merged master and resolved conflicts

.devcontainer/Dockerfile

@@ -0,0 +1,23 @@
+# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster
+ARG VARIANT=3-bullseye
+FROM python:3.8
+
+RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+    # Remove imagemagick due to https://security-tracker.debian.org/tracker/CVE-2019-10131
+    && apt-get purge -y imagemagick imagemagick-6-common 
+
+# Temporary: Upgrade python packages due to https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-40897
+# They are installed by the base image (python) which does not have the patch.
+RUN python3 -m pip install --upgrade setuptools
+
+# [Optional] If your pip requirements rarely change, uncomment this section to add them to the image.
+# COPY requirements.txt /tmp/pip-tmp/
+# RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
+#    && rm -rf /tmp/pip-tmp
+
+# [Optional] Uncomment this section to install additional OS packages.
+# RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \
+#     && apt-get -y install --no-install-recommends <your-package-list-here>
+
+# [Optional] Uncomment this line to install global node packages.
+# RUN su vscode -c "source /usr/local/share/nvm/nvm.sh && npm install -g <your-package-here>" 2>&1

.devcontainer/devcontainer.json

@@ -0,0 +1,39 @@
+{
+  "build": {
+    "dockerfile": "./Dockerfile",
+    "context": "."
+  },
+  "features": {
+    "ghcr.io/devcontainers/features/common-utils:2": {
+      "installZsh": "true",
+      "username": "vscode",
+      "userUid": "1000",
+      "userGid": "1000",
+      "upgradePackages": "true"
+    },
+    "ghcr.io/devcontainers/features/python:1": "none",
+    "ghcr.io/devcontainers/features/node:1": "none",
+    "ghcr.io/devcontainers/features/git:1": {
+      "version": "latest",
+      "ppa": "false"
+    }
+  },
+  // Configure tool-specific properties.
+  "customizations": {
+    // Configure properties specific to VS Code.
+    "vscode": {
+      // Set *default* container specific settings.json values on container create.
+      "settings": {
+        "python.defaultInterpreterPath": "/usr/local/bin/python"
+      }
+    }
+  },
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // "forwardPorts": [],
+
+  // Use 'postCreateCommand' to run commands after the container is created.
+  // "postCreateCommand": "pip3 install --user -r requirements.txt",
+
+  // Set `remoteUser` to `root` to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
+  "remoteUser": "vscode"
+}

.env.template

@@ -1,19 +1,94 @@
-PINECONE_API_KEY=your-pinecone-api-key
+################################################################################
-PINECONE_ENV=your-pinecone-region
+### AUTO-GPT - GENERAL SETTINGS
+################################################################################
+# EXECUTE_LOCAL_COMMANDS - Allow local command execution (Example: False)
+EXECUTE_LOCAL_COMMANDS=False
+# BROWSE_CHUNK_MAX_LENGTH - When browsing website, define the length of chunk stored in memory
+BROWSE_CHUNK_MAX_LENGTH=8192
+# BROWSE_SUMMARY_MAX_TOKEN - Define the maximum length of the summary generated by GPT agent when browsing website
+BROWSE_SUMMARY_MAX_TOKEN=300
+# USER_AGENT - Define the user-agent used by the requests library to browse website (string)
+# USER_AGENT="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"
+# AI_SETTINGS_FILE - Specifies which AI Settings file to use (defaults to ai_settings.yaml)
+AI_SETTINGS_FILE=ai_settings.yaml
+
+################################################################################
+### LLM PROVIDER
+################################################################################
+
+### OPENAI
+# OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
+# TEMPERATURE - Sets temperature in OpenAI (Default: 1)
+# USE_AZURE - Use Azure OpenAI or not (Default: False)
 OPENAI_API_KEY=your-openai-api-key
 TEMPERATURE=1
-ELEVENLABS_API_KEY=your-elevenlabs-api-key
+USE_AZURE=False
-ELEVENLABS_VOICE_1_ID=your-voice-id
+
-ELEVENLABS_VOICE_2_ID=your-voice-id
+### AZURE
+# OPENAI_AZURE_API_BASE - OpenAI API base URL for Azure (Example: https://my-azure-openai-url.com)
+# OPENAI_AZURE_API_VERSION - OpenAI API version for Azure (Example: v1)
+# OPENAI_AZURE_DEPLOYMENT_ID - OpenAI deployment ID for Azure (Example: my-deployment-id)
+# OPENAI_AZURE_CHAT_DEPLOYMENT_ID - OpenAI deployment ID for Azure Chat (Example: my-deployment-id-for-azure-chat)
+# OPENAI_AZURE_EMBEDDINGS_DEPLOYMENT_ID - OpenAI deployment ID for Embedding (Example: my-deployment-id-for-azure-embeddigs)
+OPENAI_AZURE_API_BASE=your-base-url-for-azure
+OPENAI_AZURE_API_VERSION=api-version-for-azure
+OPENAI_AZURE_DEPLOYMENT_ID=deployment-id-for-azure
+OPENAI_AZURE_CHAT_DEPLOYMENT_ID=deployment-id-for-azure-chat
+OPENAI_AZURE_EMBEDDINGS_DEPLOYMENT_ID=deployment-id-for-azure-embeddigs
+
+################################################################################
+### LLM MODELS
+################################################################################
+
+# SMART_LLM_MODEL - Smart language model (Default: gpt-4)
+# FAST_LLM_MODEL - Fast language model (Default: gpt-3.5-turbo)
 SMART_LLM_MODEL=gpt-4
 FAST_LLM_MODEL=gpt-3.5-turbo
-GOOGLE_API_KEY=
+
-CUSTOM_SEARCH_ENGINE_ID=
+### LLM MODEL SETTINGS
-USE_AZURE=False
+# FAST_TOKEN_LIMIT - Fast token limit for OpenAI (Default: 4000)
-EXECUTE_LOCAL_COMMANDS=False
+# SMART_TOKEN_LIMIT - Smart token limit for OpenAI (Default: 8000)
-IMAGE_PROVIDER=dalle
+# When using --gpt3onlythis needs to be set to 4000.
-HUGGINGFACE_API_TOKEN=
+FAST_TOKEN_LIMIT=4000
-USE_MAC_OS_TTS=False
+SMART_TOKEN_LIMIT=8000
+
+################################################################################
+### MEMORY
+################################################################################
+
+# MEMORY_BACKEND - Memory backend type (Default: local)
+>>>>>>> master
+MEMORY_BACKEND=local
+
+### PINECONE
+# PINECONE_API_KEY - Pinecone API Key (Example: my-pinecone-api-key)
+# PINECONE_ENV - Pinecone environment (region) (Example: us-west-2)
+PINECONE_API_KEY=your-pinecone-api-key
+PINECONE_ENV=your-pinecone-region
+
+### REDIS
+# REDIS_HOST - Redis host (Default: localhost)
+# REDIS_PORT - Redis port (Default: 6379)
+# REDIS_PASSWORD - Redis password (Default: "")
+# WIPE_REDIS_ON_START - Wipes data / index on start (Default: False)
+# MEMORY_INDEX - Name of index created in Redis database (Default: auto-gpt)
+REDIS_HOST=localhost
+REDIS_PORT=6379
+REDIS_PASSWORD=
+WIPE_REDIS_ON_START=False
+MEMORY_INDEX=auto-gpt
+
+### WEAVIATE
+# MEMORY_BACKEND - Use 'weaviate' to use Weaviate vector storage
+# WEAVIATE_HOST - Weaviate host IP
+# WEAVIATE_PORT - Weaviate host port
+# WEAVIATE_PROTOCOL - Weaviate host protocol (e.g. 'http')
+# USE_WEAVIATE_EMBEDDED - Whether to use Embedded Weaviate
+# WEAVIATE_EMBEDDED_PATH - File system path were to persist data when running Embedded Weaviate
+# WEAVIATE_USERNAME - Weaviate username
+# WEAVIATE_PASSWORD - Weaviate password
+# WEAVIATE_API_KEY - Weaviate API key if using API-key-based authentication
+# MEMORY_INDEX - Name of index to create in Weaviate
 WEAVIATE_HOST="127.0.0.1"
 WEAVIATE_PORT=8080
 WEAVIATE_PROTOCOL="http"
@@ -22,5 +97,49 @@ WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate"
 WEAVIATE_USERNAME=
 WEAVIATE_PASSWORD=
 WEAVIATE_API_KEY=
-MEMORY_INDEX="auto-gpt"
+MEMORY_INDEX=AutoGpt
-MEMORY_BACKEND=local
+
+################################################################################
+### IMAGE GENERATION PROVIDER
+################################################################################
+
+### OPEN AI
+# IMAGE_PROVIDER - Image provider (Example: dalle)
+IMAGE_PROVIDER=dalle
+
+### HUGGINGFACE
+# STABLE DIFFUSION
+# (Default URL: https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4)
+# Set in image_gen.py)
+# HUGGINGFACE_API_TOKEN - HuggingFace API token (Example: my-huggingface-api-token)
+HUGGINGFACE_API_TOKEN=your-huggingface-api-token
+
+################################################################################
+### SEARCH PROVIDER
+################################################################################
+
+### GOOGLE
+# GOOGLE_API_KEY - Google API key (Example: my-google-api-key)
+# CUSTOM_SEARCH_ENGINE_ID - Custom search engine ID (Example: my-custom-search-engine-id)
+GOOGLE_API_KEY=your-google-api-key
+CUSTOM_SEARCH_ENGINE_ID=your-custom-search-engine-id
+
+################################################################################
+### TTS PROVIDER
+################################################################################
+
+### MAC OS
+# USE_MAC_OS_TTS - Use Mac OS TTS or not (Default: False)
+USE_MAC_OS_TTS=False
+
+### STREAMELEMENTS
+# USE_BRIAN_TTS - Use Brian TTS or not (Default: False)
+USE_BRIAN_TTS=False
+
+### ELEVENLABS
+# ELEVENLABS_API_KEY - Eleven Labs API key (Example: my-elevenlabs-api-key)
+# ELEVENLABS_VOICE_1_ID - Eleven Labs voice 1 ID (Example: my-voice-id-1)
+# ELEVENLABS_VOICE_2_ID - Eleven Labs voice 2 ID (Example: my-voice-id-2)
+ELEVENLABS_API_KEY=your-elevenlabs-api-key
+ELEVENLABS_VOICE_1_ID=your-voice-id-1
+ELEVENLABS_VOICE_2_ID=your-voice-id-2

.flake8

@@ -0,0 +1,12 @@
+[flake8]
+max-line-length = 88
+extend-ignore = E203
+exclude =
+    .tox,
+    __pycache__,
+    *.pyc,
+    .env
+    venv/*
+    .venv/*
+    reports/*
+    dist/*

.github/workflows/ci.yml

@@ -32,11 +32,11 @@ jobs:
 
     - name: Lint with flake8
       continue-on-error: false
-      run: flake8 scripts/ tests/ --select E303,W293,W291,W292,E305,E231,E302
+      run: flake8 autogpt/ tests/ --select E303,W293,W291,W292,E305,E231,E302
 
     - name: Run unittest tests with coverage
       run: |
-        coverage run --source=scripts -m unittest discover tests
+        coverage run --source=autogpt -m unittest discover tests
 
     - name: Generate coverage report
       run: |

.gitignore

@@ -1,7 +1,8 @@
-scripts/keys.py
+## Original ignores
-scripts/*json
+autogpt/keys.py
-scripts/node_modules/
+autogpt/*json
-scripts/__pycache__/keys.cpython-310.pyc
+autogpt/node_modules/
+autogpt/__pycache__/keys.cpython-310.pyc
 package-lock.json
 *.pyc
 auto_gpt_workspace/*
@@ -16,11 +17,138 @@ last_run_ai_settings.yaml
 .idea/*
 auto-gpt.json
 log.txt
+log-ingestion.txt
+logs
 
-# Coverage reports
+# Byte-compiled / optimized / DLL files
-.coverage
+__pycache__/
-coverage.xml
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+plugins/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
 htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
 
-# For Macs Dev Environs: ignoring .Desktop Services_Store
+# Translations
-.DS_Store
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+llama-*
+vicuna-*

.isort.cfg

@@ -0,0 +1,10 @@
+[settings]
+profile = black
+multi_line_output = 3
+include_trailing_comma = True
+force_grid_wrap = 0
+use_parentheses = True
+ensure_newline_before_comments = True
+line_length = 88
+skip = venv,env,node_modules,.env,.venv,dist
+sections = FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+  - repo: https://github.com/sourcery-ai/sourcery
+    rev: v1.1.0 # Get the latest tag from https://github.com/sourcery-ai/sourcery/tags
+    hooks:
+      - id: sourcery
+
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v0.9.2
+    hooks:
+      - id: check-added-large-files
+        args: [ '--maxkb=500' ]
+      - id: check-byte-order-marker
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: check-symlinks
+      - id: debug-statements
+    
+  - repo: local
+    hooks:
+      - id: isort
+        name: isort-local
+        entry: isort
+        language: python
+        types: [ python ]
+        exclude: .+/(dist|.venv|venv|build)/.+
+        pass_filenames: true
+      - id: black
+        name: black-local
+        entry: black
+        language: python
+        types: [ python ]
+        exclude: .+/(dist|.venv|venv|build)/.+
+        pass_filenames: true

.sourcery.yaml

@@ -0,0 +1,71 @@
+# 🪄 This is your project's Sourcery configuration file.
+
+# You can use it to get Sourcery working in the way you want, such as
+# ignoring specific refactorings, skipping directories in your project,
+# or writing custom rules.
+
+# 📚 For a complete reference to this file, see the documentation at
+# https://docs.sourcery.ai/Configuration/Project-Settings/
+
+# This file was auto-generated by Sourcery on 2023-02-25 at 21:07.
+
+version: '1'  # The schema version of this config file
+
+ignore: # A list of paths or files which Sourcery will ignore.
+- .git
+- venv
+- .venv
+- build
+- dist
+- env
+- .env
+- .tox
+
+rule_settings:
+  enable:
+  - default
+  - gpsg
+  disable: []  # A list of rule IDs Sourcery will never suggest.
+  rule_types:
+  - refactoring
+  - suggestion
+  - comment
+  python_version: '3.9' # A string specifying the lowest Python version your project supports. Sourcery will not suggest refactorings requiring a higher Python version.
+
+# rules:  # A list of custom rules Sourcery will include in its analysis.
+# - id: no-print-statements
+#   description: Do not use print statements in the test directory.
+#   pattern: print(...)
+#   language: python
+#   replacement:
+#   condition:
+#   explanation:
+#   paths:
+#     include:
+#     - test
+#     exclude:
+#     - conftest.py
+#   tests: []
+#   tags: []
+
+# rule_tags: {} # Additional rule tags.
+
+# metrics:
+#   quality_threshold: 25.0
+
+# github:
+#   labels: []
+#   ignore_labels:
+#   - sourcery-ignore
+#   request_review: author
+#   sourcery_branch: sourcery/{base_branch}
+
+# clone_detection:
+#   min_lines: 3
+#   min_duplicates: 2
+#   identical_clones_only: false
+
+# proxy:
+#   url:
+#   ssl_certs_file:
+#   no_ssl_verify: false

CONTRIBUTING.md

@@ -8,41 +8,49 @@ To contribute to this GitHub project, you can follow these steps:
 ```
 git clone https://github.com/<YOUR-GITHUB-USERNAME>/Auto-GPT
 ```
-3. Create a new branch for your changes using the following command:
+3. Install the project requirements
+```
+pip install -r requirements.txt
+```
+4. Install pre-commit hooks
+```
+pre-commit install
+```
+5. Create a new branch for your changes using the following command:
 
 ```
 git checkout -b "branch-name"
 ```
-4. Make your changes to the code or documentation.
+6. Make your changes to the code or documentation.
 - Example: Improve User Interface or Add Documentation.
 
 
-5. Add the changes to the staging area using the following command:
+7. Add the changes to the staging area using the following command:
 ```
 git add .
 ```
 
-6. Commit the changes with a meaningful commit message using the following command:
+8. Commit the changes with a meaningful commit message using the following command:
 ```
 git commit -m "your commit message"
 ```
-7. Push the changes to your forked repository using the following command:
+9. Push the changes to your forked repository using the following command:
 ```
 git push origin branch-name
 ```
-8. Go to the GitHub website and navigate to your forked repository.
+10. Go to the GitHub website and navigate to your forked repository.
 
-9. Click the "New pull request" button.
+11. Click the "New pull request" button.
 
-10. Select the branch you just pushed to and the branch you want to merge into on the original repository.
+12. Select the branch you just pushed to and the branch you want to merge into on the original repository.
 
-11. Add a description of your changes and click the "Create pull request" button.
+13. Add a description of your changes and click the "Create pull request" button.
 
-12. Wait for the project maintainer to review your changes and provide feedback.
+14. Wait for the project maintainer to review your changes and provide feedback.
 
-13. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
+15. Make any necessary changes based on feedback and repeat steps 5-12 until your changes are accepted and merged into the main project.
 
-14. Once your changes are merged, you can update your forked repository and local copy of the repository with the following commands:
+16. Once your changes are merged, you can update your forked repository and local copy of the repository with the following commands:
 
 ```
 git fetch upstream

Dockerfile

@@ -1,7 +1,23 @@
+# Use an official Python base image from the Docker Hub
 FROM python:3.11-slim
-ENV PIP_NO_CACHE_DIR=yes
+
-WORKDIR /app
+# Set environment variables
-COPY requirements.txt .
+ENV PIP_NO_CACHE_DIR=yes \
-RUN pip install -r requirements.txt
+    PYTHONUNBUFFERED=1 \
-COPY scripts/ .
+    PYTHONDONTWRITEBYTECODE=1
-ENTRYPOINT ["python", "main.py"]
+
+# Create a non-root user and set permissions
+RUN useradd --create-home appuser
+WORKDIR /home/appuser
+RUN chown appuser:appuser /home/appuser
+USER appuser
+
+# Copy the requirements.txt file and install the requirements
+COPY --chown=appuser:appuser requirements.txt .
+RUN pip install --no-cache-dir --user -r requirements.txt
+
+# Copy the application files
+COPY --chown=appuser:appuser autogpt/ .
+
+# Set the entrypoint
+ENTRYPOINT ["python", "-m", "autogpt"]

README.md

@@ -1,9 +1,13 @@
 # Auto-GPT: An Autonomous GPT-4 Experiment
+### 🔴 🔴 🔴  Urgent: USE `stable` not `master`  🔴 🔴 🔴
+We've improved our workflow. `master` will often be **in a broken state**.
+Download the latest `stable` release here: https://github.com/Torantulino/Auto-GPT/releases/latest
+This information SUPERCEDES any following information. Takes precedence. Do this not that.
 
 ![GitHub Repo stars](https://img.shields.io/github/stars/Torantulino/auto-gpt?style=social)
-![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)
+[![Twitter Follow](https://img.shields.io/twitter/follow/siggravitas?style=social)](https://twitter.com/SigGravitas)
-[![Discord Follow](https://dcbadge.vercel.app/api/server/PQ7VX6TY4t?style=flat)](https://discord.gg/PQ7VX6TY4t)
+[![Discord Follow](https://dcbadge.vercel.app/api/server/autogpt?style=flat)](https://discord.gg/autogpt)
-[![Unit Tests](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml/badge.svg)](https://github.com/Torantulino/Auto-GPT/actions/workflows/unit_tests.yml)
+[![Unit Tests](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml/badge.svg)](https://github.com/Torantulino/Auto-GPT/actions/workflows/ci.yml)
 
 Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, Auto-GPT pushes the boundaries of what is possible with AI.
 
@@ -46,6 +50,7 @@ Your support is greatly appreciated
     - [Setting up environment variables](#setting-up-environment-variables-1)
   - [Setting Your Cache Type](#setting-your-cache-type)
   - [View Memory Usage](#view-memory-usage)
+  - [🧠 Memory pre-seeding](#memory-pre-seeding)
   - [💀 Continuous Mode ⚠️](#-continuous-mode-️)
   - [GPT3.5 ONLY Mode](#gpt35-only-mode)
   - [🖼 Image Generation](#-image-generation)
@@ -65,12 +70,15 @@ Your support is greatly appreciated
 
 ## 📋 Requirements
 
-- [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
+- environments(just choose one)
+  - [vscode + devcontainer](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers): It has been configured in the .devcontainer folder and can be used directly
+  - [Python 3.8 or later](https://www.tutorialspoint.com/how-to-install-python-in-windows)
 - [OpenAI API key](https://platform.openai.com/account/api-keys)
-- [PINECONE API key](https://www.pinecone.io/)
+
 
 Optional:
 
+- [PINECONE API key](https://www.pinecone.io/) (If you want Pinecone backed memory)
 - ElevenLabs Key (If you want the AI to speak)
 
 ## 💾 Installation
@@ -115,15 +123,15 @@ pip install -r requirements.txt
 
 ## 🔧 Usage
 
-1. Run the `main.py` Python script in your terminal:
+1. Run the `autogpt` Python module in your terminal:
    _(Type this into your CMD window)_
 
 ```
-python scripts/main.py
+python -m autogpt
 ```
 
-2. After each of AUTO-GPT's actions, type "NEXT COMMAND" to authorise them to continue.
+2. After each of action, enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter additional feedback for the AI.
-3. To exit the program, type "exit" and press Enter.
+
 
 ### Logs
 
@@ -132,15 +140,37 @@ You will find activity and error logs in the folder `./output/logs`
 To output debug logs:
 
 ```
-python scripts/main.py --debug
+python -m autogpt --debug
 ```
 
+### Docker
+
+You can also build this into a docker image and run it:
+
+```
+docker build -t autogpt .
+docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt
+```
+
+You can pass extra arguments, for instance, running with `--gpt3only` and `--continuous` mode:
+```
+docker run -it --env-file=./.env -v $PWD/auto_gpt_workspace:/app/auto_gpt_workspace autogpt --gpt3only --continuous
+```
+### Command Line Arguments
+Here are some common arguments you can use when running Auto-GPT:
+> Replace anything in angled brackets (<>) to a value you want to specify
+* `python scripts/main.py --help` to see a list of all available command line arguments.
+* `python scripts/main.py --ai-settings <filename>` to run Auto-GPT with a different AI Settings file.
+* `python scripts/main.py --use-memory  <memory-backend>` to specify one of 3 memory backends: `local`, `redis`, `pinecone` or 'no_memory'.
+
+> **NOTE**: There are shorthands for some of these flags, for example `-m` for `--use-memory`. Use `python scripts/main.py --help` for more information
+
 ## 🗣️ Speech Mode
 
 Use this to use TTS for Auto-GPT
 
