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25cc6ad6ae
* refactor(benchmark): Deduplicate configuration loading logic
- Move the configuration loading logic to a separate `load_agbenchmark_config` function in `agbenchmark/config.py` module.
- Replace the duplicate loading logic in `conftest.py`, `generate_test.py`, `ReportManager.py`, `reports.py`, and `__main__.py` with calls to `load_agbenchmark_config` function.
* fix(benchmark): Fix type errors, linting errors, and clean up CLI validation in __main__.py
- Fixed type errors and linting errors in `__main__.py`
- Improved the readability of CLI argument validation by introducing a separate function for it
* refactor(benchmark): Lint and typefix app.py
- Rearranged and cleaned up import statements
- Fixed type errors caused by improper use of `psutil` objects
- Simplified a number of `os.path` usages by converting to `pathlib`
- Use `Task` and `TaskRequestBody` classes from `agent_protocol_client` instead of `.schema`
* refactor(benchmark): Replace `.agent_protocol_client` by `agent-protcol-client`, clean up schema.py
- Remove `agbenchmark.agent_protocol_client` (an offline copy of `agent-protocol-client`).
- Add `agent-protocol-client` as a dependency and change imports to `agent_protocol_client`.
- Fix type annotation on `agent_api_interface.py::upload_artifacts` (`ApiClient` -> `AgentApi`).
- Remove all unused types from schema.py (= most of them).
* refactor(benchmark): Use pathlib in agent_interface.py and agent_api_interface.py
* refactor(benchmark): Improve typing, response validation, and readability in app.py
- Simplified response generation by leveraging type checking and conversion by FastAPI.
- Introduced use of `HTTPException` for error responses.
- Improved naming, formatting, and typing in `app.py::create_evaluation`.
- Updated the docstring on `app.py::create_agent_task`.
- Fixed return type annotations of `create_single_test` and `create_challenge` in generate_test.py.
- Added default values to optional attributes on models in report_types_v2.py.
- Removed unused imports in `generate_test.py`
* refactor(benchmark): Clean up logging and print statements
- Introduced use of the `logging` library for unified logging and better readability.
- Converted most print statements to use `logger.debug`, `logger.warning`, and `logger.error`.
- Improved descriptiveness of log statements.
- Removed unnecessary print statements.
- Added log statements to unspecific and non-verbose `except` blocks.
- Added `--debug` flag, which sets the log level to `DEBUG` and enables a more comprehensive log format.
- Added `.utils.logging` module with `configure_logging` function to easily configure the logging library.
- Converted raw escape sequences in `.utils.challenge` to use `colorama`.
- Renamed `generate_test.py::generate_tests` to `load_challenges`.
* refactor(benchmark): Remove unused server.py and agent_interface.py::run_agent
- Remove unused server.py file
- Remove unused run_agent function from agent_interface.py
* refactor(benchmark): Clean up conftest.py
- Fix and add type annotations
- Rewrite docstrings
- Disable or remove unused code
- Fix definition of arguments and their types in `pytest_addoption`
* refactor(benchmark): Clean up generate_test.py file
- Refactored the `create_single_test` function for clarity and readability
- Removed unused variables
- Made creation of `Challenge` subclasses more straightforward
- Made bare `except` more specific
- Renamed `Challenge.setup_challenge` method to `run_challenge`
- Updated type hints and annotations
- Made minor code/readability improvements in `load_challenges`
- Added a helper function `_add_challenge_to_module` for attaching a Challenge class to the current module
* fix(benchmark): Fix and add type annotations in execute_sub_process.py
* refactor(benchmark): Simplify const determination in agent_interface.py
- Simplify the logic that determines the value of `HELICONE_GRAPHQL_LOGS`
* fix(benchmark): Register category markers to prevent warnings
- Use the `pytest_configure` hook to register the known challenge categories as markers. Otherwise, Pytest will raise "unknown marker" warnings at runtime.
* refactor(benchmark/challenges): Fix indentation in 4_revenue_retrieval_2/data.json
* refactor(benchmark): Update agent_api_interface.py
- Add type annotations to `copy_agent_artifacts_into_temp_folder` function
- Add note about broken endpoint in the `agent_protocol_client` library
- Remove unused variable in `run_api_agent` function
- Improve readability and resolve linting error
* feat(benchmark): Improve and centralize pathfinding
- Search path hierarchy for applicable `agbenchmark_config`, rather than assuming it's in the current folder.
