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a5f61caae9
* Added a push action * Tests * Add tests * Fix capitalization * Update * Fix typo * Fix integration tests * Added poetry.lock * Set lock * Fix action parsing * Update integration test output * Updated prompt * Update integration test * Add github token to default config --------- Co-authored-by: Engel Nyst <enyst@users.noreply.github.com>
99 lines
2.9 KiB
Markdown
99 lines
2.9 KiB
Markdown
# Agents and Capabilities
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## Monologue Agent:
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### Description:
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The Monologue Agent utilizes long and short term memory to complete tasks.
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Long term memory is stored as a LongTermMemory object and the model uses it to search for examples from the past.
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Short term memory is stored as a Monologue object and the model can condense it as necessary.
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### Actions:
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`Action`,
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`NullAction`,
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`CmdRunAction`,
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`FileWriteAction`,
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`FileReadAction`,
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`AgentRecallAction`,
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`BrowseURLAction`,
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`GithubPushAction`,
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`AgentThinkAction`
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### Observations:
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`Observation`,
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`NullObservation`,
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`CmdOutputObservation`,
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`FileReadObservation`,
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`AgentRecallObservation`,
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`BrowserOutputObservation`
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### Methods:
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`__init__`: Initializes the agent with a long term memory, and an internal monologue
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`_add_event`: Appends events to the monologue of the agent and condenses with summary automatically if the monologue is too long
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`_initialize`: Utilizes the `INITIAL_THOUGHTS` list to give the agent a context for its capabilities and how to navigate the `/workspace`
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`step`: Modifies the current state by adding the most recent actions and observations, then prompts the model to think about its next action to take.
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`search_memory`: Uses `VectorIndexRetriever` to find related memories within the long term memory.
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## Planner Agent:
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### Description:
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The planner agent utilizes a special prompting strategy to create long term plans for solving problems.
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The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.
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### Actions:
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`NullAction`,
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`CmdRunAction`,
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`CmdKillAction`,
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`BrowseURLAction`,
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`GithubPushAction`,
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`FileReadAction`,
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`FileWriteAction`,
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`AgentRecallAction`,
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`AgentThinkAction`,
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`AgentFinishAction`,
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`AgentSummarizeAction`,
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`AddTaskAction`,
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`ModifyTaskAction`,
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### Observations:
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`Observation`,
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`NullObservation`,
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`CmdOutputObservation`,
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`FileReadObservation`,
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`AgentRecallObservation`,
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`BrowserOutputObservation`
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### Methods:
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`__init__`: Initializes an agent with `llm`
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`step`: Checks to see if current step is completed, returns `AgentFinishAction` if True. Otherwise, creates a plan prompt and sends to model for inference, adding the result as the next action.
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`search_memory`: Not yet implemented
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## CodeAct Agent:
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### Description:
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The Code Act Agent is a minimalist agent. The agent works by passing the model a list of action-observation pairs and prompting the model to take the next step.
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### Actions:
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`Action`,
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`CmdRunAction`,
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`AgentEchoAction`,
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`AgentFinishAction`,
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### Observations:
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`CmdOutputObservation`,
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`AgentMessageObservation`,
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### Methods:
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`__init__`: Initializes an agent with `llm` and a list of messages `List[Mapping[str, str]]`
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`step`: First, gets messages from state and then compiles them into a list for context. Next, pass the context list with the prompt to get the next command to execute. Finally, Execute command if valid, else return `AgentEchoAction(INVALID_INPUT_MESSAGE)`
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`search_memory`: Not yet implemented
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