agent.ts

import type {
  StructuredToolInterface,
  ToolInterface,
} from "@langchain/core/tools";
import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
import { CallbackManager, Callbacks } from "@langchain/core/callbacks/manager";
import { BasePromptTemplate } from "@langchain/core/prompts";
import { AgentAction, AgentFinish, AgentStep } from "@langchain/core/agents";
import { BaseMessage } from "@langchain/core/messages";
import { ChainValues } from "@langchain/core/utils/types";
import { Serializable } from "@langchain/core/load/serializable";
import { Runnable } from "@langchain/core/runnables";
import { LLMChain } from "../chains/llm_chain.js";
import {
  AgentActionOutputParser,
  AgentInput,
  RunnableAgentInput,
  SerializedAgent,
  StoppingMethod,
} from "./types.js";

/**
 * Record type for arguments passed to output parsers.
 */
// eslint-disable-next-line @typescript-eslint/no-explicit-any
export type OutputParserArgs = Record<string, any>;

/**
 * Error class for parse errors in LangChain. Contains information about
 * the error message and the output that caused the error.
 */
class ParseError extends Error {
  output: string;

  constructor(msg: string, output: string) {
    super(msg);
    this.output = output;
  }
}

/**
 * Abstract base class for agents in LangChain. Provides common
 * functionality for agents, such as handling inputs and outputs.
 */
export abstract class BaseAgent extends Serializable {
  declare ToolType: StructuredToolInterface;

  abstract get inputKeys(): string[];

  get returnValues(): string[] {
    return ["output"];
  }

  get allowedTools(): string[] | undefined {
    return undefined;
  }

  /**
   * Return the string type key uniquely identifying this class of agent.
   */
  _agentType(): string {
    throw new Error("Not implemented");
  }

  /**
   * Return the string type key uniquely identifying multi or single action agents.
   */
  abstract _agentActionType(): string;

  /**
   * Return response when agent has been stopped due to max iterations
   */
  returnStoppedResponse(
    earlyStoppingMethod: StoppingMethod,
    _steps: AgentStep[],
    _inputs: ChainValues,
    _callbackManager?: CallbackManager
  ): Promise<AgentFinish> {
    if (earlyStoppingMethod === "force") {
      return Promise.resolve({
        returnValues: { output: "Agent stopped due to max iterations." },
        log: "",
      });
    }

    throw new Error(`Invalid stopping method: ${earlyStoppingMethod}`);
  }

  /**
   * Prepare the agent for output, if needed
   */
  async prepareForOutput(
    _returnValues: AgentFinish["returnValues"],
    _steps: AgentStep[]
  ): Promise<AgentFinish["returnValues"]> {
    return {};
  }
}

/**
 * Abstract base class for single action agents in LangChain. Extends the
 * BaseAgent class and provides additional functionality specific to
 * single action agents.
 */
export abstract class BaseSingleActionAgent extends BaseAgent {
  _agentActionType(): string {
    return "single" as const;
  }

  /**
   * Decide what to do, given some input.
   *
   * @param steps - Steps the LLM has taken so far, along with observations from each.
   * @param inputs - User inputs.
   * @param callbackManager - Callback manager.
   *
   * @returns Action specifying what tool to use.
   */
  abstract plan(
    steps: AgentStep[],
    inputs: ChainValues,
    callbackManager?: CallbackManager
  ): Promise<AgentAction | AgentFinish>;
}

/**
 * Abstract base class for multi-action agents in LangChain. Extends the
 * BaseAgent class and provides additional functionality specific to
 * multi-action agents.
 */
export abstract class BaseMultiActionAgent extends BaseAgent {
  _agentActionType(): string {
    return "multi" as const;
  }

  /**
   * Decide what to do, given some input.
   *
   * @param steps - Steps the LLM has taken so far, along with observations from each.
   * @param inputs - User inputs.
   * @param callbackManager - Callback manager.
   *
   * @returns Actions specifying what tools to use.
   */
  abstract plan(
    steps: AgentStep[],
    inputs: ChainValues,
    callbackManager?: CallbackManager
  ): Promise<AgentAction[] | AgentFinish>;
}

function isAgentAction(input: unknown): input is AgentAction {
  return !Array.isArray(input) && (input as AgentAction)?.tool !== undefined;
}

/**
 * Class representing a single action agent which accepts runnables.
 * Extends the BaseSingleActionAgent class and provides methods for
 * planning agent actions with runnables.
 */
export class RunnableAgent extends BaseMultiActionAgent {
  protected lc_runnable = true;

  lc_namespace = ["langchain", "agents", "runnable"];

