Abstract Abstract lc_A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.
Abstract _agentConstruct a scratchpad to let the agent continue its thought process
Abstract llmAbstract observationDecide what to do given some input.
Steps the LLM has taken so far, along with observations from each.
User inputs.
Optional callbackManager: CallbackManagerCallback manager to use for this call.
Action specifying what tool to use.
Prepare the agent for output, if needed
Return response when agent has been stopped due to max iterations
Optional callbackManager: CallbackManagerStatic createCreate a prompt for this class
List of tools the agent will have access to, used to format the prompt.
Optional _fields: Record<string, any>Additional fields used to format the prompt.
A PromptTemplate assembled from the given tools and fields.
Static deserializeStatic fromLLMAndConstruct an agent from an LLM and a list of tools
Optional _args: AgentArgsStatic getGet the default output parser for this agent.
Optional _fields: OutputParserArgsStatic validateValidate that appropriate tools are passed in
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Class responsible for calling a language model and deciding an action.
Remarks
This is driven by an LLMChain. The prompt in the LLMChain must include a variable called "agent_scratchpad" where the agent can put its intermediary work.