Class LLMSingleActionAgent

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

class CustomPromptTemplate extends BaseStringPromptTemplate {
  tools: Tool[];

  constructor(args: { tools: Tool[]; inputVariables: string[] }) {
    super({ inputVariables: args.inputVariables });
    this.tools = args.tools;
  }

  async format(input: InputValues): Promise<string> {
    const toolStrings = this.tools
      .map((tool) => `${tool.name}: ${tool.description}`)
      .join("\n");
    const toolNames = this.tools.map((tool) => tool.name).join(", ");
    const instructions = formatInstructions(toolNames);
    const template = [PREFIX, toolStrings, instructions, SUFFIX].join("\n\n");
    const newInput = {
      ...input,
      agent_scratchpad: input.intermediate_steps
        ?.map(
          (step) =>
            `Action: ${step.action.log}\nObservation: ${step.observation}\nThought:`,
        )
        .join("\n"),
    };
    return renderTemplate(template, "f-string", newInput);
  }
}

class CustomOutputParser extends AgentActionOutputParser {
  async parse(text: string): Promise<AgentAction | AgentFinish> {
    if (text.includes("Final Answer:")) {
      const finalAnswer = text.split("Final Answer:")[1].trim();
      return { log: text, returnValues: { output: finalAnswer } };
    }
    const match = /Action: (.*)\nAction Input: (.*)/s.exec(text);
    if (!match) throw new Error(`Could not parse LLM output: ${text}`);
    return {
      tool: match[1].trim(),
      toolInput: match[2].trim().replace(/^"+|"+$/g, ""),
      log: text,
    };
  }
}

// Example usage
const tools = [new SerpAPI(), new Calculator()];
const llmChain = new LLMChain({
  prompt: new CustomPromptTemplate({
    tools,
    inputVariables: ["input", "agent_scratchpad"],
  }),
  llm: new OpenAI({ temperature: 0 }),
});
const agent = new LLMSingleActionAgent({
  llmChain,
  outputParser: new CustomOutputParser(),
  stop: ["\nObservation"],
});
const executor = new AgentExecutor({ agent, tools });
const input = `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`;
const result = await executor.invoke({ input });
console.log(`Got output ${result.output}`);

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