 ```
-python scripts/main.py --speak
+python -m autogpt --speak
 ```
 
 ## 🔍 Google API Keys Configuration
@@ -154,9 +184,10 @@ To use the `google_official_search` command, you need to set up your Google API
 4. Go to the [APIs & Services Dashboard](https://console.cloud.google.com/apis/dashboard) and click "Enable APIs and Services". Search for "Custom Search API" and click on it, then click "Enable".
 5. Go to the [Credentials](https://console.cloud.google.com/apis/credentials) page and click "Create Credentials". Choose "API Key".
 6. Copy the API key and set it as an environment variable named `GOOGLE_API_KEY` on your machine. See setting up environment variables below.
-7. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page and click "Add".
+7. [Enable](https://console.developers.google.com/apis/api/customsearch.googleapis.com) the Custom Search API on your project. (Might need to wait few minutes to propagate)
-8. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
+8. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page and click "Add".
-9. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
+9. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites.
+10. Once you've created your search engine, click on "Control Panel" and then "Basics". Copy the "Search engine ID" and set it as an environment variable named `CUSTOM_SEARCH_ENGINE_ID` on your machine. See setting up environment variables below.
 
 _Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches._
 
@@ -225,7 +256,10 @@ Pinecone enables the storage of vast amounts of vector-based memory, allowing fo
 
 ### Setting up environment variables
 
-Simply set them in the `.env` file.
+In the `.env` file set:
+- `PINECONE_API_KEY`
+- `PINECONE_ENV` (something like: us-east4-gcp)
+- `MEMORY_BACKEND=pinecone`
 
 Alternatively, you can set them from the command line (advanced):
 
@@ -234,7 +268,7 @@ For Windows Users:
 ```
 setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
 setx PINECONE_ENV "Your pinecone region" # something like: us-east4-gcp
-
+setx MEMORY_BACKEND "pinecone"
 ```
 
 For macOS and Linux users:
@@ -242,7 +276,7 @@ For macOS and Linux users:
 ```
 export PINECONE_API_KEY="YOUR_PINECONE_API_KEY"
 export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
-
+export MEMORY_BACKEND="pinecone"
 ```
 
 ## Weaviate Setup
@@ -281,6 +315,52 @@ To switch to either, change the `MEMORY_BACKEND` env variable to the value that
 
 1. View memory usage by using the `--debug` flag :)
 
+
+## 🧠 Memory pre-seeding
+
+```
+# python scripts/data_ingestion.py -h 
+usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH]
+
+Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script.
+
+options:
+  -h, --help               show this help message and exit
+  --file FILE              The file to ingest.
+  --dir DIR                The directory containing the files to ingest.
+  --init                   Init the memory and wipe its content (default: False)
+  --overlap OVERLAP        The overlap size between chunks when ingesting files (default: 200)
+  --max_length MAX_LENGTH  The max_length of each chunk when ingesting files (default: 4000
+
+# python scripts/data_ingestion.py --dir seed_data --init --overlap 200 --max_length 1000
+```
+
+This script located at scripts/data_ingestion.py, allows you to ingest files into memory and pre-seed it before running Auto-GPT. 
+
+Memory pre-seeding is a technique that involves ingesting relevant documents or data into the AI's memory so that it can use this information to generate more informed and accurate responses.
+
+To pre-seed the memory, the content of each document is split into chunks of a specified maximum length with a specified overlap between chunks, and then each chunk is added to the memory backend set in the .env file. When the AI is prompted to recall information, it can then access those pre-seeded memories to generate more informed and accurate responses.
+
+This technique is particularly useful when working with large amounts of data or when there is specific information that the AI needs to be able to access quickly. 
+By pre-seeding the memory, the AI can retrieve and use this information more efficiently, saving time, API call and improving the accuracy of its responses. 
+
+You could for example download the documentation of an API, a Github repository, etc. and ingest it into memory before running Auto-GPT. 
+
+⚠️ If you use Redis as your memory, make sure to run Auto-GPT with the WIPE_REDIS_ON_START set to False in your .env file.
+
+⚠️For other memory backend, we currently forcefully wipe the memory when starting Auto-GPT. To ingest data with those memory backend, you can call the data_ingestion.py script anytime during an Auto-GPT run. 
+
+Memories will be available to the AI immediately as they are ingested, even if ingested while Auto-GPT is running.
+
+In the example above, the script initializes the memory, ingests all files within the seed_data directory into memory with an overlap between chunks of 200 and a maximum length of each chunk of 4000.
+Note that you can also use the --file argument to ingest a single file into memory and that the script will only ingest files within the auto_gpt_workspace directory.
+
+You can adjust the max_length and overlap parameters to fine-tune the way the docuents are presented to the AI when it "recall" that memory:
+
+- Adjusting the overlap value allows the AI to access more contextual information from each chunk when recalling information, but will result in more chunks being created and therefore increase memory backend usage and OpenAI API requests.
+- Reducing the max_length value will create more chunks, which can save prompt tokens by allowing for more message history in the context, but will also increase the number of chunks.
+- Increasing the max_length value will provide the AI with more contextual information from each chunk, reducing the number of chunks created and saving on OpenAI API requests. However, this may also use more prompt tokens and decrease the overall context available to the AI.
+
 ## 💀 Continuous Mode ⚠️
 
 Run the AI **without** user authorisation, 100% automated.
@@ -288,10 +368,10 @@ Continuous mode is not recommended.
 It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise.
 Use at your own risk.
 
-1. Run the `main.py` Python script in your terminal:
+1. Run the `autogpt` python module in your terminal:
 
 ```
-python scripts/main.py --continuous
+python -m autogpt --speak --continuous
 
 ```
 
@@ -302,7 +382,7 @@ python scripts/main.py --continuous
 If you don't have access to the GPT4 api, this mode will allow you to use Auto-GPT!
 
 ```
-python scripts/main.py --gpt3only
+python -m autogpt --speak --gpt3only
 ```
 
 It is recommended to use a virtual machine for tasks that require high security measures to prevent any potential harm to the main computer's system and data.
@@ -375,8 +455,8 @@ This project uses [flake8](https://flake8.pycqa.org/en/latest/) for linting. We
 To run the linter, run the following command:
 