- Create `agbenchmark.utils.path_manager` with `AGBenchmarkPathManager` and exporting a `PATH_MANAGER` const.
- Replace path constants defined in __main__.py with usages of `PATH_MANAGER`.
* feat(benchmark/cli): Clean up and improve CLI
- Updated commands, options, and their descriptions to be more intuitive and consistent
- Moved slow imports into the entrypoints that use them to speed up application startup
- Fixed type hints to match output types of Click options
- Hid deprecated `agbenchmark start` command
- Refactored code to improve readability and maintainability
- Moved main entrypoint into `run` subcommand
- Fixed `version` and `serve` subcommands
- Added `click-default-group` package to allow using `run` implicitly (for backwards compatibility)
- Renamed `--no_dep` to `--no-dep` for consistency
- Fixed string formatting issues in log statements
* refactor(benchmark/config): Move AgentBenchmarkConfig and related functions to config.py
- Move the `AgentBenchmarkConfig` class from `utils/data_types.py` to `config.py`.
- Extract the `calculate_info_test_path` function from `utils/data_types.py` and move it to `config.py` as a private helper function `_calculate_info_test_path`.
- Move `load_agent_benchmark_config()` to `AgentBenchmarkConfig.load()`.
- Changed simple getter methods on `AgentBenchmarkConfig` to calculated properties.
- Update all code references according to the changes mentioned above.
* refactor(benchmark): Fix ReportManager init parameter types and use pathlib
- Fix the type annotation of the `benchmark_start_time` parameter in `ReportManager.__init__`, was mistyped as `str` instead of `datetime`.
- Change the type of the `filename` parameter in the `ReportManager.__init__` method from `str` to `Path`.
- Rename `self.filename` with `self.report_file` in `ReportManager`.
- Change the way the report file is created, opened and saved to use the `Path` object.
* refactor(benchmark): Improve typing surrounding ChallengeData and clean up its implementation
- Use `ChallengeData` objects instead of untyped `dict` in app.py, generate_test.py, reports.py.
- Remove unnecessary methods `serialize`, `get_data`, `get_json_from_path`, `deserialize` from `ChallengeData` class.
- Remove unused methods `challenge_from_datum` and `challenge_from_test_data` from `ChallengeData class.
- Update function signatures and annotations of `create_challenge` and `generate_single_test` functions in generate_test.py.
- Add types to function signatures of `generate_single_call_report` and `finalize_reports` in reports.py.
- Remove unnecessary `challenge_data` parameter (in generate_test.py) and fixture (in conftest.py).
* refactor(benchmark): Clean up generate_test.py, conftest.py and __main__.py
- Cleaned up generate_test.py and conftest.py
- Consolidated challenge creation logic in the `Challenge` class itself, most notably the new `Challenge.from_challenge_spec` method.
- Moved challenge selection logic from generate_test.py to the `pytest_collection_modifyitems` hook in conftest.py.
- Converted methods in the `Challenge` class to class methods where appropriate.
- Improved argument handling in the `run_benchmark` function in `__main__.py`.
* refactor(benchmark/config): Merge AGBenchmarkPathManager into AgentBenchmarkConfig and reduce fragmented/global state
- Merge the functionality of `AGBenchmarkPathManager` into `AgentBenchmarkConfig` to consolidate the configuration management.
- Remove the `.path_manager` module containing `AGBenchmarkPathManager`.
- Pass the `AgentBenchmarkConfig` and its attributes through function arguments to reduce global state and improve code clarity.
* feat(benchmark/serve): Configurable port for `serve` subcommand
- Added `--port` option to `serve` subcommand to allow for specifying the port to run the API on.
- If no `--port` option is provided, the port will default to the value specified in the `PORT` environment variable, or 8080 if not set.
* feat(benchmark/cli): Add `config` subcommand
- Added a new subcommand `config` to the AGBenchmark CLI, to display information about the present AGBenchmark config.