  // TODO: Rename input to "intermediate_steps"
  runnable: Runnable<
    ChainValues & { steps: AgentStep[] },
    AgentAction[] | AgentAction | AgentFinish
  >;

  stop?: string[];

  get inputKeys(): string[] {
    return [];
  }

  constructor(fields: RunnableAgentInput) {
    super();
    this.runnable = fields.runnable;
    this.stop = fields.stop;
  }

  async plan(
    steps: AgentStep[],
    inputs: ChainValues,
    callbackManager?: CallbackManager
  ): Promise<AgentAction[] | AgentFinish> {
    const invokeInput = { ...inputs, steps };

    const output = await this.runnable.invoke(invokeInput, {
      callbacks: callbackManager,
      runName: "RunnableAgent",
    });

    if (isAgentAction(output)) {
      return [output];
    }

    return output;
  }
}

/**
 * Interface for input data for creating a LLMSingleActionAgent.
 */
export interface LLMSingleActionAgentInput {
  llmChain: LLMChain;
  outputParser: AgentActionOutputParser;
  stop?: string[];
}

/**
 * Class representing a single action agent using a LLMChain in LangChain.
 * Extends the BaseSingleActionAgent class and provides methods for
 * planning agent actions based on LLMChain outputs.
 * @example
 * ```typescript
 * const customPromptTemplate = new CustomPromptTemplate({
 *   tools: [new Calculator()],
 *   inputVariables: ["input", "agent_scratchpad"],
 * });
 * const customOutputParser = new CustomOutputParser();
 * const agent = new LLMSingleActionAgent({
 *   llmChain: new LLMChain({
 *     prompt: customPromptTemplate,
 *     llm: new ChatOpenAI({ temperature: 0 }),
 *   }),
 *   outputParser: customOutputParser,
 *   stop: ["\nObservation"],
 * });
 * const executor = new AgentExecutor({
 *   agent,
 *   tools: [new Calculator()],
 * });
 * const result = await executor.invoke({
 *   input:
 *     "Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?",
 * });
 * ```
 */
export class LLMSingleActionAgent extends BaseSingleActionAgent {
  lc_namespace = ["langchain", "agents"];

  llmChain: LLMChain;

  outputParser: AgentActionOutputParser;

  stop?: string[];

  constructor(input: LLMSingleActionAgentInput) {
    super(input);
    this.stop = input.stop;
    this.llmChain = input.llmChain;
    this.outputParser = input.outputParser;
  }

  get inputKeys(): string[] {
    return this.llmChain.inputKeys;
  }

  /**
   * Decide what to do given some input.
   *
   * @param steps - Steps the LLM has taken so far, along with observations from each.
   * @param inputs - User inputs.
   * @param callbackManager - Callback manager.
   *
   * @returns Action specifying what tool to use.
   */
  async plan(
    steps: AgentStep[],
    inputs: ChainValues,
    callbackManager?: CallbackManager
  ): Promise<AgentAction | AgentFinish> {
    const output = await this.llmChain.call(
      {
        intermediate_steps: steps,
        stop: this.stop,
        ...inputs,
      },
      callbackManager
    );
    return this.outputParser.parse(
      output[this.llmChain.outputKey],
      callbackManager
    );
  }
}

/**
 * Interface for arguments used to create an agent in LangChain.
 */
export interface AgentArgs {
  outputParser?: AgentActionOutputParser;

  callbacks?: Callbacks;

  /**
   * @deprecated Use `callbacks` instead.
   */
  callbackManager?: CallbackManager;
}

/**
 * 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.
 *
 * @deprecated Use {@link https://js.langchain.com/docs/modules/agents/agent_types/ | new agent creation methods}.
 */
export abstract class Agent extends BaseSingleActionAgent {
  llmChain: LLMChain;

  outputParser: AgentActionOutputParser | undefined;

  private _allowedTools?: string[] = undefined;

  get allowedTools(): string[] | undefined {
    return this._allowedTools;
  }

  get inputKeys(): string[] {
    return this.llmChain.inputKeys.filter((k) => k !== "agent_scratchpad");
  }

  constructor(input: AgentInput) {
    super(input);

    this.llmChain = input.llmChain;
    this._allowedTools = input.allowedTools;
    this.outputParser = input.outputParser;
  }

  /**
   * Prefix to append the observation with.
   */
  abstract observationPrefix(): string;

  /**
   * Prefix to append the LLM call with.
   */
  abstract llmPrefix(): string;

  /**
   * Return the string type key uniquely identifying this class of agent.
   */
  abstract _agentType(): string;