 ```
-flake8 scripts/ tests/
+flake8 autogpt/ tests/
 
 # Or, if you want to run flake8 with the same configuration as the CI:
-flake8 scripts/ tests/ --select E303,W293,W291,W292,E305,E231,E302
+flake8 autogpt/ tests/ --select E303,W293,W291,W292,E305,E231,E302
 ```

autogpt/__main__.py

@@ -0,0 +1,572 @@
+import argparse
+import json
+import logging
+import traceback
+
+from colorama import Fore, Style
+
+from autogpt import chat
+from autogpt import commands as cmd
+from autogpt import speak, utils
+from autogpt.ai_config import AIConfig
+from autogpt.config import Config
+from autogpt.json_parser import fix_and_parse_json
+from autogpt.logger import logger
+from autogpt.memory import get_memory, get_supported_memory_backends
+from autogpt.spinner import Spinner
+
+cfg = Config()
+config = None
+
+
+def check_openai_api_key():
+    """Check if the OpenAI API key is set in config.py or as an environment variable."""
+    if not cfg.openai_api_key:
+        print(
+            Fore.RED
+            + "Please set your OpenAI API key in .env or as an environment variable."
+        )
+        print("You can get your key from https://beta.openai.com/account/api-keys")
+        exit(1)
+
+
+def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
+    if cfg.speak_mode and cfg.debug_mode:
+        speak.say_text(
+            "I have received an invalid JSON response from the OpenAI API. "
+            "Trying to fix it now."
+        )
+    logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
+
+    try:
+        # Use regex to search for JSON objects
+        import regex
+
+        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
+        json_match = json_pattern.search(json_string)
+
+        if json_match:
+            # Extract the valid JSON object from the string
+            json_string = json_match.group(0)
+            logger.typewriter_log(
+                title="Apparently json was fixed.", title_color=Fore.GREEN
+            )
+            if cfg.speak_mode and cfg.debug_mode:
+                speak.say_text("Apparently json was fixed.")
+        else:
+            raise ValueError("No valid JSON object found")
+
+    except (json.JSONDecodeError, ValueError) as e:
+        if cfg.debug_mode:
+            logger.error("Error: Invalid JSON: %s\n", json_string)
+        if cfg.speak_mode:
+            speak.say_text("Didn't work. I will have to ignore this response then.")
+        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
+        json_string = {}
+
+    return json_string
+
+
+def print_assistant_thoughts(assistant_reply):
+    """Prints the assistant's thoughts to the console"""
+    global ai_name
+    global cfg
+    try:
+        try:
+            # Parse and print Assistant response
+            assistant_reply_json = fix_and_parse_json(assistant_reply)
+        except json.JSONDecodeError as e:
+            logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
+            assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
+                assistant_reply
+            )
+            assistant_reply_json = fix_and_parse_json(assistant_reply_json)
+
+        # Check if assistant_reply_json is a string and attempt to parse it into a
+        #  JSON object
+        if isinstance(assistant_reply_json, str):
+            try:
+                assistant_reply_json = json.loads(assistant_reply_json)
+            except json.JSONDecodeError as e:
+                logger.error("Error: Invalid JSON\n", assistant_reply)
+                assistant_reply_json = (
+                    attempt_to_fix_json_by_finding_outermost_brackets(
+                        assistant_reply_json
+                    )
+                )
+
+        assistant_thoughts_reasoning = None
+        assistant_thoughts_plan = None
+        assistant_thoughts_speak = None
+        assistant_thoughts_criticism = None
+        assistant_thoughts = assistant_reply_json.get("thoughts", {})
+        assistant_thoughts_text = assistant_thoughts.get("text")
+
+        if assistant_thoughts:
+            assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
+            assistant_thoughts_plan = assistant_thoughts.get("plan")
+            assistant_thoughts_criticism = assistant_thoughts.get("criticism")
+            assistant_thoughts_speak = assistant_thoughts.get("speak")
+
+        logger.typewriter_log(
+            f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
+        )
+        logger.typewriter_log(
+            "REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
+        )
+
+        if assistant_thoughts_plan:
+            logger.typewriter_log("PLAN:", Fore.YELLOW, "")
+            # If it's a list, join it into a string
+            if isinstance(assistant_thoughts_plan, list):
+                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
+            elif isinstance(assistant_thoughts_plan, dict):
+                assistant_thoughts_plan = str(assistant_thoughts_plan)
+
+            # Split the input_string using the newline character and dashes
+            lines = assistant_thoughts_plan.split("\n")
+            for line in lines:
+                line = line.lstrip("- ")
+                logger.typewriter_log("- ", Fore.GREEN, line.strip())
+
+        logger.typewriter_log(
+            "CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
+        )
+        # Speak the assistant's thoughts
+        if cfg.speak_mode and assistant_thoughts_speak:
+            speak.say_text(assistant_thoughts_speak)
+
+        return assistant_reply_json
+    except json.decoder.JSONDecodeError:
+        call_stack = traceback.format_exc()
+        logger.error("Error: Invalid JSON\n", assistant_reply)
+        logger.error("Traceback: \n", call_stack)
+        if cfg.speak_mode:
+            speak.say_text(
+                "I have received an invalid JSON response from the OpenAI API."
+                " I cannot ignore this response."
+            )
+
+    # All other errors, return "Error: + error message"
+    except Exception:
+        call_stack = traceback.format_exc()
+        logger.error("Error: \n", call_stack)
+
+
+def construct_prompt():
+    """Construct the prompt for the AI to respond to"""
+    config: AIConfig = AIConfig.load(cfg.ai_settings_file)
+    if cfg.skip_reprompt and config.ai_name:
+        logger.typewriter_log("Name :", Fore.GREEN, config.ai_name)
+        logger.typewriter_log("Role :", Fore.GREEN, config.ai_role)
+        logger.typewriter_log("Goals:", Fore.GREEN, f"{config.ai_goals}")
+    elif config.ai_name:
+        logger.typewriter_log(
+            "Welcome back! ",
+            Fore.GREEN,
+            f"Would you like me to return to being {config.ai_name}?",
+            speak_text=True,
+        )
+        should_continue = utils.clean_input(
+            f"""Continue with the last settings?
+Name:  {config.ai_name}
+Role:  {config.ai_role}
+Goals: {config.ai_goals}
+Continue (y/n): """
+        )
+        if should_continue.lower() == "n":
+            config = AIConfig()
+
+    if not config.ai_name:
+        config = prompt_user()
+        config.save()
+
+    # Get rid of this global:
+    global ai_name
+    ai_name = config.ai_name
+
+    return config.construct_full_prompt()
+
+
+def prompt_user():
+    """Prompt the user for input"""
+    ai_name = ""
+    # Construct the prompt
+    logger.typewriter_log(
+        "Welcome to Auto-GPT! ",
+        Fore.GREEN,
+        "Enter the name of your AI and its role below. Entering nothing will load"
+        " defaults.",
+        speak_text=True,
+    )
+
+    # Get AI Name from User
+    logger.typewriter_log(
+        "Name your AI: ", Fore.GREEN, "For example, 'Entrepreneur-GPT'"
+    )
+    ai_name = utils.clean_input("AI Name: ")
+    if ai_name == "":
+        ai_name = "Entrepreneur-GPT"
+
+    logger.typewriter_log(
+        f"{ai_name} here!", Fore.LIGHTBLUE_EX, "I am at your service.", speak_text=True
+    )
+
+    # Get AI Role from User
+    logger.typewriter_log(
+        "Describe your AI's role: ",
+        Fore.GREEN,
+        "For example, 'an AI designed to autonomously develop and run businesses with"
+        " the sole goal of increasing your net worth.'",
+    )
+    ai_role = utils.clean_input(f"{ai_name} is: ")
+    if ai_role == "":
+        ai_role = "an AI designed to autonomously develop and run businesses with the"
+        " sole goal of increasing your net worth."
+
+    # Enter up to 5 goals for the AI
+    logger.typewriter_log(
+        "Enter up to 5 goals for your AI: ",
+        Fore.GREEN,
+        "For example: \nIncrease net worth, Grow Twitter Account, Develop and manage"
+        " multiple businesses autonomously'",
+    )
+    print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
+    ai_goals = []
+    for i in range(5):
+        ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
+        if ai_goal == "":
+            break
+        ai_goals.append(ai_goal)
+    if len(ai_goals) == 0:
+        ai_goals = [
+            "Increase net worth",
+            "Grow Twitter Account",
+            "Develop and manage multiple businesses autonomously",
+        ]
+
+    config = AIConfig(ai_name, ai_role, ai_goals)
+    return config
+
+
+def parse_arguments():
+    """Parses the arguments passed to the script"""
+    global cfg
+    cfg.set_debug_mode(False)
+    cfg.set_continuous_mode(False)
+    cfg.set_speak_mode(False)
+
+    parser = argparse.ArgumentParser(description="Process arguments.")
+    parser.add_argument(
+        "--continuous", "-c", action="store_true", help="Enable Continuous Mode"
+    )
+    parser.add_argument(
+        "--continuous-limit",
+        "-l",
+        type=int,
+        dest="continuous_limit",
+        help="Defines the number of times to run in continuous mode",
+    )
+    parser.add_argument("--speak", action="store_true", help="Enable Speak Mode")
+    parser.add_argument("--debug", action="store_true", help="Enable Debug Mode")
+    parser.add_argument(
+        "--gpt3only", action="store_true", help="Enable GPT3.5 Only Mode"
+    )
+    parser.add_argument("--gpt4only", action="store_true", help="Enable GPT4 Only Mode")
+    parser.add_argument(
+        "--use-memory",
+        "-m",
+        dest="memory_type",
+        help="Defines which Memory backend to use",
+    )
+    parser.add_argument(
+        "--skip-reprompt",
+        "-y",
+        dest="skip_reprompt",
+        action="store_true",
+        help="Skips the re-prompting messages at the beginning of the script",
+    )
+    parser.add_argument(
+        "--ai-settings",
+        "-C",
+        dest="ai_settings_file",
+        help="Specifies which ai_settings.yaml file to use, will also automatically"
+        " skip the re-prompt.",
+    )
+    args = parser.parse_args()
+
+    if args.debug:
+        logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_debug_mode(True)
+
+    if args.continuous:
+        logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
+        logger.typewriter_log(
+            "WARNING: ",
+            Fore.RED,
+            "Continuous mode is not recommended. It is potentially dangerous and may"
+            " cause your AI to run forever or carry out actions you would not usually"
+            " authorise. Use at your own risk.",
+        )
+        cfg.set_continuous_mode(True)
+
+        if args.continuous_limit:
+            logger.typewriter_log(
+                "Continuous Limit: ", Fore.GREEN, f"{args.continuous_limit}"
+            )
+            cfg.set_continuous_limit(args.continuous_limit)
+
+    # Check if continuous limit is used without continuous mode
+    if args.continuous_limit and not args.continuous:
+        parser.error("--continuous-limit can only be used with --continuous")
+
+    if args.speak:
+        logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_speak_mode(True)
+
+    if args.gpt3only:
+        logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_smart_llm_model(cfg.fast_llm_model)
+
+    if args.gpt4only:
+        logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
+        cfg.set_fast_llm_model(cfg.smart_llm_model)
+
+    if args.memory_type:
+        supported_memory = get_supported_memory_backends()
+        chosen = args.memory_type
+        if not chosen in supported_memory:
+            logger.typewriter_log(
+                "ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
+                Fore.RED,
+                f"{supported_memory}",
+            )
+            logger.typewriter_log("Defaulting to: ", Fore.YELLOW, cfg.memory_backend)
+        else:
+            cfg.memory_backend = chosen
+
+    if args.skip_reprompt:
+        logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
+        cfg.skip_reprompt = True
+
+    if args.ai_settings_file:
+        file = args.ai_settings_file
+
+        # Validate file
+        (validated, message) = utils.validate_yaml_file(file)
+        if not validated:
+            logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
+            logger.double_check()
+            exit(1)
+
+        logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
+        cfg.ai_settings_file = file
+        cfg.skip_reprompt = True
+
+
+def main():
+    global ai_name, memory
+    # TODO: fill in llm values here
+    check_openai_api_key()
+    parse_arguments()
+    logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
+    ai_name = ""
+    prompt = construct_prompt()
+    # print(prompt)
+    # Initialize variables
+    full_message_history = []
+    next_action_count = 0
+    # Make a constant:
+    user_input = (
+        "Determine which next command to use, and respond using the"
+        " format specified above:"
+    )
+    # Initialize memory and make sure it is empty.
+    # this is particularly important for indexing and referencing pinecone memory
+    memory = get_memory(cfg, init=True)
+    print(f"Using memory of type: {memory.__class__.__name__}")
+    agent = Agent(
+        ai_name=ai_name,
+        memory=memory,
+        full_message_history=full_message_history,
+        next_action_count=next_action_count,
+        prompt=prompt,
+        user_input=user_input,
+    )
+    agent.start_interaction_loop()
+
+
+class Agent:
+    """Agent class for interacting with Auto-GPT.
+
+    Attributes:
+        ai_name: The name of the agent.
+        memory: The memory object to use.
+        full_message_history: The full message history.
+        next_action_count: The number of actions to execute.
+        prompt: The prompt to use.
+        user_input: The user input.
+
+    """
+
+    def __init__(
+        self,
+        ai_name,
+        memory,
+        full_message_history,
+        next_action_count,
+        prompt,
+        user_input,
+    ):
+        self.ai_name = ai_name
+        self.memory = memory
+        self.full_message_history = full_message_history
+        self.next_action_count = next_action_count
+        self.prompt = prompt
+        self.user_input = user_input
+
+    def start_interaction_loop(self):
+        # Interaction Loop
+        loop_count = 0
+        command_name = None
+        arguments = None
+        while True:
+            # Discontinue if continuous limit is reached
+            loop_count += 1
+            if (
+                cfg.continuous_mode
+                and cfg.continuous_limit > 0
+                and loop_count > cfg.continuous_limit
+            ):
+                logger.typewriter_log(
+                    "Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
+                )
+                break
+
+            # Send message to AI, get response
+            with Spinner("Thinking... "):
+                assistant_reply = chat.chat_with_ai(
+                    self.prompt,
+                    self.user_input,
+                    self.full_message_history,
+                    self.memory,
+                    cfg.fast_token_limit,
+                )  # TODO: This hardcodes the model to use GPT3.5. Make this an argument
+
+            # Print Assistant thoughts
+            print_assistant_thoughts(assistant_reply)
+
+            # Get command name and arguments
+            try:
+                command_name, arguments = cmd.get_command(
+                    attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
+                )
+                if cfg.speak_mode:
+                    speak.say_text(f"I want to execute {command_name}")
+            except Exception as e:
+                logger.error("Error: \n", str(e))
+
+            if not cfg.continuous_mode and self.next_action_count == 0:
+                ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
+                # Get key press: Prompt the user to press enter to continue or escape
+                # to exit
+                self.user_input = ""
+                logger.typewriter_log(
+                    "NEXT ACTION: ",
+                    Fore.CYAN,
+                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
+                    f"  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+                print(
+                    "Enter 'y' to authorise command, 'y -N' to run N continuous"
+                    " commands, 'n' to exit program, or enter feedback for"
+                    f" {self.ai_name}...",
+                    flush=True,
+                )
+                while True:
+                    console_input = utils.clean_input(
+                        Fore.MAGENTA + "Input:" + Style.RESET_ALL
+                    )
+                    if console_input.lower().rstrip() == "y":
+                        self.user_input = "GENERATE NEXT COMMAND JSON"
+                        break
+                    elif console_input.lower().startswith("y -"):
+                        try:
+                            self.next_action_count = abs(
+                                int(console_input.split(" ")[1])
+                            )
+                            self.user_input = "GENERATE NEXT COMMAND JSON"
+                        except ValueError:
+                            print(
+                                "Invalid input format. Please enter 'y -n' where n"
+                                " is the number of continuous tasks."
+                            )
+                            continue
+                        break
+                    elif console_input.lower() == "n":
+                        self.user_input = "EXIT"
+                        break
+                    else:
+                        self.user_input = console_input
+                        command_name = "human_feedback"
+                        break
+
+                if self.user_input == "GENERATE NEXT COMMAND JSON":
+                    logger.typewriter_log(
+                        "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
+                        Fore.MAGENTA,
+                        "",
+                    )
+                elif self.user_input == "EXIT":
+                    print("Exiting...", flush=True)
+                    break
+            else:
+                # Print command
+                logger.typewriter_log(
+                    "NEXT ACTION: ",
+                    Fore.CYAN,
+                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
+                    f"  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+
+            # Execute command
+            if command_name is not None and command_name.lower().startswith("error"):
+                result = (
+                    f"Command {command_name} threw the following error: {arguments}"
+                )
+            elif command_name == "human_feedback":
+                result = f"Human feedback: {self.user_input}"
+            else:
+                result = (
+                    f"Command {command_name} "
+                    f"returned: {cmd.execute_command(command_name, arguments)}"
+                )
+                if self.next_action_count > 0:
+                    self.next_action_count -= 1
+
+            memory_to_add = (
+                f"Assistant Reply: {assistant_reply} "
+                f"\nResult: {result} "
+                f"\nHuman Feedback: {self.user_input} "
+            )
+
+            self.memory.add(memory_to_add)
+
+            # Check if there's a result from the command append it to the message
+            # history
+            if result is not None:
+                self.full_message_history.append(
+                    chat.create_chat_message("system", result)
+                )
+                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
+            else:
+                self.full_message_history.append(
+                    chat.create_chat_message("system", "Unable to execute command")
+                )
+                logger.typewriter_log(
+                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
+                )
+
+
+if __name__ == "__main__":
+    main()

autogpt/agent.py

@@ -0,0 +1,304 @@
+import json
+import regex
+import traceback
+
+from colorama import Fore, Style
+
+from autogpt.chat import chat_with_ai, create_chat_message
+import autogpt.commands as cmd
+from autogpt.config import Config
+from autogpt.json_parser import fix_and_parse_json
+from autogpt.logger import logger
+from autogpt.speak import say_text
+from autogpt.spinner import Spinner
+from autogpt.utils import clean_input
+
+
+class Agent:
+    """Agent class for interacting with Auto-GPT.
+
+    Attributes:
+        ai_name: The name of the agent.
+        memory: The memory object to use.
+        full_message_history: The full message history.
+        next_action_count: The number of actions to execute.
+        prompt: The prompt to use.
+        user_input: The user input.
+
+    """
+
+    def __init__(
+        self,
+        ai_name,
+        memory,
+        full_message_history,
+        next_action_count,
+        prompt,
+        user_input,
+    ):
+        self.ai_name = ai_name
+        self.memory = memory
+        self.full_message_history = full_message_history
+        self.next_action_count = next_action_count
+        self.prompt = prompt
+        self.user_input = user_input
+
+    def start_interaction_loop(self):
+        # Interaction Loop
+        cfg = Config()
+        loop_count = 0
+        command_name = None
+        arguments = None
+        while True:
+            # Discontinue if continuous limit is reached
+            loop_count += 1
+            if (
+                cfg.continuous_mode
+                and cfg.continuous_limit > 0
+                and loop_count > cfg.continuous_limit
+            ):
+                logger.typewriter_log(
+                    "Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
+                )
+                break
+
+            # Send message to AI, get response
+            with Spinner("Thinking... "):
+                assistant_reply = chat_with_ai(
+                    self.prompt,
+                    self.user_input,
+                    self.full_message_history,
+                    self.memory,
+                    cfg.fast_token_limit,
+                )  # TODO: This hardcodes the model to use GPT3.5. Make this an argument
+
+            # Print Assistant thoughts
+            print_assistant_thoughts(self.ai_name, assistant_reply)
+
+            # Get command name and arguments
+            try:
+                command_name, arguments = cmd.get_command(
+                    attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
+                )
+                if cfg.speak_mode:
+                    say_text(f"I want to execute {command_name}")
+            except Exception as e:
+                logger.error("Error: \n", str(e))
+
+            if not cfg.continuous_mode and self.next_action_count == 0:
+                ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
+                # Get key press: Prompt the user to press enter to continue or escape
+                # to exit
+                self.user_input = ""
+                logger.typewriter_log(
+                    "NEXT ACTION: ",
+                    Fore.CYAN,
+                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}  "
+                    f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+                print(
+                    "Enter 'y' to authorise command, 'y -N' to run N continuous "
+                    "commands, 'n' to exit program, or enter feedback for "
+                    f"{self.ai_name}...",
+                    flush=True,
+                )
+                while True:
+                    console_input = clean_input(
+                        Fore.MAGENTA + "Input:" + Style.RESET_ALL
+                    )
+                    if console_input.lower().rstrip() == "y":
+                        self.user_input = "GENERATE NEXT COMMAND JSON"
+                        break
+                    elif console_input.lower().startswith("y -"):
+                        try:
+                            self.next_action_count = abs(
+                                int(console_input.split(" ")[1])
+                            )
+                            self.user_input = "GENERATE NEXT COMMAND JSON"
+                        except ValueError:
+                            print(
+                                "Invalid input format. Please enter 'y -n' where n is"
+                                " the number of continuous tasks."
+                            )
+                            continue
+                        break
+                    elif console_input.lower() == "n":
+                        self.user_input = "EXIT"
+                        break
+                    else:
+                        self.user_input = console_input
+                        command_name = "human_feedback"
+                        break
+
+                if self.user_input == "GENERATE NEXT COMMAND JSON":
+                    logger.typewriter_log(
+                        "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
+                        Fore.MAGENTA,
+                        "",
+                    )
+                elif self.user_input == "EXIT":
+                    print("Exiting...", flush=True)
+                    break
+            else:
+                # Print command
+                logger.typewriter_log(
+                    "NEXT ACTION: ",
+                    Fore.CYAN,
+                    f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
+                    f"  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
+                )
+
+            # Execute command
+            if command_name is not None and command_name.lower().startswith("error"):
+                result = (
+                    f"Command {command_name} threw the following error: {arguments}"
+                )
+            elif command_name == "human_feedback":
+                result = f"Human feedback: {self.user_input}"
+            else:
+                result = (
+                    f"Command {command_name} returned: "
+                    f"{cmd.execute_command(command_name, arguments)}"
+                )
+                if self.next_action_count > 0:
+                    self.next_action_count -= 1
+
+            memory_to_add = (
+                f"Assistant Reply: {assistant_reply} "
+                f"\nResult: {result} "
+                f"\nHuman Feedback: {self.user_input} "
+            )
+
+            self.memory.add(memory_to_add)
+
+            # Check if there's a result from the command append it to the message
+            # history
+            if result is not None:
+                self.full_message_history.append(create_chat_message("system", result))
+                logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
+            else:
+                self.full_message_history.append(
+                    create_chat_message("system", "Unable to execute command")
+                )
+                logger.typewriter_log(
+                    "SYSTEM: ", Fore.YELLOW, "Unable to execute command"
+                )
+
+
+def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
+    cfg = Config()
+    if cfg.speak_mode and cfg.debug_mode:
+        say_text(
+            "I have received an invalid JSON response from the OpenAI API. "
+            "Trying to fix it now."
+        )
+    logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
+
+    try:
+        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
+        json_match = json_pattern.search(json_string)
+
+        if json_match:
+            # Extract the valid JSON object from the string
+            json_string = json_match.group(0)
+            logger.typewriter_log(
+                title="Apparently json was fixed.", title_color=Fore.GREEN
+            )
+            if cfg.speak_mode and cfg.debug_mode:
+                say_text("Apparently json was fixed.")
+        else:
+            raise ValueError("No valid JSON object found")
+
+    except (json.JSONDecodeError, ValueError):
+        if cfg.speak_mode:
+            say_text("Didn't work. I will have to ignore this response then.")
+        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
+        json_string = {}
+
+    return json_string
+
+
+def print_assistant_thoughts(ai_name, assistant_reply):
+    """Prints the assistant's thoughts to the console"""
+    cfg = Config()
+    try:
+        try:
+            # Parse and print Assistant response
+            assistant_reply_json = fix_and_parse_json(assistant_reply)
+        except json.JSONDecodeError:
+            logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
+            assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
+                assistant_reply
+            )
+            if isinstance(assistant_reply_json, str):
+                assistant_reply_json = fix_and_parse_json(assistant_reply_json)
+
+        # Check if assistant_reply_json is a string and attempt to parse
+        # it into a JSON object
+        if isinstance(assistant_reply_json, str):
+            try:
+                assistant_reply_json = json.loads(assistant_reply_json)
+            except json.JSONDecodeError:
+                logger.error("Error: Invalid JSON\n", assistant_reply)
+                assistant_reply_json = (
+                    attempt_to_fix_json_by_finding_outermost_brackets(
+                        assistant_reply_json
+                    )
+                )
+
+        assistant_thoughts_reasoning = None
+        assistant_thoughts_plan = None
+        assistant_thoughts_speak = None
+        assistant_thoughts_criticism = None
+        if not isinstance(assistant_reply_json, dict):
+            assistant_reply_json = {}
+        assistant_thoughts = assistant_reply_json.get("thoughts", {})
+        assistant_thoughts_text = assistant_thoughts.get("text")
+
+        if assistant_thoughts:
+            assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
+            assistant_thoughts_plan = assistant_thoughts.get("plan")
+            assistant_thoughts_criticism = assistant_thoughts.get("criticism")
+            assistant_thoughts_speak = assistant_thoughts.get("speak")
+
+        logger.typewriter_log(
+            f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, f"{assistant_thoughts_text}"
+        )
+        logger.typewriter_log(
+            "REASONING:", Fore.YELLOW, f"{assistant_thoughts_reasoning}"
+        )
+
+        if assistant_thoughts_plan:
+            logger.typewriter_log("PLAN:", Fore.YELLOW, "")
+            # If it's a list, join it into a string
+            if isinstance(assistant_thoughts_plan, list):
+                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
+            elif isinstance(assistant_thoughts_plan, dict):
+                assistant_thoughts_plan = str(assistant_thoughts_plan)
