Class AgentAbstract

constructor

• new Agent(input: AgentInput): Agent

  Parameters

  • input: AgentInput

  Returns Agent

llmChain

outputParser

allowedTools

• get allowedTools(): undefined | string[]

  Returns undefined | string[]

inputKeys

• get inputKeys(): string[]

  Returns string[]

Abstract_agentType

• _agentType(): string

  Return the string type key uniquely identifying this class of agent.

  Returns string

_stop

• _stop(): string[]

  Returns string[]

constructScratchPad

• constructScratchPad(steps: AgentStep[]): Promise<string | BaseMessage[]>

  Construct a scratchpad to let the agent continue its thought process

  Parameters

  • steps: AgentStep[]

  Returns Promise<string | BaseMessage[]>

finishToolName

• finishToolName(): string

  Name of tool to use to terminate the chain.

  Returns string

AbstractllmPrefix

• llmPrefix(): string

  Prefix to append the LLM call with.

  Returns string

AbstractobservationPrefix

• observationPrefix(): string

  Prefix to append the observation with.

  Returns string

plan

• plan(
      steps: AgentStep[],
      inputs: ChainValues,
      callbackManager?: any,
  ): Promise<any>

  Decide what to do given some input.

  Parameters

  • steps: AgentStep[]

    Steps the LLM has taken so far, along with observations from each.

  • inputs: ChainValues

    User inputs.

  • OptionalcallbackManager: any

    Callback manager to use for this call.

  Returns Promise<any>

  Action specifying what tool to use.

returnStoppedResponse

• returnStoppedResponse(
      earlyStoppingMethod: StoppingMethod,
      steps: AgentStep[],
      inputs: ChainValues,
      callbackManager?: any,
  ): Promise<AgentFinish>

  Return response when agent has been stopped due to max iterations

  Parameters

  • earlyStoppingMethod: StoppingMethod
  • steps: AgentStep[]
  • inputs: ChainValues
  • OptionalcallbackManager: any

  Returns Promise<AgentFinish>

StaticcreatePrompt

• createPrompt(
      _tools: StructuredToolInterface[],
      _fields?: Record<string, any>,
  ): BasePromptTemplate

  Create a prompt for this class

  Parameters

  • _tools: StructuredToolInterface[]

    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.

  Returns BasePromptTemplate

  A PromptTemplate assembled from the given tools and fields.

Staticdeserialize

• deserialize(
      data: SerializedZeroShotAgent & { llm?: any; tools?: ToolInterface[] },
  ): Promise<Agent>

  Load an agent from a json-like object describing it.

  Parameters

  • data: SerializedZeroShotAgent & { llm?: any; tools?: ToolInterface[] }

  Returns Promise<Agent>

StaticfromLLMAndTools

• fromLLMAndTools(
      _llm: BaseLanguageModelInterface,
      _tools: StructuredToolInterface[],
      _args?: AgentArgs,
  ): Agent

  Construct an agent from an LLM and a list of tools

  Parameters

  • _llm: BaseLanguageModelInterface
  • _tools: StructuredToolInterface[]
  • Optional_args: AgentArgs

  Returns Agent

StaticgetDefaultOutputParser

• getDefaultOutputParser(_fields?: OutputParserArgs): AgentActionOutputParser

  Get the default output parser for this agent.

  Parameters

  • Optional_fields: OutputParserArgs

  Returns AgentActionOutputParser

StaticvalidateTools

• validateTools(_tools: StructuredToolInterface[]): void

  Validate that appropriate tools are passed in

  Parameters

  • _tools: StructuredToolInterface[]

  Returns void