  /**
   * Get the default output parser for this agent.
   */
  static getDefaultOutputParser(
    _fields?: OutputParserArgs
  ): AgentActionOutputParser {
    throw new Error("Not implemented");
  }

  /**
   * Create a prompt for this class
   *
   * @param _tools - List of tools the agent will have access to, used to format the prompt.
   * @param _fields - Additional fields used to format the prompt.
   *
   * @returns A PromptTemplate assembled from the given tools and fields.
   * */
  static createPrompt(
    _tools: StructuredToolInterface[],
    // eslint-disable-next-line @typescript-eslint/no-explicit-any
    _fields?: Record<string, any>
  ): BasePromptTemplate {
    throw new Error("Not implemented");
  }

  /** Construct an agent from an LLM and a list of tools */
  static fromLLMAndTools(
    _llm: BaseLanguageModelInterface,
    _tools: StructuredToolInterface[],
    // eslint-disable-next-line @typescript-eslint/no-explicit-any
    _args?: AgentArgs
  ): Agent {
    throw new Error("Not implemented");
  }

  /**
   * Validate that appropriate tools are passed in
   */
  static validateTools(_tools: StructuredToolInterface[]): void {}

  _stop(): string[] {
    return [`\n${this.observationPrefix()}`];
  }

  /**
   * Name of tool to use to terminate the chain.
   */
  finishToolName(): string {
    return "Final Answer";
  }

  /**
   * Construct a scratchpad to let the agent continue its thought process
   */
  async constructScratchPad(
    steps: AgentStep[]
  ): Promise<string | BaseMessage[]> {
    return steps.reduce(
      (thoughts, { action, observation }) =>
        thoughts +
        [
          action.log,
          `${this.observationPrefix()}${observation}`,
          this.llmPrefix(),
        ].join("\n"),
      ""
    );
  }

  private async _plan(
    steps: AgentStep[],
    inputs: ChainValues,
    suffix?: string,
    callbackManager?: CallbackManager
  ): Promise<AgentAction | AgentFinish> {
    const thoughts = await this.constructScratchPad(steps);
    const newInputs: ChainValues = {
      ...inputs,
      agent_scratchpad: suffix ? `${thoughts}${suffix}` : thoughts,
    };

    if (this._stop().length !== 0) {
      newInputs.stop = this._stop();
    }

    const output = await this.llmChain.predict(newInputs, callbackManager);
    if (!this.outputParser) {
      throw new Error("Output parser not set");
    }
    return this.outputParser.parse(output, callbackManager);
  }

  /**
   * Decide what to do given some input.
   *
   * @param steps - Steps the LLM has taken so far, along with observations from each.
   * @param inputs - User inputs.
   * @param callbackManager - Callback manager to use for this call.
   *
   * @returns Action specifying what tool to use.
   */
  plan(
    steps: AgentStep[],
    inputs: ChainValues,
    callbackManager?: CallbackManager
  ): Promise<AgentAction | AgentFinish> {
    return this._plan(steps, inputs, undefined, callbackManager);
  }

  /**
   * Return response when agent has been stopped due to max iterations
   */
  async returnStoppedResponse(
    earlyStoppingMethod: StoppingMethod,
    steps: AgentStep[],
    inputs: ChainValues,
    callbackManager?: CallbackManager
  ): Promise<AgentFinish> {
    if (earlyStoppingMethod === "force") {
      return {
        returnValues: { output: "Agent stopped due to max iterations." },
        log: "",
      };
    }

    if (earlyStoppingMethod === "generate") {
      try {
        const action = await this._plan(
          steps,
          inputs,
          "\n\nI now need to return a final answer based on the previous steps:",
          callbackManager
        );
        if ("returnValues" in action) {
          return action;
        }

        return { returnValues: { output: action.log }, log: action.log };
      } catch (err) {
        // fine to use instanceof because we're in the same module
        // eslint-disable-next-line no-instanceof/no-instanceof
        if (!(err instanceof ParseError)) {
          throw err;
        }
        return { returnValues: { output: err.output }, log: err.output };
      }
    }

    throw new Error(`Invalid stopping method: ${earlyStoppingMethod}`);
  }

  /**
   * Load an agent from a json-like object describing it.
   */
  static async deserialize(
    data: SerializedAgent & {
      llm?: BaseLanguageModelInterface;
      tools?: ToolInterface[];
    }
  ): Promise<Agent> {
    switch (data._type) {
      case "zero-shot-react-description": {
        const { ZeroShotAgent } = await import("./mrkl/index.js");
        return ZeroShotAgent.deserialize(data);
      }
      default:
        throw new Error("Unknown agent type");
    }
  }
}