+
+            # Split the input_string using the newline character and dashes
+            lines = assistant_thoughts_plan.split("\n")
+            for line in lines:
+                line = line.lstrip("- ")
+                logger.typewriter_log("- ", Fore.GREEN, line.strip())
+
+        logger.typewriter_log(
+            "CRITICISM:", Fore.YELLOW, f"{assistant_thoughts_criticism}"
+        )
+        # Speak the assistant's thoughts
+        if cfg.speak_mode and assistant_thoughts_speak:
+            say_text(assistant_thoughts_speak)
+
+        return assistant_reply_json
+    except json.decoder.JSONDecodeError:
+        logger.error("Error: Invalid JSON\n", assistant_reply)
+        if cfg.speak_mode:
+            say_text(
+                "I have received an invalid JSON response from the OpenAI API."
+                " I cannot ignore this response."
+            )
+
+    # All other errors, return "Error: + error message"
+    except Exception:
+        call_stack = traceback.format_exc()
+        logger.error("Error: \n", call_stack)

autogpt/agent_manager.py

@@ -1,4 +1,4 @@
-from llm_utils import create_chat_completion
+from autogpt.llm_utils import create_chat_completion
 
 next_key = 0
 agents = {}  # key, (task, full_message_history, model)
@@ -12,7 +12,9 @@ def create_agent(task, prompt, model):
     global next_key
     global agents
 
-    messages = [{"role": "user", "content": prompt}, ]
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
 
     # Start GPT instance
     agent_reply = create_chat_completion(

autogpt/ai_config.py

@@ -1,6 +1,8 @@
-import yaml
-import data
 import os
+from typing import Type
+import yaml
+
+from autogpt.prompt import get_prompt
 
 
 class AIConfig:
@@ -13,7 +15,9 @@ class AIConfig:
         ai_goals (list): The list of objectives the AI is supposed to complete.
     """
 
-    def __init__(self, ai_name: str="", ai_role: str="", ai_goals: list=[]) -> None:
+    def __init__(
+        self, ai_name: str = "", ai_role: str = "", ai_goals: list = []
+    ) -> None:
         """
         Initialize a class instance
 
@@ -30,24 +34,26 @@ class AIConfig:
         self.ai_goals = ai_goals
 
     # Soon this will go in a folder where it remembers more stuff about the run(s)
-    SAVE_FILE = os.path.join(os.path.dirname(__file__), '..', 'ai_settings.yaml')
+    SAVE_FILE = os.path.join(os.path.dirname(__file__), "..", "ai_settings.yaml")
 
     @classmethod
-    def load(cls: object, config_file: str=SAVE_FILE) -> object:
+    def load(cls: "Type[AIConfig]", config_file: str = SAVE_FILE) -> "Type[AIConfig]":
         """
-        Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from yaml file if yaml file exists,
+        Returns class object with parameters (ai_name, ai_role, ai_goals) loaded from
+          yaml file if yaml file exists,
         else returns class with no parameters.
 
         Parameters:
            cls (class object): An AIConfig Class object.
-           config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
+           config_file (int): The path to the config yaml file.
+             DEFAULT: "../ai_settings.yaml"
 
         Returns:
             cls (object): An instance of given cls object
         """
 
         try:
-            with open(config_file) as file:
+            with open(config_file, encoding="utf-8") as file:
                 config_params = yaml.load(file, Loader=yaml.FullLoader)
         except FileNotFoundError:
             config_params = {}
@@ -55,23 +61,28 @@ class AIConfig:
         ai_name = config_params.get("ai_name", "")
         ai_role = config_params.get("ai_role", "")
         ai_goals = config_params.get("ai_goals", [])
-
+        # type: Type[AIConfig]
         return cls(ai_name, ai_role, ai_goals)
 
-    def save(self, config_file: str=SAVE_FILE) -> None:
+    def save(self, config_file: str = SAVE_FILE) -> None:
         """
         Saves the class parameters to the specified file yaml file path as a yaml file.
 
         Parameters:
-            config_file(str): The path to the config yaml file. DEFAULT: "../ai_settings.yaml"
+            config_file(str): The path to the config yaml file.
+              DEFAULT: "../ai_settings.yaml"
 
         Returns:
             None
         """
 
-        config = {"ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals}
+        config = {
-        with open(config_file, "w") as file:
+            "ai_name": self.ai_name,
-            yaml.dump(config, file)
+            "ai_role": self.ai_role,
+            "ai_goals": self.ai_goals,
+        }
+        with open(config_file, "w", encoding="utf-8") as file:
+            yaml.dump(config, file, allow_unicode=True)
 
     def construct_full_prompt(self) -> str:
         """
@@ -81,15 +92,23 @@ class AIConfig:
             None
 
         Returns:
-            full_prompt (str): A string containing the initial prompt for the user including the ai_name, ai_role and ai_goals.
+            full_prompt (str): A string containing the initial prompt for the user
+              including the ai_name, ai_role and ai_goals.
         """
 
-        prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications."""
+        prompt_start = (
+            "Your decisions must always be made independently without"
+            "seeking user assistance. Play to your strengths as an LLM and pursue"
+            " simple strategies with no legal complications."
+            ""
+        )
 
         # Construct full prompt
-        full_prompt = f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
+        full_prompt = (
+            f"You are {self.ai_name}, {self.ai_role}\n{prompt_start}\n\nGOALS:\n\n"
+        )
         for i, goal in enumerate(self.ai_goals):
             full_prompt += f"{i+1}. {goal}\n"
 
-        full_prompt += f"\n\n{data.load_prompt()}"
+        full_prompt += f"\n\n{get_prompt()}"
         return full_prompt

autogpt/ai_functions.py

@@ -1,33 +1,37 @@
-from typing import List, Optional
 import json
-from config import Config
+from typing import List
-from call_ai_function import call_ai_function
+
-from json_parser import fix_and_parse_json
+from autogpt.call_ai_function import call_ai_function
+from autogpt.config import Config
+
 cfg = Config()
 
 
 def evaluate_code(code: str) -> List[str]:
     """
-    A function that takes in a string and returns a response from create chat completion api call.
+    A function that takes in a string and returns a response from create chat
+      completion api call.
 
     Parameters:
         code (str): Code to be evaluated.
     Returns:
-        A result string from create chat completion. A list of suggestions to improve the code.
+        A result string from create chat completion. A list of suggestions to
+            improve the code.
     """
 
     function_string = "def analyze_code(code: str) -> List[str]:"
     args = [code]
-    description_string = """Analyzes the given code and returns a list of suggestions for improvements."""
+    description_string = (
+        "Analyzes the given code and returns a list of suggestions" " for improvements."
+    )
 
-    result_string = call_ai_function(function_string, args, description_string)
+    return call_ai_function(function_string, args, description_string)
-
-    return result_string
 
 
 def improve_code(suggestions: List[str], code: str) -> str:
     """
-    A function that takes in code and suggestions and returns a response from create chat completion api call.
+    A function that takes in code and suggestions and returns a response from create
+      chat completion api call.
 
     Parameters:
         suggestions (List): A list of suggestions around what needs to be improved.
@@ -40,28 +44,34 @@ def improve_code(suggestions: List[str], code: str) -> str:
         "def generate_improved_code(suggestions: List[str], code: str) -> str:"
     )
     args = [json.dumps(suggestions), code]
-    description_string = """Improves the provided code based on the suggestions provided, making no other changes."""
+    description_string = (
+        "Improves the provided code based on the suggestions"
+        " provided, making no other changes."
+    )
 
-    result_string = call_ai_function(function_string, args, description_string)
+    return call_ai_function(function_string, args, description_string)
-    return result_string
 
 
 def write_tests(code: str, focus: List[str]) -> str:
     """
-    A function that takes in code and focus topics and returns a response from create chat completion api call.
+    A function that takes in code and focus topics and returns a response from create
+      chat completion api call.
 
     Parameters:
         focus (List): A list of suggestions around what needs to be improved.
         code (str): Code for test cases to be generated against.
     Returns:
-        A result string from create chat completion. Test cases for the submitted code in response.
+        A result string from create chat completion. Test cases for the submitted code
+          in response.
     """
 
     function_string = (
         "def create_test_cases(code: str, focus: Optional[str] = None) -> str:"
     )
     args = [code, json.dumps(focus)]
-    description_string = """Generates test cases for the existing code, focusing on specific areas if required."""
+    description_string = (
+        "Generates test cases for the existing code, focusing on"
+        " specific areas if required."
+    )
 
-    result_string = call_ai_function(function_string, args, description_string)
+    return call_ai_function(function_string, args, description_string)
-    return result_string

autogpt/browse.py

@@ -1,10 +1,17 @@
+from urllib.parse import urljoin, urlparse
+
 import requests
 from bs4 import BeautifulSoup
-from config import Config
+
-from llm_utils import create_chat_completion
+from autogpt.config import Config
-from urllib.parse import urlparse, urljoin
+from autogpt.llm_utils import create_chat_completion
+from autogpt.memory import get_memory
 
 cfg = Config()
+memory = get_memory(cfg)
+
+session = requests.Session()
+session.headers.update({"User-Agent": cfg.user_agent})
 
 
 # Function to check if the URL is valid
@@ -23,23 +30,28 @@ def sanitize_url(url):
 
 # Define and check for local file address prefixes
 def check_local_file_access(url):
-    local_prefixes = ['file:///', 'file://localhost', 'http://localhost', 'https://localhost']
+    local_prefixes = [
+        "file:///",
+        "file://localhost",
+        "http://localhost",
+        "https://localhost",
+    ]
     return any(url.startswith(prefix) for prefix in local_prefixes)
 
 
-def get_response(url, headers=cfg.user_agent_header, timeout=10):
+def get_response(url, timeout=10):
     try:
         # Restrict access to local files
         if check_local_file_access(url):
-            raise ValueError('Access to local files is restricted')
+            raise ValueError("Access to local files is restricted")
 
         # Most basic check if the URL is valid:
-        if not url.startswith('http://') and not url.startswith('https://'):
+        if not url.startswith("http://") and not url.startswith("https://"):
-            raise ValueError('Invalid URL format')
+            raise ValueError("Invalid URL format")
 
         sanitized_url = sanitize_url(url)
 
-        response = requests.get(sanitized_url, headers=headers, timeout=timeout)
+        response = session.get(sanitized_url, timeout=timeout)
 
         # Check if the response contains an HTTP error
         if response.status_code >= 400:
@@ -51,7 +63,8 @@ def get_response(url, headers=cfg.user_agent_header, timeout=10):
         return None, "Error: " + str(ve)
 
     except requests.exceptions.RequestException as re:
-        # Handle exceptions related to the HTTP request (e.g., connection errors, timeouts, etc.)
+        # Handle exceptions related to the HTTP request
+        #  (e.g., connection errors, timeouts, etc.)
         return None, "Error: " + str(re)
 
 
@@ -60,6 +73,8 @@ def scrape_text(url):
     response, error_message = get_response(url)
     if error_message:
         return error_message
+    if not response:
+        return "Error: Could not get response"
 
     soup = BeautifulSoup(response.text, "html.parser")
 
@@ -69,7 +84,7 @@ def scrape_text(url):
     text = soup.get_text()
     lines = (line.strip() for line in text.splitlines())
     chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-    text = '\n'.join(chunk for chunk in chunks if chunk)
+    text = "\n".join(chunk for chunk in chunks if chunk)
 
     return text
 
@@ -77,8 +92,8 @@ def scrape_text(url):
 def extract_hyperlinks(soup):
     """Extract hyperlinks from a BeautifulSoup object"""
     hyperlinks = []
-    for link in soup.find_all('a', href=True):
+    for link in soup.find_all("a", href=True):
-        hyperlinks.append((link.text, link['href']))
+        hyperlinks.append((link.text, link["href"]))
     return hyperlinks
 
 
@@ -95,7 +110,8 @@ def scrape_links(url):
     response, error_message = get_response(url)
     if error_message:
         return error_message
-
+    if not response:
+        return "Error: Could not get response"
     soup = BeautifulSoup(response.text, "html.parser")
 
     for script in soup(["script", "style"]):
@@ -106,7 +122,7 @@ def scrape_links(url):
     return format_hyperlinks(hyperlinks)
 
 
-def split_text(text, max_length=8192):
+def split_text(text, max_length=cfg.browse_chunk_max_length):
     """Split text into chunks of a maximum length"""
     paragraphs = text.split("\n")
     current_length = 0
@@ -129,11 +145,13 @@ def create_message(chunk, question):
     """Create a message for the user to summarize a chunk of text"""
     return {
         "role": "user",
-        "content": f"\"\"\"{chunk}\"\"\" Using the above text, please answer the following question: \"{question}\" -- if the question cannot be answered using the text, please summarize the text."
+        "content": f'"""{chunk}""" Using the above text, please answer the following'
+        f' question: "{question}" -- if the question cannot be answered using the'
+        " text, please summarize the text.",
     }
 
 
-def summarize_text(text, question):
+def summarize_text(url, text, question):
     """Summarize text using the LLM model"""
     if not text:
         return "Error: No text to summarize"
@@ -145,15 +163,26 @@ def summarize_text(text, question):
     chunks = list(split_text(text))
 
     for i, chunk in enumerate(chunks):
+        print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
+
+        memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
+
+        memory.add(memory_to_add)
+
         print(f"Summarizing chunk {i + 1} / {len(chunks)}")
         messages = [create_message(chunk, question)]
 
         summary = create_chat_completion(
             model=cfg.fast_llm_model,
             messages=messages,
-            max_tokens=300,
+            max_tokens=cfg.browse_summary_max_token,
         )
         summaries.append(summary)
+        print(f"Added chunk {i + 1} summary to memory")
+
+        memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
+
+        memory.add(memory_to_add)
 
     print(f"Summarized {len(chunks)} chunks.")
 
@@ -163,7 +192,7 @@ def summarize_text(text, question):
     final_summary = create_chat_completion(
         model=cfg.fast_llm_model,
         messages=messages,
-        max_tokens=300,
+        max_tokens=cfg.browse_summary_max_token,
     )
 
     return final_summary

autogpt/call_ai_function.py

@@ -1,30 +1,26 @@
-from config import Config
+from autogpt.config import Config
+from autogpt.llm_utils import create_chat_completion
 
 cfg = Config()
 
-from llm_utils import create_chat_completion
-
 
 # This is a magic function that can do anything with no-code. See
 # https://github.com/Torantulino/AI-Functions for more info.
-def call_ai_function(function, args, description, model=None):
+def call_ai_function(function, args, description, model=None) -> str:
     """Call an AI function"""
     if model is None:
         model = cfg.smart_llm_model
     # For each arg, if any are None, convert to "None":
     args = [str(arg) if arg is not None else "None" for arg in args]
-    # parse args to comma seperated string
+    # parse args to comma separated string
     args = ", ".join(args)
     messages = [
         {
             "role": "system",
-            "content": f"You are now the following python function: ```# {description}\n{function}```\n\nOnly respond with your `return` value.",
+            "content": f"You are now the following python function: ```# {description}"
+            f"\n{function}```\n\nOnly respond with your `return` value.",
         },
         {"role": "user", "content": args},
     ]
 
-    response = create_chat_completion(
+    return create_chat_completion(model=model, messages=messages, temperature=0)
-        model=model, messages=messages, temperature=0
-    )
-
-    return response

autogpt/chat.py

@@ -1,11 +1,11 @@
 import time
-import openai
+
-from dotenv import load_dotenv
+from openai.error import RateLimitError
-from config import Config
+
-import token_counter
+from autogpt import token_counter
-from llm_utils import create_chat_completion
+from autogpt.config import Config
-from logger import logger
+from autogpt.llm_utils import create_chat_completion
-import logging
+from autogpt.logger import logger
 
 cfg = Config()
 
@@ -26,75 +26,105 @@ def create_chat_message(role, content):
 
 def generate_context(prompt, relevant_memory, full_message_history, model):
     current_context = [
+        create_chat_message("system", prompt),
         create_chat_message(
-            "system", prompt),
+            "system", f"The current time and date is {time.strftime('%c')}"
+        ),
         create_chat_message(
-            "system", f"The current time and date is {time.strftime('%c')}"),
+            "system",
-        create_chat_message(
+            f"This reminds you of these events from your past:\n{relevant_memory}\n\n",
-            "system", f"This reminds you of these events from your past:\n{relevant_memory}\n\n")]
+        ),
+    ]
 
     # Add messages from the full message history until we reach the token limit
     next_message_to_add_index = len(full_message_history) - 1
     insertion_index = len(current_context)
     # Count the currently used tokens
     current_tokens_used = token_counter.count_message_tokens(current_context, model)
-    return next_message_to_add_index, current_tokens_used, insertion_index, current_context
+    return (
+        next_message_to_add_index,
+        current_tokens_used,
+        insertion_index,
+        current_context,
+    )
 
 
 # TODO: Change debug from hardcode to argument
 def chat_with_ai(
-        prompt,
+    prompt, user_input, full_message_history, permanent_memory, token_limit
-        user_input,
+):
-        full_message_history,
+    """Interact with the OpenAI API, sending the prompt, user input, message history,
-        permanent_memory,
+    and permanent memory."""
-        token_limit):
-    """Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory."""
     while True:
         try:
             """
-            Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory.
+            Interact with the OpenAI API, sending the prompt, user input,
+                message history, and permanent memory.
 
             Args:
-            prompt (str): The prompt explaining the rules to the AI.
+                prompt (str): The prompt explaining the rules to the AI.
-            user_input (str): The input from the user.
+                user_input (str): The input from the user.
-            full_message_history (list): The list of all messages sent between the user and the AI.
+                full_message_history (list): The list of all messages sent between the
-            permanent_memory (Obj): The memory object containing the permanent memory.
+                    user and the AI.
-            token_limit (int): The maximum number of tokens allowed in the API call.
+                permanent_memory (Obj): The memory object containing the permanent
+                  memory.
+                token_limit (int): The maximum number of tokens allowed in the API call.
 
             Returns:
             str: The AI's response.
             """
-            model = cfg.fast_llm_model # TODO: Change model from hardcode to argument
+            model = cfg.fast_llm_model  # TODO: Change model from hardcode to argument
             # Reserve 1000 tokens for the response
 
             logger.debug(f"Token limit: {token_limit}")
             send_token_limit = token_limit - 1000
 
-            relevant_memory = permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
+            relevant_memory = (
+                ""
+                if len(full_message_history) == 0
+                else permanent_memory.get_relevant(str(full_message_history[-9:]), 10)
+            )
 
-            logger.debug(f'Memory Stats: {permanent_memory.get_stats()}')
+            logger.debug(f"Memory Stats: {permanent_memory.get_stats()}")
 
-            next_message_to_add_index, current_tokens_used, insertion_index, current_context = generate_context(
+            (
-                prompt, relevant_memory, full_message_history, model)
+                next_message_to_add_index,
+                current_tokens_used,
+                insertion_index,
+                current_context,
+            ) = generate_context(prompt, relevant_memory, full_message_history, model)
 
             while current_tokens_used > 2500:
                 # remove memories until we are under 2500 tokens
                 relevant_memory = relevant_memory[1:]
-                next_message_to_add_index, current_tokens_used, insertion_index, current_context = generate_context(
+                (
-                    prompt, relevant_memory, full_message_history, model)
+                    next_message_to_add_index,
+                    current_tokens_used,
+                    insertion_index,
+                    current_context,
+                ) = generate_context(
+                    prompt, relevant_memory, full_message_history, model
+                )
 
-            current_tokens_used += token_counter.count_message_tokens([create_chat_message("user", user_input)], model) # Account for user input (appended later)
+            current_tokens_used += token_counter.count_message_tokens(
+                [create_chat_message("user", user_input)], model
+            )  # Account for user input (appended later)
 
             while next_message_to_add_index >= 0:
                 # print (f"CURRENT TOKENS USED: {current_tokens_used}")
                 message_to_add = full_message_history[next_message_to_add_index]
 
-                tokens_to_add = token_counter.count_message_tokens([message_to_add], model)
+                tokens_to_add = token_counter.count_message_tokens(
+                    [message_to_add], model
+                )
                 if current_tokens_used + tokens_to_add > send_token_limit:
                     break
 
-                # Add the most recent message to the start of the current context, after the two system prompts.
+                # Add the most recent message to the start of the current context,
-                current_context.insert(insertion_index, full_message_history[next_message_to_add_index])
+                #  after the two system prompts.
+                current_context.insert(
+                    insertion_index, full_message_history[next_message_to_add_index]
+                )
 
                 # Count the currently used tokens
                 current_tokens_used += tokens_to_add
@@ -107,7 +137,9 @@ def chat_with_ai(
 
             # Calculate remaining tokens
             tokens_remaining = token_limit - current_tokens_used
-            # assert tokens_remaining >= 0, "Tokens remaining is negative. This should never happen, please submit a bug report at https://www.github.com/Torantulino/Auto-GPT"
+            # assert tokens_remaining >= 0, "Tokens remaining is negative.
+            # This should never happen, please submit a bug report at
+            #  https://www.github.com/Torantulino/Auto-GPT"
 
             # Debug print the current context
             logger.debug(f"Token limit: {token_limit}")
@@ -122,7 +154,8 @@ def chat_with_ai(
                 logger.debug("")
             logger.debug("----------- END OF CONTEXT ----------------")
 
-            # TODO: use a model defined elsewhere, so that model can contain temperature and other settings we care about
+            # TODO: use a model defined elsewhere, so that model can contain
+            # temperature and other settings we care about
             assistant_reply = create_chat_completion(
                 model=model,
                 messages=current_context,
@@ -130,15 +163,13 @@ def chat_with_ai(
             )
 
             # Update full message history
+            full_message_history.append(create_chat_message("user", user_input))
             full_message_history.append(
-                create_chat_message(
+                create_chat_message("assistant", assistant_reply)
-                    "user", user_input))
+            )
-            full_message_history.append(
-                create_chat_message(
-                    "assistant", assistant_reply))
 
             return assistant_reply
-        except openai.error.RateLimitError:
+        except RateLimitError:
             # TODO: When we switch to langchain, this is built in
             print("Error: ", "API Rate Limit Reached. Waiting 10 seconds...")
             time.sleep(10)

autogpt/commands.py

@@ -1,23 +1,29 @@
-import browse
 import json
-from memory import get_memory
 import datetime
-import agent_manager as agents
+import autogpt.agent_manager as agents
-import speak
+from autogpt.config import Config
-from config import Config
+from autogpt.json_parser import fix_and_parse_json
-import ai_functions as ai
+from autogpt.image_gen import generate_image
-from file_operations import read_file, write_to_file, append_to_file, delete_file, search_files
-from execute_code import execute_python_file, execute_shell
-from json_parser import fix_and_parse_json
-from image_gen import generate_image
 from duckduckgo_search import ddg
-from googleapiclient.discovery import build
+from autogpt.ai_functions import evaluate_code, improve_code, write_tests
-from googleapiclient.errors import HttpError
+from autogpt.browse import scrape_links, scrape_text, summarize_text
+from autogpt.execute_code import execute_python_file, execute_shell
+from autogpt.file_operations import (
+    append_to_file,
+    delete_file,
+    read_file,
+    search_files,
+    write_to_file,
+)
+from autogpt.memory import get_memory
+from autogpt.speak import say_text
+from autogpt.web import browse_website
+
 
 cfg = Config()
 
 
-def is_valid_int(value):
+def is_valid_int(value) -> bool:
     try:
         int(value)
         return True
@@ -31,9 +37,14 @@ def get_command(response):
         response_json = fix_and_parse_json(response)
 
         if "command" not in response_json:
-            return "Error:" , "Missing 'command' object in JSON"
+            return "Error:", "Missing 'command' object in JSON"
+
+        if not isinstance(response_json, dict):
+            return "Error:", f"'response_json' object is not dictionary {response_json}"
 
         command = response_json["command"]
+        if not isinstance(command, dict):
+            return "Error:", "'command' object is not a dictionary"
 
         if "name" not in command:
             return "Error:", "Missing 'name' field in 'command' object"
@@ -57,10 +68,11 @@ def execute_command(command_name, arguments):
 
     try:
         if command_name == "google":
-
             # Check if the Google API key is set and use the official search method
-            # If the API key is not set or has only whitespaces, use the unofficial search method
+            # If the API key is not set or has only whitespaces, use the unofficial
-            if cfg.google_api_key and (cfg.google_api_key.strip() if cfg.google_api_key else None):
+            # search method
+            key = cfg.google_api_key
+            if key and key.strip() and key != "your-google-api-key":
                 return google_official_search(arguments["input"])
             else:
                 return google_search(arguments["input"])
@@ -68,9 +80,8 @@ def execute_command(command_name, arguments):
             return memory.add(arguments["string"])
         elif command_name == "start_agent":
             return start_agent(
-                arguments["name"],
+                arguments["name"], arguments["task"], arguments["prompt"]
-                arguments["task"],
+            )
-                arguments["prompt"])
         elif command_name == "message_agent":
             return message_agent(arguments["key"], arguments["message"])
         elif command_name == "list_agents":
@@ -97,18 +108,22 @@ def execute_command(command_name, arguments):
         # non-file is given, return instructions "Input should be a python
         # filepath, write your code to file and try again"
         elif command_name == "evaluate_code":
-            return ai.evaluate_code(arguments["code"])
+            return evaluate_code(arguments["code"])
         elif command_name == "improve_code":
-            return ai.improve_code(arguments["suggestions"], arguments["code"])
+            return improve_code(arguments["suggestions"], arguments["code"])
         elif command_name == "write_tests":
-            return ai.write_tests(arguments["code"], arguments.get("focus"))
+            return write_tests(arguments["code"], arguments.get("focus"))
         elif command_name == "execute_python_file":  # Add this command
             return execute_python_file(arguments["file"])
         elif command_name == "execute_shell":
             if cfg.execute_local_commands:
                 return execute_shell(arguments["command_line"])
             else:
-                return "You are not allowed to run local shell commands. To execute shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' in your config. Do not attempt to bypass the restriction."
+                return (
+                    "You are not allowed to run local shell commands. To execute"
+                    " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
+                    "in your config. Do not attempt to bypass the restriction."
+                )
         elif command_name == "generate_image":
             return generate_image(arguments["prompt"])
         elif command_name == "do_nothing":
@@ -116,7 +131,11 @@ def execute_command(command_name, arguments):
         elif command_name == "task_complete":
             shutdown()
         else:
-            return f"Unknown command '{command_name}'. Please refer to the 'COMMANDS' list for available commands and only respond in the specified JSON format."
+            return (
+                f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
+                " list for available commands and only respond in the specified JSON"
+                " format."
+            )
     # All errors, return "Error: + error message"
     except Exception as e:
         return "Error: " + str(e)
@@ -124,13 +143,17 @@ def execute_command(command_name, arguments):
 
 def get_datetime():
     """Return the current date and time"""
-    return "Current date and time: " + \
+    return "Current date and time: " + datetime.datetime.now().strftime(
-        datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        "%Y-%m-%d %H:%M:%S"
+    )
 
 
 def google_search(query, num_results=8):
     """Return the results of a google search"""
     search_results = []
+    if not query:
+        return json.dumps(search_results)
+
     for j in ddg(query, max_results=num_results):
         search_results.append(j)
 
@@ -139,9 +162,10 @@ def google_search(query, num_results=8):
 
 def google_official_search(query, num_results=8):
     """Return the results of a google search using the official Google API"""
+    import json
+
     from googleapiclient.discovery import build
     from googleapiclient.errors import HttpError
-    import json
 
     try:
         # Get the Google API key and Custom Search Engine ID from the config file
@@ -152,7 +176,11 @@ def google_official_search(query, num_results=8):
         service = build("customsearch", "v1", developerKey=api_key)
 
         # Send the search query and retrieve the results
-        result = service.cse().list(q=query, cx=custom_search_engine_id, num=num_results).execute()
+        result = (
+            service.cse()
+            .list(q=query, cx=custom_search_engine_id, num=num_results)
+            .execute()
+        )
 
         # Extract the search result items from the response
         search_results = result.get("items", [])
@@ -165,7 +193,11 @@ def google_official_search(query, num_results=8):
         error_details = json.loads(e.content.decode())
 
         # Check if the error is related to an invalid or missing API key
-        if error_details.get("error", {}).get("code") == 403 and "invalid API key" in error_details.get("error", {}).get("message", ""):
+        if error_details.get("error", {}).get(
+            "code"
+        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
+            "message", ""
+        ):
             return "Error: The provided Google API key is invalid or missing."
         else:
             return f"Error: {e}"
@@ -174,77 +206,16 @@ def google_official_search(query, num_results=8):
     return search_results_links
 
 
-def browse_website(url, question):
-    """Browse a website and return the summary and links"""
-    summary = get_text_summary(url, question)
-    links = get_hyperlinks(url)
-
-    # Limit links to 5
-    if len(links) > 5:
-        links = links[:5]
-
-    result = f"""Website Content Summary: {summary}\n\nLinks: {links}"""
-
-    return result
-
-
 def get_text_summary(url, question):
     """Return the results of a google search"""
-    text = browse.scrape_text(url)
+    text = scrape_text(url)
-    summary = browse.summarize_text(text, question)
+    summary = summarize_text(url, text, question)
     return """ "Result" : """ + summary
 
 
 def get_hyperlinks(url):
     """Return the results of a google search"""
-    link_list = browse.scrape_links(url)
+    return scrape_links(url)
-    return link_list
-
-
-def commit_memory(string):
-    """Commit a string to memory"""
-    _text = f"""Committing memory with string "{string}" """
-    mem.permanent_memory.append(string)
-    return _text
-
-
-def delete_memory(key):
-    """Delete a memory with a given key"""
-    if key >= 0 and key < len(mem.permanent_memory):
-        _text = "Deleting memory with key " + str(key)
-        del mem.permanent_memory[key]
-        print(_text)
-        return _text
-    else:
-        print("Invalid key, cannot delete memory.")
-        return None
-
-
-def overwrite_memory(key, string):
-    """Overwrite a memory with a given key and string"""
-    # Check if the key is a valid integer
-    if is_valid_int(key):
-        key_int = int(key)
-        # Check if the integer key is within the range of the permanent_memory list
-        if 0 <= key_int < len(mem.permanent_memory):
-            _text = "Overwriting memory with key " + str(key) + " and string " + string
-            # Overwrite the memory slot with the given integer key and string
-            mem.permanent_memory[key_int] = string
-            print(_text)
-            return _text
-        else:
-            print(f"Invalid key '{key}', out of range.")
-            return None
-    # Check if the key is a valid string
-    elif isinstance(key, str):
-        _text = "Overwriting memory with key " + key + " and string " + string
-        # Overwrite the memory slot with the given string key and string
-        mem.permanent_memory[key] = string
-        print(_text)
-        return _text
-    else:
-        print(f"Invalid key '{key}', must be an integer or a string.")
-        return None
 
 
 def shutdown():
@@ -255,8 +226,6 @@ def shutdown():
 
 def start_agent(name, task, prompt, model=cfg.fast_llm_model):
     """Start an agent with a given name, task, and prompt"""
-    global cfg
-
     # Remove underscores from name
     voice_name = name.replace("_", " ")
 
@@ -265,22 +234,20 @@ def start_agent(name, task, prompt, model=cfg.fast_llm_model):
 
     # Create agent
     if cfg.speak_mode:
-        speak.say_text(agent_intro, 1)
+        say_text(agent_intro, 1)
     key, ack = agents.create_agent(task, first_message, model)
 
     if cfg.speak_mode:
-        speak.say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
+        say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
 
     # Assign task (prompt), get response
-    agent_response = message_agent(key, prompt)
+    agent_response = agents.message_agent(key, prompt)
 
     return f"Agent {name} created with key {key}. First response: {agent_response}"
 
 
 def message_agent(key, message):
     """Message an agent with a given key and message"""
-    global cfg
-
     # Check if the key is a valid integer
     if is_valid_int(key):
         agent_response = agents.message_agent(int(key), message)
@@ -292,18 +259,16 @@ def message_agent(key, message):
 
     # Speak response
     if cfg.speak_mode:
-        speak.say_text(agent_response, 1)
+        say_text(agent_response, 1)
     return agent_response
 
 
 def list_agents():
     """List all agents"""
-    return agents.list_agents()
+    return list_agents()
 
 
 def delete_agent(key):
     """Delete an agent with a given key"""
     result = agents.delete_agent(key)
-    if not result:
+    return f"Agent {key} deleted." if result else f"Agent {key} does not exist."
-        return f"Agent {key} does not exist."
-    return f"Agent {key} deleted."

autogpt/config.py

@@ -1,8 +1,10 @@
 import abc
 import os
+
 import openai
 import yaml
 from dotenv import load_dotenv
+
 # Load environment variables from .env file
 load_dotenv()
 
@@ -17,9 +19,7 @@ class Singleton(abc.ABCMeta, type):
     def __call__(cls, *args, **kwargs):
         """Call method for the singleton metaclass."""
         if cls not in cls._instances:
-            cls._instances[cls] = super(
+            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
-                Singleton, cls).__call__(
-                *args, **kwargs)
         return cls._instances[cls]
 
 
@@ -36,17 +36,24 @@ class Config(metaclass=Singleton):
         """Initialize the Config class"""
         self.debug_mode = False
         self.continuous_mode = False
+        self.continuous_limit = 0
         self.speak_mode = False
+        self.skip_reprompt = False
 
+        self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml")
         self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo")
         self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4")
         self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000))
         self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000))
+        self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 8192))
+        self.browse_summary_max_token = int(os.getenv("BROWSE_SUMMARY_MAX_TOKEN", 300))
 
         self.openai_api_key = os.getenv("OPENAI_API_KEY")
         self.temperature = float(os.getenv("TEMPERATURE", "1"))
-        self.use_azure = os.getenv("USE_AZURE") == 'True'
+        self.use_azure = os.getenv("USE_AZURE") == "True"
-        self.execute_local_commands = os.getenv('EXECUTE_LOCAL_COMMANDS', 'False') == 'True'
+        self.execute_local_commands = (
+            os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True"
+        )
 
         if self.use_azure:
             self.load_azure_config()
@@ -61,6 +68,9 @@ class Config(metaclass=Singleton):
         self.use_mac_os_tts = False
         self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS")
 
+        self.use_brian_tts = False
+        self.use_brian_tts = os.getenv("USE_BRIAN_TTS")
+
         self.google_api_key = os.getenv("GOOGLE_API_KEY")
         self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
 
@@ -82,15 +92,18 @@ class Config(metaclass=Singleton):
 
         # User agent headers to use when browsing web
         # Some websites might just completely deny request with an error code if no user agent was found.
-        self.user_agent_header = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}
+        self.user_agent = os.getenv(
+            "USER_AGENT",
+            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",
+        )
         self.redis_host = os.getenv("REDIS_HOST", "localhost")
         self.redis_port = os.getenv("REDIS_PORT", "6379")
         self.redis_password = os.getenv("REDIS_PASSWORD", "")
-        self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == 'True'
+        self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True"
-        self.memory_index = os.getenv("MEMORY_INDEX", 'auto-gpt')
+        self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt")
         # Note that indexes must be created on db 0 in redis, this is not configurable.
 
-        self.memory_backend = os.getenv("MEMORY_BACKEND", 'local')
+        self.memory_backend = os.getenv("MEMORY_BACKEND", "local")
         # Initialize the OpenAI API client
         openai.api_key = self.openai_api_key
 
@@ -107,15 +120,19 @@ class Config(metaclass=Singleton):
         if model == self.fast_llm_model:
             return self.azure_model_to_deployment_id_map["fast_llm_model_deployment_id"]
         elif model == self.smart_llm_model:
-            return self.azure_model_to_deployment_id_map["smart_llm_model_deployment_id"]
+            return self.azure_model_to_deployment_id_map[
+                "smart_llm_model_deployment_id"
+            ]
         elif model == "text-embedding-ada-002":
-            return self.azure_model_to_deployment_id_map["embedding_model_deployment_id"]
+            return self.azure_model_to_deployment_id_map[
+                "embedding_model_deployment_id"
+            ]
         else:
             return ""
 
-    AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), '..', 'azure.yaml')
+    AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "..", "azure.yaml")
 
-    def load_azure_config(self, config_file: str=AZURE_CONFIG_FILE) -> None:
+    def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None:
         """
         Loads the configuration parameters for Azure hosting from the specified file path as a yaml file.
 
@@ -130,15 +147,25 @@ class Config(metaclass=Singleton):
                 config_params = yaml.load(file, Loader=yaml.FullLoader)
         except FileNotFoundError:
             config_params = {}
-        self.openai_api_type = os.getenv("OPENAI_API_TYPE", config_params.get("azure_api_type", "azure"))
+        self.openai_api_type = os.getenv(
-        self.openai_api_base = os.getenv("OPENAI_AZURE_API_BASE", config_params.get("azure_api_base", ""))
+            "OPENAI_API_TYPE", config_params.get("azure_api_type", "azure")
-        self.openai_api_version = os.getenv("OPENAI_AZURE_API_VERSION", config_params.get("azure_api_version", ""))
+        )
+        self.openai_api_base = os.getenv(
+            "OPENAI_AZURE_API_BASE", config_params.get("azure_api_base", "")
+        )
+        self.openai_api_version = os.getenv(
+            "OPENAI_AZURE_API_VERSION", config_params.get("azure_api_version", "")
+        )
         self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", [])
 
     def set_continuous_mode(self, value: bool):
         """Set the continuous mode value."""
         self.continuous_mode = value
 
+    def set_continuous_limit(self, value: int):
+        """Set the continuous limit value."""
+        self.continuous_limit = value
+
     def set_speak_mode(self, value: bool):
         """Set the speak mode value."""
         self.speak_mode = value
@@ -159,6 +186,14 @@ class Config(metaclass=Singleton):
         """Set the smart token limit value."""
         self.smart_token_limit = value
 
+    def set_browse_chunk_max_length(self, value: int):
+        """Set the browse_website command chunk max length value."""
+        self.browse_chunk_max_length = value
+
+    def set_browse_summary_max_token(self, value: int):
+        """Set the browse_website command summary max token value."""
+        self.browse_summary_max_token = value
+
     def set_openai_api_key(self, value: str):
         """Set the OpenAI API key value."""
         self.openai_api_key = value

autogpt/data_ingestion.py

@@ -0,0 +1,95 @@
+import argparse
+import logging
+
+from autogpt.config import Config
+from autogpt.file_operations import ingest_file, search_files
+from autogpt.memory import get_memory
+
+cfg = Config()
+
+
+def configure_logging():
+    logging.basicConfig(
+        filename="log-ingestion.txt",
+        filemode="a",
+        format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
+        datefmt="%H:%M:%S",
+        level=logging.DEBUG,
+    )
+    return logging.getLogger("AutoGPT-Ingestion")
+
+
+def ingest_directory(directory, memory, args):
+    """
+    Ingest all files in a directory by calling the ingest_file function for each file.
+
+    :param directory: The directory containing the files to ingest
+    :param memory: An object with an add() method to store the chunks in memory
+    """
+    try:
+        files = search_files(directory)
+        for file in files:
+            ingest_file(file, memory, args.max_length, args.overlap)
+    except Exception as e:
+        print(f"Error while ingesting directory '{directory}': {str(e)}")
+
+
+def main() -> None:
+    logger = configure_logging()
+
+    parser = argparse.ArgumentParser(
+        description="Ingest a file or a directory with multiple files into memory. "
+        "Make sure to set your .env before running this script."
+    )
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("--file", type=str, help="The file to ingest.")
+    group.add_argument(
+        "--dir", type=str, help="The directory containing the files to ingest."
+    )
+    parser.add_argument(
+        "--init",
+        action="store_true",
+        help="Init the memory and wipe its content (default: False)",
+        default=False,
+    )
+    parser.add_argument(
+        "--overlap",
+        type=int,
+        help="The overlap size between chunks when ingesting files (default: 200)",
+        default=200,
+    )
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        help="The max_length of each chunk when ingesting files (default: 4000)",
+        default=4000,
+    )
+
+    args = parser.parse_args()
+
+    # Initialize memory
+    memory = get_memory(cfg, init=args.init)
+    print("Using memory of type: " + memory.__class__.__name__)
+
+    if args.file:
+        try:
+            ingest_file(args.file, memory, args.max_length, args.overlap)
+            print(f"File '{args.file}' ingested successfully.")
+        except Exception as e:
+            logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
+            print(f"Error while ingesting file '{args.file}': {str(e)}")
+    elif args.dir:
+        try:
+            ingest_directory(args.dir, memory, args)
+            print(f"Directory '{args.dir}' ingested successfully.")
+        except Exception as e:
+            logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
+            print(f"Error while ingesting directory '{args.dir}': {str(e)}")
+    else:
+        print(
+            "Please provide either a file path (--file) or a directory name (--dir) inside the auto_gpt_workspace directory as input."
+        )
+
+
+if __name__ == "__main__":
+    main()

autogpt/execute_code.py

@@ -0,0 +1,105 @@
+import os
+import subprocess
+
+import docker
+from docker.errors import ImageNotFound
+
+WORKSPACE_FOLDER = "auto_gpt_workspace"
+
+
+def execute_python_file(file):
+    """Execute a Python file in a Docker container and return the output"""
+
+    print(f"Executing file '{file}' in workspace '{WORKSPACE_FOLDER}'")
+
+    if not file.endswith(".py"):
+        return "Error: Invalid file type. Only .py files are allowed."
+
+    file_path = os.path.join(WORKSPACE_FOLDER, file)
+
+    if not os.path.isfile(file_path):
+        return f"Error: File '{file}' does not exist."
+
+    if we_are_running_in_a_docker_container():
+        result = subprocess.run(
+            f"python {file_path}", capture_output=True, encoding="utf8", shell=True
+        )
+        if result.returncode == 0:
+            return result.stdout
+        else:
+            return f"Error: {result.stderr}"
+    else:
+        try:
+            client = docker.from_env()
+
+            image_name = "python:3.10"
+            try:
+                client.images.get(image_name)
+                print(f"Image '{image_name}' found locally")
+            except ImageNotFound:
+                print(
+                    f"Image '{image_name}' not found locally, pulling from Docker Hub"
+                )
+                # Use the low-level API to stream the pull response
+                low_level_client = docker.APIClient()
+                for line in low_level_client.pull(image_name, stream=True, decode=True):
+                    # Print the status and progress, if available
+                    status = line.get("status")
+                    progress = line.get("progress")
+                    if status and progress:
+                        print(f"{status}: {progress}")
+                    elif status:
+                        print(status)
+
+            # You can replace 'python:3.8' with the desired Python image/version
+            # You can find available Python images on Docker Hub:
+            # https://hub.docker.com/_/python
+            container = client.containers.run(
+                image_name,
+                f"python {file}",
+                volumes={
+                    os.path.abspath(WORKSPACE_FOLDER): {
+                        "bind": "/workspace",
+                        "mode": "ro",
+                    }
+                },
+                working_dir="/workspace",
+                stderr=True,
+                stdout=True,
+                detach=True,
+            )
+
+            container.wait()
+            logs = container.logs().decode("utf-8")
+            container.remove()
+
+            # print(f"Execution complete. Output: {output}")
+            # print(f"Logs: {logs}")
+
+            return logs
+
+        except Exception as e:
+            return f"Error: {str(e)}"
+
+
+def execute_shell(command_line):
+    current_dir = os.getcwd()
+
+    if WORKSPACE_FOLDER not in current_dir:  # Change dir into workspace if necessary
+        work_dir = os.path.join(os.getcwd(), WORKSPACE_FOLDER)
+        os.chdir(work_dir)
+
+    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
+
+    result = subprocess.run(command_line, capture_output=True, shell=True)
+    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
+
+    # Change back to whatever the prior working dir was
+
+    os.chdir(current_dir)
+
+    return output
+
+
+def we_are_running_in_a_docker_container():
+    os.path.exists("/.dockerenv")

autogpt/file_operations.py

@@ -0,0 +1,141 @@
+import os
+import os.path
+
+# Set a dedicated folder for file I/O
+working_directory = "auto_gpt_workspace"
+
+# Create the directory if it doesn't exist
+if not os.path.exists(working_directory):
+    os.makedirs(working_directory)
+
+
+def safe_join(base, *paths):
+    """Join one or more path components intelligently."""
+    new_path = os.path.join(base, *paths)
+    norm_new_path = os.path.normpath(new_path)
+
+    if os.path.commonprefix([base, norm_new_path]) != base:
+        raise ValueError("Attempted to access outside of working directory.")
+
+    return norm_new_path
+
+
+def split_file(content, max_length=4000, overlap=0):
+    """
+    Split text into chunks of a specified maximum length with a specified overlap
+    between chunks.
+
+    :param text: The input text to be split into chunks
+    :param max_length: The maximum length of each chunk,
+        default is 4000 (about 1k token)
+    :param overlap: The number of overlapping characters between chunks,
+        default is no overlap
+    :return: A generator yielding chunks of text
+    """
+    start = 0
+    content_length = len(content)
+
+    while start < content_length:
+        end = start + max_length
+        if end + overlap < content_length:
+            chunk = content[start : end + overlap]
+        else:
+            chunk = content[start:content_length]
+        yield chunk
+        start += max_length - overlap
+
+
+def read_file(filename) -> str:
+    """Read a file and return the contents"""
+    try:
+        filepath = safe_join(working_directory, filename)
+        with open(filepath, "r", encoding="utf-8") as f:
+            content = f.read()
+        return content
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+def ingest_file(filename, memory, max_length=4000, overlap=200):
+    """
+    Ingest a file by reading its content, splitting it into chunks with a specified
+    maximum length and overlap, and adding the chunks to the memory storage.
+
+    :param filename: The name of the file to ingest
+    :param memory: An object with an add() method to store the chunks in memory
+    :param max_length: The maximum length of each chunk, default is 4000
+    :param overlap: The number of overlapping characters between chunks, default is 200
+    """
+    try:
+        print(f"Working with file {filename}")
+        content = read_file(filename)
+        content_length = len(content)
+        print(f"File length: {content_length} characters")
+
+        chunks = list(split_file(content, max_length=max_length, overlap=overlap))
+
+        num_chunks = len(chunks)
+        for i, chunk in enumerate(chunks):
+            print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
+            memory_to_add = (
+                f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
+            )
+
+            memory.add(memory_to_add)
+
+        print(f"Done ingesting {num_chunks} chunks from {filename}.")
+    except Exception as e:
+        print(f"Error while ingesting file '{filename}': {str(e)}")
+
+
+def write_to_file(filename, text):
+    """Write text to a file"""
+    try:
+        filepath = safe_join(working_directory, filename)
+        directory = os.path.dirname(filepath)
+        if not os.path.exists(directory):
+            os.makedirs(directory)
+        with open(filepath, "w", encoding="utf-8") as f:
+            f.write(text)
+        return "File written to successfully."
+    except Exception as e:
+        return "Error: " + str(e)
+
+
+def append_to_file(filename, text):
+    """Append text to a file"""
+    try:
+        filepath = safe_join(working_directory, filename)
+        with open(filepath, "a") as f:
+            f.write(text)
+        return "Text appended successfully."
+    except Exception as e:
+        return "Error: " + str(e)
+
+
+def delete_file(filename):
+    """Delete a file"""
+    try:
+        filepath = safe_join(working_directory, filename)
+        os.remove(filepath)
+        return "File deleted successfully."
+    except Exception as e:
+        return "Error: " + str(e)
+
+
+def search_files(directory):
+    found_files = []
+
+    if directory == "" or directory == "/":
+        search_directory = working_directory
+    else:
+        search_directory = safe_join(working_directory, directory)
+
+    for root, _, files in os.walk(search_directory):
+        for file in files:
+            if file.startswith("."):
+                continue
+            relative_path = os.path.relpath(os.path.join(root, file), working_directory)
+            found_files.append(relative_path)
+
+    return found_files

autogpt/image_gen.py

@@ -1,24 +1,24 @@
-import requests
 import io
 import os.path
-from PIL import Image
-from config import Config
 import uuid
-import openai
 from base64 import b64decode
 
+import openai
+import requests
+from PIL import Image
+
+from autogpt.config import Config
+
 cfg = Config()
 
 working_directory = "auto_gpt_workspace"
 
 
 def generate_image(prompt):
-
     filename = str(uuid.uuid4()) + ".jpg"
 
     # DALL-E
-    if cfg.image_provider == 'dalle':
+    if cfg.image_provider == "dalle":
-
         openai.api_key = cfg.openai_api_key
 
         response = openai.Image.create(
@@ -38,14 +38,23 @@ def generate_image(prompt):
         return "Saved to disk:" + filename
 
     # STABLE DIFFUSION
-    elif cfg.image_provider == 'sd':
+    elif cfg.image_provider == "sd":
-
+        API_URL = (
-        API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+            "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+        )
+        if cfg.huggingface_api_token is None:
+            raise ValueError(
+                "You need to set your Hugging Face API token in the config file."
+            )
         headers = {"Authorization": "Bearer " + cfg.huggingface_api_token}
 
-        response = requests.post(API_URL, headers=headers, json={
+        response = requests.post(
-            "inputs": prompt,
+            API_URL,
-        })
+            headers=headers,
+            json={
+                "inputs": prompt,
+            },
+        )
 
         image = Image.open(io.BytesIO(response.content))
         print("Image Generated for prompt:" + prompt)

autogpt/js/overlay.js

@@ -0,0 +1,29 @@
+const overlay = document.createElement('div');
+Object.assign(overlay.style, {
+    position: 'fixed',
+    zIndex: 999999,
+    top: 0,
+    left: 0,
+    width: '100%',
+    height: '100%',
+    background: 'rgba(0, 0, 0, 0.7)',
+    color: '#fff',
+    fontSize: '24px',
+    fontWeight: 'bold',
+    display: 'flex',
+    justifyContent: 'center',
+    alignItems: 'center',
+});
+const textContent = document.createElement('div');
+Object.assign(textContent.style, {
+    textAlign: 'center',
+});
+textContent.textContent = 'AutoGPT Analyzing Page';
+overlay.appendChild(textContent);
+document.body.append(overlay);
+document.body.style.overflow = 'hidden';
+let dotCount = 0;
+setInterval(() => {
+    textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
+    dotCount = (dotCount + 1) % 4;
+}, 1000);

autogpt/json_parser.py

@@ -1,9 +1,10 @@
 import json
 from typing import Any, Dict, Union
-from call_ai_function import call_ai_function
+
-from config import Config
+from autogpt.call_ai_function import call_ai_function
-from json_utils import correct_json
+from autogpt.config import Config
-from logger import logger
+from autogpt.json_utils import correct_json
+from autogpt.logger import logger
 
 cfg = Config()
 
@@ -11,7 +12,7 @@ JSON_SCHEMA = """
 {
     "command": {
         "name": "command name",
-        "args":{
+        "args": {
             "arg name": "value"
         }
     },
@@ -28,12 +29,11 @@ JSON_SCHEMA = """
 
 
 def fix_and_parse_json(
-    json_str: str,
+    json_str: str, try_to_fix_with_gpt: bool = True
-    try_to_fix_with_gpt: bool = True
 ) -> Union[str, Dict[Any, Any]]:
     """Fix and parse JSON string"""
     try:
-        json_str = json_str.replace('\t', '')
+        json_str = json_str.replace("\t", "")
         return json.loads(json_str)
     except json.JSONDecodeError as _:  # noqa: F841
         try:
@@ -52,15 +52,17 @@ def fix_and_parse_json(
         brace_index = json_str.index("{")
         json_str = json_str[brace_index:]
         last_brace_index = json_str.rindex("}")
-        json_str = json_str[:last_brace_index+1]
+        json_str = json_str[: last_brace_index + 1]
         return json.loads(json_str)
     # Can throw a ValueError if there is no "{" or "}" in the json_str
     except (json.JSONDecodeError, ValueError) as e:  # noqa: F841
         if try_to_fix_with_gpt:
-            logger.warn("Warning: Failed to parse AI output, attempting to fix."
+            logger.warn(
-                  "\n If you see this warning frequently, it's likely that"
+                "Warning: Failed to parse AI output, attempting to fix."
-                  " your prompt is confusing the AI. Try changing it up"
+                "\n If you see this warning frequently, it's likely that"
-                  " slightly.")
+                " your prompt is confusing the AI. Try changing it up"
+                " slightly."
+            )
             # Now try to fix this up using the ai_functions
             ai_fixed_json = fix_json(json_str, JSON_SCHEMA)
 
@@ -80,11 +82,13 @@ def fix_json(json_str: str, schema: str) -> str:
     # Try to fix the JSON using GPT:
     function_string = "def fix_json(json_str: str, schema:str=None) -> str:"
     args = [f"'''{json_str}'''", f"'''{schema}'''"]
-    description_string = "Fixes the provided JSON string to make it parseable"\
+    description_string = (
-        " and fully compliant with the provided schema.\n If an object or"\
+        "Fixes the provided JSON string to make it parseable"
-        " field specified in the schema isn't contained within the correct"\
+        " and fully compliant with the provided schema.\n If an object or"
-        " JSON, it is omitted.\n This function is brilliant at guessing"\
+        " field specified in the schema isn't contained within the correct"
+        " JSON, it is omitted.\n This function is brilliant at guessing"
         " when the format is incorrect."
+    )
 
     # If it doesn't already start with a "`", add one:
     if not json_str.startswith("`"):

autogpt/json_utils.py

@@ -1,6 +1,8 @@
-import re
 import json
-from config import Config
+import re
+from typing import Optional
+
+from autogpt.config import Config
 
 cfg = Config()
 
@@ -17,7 +19,7 @@ def extract_char_position(error_message: str) -> int:
     """
     import re
 
-    char_pattern = re.compile(r'\(char (\d+)\)')
+    char_pattern = re.compile(r"\(char (\d+)\)")
     if match := char_pattern.search(error_message):
         return int(match[1])
     else:
@@ -38,10 +40,8 @@ def add_quotes_to_property_names(json_string: str) -> str:
     def replace_func(match):
         return f'"{match.group(1)}":'
 
-    property_name_pattern = re.compile(r'(\w+):')
+    property_name_pattern = re.compile(r"(\w+):")
-    corrected_json_string = property_name_pattern.sub(
+    corrected_json_string = property_name_pattern.sub(replace_func, json_string)
-        replace_func,
-        json_string)
 
     try:
         json.loads(corrected_json_string)
@@ -50,7 +50,7 @@ def add_quotes_to_property_names(json_string: str) -> str:
         raise e
 
 
-def balance_braces(json_string: str) -> str:
+def balance_braces(json_string: str) -> Optional[str]:
     """
     Balance the braces in a JSON string.
 
@@ -61,35 +61,34 @@ def balance_braces(json_string: str) -> str:
         str: The JSON string with braces balanced.
     """
 
-    open_braces_count = json_string.count('{')
+    open_braces_count = json_string.count("{")
-    close_braces_count = json_string.count('}')
+    close_braces_count = json_string.count("}")
 
     while open_braces_count > close_braces_count:
-        json_string += '}'
+        json_string += "}"
         close_braces_count += 1
 
     while close_braces_count > open_braces_count:
-        json_string = json_string.rstrip('}')
+        json_string = json_string.rstrip("}")
         close_braces_count -= 1
 
     try:
         json.loads(json_string)
         return json_string
-    except json.JSONDecodeError as e:
+    except json.JSONDecodeError:
         pass
 
 
 def fix_invalid_escape(json_str: str, error_message: str) -> str:
-    while error_message.startswith('Invalid \\escape'):
+    while error_message.startswith("Invalid \\escape"):
         bad_escape_location = extract_char_position(error_message)
-        json_str = json_str[:bad_escape_location] + \
+        json_str = json_str[:bad_escape_location] + json_str[bad_escape_location + 1 :]
-            json_str[bad_escape_location + 1:]
         try:
             json.loads(json_str)
             return json_str
         except json.JSONDecodeError as e:
             if cfg.debug_mode:
-                print('json loads error - fix invalid escape', e)
+                print("json loads error - fix invalid escape", e)
             error_message = str(e)
     return json_str
 
@@ -109,18 +108,20 @@ def correct_json(json_str: str) -> str:
         return json_str
     except json.JSONDecodeError as e:
         if cfg.debug_mode:
-            print('json loads error', e)
+            print("json loads error", e)
         error_message = str(e)
-        if error_message.startswith('Invalid \\escape'):
+        if error_message.startswith("Invalid \\escape"):
             json_str = fix_invalid_escape(json_str, error_message)
-        if error_message.startswith('Expecting property name enclosed in double quotes'):
+        if error_message.startswith(
+            "Expecting property name enclosed in double quotes"
+        ):
             json_str = add_quotes_to_property_names(json_str)
             try:
                 json.loads(json_str)
                 return json_str
             except json.JSONDecodeError as e:
                 if cfg.debug_mode:
-                    print('json loads error - add quotes', e)
+                    print("json loads error - add quotes", e)
                 error_message = str(e)
         if balanced_str := balance_braces(json_str):
             return balanced_str

autogpt/llm_utils.py

@@ -0,0 +1,69 @@
+import time
+
+import openai
+from openai.error import APIError, RateLimitError
+from colorama import Fore
+
+from autogpt.config import Config
+
+cfg = Config()
+
+openai.api_key = cfg.openai_api_key
+
+
+# Overly simple abstraction until we create something better
+# simple retry mechanism when getting a rate error or a bad gateway
+def create_chat_completion(
+    messages, model=None, temperature=cfg.temperature, max_tokens=None
+) -> str:
+    """Create a chat completion using the OpenAI API"""
+    response = None
+    num_retries = 5
+    if cfg.debug_mode:
+        print(
+            Fore.GREEN
+            + f"Creating chat completion with model {model}, temperature {temperature},"
+            f" max_tokens {max_tokens}" + Fore.RESET
+        )
+    for attempt in range(num_retries):
+        try:
+            if cfg.use_azure:
+                response = openai.ChatCompletion.create(
+                    deployment_id=cfg.get_azure_deployment_id_for_model(model),
+                    model=model,
+                    messages=messages,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                )
+            else:
+                response = openai.ChatCompletion.create(
+                    model=model,
+                    messages=messages,
+                    temperature=temperature,
+                    max_tokens=max_tokens,
+                )
+            break
+        except RateLimitError:
+            if cfg.debug_mode:
+                print(
+                    Fore.RED + "Error: ",
+                    "API Rate Limit Reached. Waiting 20 seconds..." + Fore.RESET,
+                )
+            time.sleep(20)
+        except APIError as e:
+            if e.http_status == 502:
+                if cfg.debug_mode:
+                    print(
+                        Fore.RED + "Error: ",
+                        "API Bad gateway. Waiting 20 seconds..." + Fore.RESET,
+                    )
+                time.sleep(20)
+            else:
+                raise
+            if attempt == num_retries - 1:
+                raise
+
+    if response is None:
+        raise RuntimeError("Failed to get response after 5 retries")
+
+    return response.choices[0].message["content"]

autogpt/logger.py

@@ -4,34 +4,33 @@ import random
 import re
 import time
 from logging import LogRecord
-from colorama import Fore
 
-from colorama import Style
+from colorama import Fore, Style
 
-import speak
+from autogpt import speak
-from config import Config
+from autogpt.config import Config, Singleton
-from config import Singleton
 
 cfg = Config()
 
-'''
+"""
 Logger that handle titles in different colors.
 Outputs logs in console, activity.log, and errors.log
 For console handler: simulates typing
-'''
+"""
 
 
 class Logger(metaclass=Singleton):
     def __init__(self):
         # create log directory if it doesn't exist
-        log_dir = os.path.join('..', 'logs')
+        this_files_dir_path = os.path.dirname(__file__)
+        log_dir = os.path.join(this_files_dir_path, "../logs")
         if not os.path.exists(log_dir):
             os.makedirs(log_dir)
 
         log_file = "activity.log"
         error_file = "error.log"
 
-        console_formatter = AutoGptFormatter('%(title_color)s %(message)s')
+        console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
 
         # Create a handler for console which simulate typing
         self.typing_console_handler = TypingConsoleHandler()
@@ -46,35 +45,35 @@ class Logger(metaclass=Singleton):
         # Info handler in activity.log
         self.file_handler = logging.FileHandler(os.path.join(log_dir, log_file))
         self.file_handler.setLevel(logging.DEBUG)
-        info_formatter = AutoGptFormatter('%(asctime)s %(levelname)s %(title)s %(message_no_color)s')
+        info_formatter = AutoGptFormatter(
+            "%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
+        )
         self.file_handler.setFormatter(info_formatter)
 
         # Error handler error.log
         error_handler = logging.FileHandler(os.path.join(log_dir, error_file))
         error_handler.setLevel(logging.ERROR)
         error_formatter = AutoGptFormatter(
-            '%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s %(message_no_color)s')
+            "%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
+            " %(message_no_color)s"
+        )
         error_handler.setFormatter(error_formatter)
 
-        self.typing_logger = logging.getLogger('TYPER')
+        self.typing_logger = logging.getLogger("TYPER")
         self.typing_logger.addHandler(self.typing_console_handler)
         self.typing_logger.addHandler(self.file_handler)
         self.typing_logger.addHandler(error_handler)
         self.typing_logger.setLevel(logging.DEBUG)
 
-        self.logger = logging.getLogger('LOGGER')
+        self.logger = logging.getLogger("LOGGER")
         self.logger.addHandler(self.console_handler)
         self.logger.addHandler(self.file_handler)
         self.logger.addHandler(error_handler)
         self.logger.setLevel(logging.DEBUG)
 
     def typewriter_log(
-            self,
+        self, title="", title_color="", content="", speak_text=False, level=logging.INFO
-            title='',
+    ):
-            title_color='',
-            content='',
-            speak_text=False,
-            level=logging.INFO):
         if speak_text and cfg.speak_mode:
             speak.say_text(f"{title}. {content}")
 
@@ -84,41 +83,34 @@ class Logger(metaclass=Singleton):
         else:
             content = ""
 
-        self.typing_logger.log(level, content, extra={'title': title, 'color': title_color})
+        self.typing_logger.log(
+            level, content, extra={"title": title, "color": title_color}
+        )
 
     def debug(
-            self,
+        self,
-            message,
+        message,
-            title='',
+        title="",
-            title_color='',
+        title_color="",
     ):
         self._log(title, title_color, message, logging.DEBUG)
 
     def warn(
-            self,
+        self,
-            message,
+        message,
-            title='',
+        title="",
-            title_color='',
+        title_color="",
     ):
         self._log(title, title_color, message, logging.WARN)
 
-    def error(
+    def error(self, title, message=""):
-            self,
-            title,
-            message=''
-    ):
         self._log(title, Fore.RED, message, logging.ERROR)
 
-    def _log(
+    def _log(self, title="", title_color="", message="", level=logging.INFO):
-            self,
-            title='',
-            title_color='',
-            message='',
-            level=logging.INFO):
         if message:
             if isinstance(message, list):
                 message = " ".join(message)
-        self.logger.log(level, message, extra={'title': title, 'color': title_color})
+        self.logger.log(level, message, extra={"title": title, "color": title_color})
 
     def set_level(self, level):
         self.logger.setLevel(level)
@@ -126,14 +118,19 @@ class Logger(metaclass=Singleton):
 
     def double_check(self, additionalText=None):
         if not additionalText:
-            additionalText = "Please ensure you've setup and configured everything correctly. Read https://github.com/Torantulino/Auto-GPT#readme to double check. You can also create a github issue or join the discord and ask there!"
+            additionalText = (
+                "Please ensure you've setup and configured everything"
+                " correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
+                "double check. You can also create a github issue or join the discord"
+                " and ask there!"
+            )
 
         self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
 
 
-'''
+"""
 Output stream to console using simulated typing
-'''
+"""
 
 
 class TypingConsoleHandler(logging.StreamHandler):
@@ -159,7 +156,7 @@ class TypingConsoleHandler(logging.StreamHandler):
 
 
 class ConsoleHandler(logging.StreamHandler):
-    def emit(self, record):
+    def emit(self, record) -> None:
         msg = self.format(record)
         try:
             print(msg)
@@ -172,21 +169,27 @@ class AutoGptFormatter(logging.Formatter):
     Allows to handle custom placeholders 'title_color' and 'message_no_color'.
     To use this formatter, make sure to pass 'color', 'title' as log extras.
     """
+
     def format(self, record: LogRecord) -> str:
-        if (hasattr(record, 'color')):
+        if hasattr(record, "color"):
-            record.title_color = getattr(record, 'color') + getattr(record, 'title') + " " + Style.RESET_ALL
+            record.title_color = (
+                getattr(record, "color")
+                + getattr(record, "title")
+                + " "
+                + Style.RESET_ALL
+            )
         else:
-            record.title_color = getattr(record, 'title')
+            record.title_color = getattr(record, "title")
-        if hasattr(record, 'msg'):
+        if hasattr(record, "msg"):
-            record.message_no_color = remove_color_codes(getattr(record, 'msg'))
+            record.message_no_color = remove_color_codes(getattr(record, "msg"))
         else:
-            record.message_no_color = ''
+            record.message_no_color = ""
         return super().format(record)
 
 
 def remove_color_codes(s: str) -> str:
-    ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])')
+    ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
-    return ansi_escape.sub('', s)
+    return ansi_escape.sub("", s)
 
 
 logger = Logger()

autogpt/memory/__init__.py

@@ -1,20 +1,22 @@
-from memory.local import LocalCache
+from autogpt.memory.local import LocalCache
-from memory.no_memory import NoMemory
+from autogpt.memory.no_memory import NoMemory
 
 # List of supported memory backends
 # Add a backend to this list if the import attempt is successful
-supported_memory = ['local']
+supported_memory = ["local", "no_memory"]
 
 try:
-    from memory.redismem import RedisMemory
+    from autogpt.memory.redismem import RedisMemory
-    supported_memory.append('redis')
+
+    supported_memory.append("redis")
 except ImportError:
     print("Redis not installed. Skipping import.")
     RedisMemory = None
 
 try:
-    from memory.pinecone import PineconeMemory
+    from autogpt.memory.pinecone import PineconeMemory
-    supported_memory.append('pinecone')
+
+    supported_memory.append("pinecone")
 except ImportError:
     print("Pinecone not installed. Skipping import.")
     PineconeMemory = None
@@ -29,16 +31,20 @@ def get_memory(cfg, init=False):
     memory = None
     if cfg.memory_backend == "pinecone":
         if not PineconeMemory:
-            print("Error: Pinecone is not installed. Please install pinecone"
+            print(
-                  " to use Pinecone as a memory backend.")
+                "Error: Pinecone is not installed. Please install pinecone"
+                " to use Pinecone as a memory backend."
+            )
         else:
             memory = PineconeMemory(cfg)
             if init:
                 memory.clear()
     elif cfg.memory_backend == "redis":
         if not RedisMemory:
-            print("Error: Redis is not installed. Please install redis-py to"
+            print(
-                  " use Redis as a memory backend.")
+                "Error: Redis is not installed. Please install redis-py to"
+                " use Redis as a memory backend."
+            )
         else:
             memory = RedisMemory(cfg)
     elif cfg.memory_backend == "weaviate":
@@ -62,11 +68,4 @@ def get_supported_memory_backends():
     return supported_memory
 
 
-__all__ = [
+__all__ = ["get_memory", "LocalCache", "RedisMemory", "PineconeMemory", "WeaviateMemory", "NoMemory"]
-    "get_memory",
-    "LocalCache",
-    "RedisMemory",
-    "PineconeMemory",
-    "WeaviateMemory"
-    "NoMemory"
-]

autogpt/memory/base.py

@@ -1,16 +1,23 @@
 """Base class for memory providers."""
 import abc
-from config import AbstractSingleton, Config
+
 import openai
 
+from autogpt.config import AbstractSingleton, Config
+
 cfg = Config()
 
 def get_ada_embedding(text):
     text = text.replace("\n", " ")
     if cfg.use_azure:
-        return openai.Embedding.create(input=[text], engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"))["data"][0]["embedding"]
+        return openai.Embedding.create(
+            input=[text],
+            engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"),
+        )["data"][0]["embedding"]
     else:
-        return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
+        return openai.Embedding.create(input=[text], model="text-embedding-ada-002")[
+            "data"
+        ][0]["embedding"]
 
 
 class MemoryProviderSingleton(AbstractSingleton):

autogpt/memory/local.py

@@ -1,10 +1,11 @@
 import dataclasses
-import orjson
-from typing import Any, List, Optional
-import numpy as np
 import os
-from memory.base import MemoryProviderSingleton, get_ada_embedding
+from typing import Any, List, Optional
 
+import numpy as np
+import orjson
+
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
 
 EMBED_DIM = 1536
 SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
@@ -23,16 +24,15 @@ class CacheContent:
 
 
 class LocalCache(MemoryProviderSingleton):
-
     # on load, load our database
     def __init__(self, cfg) -> None:
         self.filename = f"{cfg.memory_index}.json"
         if os.path.exists(self.filename):
             try:
-                with open(self.filename, 'w+b') as f:
+                with open(self.filename, "w+b") as f:
                     file_content = f.read()
                     if not file_content.strip():
-                        file_content = b'{}'
+                        file_content = b"{}"
                         f.write(file_content)
 
                     loaded = orjson.loads(file_content)
@@ -41,7 +41,9 @@ class LocalCache(MemoryProviderSingleton):
                 print(f"Error: The file '{self.filename}' is not in JSON format.")
                 self.data = CacheContent()
         else:
-            print(f"Warning: The file '{self.filename}' does not exist. Local memory would not be saved to a file.")
+            print(
+                f"Warning: The file '{self.filename}' does not exist. Local memory would not be saved to a file."
+            )
             self.data = CacheContent()
 
     def add(self, text: str):
@@ -54,7 +56,7 @@ class LocalCache(MemoryProviderSingleton):
 
         Returns: None
         """
-        if 'Command Error:' in text:
+        if "Command Error:" in text:
             return ""
         self.data.texts.append(text)
 
@@ -70,11 +72,8 @@ class LocalCache(MemoryProviderSingleton):
             axis=0,
         )
 
-        with open(self.filename, 'wb') as f:
+        with open(self.filename, "wb") as f:
-            out = orjson.dumps(
+            out = orjson.dumps(self.data, option=SAVE_OPTIONS)
-                self.data,
-                option=SAVE_OPTIONS
-            )
             f.write(out)
         return text
 
@@ -99,7 +98,7 @@ class LocalCache(MemoryProviderSingleton):
         return self.get_relevant(data, 1)
 
     def get_relevant(self, text: str, k: int) -> List[Any]:
-        """"
+        """ "
         matrix-vector mult to find score-for-each-row-of-matrix
          get indices for top-k winning scores
          return texts for those indices

autogpt/memory/no_memory.py

@@ -1,6 +1,6 @@
 from typing import Optional, List, Any
 
-from memory.base import MemoryProviderSingleton
+from autogpt.memory.base import MemoryProviderSingleton
 
 
 class NoMemory(MemoryProviderSingleton):

autogpt/memory/pinecone.py

@@ -1,9 +1,9 @@
 import pinecone
-
-from memory.base import MemoryProviderSingleton, get_ada_embedding
-from logger import logger
 from colorama import Fore, Style
 
+from autogpt.logger import logger
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
+
 
 class PineconeMemory(MemoryProviderSingleton):
     def __init__(self, cfg):
@@ -22,13 +22,21 @@ class PineconeMemory(MemoryProviderSingleton):
         try:
             pinecone.whoami()
         except Exception as e:
-            logger.typewriter_log("FAILED TO CONNECT TO PINECONE", Fore.RED, Style.BRIGHT + str(e) + Style.RESET_ALL)
+            logger.typewriter_log(
-            logger.double_check("Please ensure you have setup and configured Pinecone properly for use. " +
+                "FAILED TO CONNECT TO PINECONE",
-                               f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup{Style.RESET_ALL} to ensure you've set up everything correctly.")
+                Fore.RED,
+                Style.BRIGHT + str(e) + Style.RESET_ALL,
+            )
+            logger.double_check(
+                "Please ensure you have setup and configured Pinecone properly for use. "
+                + f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup{Style.RESET_ALL} to ensure you've set up everything correctly."
+            )
             exit(1)
 
         if table_name not in pinecone.list_indexes():
-            pinecone.create_index(table_name, dimension=dimension, metric=metric, pod_type=pod_type)
+            pinecone.create_index(
+                table_name, dimension=dimension, metric=metric, pod_type=pod_type
+            )
         self.index = pinecone.Index(table_name)
 
     def add(self, data):
@@ -53,9 +61,11 @@ class PineconeMemory(MemoryProviderSingleton):
         :param num_relevant: The number of relevant data to return. Defaults to 5
         """
         query_embedding = get_ada_embedding(data)
-        results = self.index.query(query_embedding, top_k=num_relevant, include_metadata=True)
+        results = self.index.query(
+            query_embedding, top_k=num_relevant, include_metadata=True
+        )
         sorted_results = sorted(results.matches, key=lambda x: x.score)
-        return [str(item['metadata']["raw_text"]) for item in sorted_results]
+        return [str(item["metadata"]["raw_text"]) for item in sorted_results]
 
     def get_stats(self):
         return self.index.describe_index_stats()

autogpt/memory/redismem.py

@@ -1,26 +1,22 @@
 """Redis memory provider."""
 from typing import Any, List, Optional
-import redis
+
-from redis.commands.search.field import VectorField, TextField
-from redis.commands.search.query import Query
-from redis.commands.search.indexDefinition import IndexDefinition, IndexType
 import numpy as np
-
+import redis
-from memory.base import MemoryProviderSingleton, get_ada_embedding
-from logger import logger
 from colorama import Fore, Style
+from redis.commands.search.field import TextField, VectorField
+from redis.commands.search.indexDefinition import IndexDefinition, IndexType
+from redis.commands.search.query import Query
 
+from autogpt.logger import logger
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
 
 SCHEMA = [
     TextField("data"),
     VectorField(
         "embedding",
         "HNSW",
-        {
+        {"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"},
-            "TYPE": "FLOAT32",
-            "DIM": 1536,
-            "DISTANCE_METRIC": "COSINE"
-        }
     ),
 ]
 
@@ -43,7 +39,7 @@ class RedisMemory(MemoryProviderSingleton):
             host=redis_host,
             port=redis_port,
             password=redis_password,
-            db=0  # Cannot be changed
+            db=0,  # Cannot be changed
         )
         self.cfg = cfg
 
@@ -51,9 +47,15 @@ class RedisMemory(MemoryProviderSingleton):
         try:
             self.redis.ping()
         except redis.ConnectionError as e:
-            logger.typewriter_log("FAILED TO CONNECT TO REDIS", Fore.RED, Style.BRIGHT + str(e) + Style.RESET_ALL)
+            logger.typewriter_log(
-            logger.double_check("Please ensure you have setup and configured Redis properly for use. " +
+                "FAILED TO CONNECT TO REDIS",
-                                f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL} to ensure you've set up everything correctly.")
+                Fore.RED,
+                Style.BRIGHT + str(e) + Style.RESET_ALL,
+            )
+            logger.double_check(
+                "Please ensure you have setup and configured Redis properly for use. "
+                + f"You can check out {Fore.CYAN + Style.BRIGHT}https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL} to ensure you've set up everything correctly."
+            )
             exit(1)
 
         if cfg.wipe_redis_on_start:
@@ -62,15 +64,13 @@ class RedisMemory(MemoryProviderSingleton):
             self.redis.ft(f"{cfg.memory_index}").create_index(
                 fields=SCHEMA,
                 definition=IndexDefinition(
-                    prefix=[f"{cfg.memory_index}:"],
+                    prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH
-                    index_type=IndexType.HASH
+                ),
-                    )
+            )
-                )
         except Exception as e:
             print("Error creating Redis search index: ", e)
-        existing_vec_num = self.redis.get(f'{cfg.memory_index}-vec_num')
+        existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num")
-        self.vec_num = int(existing_vec_num.decode('utf-8')) if\
+        self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0
-            existing_vec_num else 0
 
     def add(self, data: str) -> str:
         """
@@ -81,20 +81,18 @@ class RedisMemory(MemoryProviderSingleton):
 
         Returns: Message indicating that the data has been added.
         """
-        if 'Command Error:' in data:
+        if "Command Error:" in data:
             return ""
         vector = get_ada_embedding(data)
         vector = np.array(vector).astype(np.float32).tobytes()
-        data_dict = {
+        data_dict = {b"data": data, "embedding": vector}
-            b"data": data,
-            "embedding": vector
-        }
         pipe = self.redis.pipeline()
         pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict)
-        _text = f"Inserting data into memory at index: {self.vec_num}:\n"\
+        _text = (
-            f"data: {data}"
+            f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}"
+        )
         self.vec_num += 1
-        pipe.set(f'{self.cfg.memory_index}-vec_num', self.vec_num)
+        pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num)
         pipe.execute()
         return _text
 
@@ -118,11 +116,7 @@ class RedisMemory(MemoryProviderSingleton):
         self.redis.flushall()
         return "Obliviated"
 
-    def get_relevant(
+    def get_relevant(self, data: str, num_relevant: int = 5) -> Optional[List[Any]]:
-        self,
-        data: str,
-        num_relevant: int = 5
-    ) -> Optional[List[Any]]:
         """
         Returns all the data in the memory that is relevant to the given data.
         Args:
@@ -133,10 +127,12 @@ class RedisMemory(MemoryProviderSingleton):
         """
         query_embedding = get_ada_embedding(data)
         base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
-        query = Query(base_query).return_fields(
+        query = (
-            "data",
+            Query(base_query)
-            "vector_score"
+            .return_fields("data", "vector_score")
-        ).sort_by("vector_score").dialect(2)
+            .sort_by("vector_score")
+            .dialect(2)
+        )
         query_vector = np.array(query_embedding).astype(np.float32).tobytes()
 
         try:

autogpt/memory/weaviate.py

@@ -1,5 +1,5 @@
-from config import Config
+from autogpt.config import Config
-from memory.base import MemoryProviderSingleton, get_ada_embedding
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
 import uuid
 import weaviate
 from weaviate import Client

autogpt/prompt.py

@@ -0,0 +1,108 @@
+from autogpt.promptgenerator import PromptGenerator
+
+
+def get_prompt() -> str:
+    """
+    This function generates a prompt string that includes various constraints,
+        commands, resources, and performance evaluations.
+
+    Returns:
+        str: The generated prompt string.
+    """
+
+    # Initialize the PromptGenerator object
+    prompt_generator = PromptGenerator()
+
+    # Add constraints to the PromptGenerator object
+    prompt_generator.add_constraint(
+        "~4000 word limit for short term memory. Your short term memory is short, so"
+        " immediately save important information to files."
+    )
+    prompt_generator.add_constraint(
+        "If you are unsure how you previously did something or want to recall past"
+        " events, thinking about similar events will help you remember."
+    )
+    prompt_generator.add_constraint("No user assistance")
+    prompt_generator.add_constraint(
+        'Exclusively use the commands listed in double quotes e.g. "command name"'
+    )
+
+    # Define the command list
+    commands = [
+        ("Google Search", "google", {"input": "<search>"}),
+        (
+            "Browse Website",
+            "browse_website",
+            {"url": "<url>", "question": "<what_you_want_to_find_on_website>"},
+        ),
+        (
+            "Start GPT Agent",
+            "start_agent",
+            {"name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"},
+        ),
+        (
+            "Message GPT Agent",
+            "message_agent",
+            {"key": "<key>", "message": "<message>"},
+        ),
+        ("List GPT Agents", "list_agents", {}),
+        ("Delete GPT Agent", "delete_agent", {"key": "<key>"}),
+        ("Write to file", "write_to_file", {"file": "<file>", "text": "<text>"}),
+        ("Read file", "read_file", {"file": "<file>"}),
+        ("Append to file", "append_to_file", {"file": "<file>", "text": "<text>"}),
+        ("Delete file", "delete_file", {"file": "<file>"}),
+        ("Search Files", "search_files", {"directory": "<directory>"}),
+        ("Evaluate Code", "evaluate_code", {"code": "<full_code_string>"}),
+        (
+            "Get Improved Code",
+            "improve_code",
+            {"suggestions": "<list_of_suggestions>", "code": "<full_code_string>"},
+        ),
+        (
+            "Write Tests",
+            "write_tests",
+            {"code": "<full_code_string>", "focus": "<list_of_focus_areas>"},
+        ),
+        ("Execute Python File", "execute_python_file", {"file": "<file>"}),
+        (
+            "Execute Shell Command, non-interactive commands only",
+            "execute_shell",
+            {"command_line": "<command_line>"},
+        ),
+        ("Task Complete (Shutdown)", "task_complete", {"reason": "<reason>"}),
+        ("Generate Image", "generate_image", {"prompt": "<prompt>"}),
+        ("Do Nothing", "do_nothing", {}),
+    ]
+
+    # Add commands to the PromptGenerator object
+    for command_label, command_name, args in commands:
+        prompt_generator.add_command(command_label, command_name, args)
+
+    # Add resources to the PromptGenerator object
+    prompt_generator.add_resource(
+        "Internet access for searches and information gathering."
+    )
+    prompt_generator.add_resource("Long Term memory management.")
+    prompt_generator.add_resource(
+        "GPT-3.5 powered Agents for delegation of simple tasks."
+    )
+    prompt_generator.add_resource("File output.")
+
+    # Add performance evaluations to the PromptGenerator object
+    prompt_generator.add_performance_evaluation(
+        "Continuously review and analyze your actions to ensure you are performing to"
+        " the best of your abilities."
+    )
+    prompt_generator.add_performance_evaluation(
+        "Constructively self-criticize your big-picture behavior constantly."
+    )
+    prompt_generator.add_performance_evaluation(
+        "Reflect on past decisions and strategies to refine your approach."
+    )
+    prompt_generator.add_performance_evaluation(
+        "Every command has a cost, so be smart and efficient. Aim to complete tasks in"
+        " the least number of steps."
+    )
+
+    # Generate the prompt string
+    return prompt_generator.generate_prompt_string()

autogpt/promptgenerator.py

@@ -0,0 +1,134 @@
+import json
+
+
+class PromptGenerator:
+    """
+    A class for generating custom prompt strings based on constraints, commands,
+        resources, and performance evaluations.
+    """
+
+    def __init__(self):
+        """
+        Initialize the PromptGenerator object with empty lists of constraints,
+            commands, resources, and performance evaluations.
+        """
+        self.constraints = []
+        self.commands = []
+        self.resources = []
+        self.performance_evaluation = []
+        self.response_format = {
+            "thoughts": {
+                "text": "thought",
+                "reasoning": "reasoning",
+                "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+                "criticism": "constructive self-criticism",
+                "speak": "thoughts summary to say to user",
+            },
+            "command": {"name": "command name", "args": {"arg name": "value"}},
+        }
+
+    def add_constraint(self, constraint):
+        """
+        Add a constraint to the constraints list.
+
+        Args:
+            constraint (str): The constraint to be added.
+        """
+        self.constraints.append(constraint)
+
+    def add_command(self, command_label, command_name, args=None):
+        """
+        Add a command to the commands list with a label, name, and optional arguments.
+
+        Args:
+            command_label (str): The label of the command.
+            command_name (str): The name of the command.
+            args (dict, optional): A dictionary containing argument names and their
+              values. Defaults to None.
+        """
+        if args is None:
+            args = {}
+
+        command_args = {arg_key: arg_value for arg_key, arg_value in args.items()}
+
+        command = {
+            "label": command_label,
