"use strict"; Object.defineProperty(exports, "__esModule", { value: true }); exports.createReactAgent = void 0; const runnables_1 = require("@langchain/core/runnables"); const render_js_1 = require("../../tools/render.cjs"); const log_js_1 = require("../format_scratchpad/log.cjs"); const output_parser_js_1 = require("./output_parser.cjs"); const agent_js_1 = require("../agent.cjs"); /** * Create an agent that uses ReAct prompting. * @param params Params required to create the agent. Includes an LLM, tools, and prompt. * @returns A runnable sequence representing an agent. It takes as input all the same input * variables as the prompt passed in does. It returns as output either an * AgentAction or AgentFinish. * * @example * ```typescript * import { AgentExecutor, createReactAgent } from "langchain/agents"; * import { pull } from "langchain/hub"; * import type { PromptTemplate } from "@langchain/core/prompts"; * * import { OpenAI } from "@langchain/openai"; * * // Define the tools the agent will have access to. * const tools = [...]; * * // Get the prompt to use - you can modify this! * // If you want to see the prompt in full, you can at: * // https://smith.langchain.com/hub/hwchase17/react * const prompt = await pull("hwchase17/react"); * * const llm = new OpenAI({ * temperature: 0, * }); * * const agent = await createReactAgent({ * llm, * tools, * prompt, * }); * * const agentExecutor = new AgentExecutor({ * agent, * tools, * }); * * const result = await agentExecutor.invoke({ * input: "what is LangChain?", * }); * ``` */ async function createReactAgent({ llm, tools, prompt, streamRunnable, }) { const missingVariables = ["tools", "tool_names", "agent_scratchpad"].filter((v) => !prompt.inputVariables.includes(v)); if (missingVariables.length > 0) { throw new Error(`Provided prompt is missing required input variables: ${JSON.stringify(missingVariables)}`); } const toolNames = tools.map((tool) => tool.name); const partialedPrompt = await prompt.partial({ tools: (0, render_js_1.renderTextDescription)(tools), tool_names: toolNames.join(", "), }); // TODO: Add .bind to core runnable interface. const llmWithStop = llm.bind({ stop: ["\nObservation:"], }); const agent = agent_js_1.AgentRunnableSequence.fromRunnables([ runnables_1.RunnablePassthrough.assign({ agent_scratchpad: (input) => (0, log_js_1.formatLogToString)(input.steps), }), partialedPrompt, llmWithStop, new output_parser_js_1.ReActSingleInputOutputParser({ toolNames, }), ], { name: "ReactAgent", streamRunnable, singleAction: true, }); return agent; } exports.createReactAgent = createReactAgent;