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Fix typos in forge/agent.py (#5449)

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* Create FRITZLABS.md

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* fix typos

Co-authored-by: Swiftyos <craigswift13@gmail.com>
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bsenst
2023-10-02 10:16:27 +02:00
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parent ef8688b1a4
commit 062d286c23
1 changed files with 9 additions and 9 deletions
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18
autogpts/forge/forge/agent.py
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@@ -19,7 +19,7 @@ LOG = ForgeLogger(__name__)
class ForgeAgent(Agent):
"""
The goal of the Forge is to take care of the boilerplate code so you can focus on
The goal of the Forge is to take care of the boilerplate code, so you can focus on
agent design.
There is a great paper surveying the agent landscape: https://arxiv.org/abs/2308.11432
@@ -39,18 +39,18 @@ class ForgeAgent(Agent):
a coder, a planner etc. In using the profile in the llm prompt it has been shown to
improve the quality of the output. https://arxiv.org/abs/2305.14688
Additionally baed on the profile selected, the agent could be configured to use a
different llm. The possabilities are endless and the profile can be selected selected
Additionally, based on the profile selected, the agent could be configured to use a
different llm. The possibilities are endless and the profile can be selected
dynamically based on the task at hand.
Memory:
Memory is critical for the agent to acculmulate experiences, self-evolve, and behave
Memory is critical for the agent to accumulate experiences, self-evolve, and behave
in a more consistent, reasonable, and effective manner. There are many approaches to
memory. However, some thoughts: there is long term and short term or working memory.
You may want different approaches for each. There has also been work exploring the
idea of memory reflection, which is the ability to assess its memories and re-evaluate
them. For example, condensting short term memories into long term memories.
them. For example, condensing short term memories into long term memories.
Planning:
@@ -62,7 +62,7 @@ class ForgeAgent(Agent):
Action:
Actions translate the agents decisions into specific outcomes. For example, if the agent
Actions translate the agent's decisions into specific outcomes. For example, if the agent
decides to write a file, the action would be to write the file. There are many approaches you
could implement actions.
@@ -103,7 +103,7 @@ class ForgeAgent(Agent):
executing steps for that task. This method is called when the agent is asked to execute
a step.
The task that is created contains an input string, for the bechmarks this is the task
The task that is created contains an input string, for the benchmarks this is the task
the agent has been asked to solve and additional input, which is a dictionary and
could contain anything.
@@ -113,8 +113,8 @@ class ForgeAgent(Agent):
task = await self.db.get_task(task_id)
```
The step request body is essentailly the same as the task request and contains an input
string, for the bechmarks this is the task the agent has been asked to solve and
The step request body is essentially the same as the task request and contains an input
string, for the benchmarks this is the task the agent has been asked to solve and
additional input, which is a dictionary and could contain anything.
You need to implement logic that will take in this step input and output the completed step