+            "name": command_name,
+            "args": command_args,
+        }
+
+        self.commands.append(command)
+
+    def _generate_command_string(self, command):
+        """
+        Generate a formatted string representation of a command.
+
+        Args:
+            command (dict): A dictionary containing command information.
+
+        Returns:
+            str: The formatted command string.
+        """
+        args_string = ", ".join(
+            f'"{key}": "{value}"' for key, value in command["args"].items()
+        )
+        return f'{command["label"]}: "{command["name"]}", args: {args_string}'
+
+    def add_resource(self, resource: str) -> None:
+        """
+        Add a resource to the resources list.
+
+        Args:
+            resource (str): The resource to be added.
+        """
+        self.resources.append(resource)
+
+    def add_performance_evaluation(self, evaluation: str) -> None:
+        """
+        Add a performance evaluation item to the performance_evaluation list.
+
+        Args:
+            evaluation (str): The evaluation item to be added.
+        """
+        self.performance_evaluation.append(evaluation)
+
+    def _generate_numbered_list(self, items, item_type="list") -> str:
+        """
+        Generate a numbered list from given items based on the item_type.
+
+        Args:
+            items (list): A list of items to be numbered.
+            item_type (str, optional): The type of items in the list.
+                Defaults to 'list'.
+
+        Returns:
+            str: The formatted numbered list.
+        """
+        if item_type == "command":
+            return "\n".join(
+                f"{i+1}. {self._generate_command_string(item)}"
+                for i, item in enumerate(items)
+            )
+        else:
+            return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
+
+    def generate_prompt_string(self) -> str:
+        """
+        Generate a prompt string based on the constraints, commands, resources,
+            and performance evaluations.
+
+        Returns:
+            str: The generated prompt string.
+        """
+        formatted_response_format = json.dumps(self.response_format, indent=4)
+        return (
+            f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
+            "Commands:\n"
+            f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
+            f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
+            "Performance Evaluation:\n"
+            f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
+            "You should only respond in JSON format as described below \nResponse"
+            f" Format: \n{formatted_response_format} \nEnsure the response can be"
+            "parsed by Python json.loads"
+        )

autogpt/speak.py

@@ -1,12 +1,17 @@
 import os
-from playsound import playsound
+
 import requests
-from config import Config
+from playsound import playsound
-cfg = Config()
+
-import gtts
+from autogpt.config import Config
+
 import threading
 from threading import Lock, Semaphore
 
+import gtts
+
+cfg = Config()
+
 # Default voice IDs
 default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"]
 
@@ -19,26 +24,29 @@ placeholders = {"your-voice-id"}
 
 # Use custom voice IDs if provided and not placeholders, otherwise use default voice IDs
 voices = [
-    custom_voice_1 if custom_voice_1 and custom_voice_1 not in placeholders else default_voices[0],
+    custom_voice_1
-    custom_voice_2 if custom_voice_2 and custom_voice_2 not in placeholders else default_voices[1]
+    if custom_voice_1 and custom_voice_1 not in placeholders
+    else default_voices[0],
+    custom_voice_2
+    if custom_voice_2 and custom_voice_2 not in placeholders
+    else default_voices[1],
 ]
 
-tts_headers = {
+tts_headers = {"Content-Type": "application/json", "xi-api-key": cfg.elevenlabs_api_key}
-    "Content-Type": "application/json",
-    "xi-api-key": cfg.elevenlabs_api_key
-}
 
-mutex_lock = Lock() # Ensure only one sound is played at a time
+mutex_lock = Lock()  # Ensure only one sound is played at a time
-queue_semaphore = Semaphore(1) # The amount of sounds to queue before blocking the main thread
+queue_semaphore = Semaphore(
+    1
+)  # The amount of sounds to queue before blocking the main thread
 
 
 def eleven_labs_speech(text, voice_index=0):
     """Speak text using elevenlabs.io's API"""
     tts_url = "https://api.elevenlabs.io/v1/text-to-speech/{voice_id}".format(
-        voice_id=voices[voice_index])
+        voice_id=voices[voice_index]
+    )
     formatted_message = {"text": text}
-    response = requests.post(
+    response = requests.post(tts_url, headers=tts_headers, json=formatted_message)
-        tts_url, headers=tts_headers, json=formatted_message)
 
     if response.status_code == 200:
         with mutex_lock:
@@ -53,6 +61,24 @@ def eleven_labs_speech(text, voice_index=0):
         return False
 
 
+def brian_speech(text):
+    """Speak text using Brian with the streamelements API"""
+    tts_url = f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}"
+    response = requests.get(tts_url)
+
+    if response.status_code == 200:
+        with mutex_lock:
+            with open("speech.mp3", "wb") as f:
+                f.write(response.content)
+            playsound("speech.mp3")
+            os.remove("speech.mp3")
+        return True
+    else:
+        print("Request failed with status code:", response.status_code)
+        print("Response content:", response.content)
+        return False
+
+
 def gtts_speech(text):
     tts = gtts.gTTS(text)
     with mutex_lock:
@@ -72,11 +98,14 @@ def macos_tts_speech(text, voice_index=0):
 
 
 def say_text(text, voice_index=0):
-
     def speak():
         if not cfg.elevenlabs_api_key:
-            if cfg.use_mac_os_tts == 'True':
+            if cfg.use_mac_os_tts == "True":
-                macos_tts_speech(text, voice_index)
+                macos_tts_speech(text)
+            elif cfg.use_brian_tts == "True":
+                success = brian_speech(text)
+                if not success:
+                    gtts_speech(text)
             else:
                 gtts_speech(text)
         else:

autogpt/spinner.py

@@ -1,14 +1,15 @@
+import itertools
 import sys
 import threading
-import itertools
 import time
 
 
 class Spinner:
     """A simple spinner class"""
+
     def __init__(self, message="Loading...", delay=0.1):
         """Initialize the spinner class"""
-        self.spinner = itertools.cycle(['-', '/', '|', '\\'])
+        self.spinner = itertools.cycle(["-", "/", "|", "\\"])
         self.delay = delay
         self.message = message
         self.running = False
@@ -17,10 +18,10 @@ class Spinner:
     def spin(self):
         """Spin the spinner"""
         while self.running:
-            sys.stdout.write(next(self.spinner) + " " + self.message + "\r")
+            sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
             sys.stdout.flush()
             time.sleep(self.delay)
-            sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')
+            sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
 
     def __enter__(self):
         """Start the spinner"""
@@ -31,6 +32,7 @@ class Spinner:
     def __exit__(self, exc_type, exc_value, exc_traceback):
         """Stop the spinner"""
         self.running = False
-        self.spinner_thread.join()
+        if self.spinner_thread is not None:
-        sys.stdout.write('\r' + ' ' * (len(self.message) + 2) + '\r')
+            self.spinner_thread.join()
+        sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
         sys.stdout.flush()

autogpt/summary.py

@@ -0,0 +1,69 @@
+from autogpt.llm_utils import create_chat_completion
+
+
+def summarize_text(driver, text, question):
+    if not text:
+        return "Error: No text to summarize"
+
+    text_length = len(text)
+    print(f"Text length: {text_length} characters")
+
+    summaries = []
+    chunks = list(split_text(text))
+
+    scroll_ratio = 1 / len(chunks)
+    for i, chunk in enumerate(chunks):
+        scroll_to_percentage(driver, scroll_ratio * i)
+        print(f"Summarizing chunk {i + 1} / {len(chunks)}")
+        messages = [create_message(chunk, question)]
+
+        summary = create_chat_completion(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            max_tokens=300,
+        )
+        summaries.append(summary)
+
+    print(f"Summarized {len(chunks)} chunks.")
+
+    combined_summary = "\n".join(summaries)
+    messages = [create_message(combined_summary, question)]
+
+    return create_chat_completion(
+        model="gpt-3.5-turbo",
+        messages=messages,
+        max_tokens=300,
+    )
+
+
+def split_text(text, max_length=8192):
+    paragraphs = text.split("\n")
+    current_length = 0
+    current_chunk = []
+
+    for paragraph in paragraphs:
+        if current_length + len(paragraph) + 1 <= max_length:
+            current_chunk.append(paragraph)
+            current_length += len(paragraph) + 1
+        else:
+            yield "\n".join(current_chunk)
+            current_chunk = [paragraph]
+            current_length = len(paragraph) + 1
+
+    if current_chunk:
+        yield "\n".join(current_chunk)
+
+
+def create_message(chunk, question):
+    return {
+        "role": "user",
+        "content": f'"""{chunk}""" Using the above text, please answer the following'
+        f' question: "{question}" -- if the question cannot be answered using the text,'
+        " please summarize the text.",
+    }
+
+
+def scroll_to_percentage(driver, ratio):
+    if ratio < 0 or ratio > 1:
+        raise ValueError("Percentage should be between 0 and 1")
+    driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")

autogpt/token_counter.py

@@ -1,14 +1,21 @@
+from typing import Dict, List
+
 import tiktoken
-from typing import List, Dict
+
+from autogpt.logger import logger
 
 
-def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5-turbo-0301") -> int:
+def count_message_tokens(
+    messages: List[Dict[str, str]], model: str = "gpt-3.5-turbo-0301"
+) -> int:
     """
     Returns the number of tokens used by a list of messages.
 
     Args:
-    messages (list): A list of messages, each of which is a dictionary containing the role and content of the message.
+    messages (list): A list of messages, each of which is a dictionary
-    model (str): The name of the model to use for tokenization. Defaults to "gpt-3.5-turbo-0301".
+        containing the role and content of the message.
+    model (str): The name of the model to use for tokenization.
+        Defaults to "gpt-3.5-turbo-0301".
 
     Returns:
     int: The number of tokens used by the list of messages.
@@ -19,19 +26,26 @@ def count_message_tokens(messages : List[Dict[str, str]], model : str = "gpt-3.5
         logger.warn("Warning: model not found. Using cl100k_base encoding.")
         encoding = tiktoken.get_encoding("cl100k_base")
     if model == "gpt-3.5-turbo":
-        # !Node: gpt-3.5-turbo may change over time. Returning num tokens assuming gpt-3.5-turbo-0301.")
+        # !Node: gpt-3.5-turbo may change over time.
+        # Returning num tokens assuming gpt-3.5-turbo-0301.")
         return count_message_tokens(messages, model="gpt-3.5-turbo-0301")
     elif model == "gpt-4":
         # !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.")
         return count_message_tokens(messages, model="gpt-4-0314")
     elif model == "gpt-3.5-turbo-0301":
-        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        tokens_per_message = (
+            4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        )
         tokens_per_name = -1  # if there's a name, the role is omitted
     elif model == "gpt-4-0314":
         tokens_per_message = 3
         tokens_per_name = 1
     else:
-        raise NotImplementedError(f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")
+        raise NotImplementedError(
+            f"num_tokens_from_messages() is not implemented for model {model}.\n"
+            " See https://github.com/openai/openai-python/blob/main/chatml.md for"
+            " information on how messages are converted to tokens."
+        )
     num_tokens = 0
     for message in messages:
         num_tokens += tokens_per_message
@@ -55,5 +69,4 @@ def count_string_tokens(string: str, model_name: str) -> int:
     int: The number of tokens in the text string.
     """
     encoding = tiktoken.encoding_for_model(model_name)
-    num_tokens = len(encoding.encode(string))
+    return len(encoding.encode(string))
-    return num_tokens

autogpt/utils.py

@@ -0,0 +1,26 @@
+import yaml
+from colorama import Fore
+
+
+def clean_input(prompt: str = ""):
+    try:
+        return input(prompt)
+    except KeyboardInterrupt:
+        print("You interrupted Auto-GPT")
+        print("Quitting...")
+        exit(0)
+
+
+def validate_yaml_file(file: str):
+    try:
+        with open(file, encoding="utf-8") as fp:
+            yaml.load(fp.read(), Loader=yaml.FullLoader)
+    except FileNotFoundError:
+        return (False, f"The file {Fore.CYAN}`{file}`{Fore.RESET} wasn't found")
+    except yaml.YAMLError as e:
+        return (
+            False,
+            f"There was an issue while trying to read with your AI Settings file: {e}",
+        )
+
+    return (True, f"Successfully validated {Fore.CYAN}`{file}`{Fore.RESET}!")

autogpt/web.py

@@ -0,0 +1,85 @@
+from selenium import webdriver
+import autogpt.summary as summary
+from bs4 import BeautifulSoup
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.wait import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from webdriver_manager.chrome import ChromeDriverManager
+from selenium.webdriver.chrome.options import Options
+import logging
+from pathlib import Path
+from autogpt.config import Config
+
+file_dir = Path(__file__).parent
+cfg = Config()
+
+
+def browse_website(url, question):
+    driver, text = scrape_text_with_selenium(url)
+    add_header(driver)
+    summary_text = summary.summarize_text(driver, text, question)
+    links = scrape_links_with_selenium(driver)
+
+    # Limit links to 5
+    if len(links) > 5:
+        links = links[:5]
+    close_browser(driver)
+    return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
+
+
+def scrape_text_with_selenium(url):
+    logging.getLogger("selenium").setLevel(logging.CRITICAL)
+
+    options = Options()
+    options.add_argument(
+        "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
+    )
+    driver = webdriver.Chrome(
+        executable_path=ChromeDriverManager().install(), options=options
+    )
+    driver.get(url)
+
+    WebDriverWait(driver, 10).until(
+        EC.presence_of_element_located((By.TAG_NAME, "body"))
+    )
+
+    # Get the HTML content directly from the browser's DOM
+    page_source = driver.execute_script("return document.body.outerHTML;")
+    soup = BeautifulSoup(page_source, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = "\n".join(chunk for chunk in chunks if chunk)
+    return driver, text
+
+
+def scrape_links_with_selenium(driver):
+    page_source = driver.page_source
+    soup = BeautifulSoup(page_source, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    hyperlinks = extract_hyperlinks(soup)
+
+    return format_hyperlinks(hyperlinks)
+
+
+def close_browser(driver):
+    driver.quit()
+
+
+def extract_hyperlinks(soup):
+    return [(link.text, link["href"]) for link in soup.find_all("a", href=True)]
+
+
+def format_hyperlinks(hyperlinks):
+    return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
+
+
+def add_header(driver):
+    driver.execute_script(open(f"{file_dir}/js/overlay.js", "r").read())

docker-compose.yml

@@ -0,0 +1,16 @@
+# To boot the app run the following:
+# docker-compose run auto-gpt
+version: "3.9"
+
+services:
+  auto-gpt:
+    depends_on:
+      - redis
+    build: ./
+    volumes:
+      - "./autogpt:/app"
+      - ".env:/app/.env"
+    profiles: ["exclude-from-up"]
+
+  redis:
+    image: "redis/redis-stack-server:latest"

main.py

@@ -1 +1 @@
-from scripts.main import main
+from autogpt import main

pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "auto-gpt"
+version = "0.1.0"
+description = "A GPT based ai agent"
+readme = "README.md"
+
+[tool.black]
+line-length = 88
+target-version = ['py310']
+include = '\.pyi?$'
+extend-exclude = ""

requirements.txt

@@ -16,6 +16,12 @@ redis
 orjson
 Pillow
 weaviate-client==3.15.5
+selenium
+webdriver-manager
 coverage
 flake8
 numpy
+pre-commit
+black
+sourcery
+isort

scripts/data.py

@@ -1,19 +0,0 @@
-import os
-from pathlib import Path
-
-
-def load_prompt():
-    """Load the prompt from data/prompt.txt"""
-    try:
-        # get directory of this file:
-        file_dir = Path(__file__).parent
-        prompt_file_path = file_dir / "data" / "prompt.txt"
-
-        # Load the prompt from data/prompt.txt
-        with open(prompt_file_path, "r") as prompt_file:
-            prompt = prompt_file.read()
-
-        return prompt
-    except FileNotFoundError:
-        print("Error: Prompt file not found", flush=True)
-        return ""

scripts/data/prompt.txt

@@ -1,64 +0,0 @@
-CONSTRAINTS:
-
-1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
-2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
-3. No user assistance
-4. Exclusively use the commands listed in double quotes e.g. "command name"
-
-COMMANDS:
-
-1. Google Search: "google", args: "input": "<search>"
-5. Browse Website: "browse_website", args: "url": "<url>", "question": "<what_you_want_to_find_on_website>"
-6. Start GPT Agent: "start_agent",  args: "name": "<name>", "task": "<short_task_desc>", "prompt": "<prompt>"
-7. Message GPT Agent: "message_agent", args: "key": "<key>", "message": "<message>"
-8. List GPT Agents: "list_agents", args: ""
-9. Delete GPT Agent: "delete_agent", args: "key": "<key>"
-10. Write to file: "write_to_file", args: "file": "<file>", "text": "<text>"
-11. Read file: "read_file", args: "file": "<file>"
-12. Append to file: "append_to_file", args: "file": "<file>", "text": "<text>"
-13. Delete file: "delete_file", args: "file": "<file>"
-14. Search Files: "search_files", args: "directory": "<directory>"
-15. Evaluate Code: "evaluate_code", args: "code": "<full_code_string>"
-16. Get Improved Code: "improve_code", args: "suggestions": "<list_of_suggestions>", "code": "<full_code_string>"
-17. Write Tests: "write_tests", args: "code": "<full_code_string>", "focus": "<list_of_focus_areas>"
-18. Execute Python File: "execute_python_file", args: "file": "<file>"
-19. Execute Shell Command, non-interactive commands only: "execute_shell", args: "command_line": "<command_line>".
-20. Task Complete (Shutdown): "task_complete", args: "reason": "<reason>"
-21. Generate Image: "generate_image", args: "prompt": "<prompt>"
-22. Do Nothing: "do_nothing", args: ""
-
-RESOURCES:
-
-1. Internet access for searches and information gathering.
-2. Long Term memory management.
-3. GPT-3.5 powered Agents for delegation of simple tasks.
-4. File output.
-
-PERFORMANCE EVALUATION:
-
-1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
-2. Constructively self-criticize your big-picture behavior constantly.
-3. Reflect on past decisions and strategies to refine your approach.
-4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.
-
-You should only respond in JSON format as described below
-
-RESPONSE FORMAT:
-{
-    "thoughts":
-    {
-        "text": "thought",
-        "reasoning": "reasoning",
-        "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-        "criticism": "constructive self-criticism",
-        "speak": "thoughts summary to say to user"
-    },
-    "command": {
-        "name": "command name",
-        "args":{
-            "arg name": "value"
-        }
-    }
-}
-
-Ensure the response can be parsed by Python json.loads

scripts/execute_code.py

@@ -1,88 +0,0 @@
-import docker
-import os
-import subprocess
-
-
-WORKSPACE_FOLDER = "auto_gpt_workspace"
-
-
-def execute_python_file(file):
-    """Execute a Python file in a Docker container and return the output"""
-
-    print (f"Executing file '{file}' in workspace '{WORKSPACE_FOLDER}'")
-
-    if not file.endswith(".py"):
-        return "Error: Invalid file type. Only .py files are allowed."
-
-    file_path = os.path.join(WORKSPACE_FOLDER, file)
-
-    if not os.path.isfile(file_path):
-        return f"Error: File '{file}' does not exist."
-
-    try:
-        client = docker.from_env()
-
-        image_name = 'python:3.10'
-        try:
-            client.images.get(image_name)
-            print(f"Image '{image_name}' found locally")
-        except docker.errors.ImageNotFound:
-            print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
-            # Use the low-level API to stream the pull response
-            low_level_client = docker.APIClient()
-            for line in low_level_client.pull(image_name, stream=True, decode=True):
-                # Print the status and progress, if available
-                status = line.get('status')
-                progress = line.get('progress')
-                if status and progress:
-                    print(f"{status}: {progress}")
-                elif status:
-                    print(status)
-
-        # You can replace 'python:3.8' with the desired Python image/version
-        # You can find available Python images on Docker Hub:
-        # https://hub.docker.com/_/python
-        container = client.containers.run(
-            image_name,
-            f'python {file}',
-            volumes={
-                os.path.abspath(WORKSPACE_FOLDER): {
-                    'bind': '/workspace',
-                    'mode': 'ro'}},
-            working_dir='/workspace',
-            stderr=True,
-            stdout=True,
-            detach=True,
-        )
-
-        output = container.wait()
-        logs = container.logs().decode('utf-8')
-        container.remove()
-
-        # print(f"Execution complete. Output: {output}")
-        # print(f"Logs: {logs}")
-
-        return logs
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def execute_shell(command_line):
-
-    current_dir = os.getcwd()
-
-    if not WORKSPACE_FOLDER in current_dir: # Change dir into workspace if necessary
-        work_dir = os.path.join(os.getcwd(), WORKSPACE_FOLDER)
-        os.chdir(work_dir)
-
-    print (f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    result = subprocess.run(command_line, capture_output=True, shell=True)
-    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return output

scripts/file_operations.py

@@ -1,84 +0,0 @@
-import os
-import os.path
-
-# Set a dedicated folder for file I/O
-working_directory = "auto_gpt_workspace"
-
-# Create the directory if it doesn't exist
-if not os.path.exists(working_directory):
-    os.makedirs(working_directory)
-
-
-def safe_join(base, *paths):
-    """Join one or more path components intelligently."""
-    new_path = os.path.join(base, *paths)
-    norm_new_path = os.path.normpath(new_path)
-
-    if os.path.commonprefix([base, norm_new_path]) != base:
-        raise ValueError("Attempted to access outside of working directory.")
-
-    return norm_new_path
-
-
-def read_file(filename):
-    """Read a file and return the contents"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        with open(filepath, "r", encoding='utf-8') as f:
-            content = f.read()
-        return content
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def write_to_file(filename, text):
-    """Write text to a file"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        directory = os.path.dirname(filepath)
-        if not os.path.exists(directory):
-            os.makedirs(directory)
-        with open(filepath, "w", encoding='utf-8') as f:
-            f.write(text)
-        return "File written to successfully."
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def append_to_file(filename, text):
-    """Append text to a file"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        with open(filepath, "a") as f:
-            f.write(text)
-        return "Text appended successfully."
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def delete_file(filename):
-    """Delete a file"""
-    try:
-        filepath = safe_join(working_directory, filename)
-        os.remove(filepath)
-        return "File deleted successfully."
-    except Exception as e:
-        return "Error: " + str(e)
-
-
-def search_files(directory):
-    found_files = []
-
-    if directory == "" or directory == "/":
-        search_directory = working_directory
-    else:
-        search_directory = safe_join(working_directory, directory)
-
-    for root, _, files in os.walk(search_directory):
-        for file in files:
-            if file.startswith('.'):
-                continue
-            relative_path = os.path.relpath(os.path.join(root, file), working_directory)
-            found_files.append(relative_path)
-
-    return found_files

scripts/llm_utils.py

@@ -1,27 +0,0 @@
-import openai
-from config import Config
-cfg = Config()
-
-openai.api_key = cfg.openai_api_key
-
-
-# Overly simple abstraction until we create something better
-def create_chat_completion(messages, model=None, temperature=cfg.temperature, max_tokens=None)->str:
-    """Create a chat completion using the OpenAI API"""
-    if cfg.use_azure:
-        response = openai.ChatCompletion.create(
-            deployment_id=cfg.get_azure_deployment_id_for_model(model),
-            model=model,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens
-        )
-    else:
-        response = openai.ChatCompletion.create(
-            model=model,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens
-        )
-
-    return response.choices[0].message["content"]

scripts/main.py

@@ -1,442 +1,11 @@
-import json
+from colorama import Style, init
-import random
-import commands as cmd
-import utils
-from memory import get_memory, get_supported_memory_backends
-import data
-import chat
-from colorama import Fore, Style
-from spinner import Spinner
-import time
-import speak
-from config import Config
-from json_parser import fix_and_parse_json
-from ai_config import AIConfig
-import traceback
-import yaml
-import argparse
-from logger import logger
-import logging
 
-cfg = Config()
+# Initialize colorama
+init(autoreset=True)
 
-
+# Use the bold ANSI style
-def check_openai_api_key():
+print(
-    """Check if the OpenAI API key is set in config.py or as an environment variable."""
+    f"""{Style.BRIGHT}Please run:
-    if not cfg.openai_api_key:
+python -m autogpt
-        print(
+"""
-            Fore.RED +
+)
-            "Please set your OpenAI API key in .env or as an environment variable."
-        )
-        print("You can get your key from https://beta.openai.com/account/api-keys")
-        exit(1)
-
-
-def attempt_to_fix_json_by_finding_outermost_brackets(json_string):
-    if cfg.speak_mode and cfg.debug_mode:
-      speak.say_text("I have received an invalid JSON response from the OpenAI API. Trying to fix it now.")
-    logger.typewriter_log("Attempting to fix JSON by finding outermost brackets\n")
-
-    try:
-        # Use regex to search for JSON objects
-        import regex
-        json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
-        json_match = json_pattern.search(json_string)
-
-        if json_match:
-            # Extract the valid JSON object from the string
-            json_string = json_match.group(0)
-            logger.typewriter_log(title="Apparently json was fixed.", title_color=Fore.GREEN)
-            if cfg.speak_mode and cfg.debug_mode:
-               speak.say_text("Apparently json was fixed.")
-        else:
-            raise ValueError("No valid JSON object found")
-
-    except (json.JSONDecodeError, ValueError) as e:
-        if cfg.speak_mode:
-            speak.say_text("Didn't work. I will have to ignore this response then.")
-        logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
-        json_string = {}
-
-    return json_string
-
-
-def print_assistant_thoughts(assistant_reply):
-    """Prints the assistant's thoughts to the console"""
-    global ai_name
-    global cfg
-    try:
-        try:
-            # Parse and print Assistant response
-            assistant_reply_json = fix_and_parse_json(assistant_reply)
-        except json.JSONDecodeError as e:
-            logger.error("Error: Invalid JSON in assistant thoughts\n", assistant_reply)
-            assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply)
-            assistant_reply_json = fix_and_parse_json(assistant_reply_json)
-
-        # Check if assistant_reply_json is a string and attempt to parse it into a JSON object
-        if isinstance(assistant_reply_json, str):
-            try:
-                assistant_reply_json = json.loads(assistant_reply_json)
-            except json.JSONDecodeError as e:
-                logger.error("Error: Invalid JSON\n", assistant_reply)
-                assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply_json)
-
-        assistant_thoughts_reasoning = None
-        assistant_thoughts_plan = None
-        assistant_thoughts_speak = None
-        assistant_thoughts_criticism = None
-        assistant_thoughts = assistant_reply_json.get("thoughts", {})
-        assistant_thoughts_text = assistant_thoughts.get("text")
-
-        if assistant_thoughts:
-            assistant_thoughts_reasoning = assistant_thoughts.get("reasoning")
-            assistant_thoughts_plan = assistant_thoughts.get("plan")
-            assistant_thoughts_criticism = assistant_thoughts.get("criticism")
-            assistant_thoughts_speak = assistant_thoughts.get("speak")
-
-        logger.typewriter_log(f"{ai_name.upper()} THOUGHTS:", Fore.YELLOW, assistant_thoughts_text)
-        logger.typewriter_log("REASONING:", Fore.YELLOW, assistant_thoughts_reasoning)
-
-        if assistant_thoughts_plan:
-            logger.typewriter_log("PLAN:", Fore.YELLOW, "")
-            # If it's a list, join it into a string
-            if isinstance(assistant_thoughts_plan, list):
-                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
-            elif isinstance(assistant_thoughts_plan, dict):
-                assistant_thoughts_plan = str(assistant_thoughts_plan)
-
-            # Split the input_string using the newline character and dashes
-            lines = assistant_thoughts_plan.split('\n')
-            for line in lines:
-                line = line.lstrip("- ")
-                logger.typewriter_log("- ", Fore.GREEN, line.strip())
-
-        logger.typewriter_log("CRITICISM:", Fore.YELLOW, assistant_thoughts_criticism)
-        # Speak the assistant's thoughts
-        if cfg.speak_mode and assistant_thoughts_speak:
-            speak.say_text(assistant_thoughts_speak)
-
-        return assistant_reply_json
-    except json.decoder.JSONDecodeError as e:
-        logger.error("Error: Invalid JSON\n", assistant_reply)
-        if cfg.speak_mode:
-            speak.say_text("I have received an invalid JSON response from the OpenAI API. I cannot ignore this response.")
-
-    # All other errors, return "Error: + error message"
-    except Exception as e:
-        call_stack = traceback.format_exc()
-        logger.error("Error: \n", call_stack)
-
-
-def load_variables(config_file="config.yaml"):
-    """Load variables from yaml file if it exists, otherwise prompt the user for input"""
-    try:
-        with open(config_file) as file:
-            config = yaml.load(file, Loader=yaml.FullLoader)
-        ai_name = config.get("ai_name")
-        ai_role = config.get("ai_role")
-        ai_goals = config.get("ai_goals")
-    except FileNotFoundError:
-        ai_name = ""
-        ai_role = ""
-        ai_goals = []
-
-    # Prompt the user for input if config file is missing or empty values
-    if not ai_name:
-        ai_name = utils.clean_input("Name your AI: ")
-        if ai_name == "":
