import { RunnablePassthrough } from "@langchain/core/runnables";
import { renderTextDescription } from "../../tools/render.js";
import { formatLogToString } from "../format_scratchpad/log.js";
import { ReActSingleInputOutputParser } from "./output_parser.js";
import { AgentRunnableSequence } from "../agent.js";
/**
 * 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<PromptTemplate>("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?",
 * });
 * ```
 */
export 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: renderTextDescription(tools),
        tool_names: toolNames.join(", "),
    });
    // TODO: Add .bind to core runnable interface.
    const llmWithStop = llm.bind({
        stop: ["\nObservation:"],
    });
    const agent = AgentRunnableSequence.fromRunnables([
        RunnablePassthrough.assign({
            agent_scratchpad: (input) => formatLogToString(input.steps),
        }),
        partialedPrompt,
        llmWithStop,
        new ReActSingleInputOutputParser({
            toolNames,
        }),
    ], {
        name: "ReactAgent",
        streamRunnable,
        singleAction: true,
    });
    return agent;
}