* fix(benchmark): Gracefully handle incompatible challenge spec files in app.py
- Added a check to skip deprecated challenges
- Added logging to allow debugging of the loading process
- Added handling of validation errors when parsing challenge spec files
- Added missing `spec_file` attribute to `ChallengeData`
* refactor(benchmark): Move `run_benchmark` entrypoint to main.py, use it in `/reports` endpoint
- Move `run_benchmark` and `validate_args` from __main__.py to main.py
- Replace agbenchmark subprocess in `app.py:run_single_test` with `run_benchmark`
- Move `get_unique_categories` from __main__.py to challenges/__init__.py
- Move `OPTIONAL_CATEGORIES` from __main__.py to challenge.py
- Reduce operations on updates.json (including `initialize_updates_file`) outside of API
* refactor(benchmark): Remove unused `/updates` endpoint and all related code
- Remove `updates_json_file` attribute from `AgentBenchmarkConfig`
- Remove `get_updates` and `_initialize_updates_file` in app.py
- Remove `append_updates_file` and `create_update_json` functions in agent_api_interface.py
- Remove call to `append_updates_file` in challenge.py
* refactor(benchmark/config): Clean up and update docstrings on `AgentBenchmarkConfig`
- Add and update docstrings
- Change base class from `BaseModel` to `BaseSettings`, allow extras for backwards compatibility
- Make naming of path attributes on `AgentBenchmarkConfig` more consistent
- Remove unused `agent_home_directory` attribute
- Remove unused `workspace` attribute
* fix(benchmark): Restore mechanism to select (optional) categories in agent benchmark config
* fix(benchmark): Update agent-protocol-client to v1.1.0
- Fixes issue with fetching task artifact listings
AutoGPT: build & use AI agents
AutoGPT is the vision of the power of AI accessible to everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters:
- 🏗️ Building - Lay the foundation for something amazing.
- 🧪 Testing - Fine-tune your agent to perfection.
- 🤝 Delegating - Let AI work for you, and have your ideas come to life.
Be part of the revolution! AutoGPT is here to stay, at the forefront of AI innovation.
📖 Documentation | 🚀 Contributing | 🛠️ Build your own Agent - Quickstart
🥇 Current Best Agent: evo.ninja
The AutoGPT Arena Hackathon saw evo.ninja earn the top spot on our Arena Leaderboard, proving itself as the best open-source generalist agent. Try it now at https://evo.ninja!
📈 To challenge evo.ninja, AutoGPT, and others, submit your benchmark run to the Leaderboard, and maybe your agent will be up here next!
🧱 Building blocks
🏗️ Forge
Forge your own agent! – Forge is a ready-to-go template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set your agent apart. All tutorials are located here. Components from the forge.sdk can also be used individually to speed up development and reduce boilerplate in your agent project.
🚀 Getting Started with Forge – This guide will walk you through the process of creating your own agent and using the benchmark and user interface.
📘 Learn More about Forge
🎯 Benchmark
Measure your agent's performance! The agbenchmark can be used with any agent that supports the agent protocol, and the integration with the project's CLI makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
📦 agbenchmark on Pypi
|
📘 Learn More about the Benchmark
🏆 Leaderboard
Submit your benchmark run through the UI and claim your place on the AutoGPT Arena Leaderboard! The best scoring general agent earns the title of Current Best Agent, and will be adopted into our repo so people can easily run it through the CLI.
💻 UI
Makes agents easy to use! The frontend gives you a user-friendly interface to control and monitor your agents. It connects to agents through the agent protocol, ensuring compatibility with many agents from both inside and outside of our ecosystem.
The frontend works out-of-the-box with all agents in the repo. Just use the CLI to run your agent of choice!
📘 Learn More about the Frontend
⌨️ CLI
To make it as easy as possible to use all of the tools offered by the repository, a CLI is included at the root of the repo:
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
arena Commands to enter the arena
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
Just clone the repo, install dependencies with ./run setup, and you should be good to go!
🤔 Questions? Problems? Suggestions?
Get help - Discord 💬
To report a bug or request a feature, create a GitHub Issue. Please ensure someone else hasn’t created an issue for the same topic.
🤝 Sister projects
🔄 Agent Protocol
To maintain a uniform standard and ensure seamless compatibility with many current and future applications, AutoGPT employs the agent protocol standard by the AI Engineer Foundation. This standardizes the communication pathways from your agent to the frontend and benchmark.