-            ai_name = "Entrepreneur-GPT"
-
-    if not ai_role:
-        ai_role = utils.clean_input(f"{ai_name} is: ")
-        if ai_role == "":
-            ai_role = "an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth."
-
-    if not ai_goals:
-        print("Enter up to 5 goals for your AI: ")
-        print("For example: \nIncrease net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'")
-        print("Enter nothing to load defaults, enter nothing when finished.")
-        ai_goals = []
-        for i in range(5):
-            ai_goal = utils.clean_input(f"Goal {i+1}: ")
-            if ai_goal == "":
-                break
-            ai_goals.append(ai_goal)
-        if len(ai_goals) == 0:
-            ai_goals = ["Increase net worth", "Grow Twitter Account", "Develop and manage multiple businesses autonomously"]
-
-    # Save variables to yaml file
-    config = {"ai_name": ai_name, "ai_role": ai_role, "ai_goals": ai_goals}
-    with open(config_file, "w") as file:
-        documents = yaml.dump(config, file)
-
-    prompt = data.load_prompt()
-    prompt_start = """Your decisions must always be made independently without seeking user assistance. Play to your strengths as a LLM and pursue simple strategies with no legal complications."""
-
-    # Construct full prompt
-    full_prompt = f"You are {ai_name}, {ai_role}\n{prompt_start}\n\nGOALS:\n\n"
-    for i, goal in enumerate(ai_goals):
-        full_prompt += f"{i+1}. {goal}\n"
-
-    full_prompt += f"\n\n{prompt}"
-    return full_prompt
-
-
-def construct_prompt():
-    """Construct the prompt for the AI to respond to"""
-    config = AIConfig.load()
-    if config.ai_name:
-        logger.typewriter_log(
-            f"Welcome back! ",
-            Fore.GREEN,
-            f"Would you like me to return to being {config.ai_name}?",
-            speak_text=True)
-        should_continue = utils.clean_input(f"""Continue with the last settings?
-Name:  {config.ai_name}
-Role:  {config.ai_role}
-Goals: {config.ai_goals}
-Continue (y/n): """)
-        if should_continue.lower() == "n":
-            config = AIConfig()
-
-    if not config.ai_name:
-        config = prompt_user()
-        config.save()
-
-    # Get rid of this global:
-    global ai_name
-    ai_name = config.ai_name
-
-    full_prompt = config.construct_full_prompt()
-    return full_prompt
-
-
-def prompt_user():
-    """Prompt the user for input"""
-    ai_name = ""
-    # Construct the prompt
-    logger.typewriter_log(
-        "Welcome to Auto-GPT! ",
-        Fore.GREEN,
-        "Enter the name of your AI and its role below. Entering nothing will load defaults.",
-        speak_text=True)
-
-    # Get AI Name from User
-    logger.typewriter_log(
-        "Name your AI: ",
-        Fore.GREEN,
-        "For example, 'Entrepreneur-GPT'")
-    ai_name = utils.clean_input("AI Name: ")
-    if ai_name == "":
-        ai_name = "Entrepreneur-GPT"
-
-    logger.typewriter_log(
-        f"{ai_name} here!",
-        Fore.LIGHTBLUE_EX,
-        "I am at your service.",
-        speak_text=True)
-
-    # Get AI Role from User
-    logger.typewriter_log(
-        "Describe your AI's role: ",
-        Fore.GREEN,
-        "For example, 'an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth.'")
-    ai_role = utils.clean_input(f"{ai_name} is: ")
-    if ai_role == "":
-        ai_role = "an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth."
-
-    # Enter up to 5 goals for the AI
-    logger.typewriter_log(
-        "Enter up to 5 goals for your AI: ",
-        Fore.GREEN,
-        "For example: \nIncrease net worth, Grow Twitter Account, Develop and manage multiple businesses autonomously'")
-    print("Enter nothing to load defaults, enter nothing when finished.", flush=True)
-    ai_goals = []
-    for i in range(5):
-        ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}Goal{Style.RESET_ALL} {i+1}: ")
-        if ai_goal == "":
-            break
-        ai_goals.append(ai_goal)
-    if len(ai_goals) == 0:
-        ai_goals = ["Increase net worth", "Grow Twitter Account",
-                    "Develop and manage multiple businesses autonomously"]
-
-    config = AIConfig(ai_name, ai_role, ai_goals)
-    return config
-
-
-def parse_arguments():
-    """Parses the arguments passed to the script"""
-    global cfg
-    cfg.set_debug_mode(False)
-    cfg.set_continuous_mode(False)
-    cfg.set_speak_mode(False)
-
-    parser = argparse.ArgumentParser(description='Process arguments.')
-    parser.add_argument('--continuous', action='store_true', help='Enable Continuous Mode')
-    parser.add_argument('--speak', action='store_true', help='Enable Speak Mode')
-    parser.add_argument('--debug', action='store_true', help='Enable Debug Mode')
-    parser.add_argument('--gpt3only', action='store_true', help='Enable GPT3.5 Only Mode')
-    parser.add_argument('--gpt4only', action='store_true', help='Enable GPT4 Only Mode')
-    parser.add_argument('--use-memory', '-m', dest="memory_type", help='Defines which Memory backend to use')
-    args = parser.parse_args()
-
-    if args.debug:
-        logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
-        cfg.set_debug_mode(True)
-
-    if args.continuous:
-        logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.RED,
-            "Continuous mode is not recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorise. Use at your own risk.")
-        cfg.set_continuous_mode(True)
-
-    if args.speak:
-        logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
-        cfg.set_speak_mode(True)
-
-    if args.gpt3only:
-        logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
-        cfg.set_smart_llm_model(cfg.fast_llm_model)
-
-    if args.gpt4only:
-        logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
-        cfg.set_fast_llm_model(cfg.smart_llm_model)
-
-    if args.debug:
-        logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
-        cfg.set_debug_mode(True)
-
-    if args.memory_type:
-        supported_memory = get_supported_memory_backends()
-        chosen = args.memory_type
-        if not chosen in supported_memory:
-            logger.typewriter_log("ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ", Fore.RED, f'{supported_memory}')
-            logger.typewriter_log(f"Defaulting to: ", Fore.YELLOW, cfg.memory_backend)
-        else:
-            cfg.memory_backend = chosen
-
-
-def main():
-    global ai_name, memory
-    # TODO: fill in llm values here
-    check_openai_api_key()
-    parse_arguments()
-    logger.set_level(logging.DEBUG if cfg.debug_mode else logging.INFO)
-    ai_name = ""
-    prompt = construct_prompt()
-    # print(prompt)
-    # Initialize variables
-    full_message_history = []
-    result = None
-    next_action_count = 0
-    # Make a constant:
-    user_input = "Determine which next command to use, and respond using the format specified above:"
-    # Initialize memory and make sure it is empty.
-    # this is particularly important for indexing and referencing pinecone memory
-    memory = get_memory(cfg, init=True)
-    print('Using memory of type: ' + memory.__class__.__name__)
-    # Interaction Loop
-    while True:
-        # Send message to AI, get response
-        with Spinner("Thinking... "):
-            assistant_reply = chat.chat_with_ai(
-                prompt,
-                user_input,
-                full_message_history,
-                memory,
-                cfg.fast_token_limit)  # TODO: This hardcodes the model to use GPT3.5. Make this an argument
-
-        # Print Assistant thoughts
-        print_assistant_thoughts(assistant_reply)
-
-        # Get command name and arguments
-        try:
-            command_name, arguments = cmd.get_command(
-                attempt_to_fix_json_by_finding_outermost_brackets(assistant_reply))
-            if cfg.speak_mode:
-                speak.say_text(f"I want to execute {command_name}")
-        except Exception as e:
-            logger.error("Error: \n", str(e))
-
-        if not cfg.continuous_mode and next_action_count == 0:
-            ### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
-            # Get key press: Prompt the user to press enter to continue or escape
-            # to exit
-            user_input = ""
-            logger.typewriter_log(
-                "NEXT ACTION: ",
-                Fore.CYAN,
-                f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
-            print(
-                f"Enter 'y' to authorise command, 'y -N' to run N continuous commands, 'n' to exit program, or enter feedback for {ai_name}...",
-                flush=True)
-            while True:
-                console_input = utils.clean_input(Fore.MAGENTA + "Input:" + Style.RESET_ALL)
-                if console_input.lower().rstrip() == "y":
-                    user_input = "GENERATE NEXT COMMAND JSON"
-                    break
-                elif console_input.lower().startswith("y -"):
-                    try:
-                        next_action_count = abs(int(console_input.split(" ")[1]))
-                        user_input = "GENERATE NEXT COMMAND JSON"
-                    except ValueError:
-                        print("Invalid input format. Please enter 'y -n' where n is the number of continuous tasks.")
-                        continue
-                    break
-                elif console_input.lower() == "n":
-                    user_input = "EXIT"
-                    break
-                else:
-                    user_input = console_input
-                    command_name = "human_feedback"
-                    break
-
-            if user_input == "GENERATE NEXT COMMAND JSON":
-                logger.typewriter_log(
-                    "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
-                    Fore.MAGENTA,
-                    "")
-            elif user_input == "EXIT":
-                print("Exiting...", flush=True)
-                break
-        else:
-            # Print command
-            logger.typewriter_log(
-                "NEXT ACTION: ",
-                Fore.CYAN,
-                f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}  ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}")
-
-        # Execute command
-        if command_name is not None and command_name.lower().startswith("error"):
-            result = f"Command {command_name} threw the following error: " + arguments
-        elif command_name == "human_feedback":
-            result = f"Human feedback: {user_input}"
-        else:
-            result = f"Command {command_name} returned: {cmd.execute_command(command_name, arguments)}"
-            if next_action_count > 0:
-                next_action_count -= 1
-
-        memory_to_add = f"Assistant Reply: {assistant_reply} " \
-                        f"\nResult: {result} " \
-                        f"\nHuman Feedback: {user_input} "
-
-        memory.add(memory_to_add)
-
-        # Check if there's a result from the command append it to the message
-        # history
-        if result is not None:
-            full_message_history.append(chat.create_chat_message("system", result))
-            logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
-        else:
-            full_message_history.append(
-                chat.create_chat_message(
-                    "system", "Unable to execute command"))
-            logger.typewriter_log("SYSTEM: ", Fore.YELLOW, "Unable to execute command")
-
-
-if __name__ == "__main__":
-    main()

scripts/utils.py

@@ -1,8 +0,0 @@
-def clean_input(prompt: str=''):
-    try:
-        return input(prompt)
-    except KeyboardInterrupt:
-        print("You interrupted Auto-GPT")
-        print("Quitting...")
-        exit(0)
-

tests.py

@@ -1,8 +1,8 @@
 import unittest
 
 if __name__ == "__main__":
-    # Load all tests from the 'scripts/tests' package
+    # Load all tests from the 'autogpt/tests' package
-    suite = unittest.defaultTestLoader.discover('scripts/tests')
+    suite = unittest.defaultTestLoader.discover("autogpt/tests")
 
     # Run the tests
     unittest.TextTestRunner().run(suite)

tests/context.py

@@ -1,5 +1,6 @@
-import sys
 import os
+import sys
 
-sys.path.insert(0, os.path.abspath(
+sys.path.insert(
-    os.path.join(os.path.dirname(__file__), '../scripts')))
+    0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../scripts"))
+)

tests/integration/memory_tests.py

@@ -1,18 +1,16 @@
-import unittest
 import random
 import string
 import sys
+import unittest
 from pathlib import Path
-# Add the parent directory of the 'scripts' folder to the Python path
+
-sys.path.append(str(Path(__file__).resolve().parent.parent.parent / 'scripts'))
+from autogpt.config import Config
-from config import Config
+from autogpt.memory.local import LocalCache
-from memory.local import LocalCache
 
 
 class TestLocalCache(unittest.TestCase):
-
     def random_string(self, length):
-        return ''.join(random.choice(string.ascii_letters) for _ in range(length))
+        return "".join(random.choice(string.ascii_letters) for _ in range(length))
 
     def setUp(self):
         cfg = cfg = Config()
@@ -21,10 +19,10 @@ class TestLocalCache(unittest.TestCase):
 
         # Add example texts to the cache
         self.example_texts = [
-            'The quick brown fox jumps over the lazy dog',
+            "The quick brown fox jumps over the lazy dog",
-            'I love machine learning and natural language processing',
+            "I love machine learning and natural language processing",
-            'The cake is a lie, but the pie is always true',
+            "The cake is a lie, but the pie is always true",
-            'ChatGPT is an advanced AI model for conversation'
+            "ChatGPT is an advanced AI model for conversation",
         ]
 
         for text in self.example_texts:
@@ -47,5 +45,5 @@ class TestLocalCache(unittest.TestCase):
         self.assertIn(self.example_texts[1], relevant_texts)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

tests/integration/weaviate_memory_tests.py

@@ -7,10 +7,9 @@ from weaviate import Client
 from weaviate.util import get_valid_uuid
 from uuid import uuid4
 
-sys.path.append(os.path.abspath('./scripts'))
+from autogpt.config import Config
-from config import Config
+from autogpt.memory.weaviate import WeaviateMemory
-from memory.weaviate import WeaviateMemory
+from autogpt.memory.base import get_ada_embedding
-from memory.base import get_ada_embedding
 
 @mock.patch.dict(os.environ, {
     "WEAVIATE_HOST": "127.0.0.1",

tests/local_cache_test.py

@@ -1,21 +1,23 @@
 import os
 import sys
-# Probably a better way:
+
-sys.path.append(os.path.abspath('../scripts'))
+from autogpt.memory.local import LocalCache
-from memory.local import LocalCache
 
 
 def MockConfig():
-    return type('MockConfig', (object,), {
+    return type(
-        'debug_mode': False,
+        "MockConfig",
-        'continuous_mode': False,
+        (object,),
-        'speak_mode': False,
+        {
-        'memory_index': 'auto-gpt',
+            "debug_mode": False,
-    })
+            "continuous_mode": False,
+            "speak_mode": False,
+            "memory_index": "auto-gpt",
+        },
+    )
 
 
 class TestLocalCache(unittest.TestCase):
-
     def setUp(self):
         self.cfg = MockConfig()
         self.cache = LocalCache(self.cfg)
@@ -50,5 +52,5 @@ class TestLocalCache(unittest.TestCase):
         self.assertEqual(stats, (1, self.cache.data.embeddings.shape))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

tests/promptgenerator_tests.py

@@ -0,0 +1,99 @@
+# Import the required libraries for unit testing
+import os
+import sys
+import unittest
+
+from autogpt.promptgenerator import PromptGenerator
+
+
+# Create a test class for the PromptGenerator, subclassed from unittest.TestCase
+class promptgenerator_tests(unittest.TestCase):
+    # Set up the initial state for each test method by creating an instance of PromptGenerator
+    def setUp(self):
+        self.generator = PromptGenerator()
+
+    # Test whether the add_constraint() method adds a constraint to the generator's constraints list
+    def test_add_constraint(self):
+        constraint = "Constraint1"
+        self.generator.add_constraint(constraint)
+        self.assertIn(constraint, self.generator.constraints)
+
+    # Test whether the add_command() method adds a command to the generator's commands list
+    def test_add_command(self):
+        command_label = "Command Label"
+        command_name = "command_name"
+        args = {"arg1": "value1", "arg2": "value2"}
+        self.generator.add_command(command_label, command_name, args)
+        command = {
+            "label": command_label,
+            "name": command_name,
+            "args": args,
+        }
+        self.assertIn(command, self.generator.commands)
+
+    # Test whether the add_resource() method adds a resource to the generator's resources list
+    def test_add_resource(self):
+        resource = "Resource1"
+        self.generator.add_resource(resource)
+        self.assertIn(resource, self.generator.resources)
+
+    # Test whether the add_performance_evaluation() method adds an evaluation to the generator's performance_evaluation list
+    def test_add_performance_evaluation(self):
+        evaluation = "Evaluation1"
+        self.generator.add_performance_evaluation(evaluation)
+        self.assertIn(evaluation, self.generator.performance_evaluation)
+
+    # Test whether the generate_prompt_string() method generates a prompt string with all the added constraints, commands, resources and evaluations
+    def test_generate_prompt_string(self):
+        constraints = ["Constraint1", "Constraint2"]
+        commands = [
+            {
+                "label": "Command1",
+                "name": "command_name1",
+                "args": {"arg1": "value1"},
+            },
+            {
+                "label": "Command2",
+                "name": "command_name2",
+                "args": {},
+            },
+        ]
+        resources = ["Resource1", "Resource2"]
+        evaluations = ["Evaluation1", "Evaluation2"]
+
+        # Add all the constraints, commands, resources, and evaluations to the generator
+        for constraint in constraints:
+            self.generator.add_constraint(constraint)
+        for command in commands:
+            self.generator.add_command(
+                command["label"], command["name"], command["args"]
+            )
+        for resource in resources:
+            self.generator.add_resource(resource)
+        for evaluation in evaluations:
+            self.generator.add_performance_evaluation(evaluation)
+
+        # Generate the prompt string and verify its correctness
+        prompt_string = self.generator.generate_prompt_string()
+        self.assertIsNotNone(prompt_string)
+        for constraint in constraints:
+            self.assertIn(constraint, prompt_string)
+        for command in commands:
+            self.assertIn(command["name"], prompt_string)
+
+        # Check for each key-value pair in the command args dictionary
+        for key, value in command["args"].items():
+            self.assertIn(f'"{key}": "{value}"', prompt_string)
+        for resource in resources:
+            self.assertIn(resource, prompt_string)
+        for evaluation in evaluations:
+            self.assertIn(evaluation, prompt_string)
+        self.assertIn("constraints", prompt_string.lower())
+        self.assertIn("commands", prompt_string.lower())
+        self.assertIn("resources", prompt_string.lower())
+        self.assertIn("performance evaluation", prompt_string.lower())
+
+
+# Run the tests when this script is executed
+if __name__ == "__main__":
+    unittest.main()

tests/test_config.py

@@ -1,9 +1,9 @@
 import unittest
-from scripts.config import Config
+
+from autogpt.config import Config
 
 
 class TestConfig(unittest.TestCase):
-
     def test_singleton(self):
         config1 = Config()
         config2 = Config()
@@ -55,5 +55,5 @@ class TestConfig(unittest.TestCase):
         self.assertTrue(config.debug_mode)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

tests/test_json_parser.py

@@ -1,7 +1,7 @@
 import unittest
-import tests.context
 
-from scripts.json_parser import fix_and_parse_json
+import tests.context
+from autogpt.json_parser import fix_and_parse_json
 
 
 class TestParseJson(unittest.TestCase):
@@ -21,7 +21,7 @@ class TestParseJson(unittest.TestCase):
         # Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False
         json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
         with self.assertRaises(Exception):
-             fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=False)
 
     def test_invalid_json_major_without_gpt(self):
         # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@@ -51,23 +51,22 @@ class TestParseJson(unittest.TestCase):
     }
 }"""
         good_obj = {
-          "command": {
+            "command": {
-              "name": "browse_website",
+                "name": "browse_website",
-              "args": {
+                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-                  "url": "https://github.com/Torantulino/Auto-GPT"
+            },
-              }
+            "thoughts": {
-          },
+                "text": "I suggest we start browsing the repository to find any issues that we can fix.",
-          "thoughts":
+                "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
-          {
+                "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
-              "text": "I suggest we start browsing the repository to find any issues that we can fix.",
+                "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
-              "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
+                "speak": "I will start browsing the repository to find any issues we can fix.",
-              "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
+            },
-              "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
+        }
-              "speak": "I will start browsing the repository to find any issues we can fix."
-          }
-      }
         # Assert that this raises an exception:
-        self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
+        self.assertEqual(
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
+        )
 
     def test_invalid_json_leading_sentence_with_gpt(self):
         # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@@ -90,24 +89,23 @@ class TestParseJson(unittest.TestCase):
     }
 }"""
         good_obj = {
-    "command": {
+            "command": {
-        "name": "browse_website",
+                "name": "browse_website",
-        "args": {
+                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-            "url": "https://github.com/Torantulino/Auto-GPT"
+            },
+            "thoughts": {
+                "text": "Browsing the repository to identify potential bugs",
+                "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
+                "plan": "- Analyze the repository for potential bugs and areas of improvement",
+                "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
+                "speak": "I am browsing the repository to identify potential bugs.",
+            },
         }
-    },
-    "thoughts":
-    {
-        "text": "Browsing the repository to identify potential bugs",
-        "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
-        "plan": "- Analyze the repository for potential bugs and areas of improvement",
-        "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
-        "speak": "I am browsing the repository to identify potential bugs."
-    }
-}
         # Assert that this raises an exception:
-        self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
+        self.assertEqual(
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

tests/unit/json_tests.py

@@ -1,9 +1,6 @@
 import unittest
-import os
+
-import sys
+from autogpt.json_parser import fix_and_parse_json
-# Probably a better way:
-sys.path.append(os.path.abspath('../scripts'))
-from json_parser import fix_and_parse_json
 
 
 class TestParseJson(unittest.TestCase):
@@ -16,12 +13,18 @@ class TestParseJson(unittest.TestCase):
     def test_invalid_json_minor(self):
         # Test that an invalid JSON string can be fixed with gpt
         json_str = '{"name": "John", "age": 30, "city": "New York",}'
-        self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), {"name": "John", "age": 30, "city": "New York"})
+        self.assertEqual(
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=False),
+            {"name": "John", "age": 30, "city": "New York"},
+        )
 
     def test_invalid_json_major_with_gpt(self):
         # Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False
         json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END'
-        self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=True), {"name": "John", "age": 30, "city": "New York"})
+        self.assertEqual(
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=True),
+            {"name": "John", "age": 30, "city": "New York"},
+        )
 
     def test_invalid_json_major_without_gpt(self):
         # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@@ -51,23 +54,22 @@ class TestParseJson(unittest.TestCase):
     }
 }"""
         good_obj = {
-          "command": {
+            "command": {
-              "name": "browse_website",
+                "name": "browse_website",
-              "args": {
+                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-                  "url": "https://github.com/Torantulino/Auto-GPT"
+            },
-              }
+            "thoughts": {
-          },
+                "text": "I suggest we start browsing the repository to find any issues that we can fix.",
-          "thoughts":
+                "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
-          {
+                "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
-              "text": "I suggest we start browsing the repository to find any issues that we can fix.",
+                "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
-              "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.",
+                "speak": "I will start browsing the repository to find any issues we can fix.",
-              "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes",
+            },
-              "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.",
+        }
-              "speak": "I will start browsing the repository to find any issues we can fix."
-          }
-      }
         # Assert that this raises an exception:
-        self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
+        self.assertEqual(
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
+        )
 
     def test_invalid_json_leading_sentence_with_gpt(self):
         # Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False
@@ -90,24 +92,23 @@ class TestParseJson(unittest.TestCase):
     }
 }"""
         good_obj = {
-    "command": {
+            "command": {
-        "name": "browse_website",
+                "name": "browse_website",
-        "args": {
+                "args": {"url": "https://github.com/Torantulino/Auto-GPT"},
-            "url": "https://github.com/Torantulino/Auto-GPT"
+            },
+            "thoughts": {
+                "text": "Browsing the repository to identify potential bugs",
+                "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
+                "plan": "- Analyze the repository for potential bugs and areas of improvement",
+                "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
+                "speak": "I am browsing the repository to identify potential bugs.",
+            },
         }
-    },
-    "thoughts":
-    {
-        "text": "Browsing the repository to identify potential bugs",
-        "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.",
-        "plan": "- Analyze the repository for potential bugs and areas of improvement",
-        "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.",
-        "speak": "I am browsing the repository to identify potential bugs."
-    }
-}
         # Assert that this raises an exception:
-        self.assertEqual(fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj)
+        self.assertEqual(
+            fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()

tests/unit/test_browse_scrape_links.py

@@ -1,4 +1,3 @@
-
 # Generated by CodiumAI
 
 # Dependencies:
@@ -39,7 +38,6 @@ requests and parse HTML content, respectively.
 
 
 class TestScrapeLinks:
-
     # Tests that the function returns a list of formatted hyperlinks when
     # provided with a valid url that returns a webpage with hyperlinks.
     def test_valid_url_with_hyperlinks(self):
@@ -54,8 +52,10 @@ class TestScrapeLinks:
         # Mock the requests.get() function to return a response with sample HTML containing hyperlinks
         mock_response = mocker.Mock()
         mock_response.status_code = 200
-        mock_response.text = "<html><body><a href='https://www.google.com'>Google</a></body></html>"
+        mock_response.text = (
-        mocker.patch('requests.get', return_value=mock_response)
+            "<html><body><a href='https://www.google.com'>Google</a></body></html>"
+        )
+        mocker.patch("requests.get", return_value=mock_response)
 
         # Call the function with a valid URL
         result = scrape_links("https://www.example.com")
@@ -68,7 +68,7 @@ class TestScrapeLinks:
         # Mock the requests.get() function to return an HTTP error response
         mock_response = mocker.Mock()
         mock_response.status_code = 404
-        mocker.patch('requests.get', return_value=mock_response)
+        mocker.patch("requests.get", return_value=mock_response)
 
         # Call the function with an invalid URL
         result = scrape_links("https://www.invalidurl.com")
@@ -82,7 +82,7 @@ class TestScrapeLinks:
         mock_response = mocker.Mock()
         mock_response.status_code = 200
         mock_response.text = "<html><body><p>No hyperlinks here</p></body></html>"
-        mocker.patch('requests.get', return_value=mock_response)
+        mocker.patch("requests.get", return_value=mock_response)
 
         # Call the function with a URL containing no hyperlinks
         result = scrape_links("https://www.example.com")
@@ -105,7 +105,7 @@ class TestScrapeLinks:
                 </body>
             </html>
         """
-        mocker.patch('requests.get', return_value=mock_response)
+        mocker.patch("requests.get", return_value=mock_response)
 
         # Call the function being tested
         result = scrape_links("https://www.example.com")

tests/unit/test_browse_scrape_text.py

@@ -1,9 +1,8 @@
-
 # Generated by CodiumAI
 
 import requests
 
-from scripts.browse import scrape_text
+from autogpt.browse import scrape_text
 
 """
 Code Analysis
@@ -35,7 +34,6 @@ Additional aspects:
 
 
 class TestScrapeText:
-
     # Tests that scrape_text() returns the expected text when given a valid URL.
     def test_scrape_text_with_valid_url(self, mocker):
         # Mock the requests.get() method to return a response with expected text
@@ -74,7 +72,7 @@ class TestScrapeText:
     # Tests that the function returns an error message when the response status code is an http error (>=400).
     def test_http_error(self, mocker):
         # Mock the requests.get() method to return a response with a 404 status code
-        mocker.patch('requests.get', return_value=mocker.Mock(status_code=404))
+        mocker.patch("requests.get", return_value=mocker.Mock(status_code=404))
 
         # Call the function with a URL
         result = scrape_text("https://www.example.com")