langchain-core-workflow-b

LangChain Core Workflow B: Agents & Tools

Overview

Build autonomous agents that use tools, make decisions, and execute multi-step tasks. Covers tool definition with Zod schemas, createToolCallingAgent, AgentExecutor, streaming agent output, and conversation memory.

Prerequisites

• Completed langchain-core-workflow-a (chains)
• npm install langchain @langchain/core @langchain/openai zod

Step 1: Define Tools (TypeScript)

import { tool } from "@langchain/core/tools";
import { z } from "zod";

// Tool with Zod schema validation
const calculator = tool(
  async ({ expression }) => {
    try {
      // Use a safe math parser in production (e.g., mathjs)
      const result = Function(`"use strict"; return (${expression})`)();
      return String(result);
    } catch (e) {
      return `Error: invalid expression "${expression}"`;
    }
  },
  {
    name: "calculator",
    description: "Evaluate a mathematical expression. Input: a math expression string.",
    schema: z.object({
      expression: z.string().describe("Math expression like '2 + 2' or '100 * 0.15'"),
    }),
  }
);

const weatherLookup = tool(
  async ({ city }) => {
    // Replace with real API call
    const data: Record<string, string> = {
      "New York": "72F, sunny",
      "London": "58F, cloudy",
      "Tokyo": "80F, humid",
    };
    return data[city] ?? `No weather data for ${city}`;
  },
  {
    name: "weather",
    description: "Get current weather for a city.",
    schema: z.object({
      city: z.string().describe("City name"),
    }),
  }
);

const tools = [calculator, weatherLookup];

Step 2: Create Agent with AgentExecutor

import { ChatOpenAI } from "@langchain/openai";
import { createToolCallingAgent, AgentExecutor } from "langchain/agents";
import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";

const llm = new ChatOpenAI({ model: "gpt-4o-mini" });

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant. Use tools when needed."],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
  new MessagesPlaceholder("agent_scratchpad"),
]);

const agent = createToolCallingAgent({
  llm,
  tools,
  prompt,
});

const executor = new AgentExecutor({
  agent,
  tools,
  verbose: true,           // Log reasoning steps
  maxIterations: 10,       // Prevent infinite loops
  returnIntermediateSteps: true,
});

Step 3: Run the Agent

// Simple invocation
const result = await executor.invoke({
  input: "What's 25 * 4, and what's the weather in Tokyo?",
  chat_history: [],
});

console.log(result.output);
// "25 * 4 = 100. The weather in Tokyo is 80F and humid."

// The agent decided to call both tools, then composed the answer.
console.log(result.intermediateSteps);
// Shows each tool call and its result

Step 4: Agent with Conversation Memory

import { ChatMessageHistory } from "@langchain/community/stores/message/in_memory";
import { RunnableWithMessageHistory } from "@langchain/core/runnables";

const messageHistory = new ChatMessageHistory();

const agentWithHistory = new RunnableWithMessageHistory({
  runnable: executor,
  getMessageHistory: (_sessionId) => messageHistory,
  inputMessagesKey: "input",
  historyMessagesKey: "chat_history",
});

// First call
await agentWithHistory.invoke(
  { input: "My name is Alice" },
  { configurable: { sessionId: "user-1" } }
);

// Second call -- agent remembers
const res = await agentWithHistory.invoke(
  { input: "What's my name?" },
  { configurable: { sessionId: "user-1" } }
);
console.log(res.output); // "Your name is Alice!"

Step 5: Stream Agent Events

const eventStream = executor.streamEvents(
  { input: "Calculate 15% tip on $85", chat_history: [] },
  { version: "v2" }
);

for await (const event of eventStream) {
  if (event.event === "on_chat_model_stream") {
    process.stdout.write(event.data.chunk.content ?? "");
  } else if (event.event === "on_tool_start") {
    console.log(`\n[Calling tool: ${event.name}]`);
  } else if (event.event === "on_tool_end") {
    console.log(`[Tool result: ${event.data.output}]`);
  }
}

Step 6: Bind Tools Directly (Without AgentExecutor)

import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";

const model = new ChatOpenAI({ model: "gpt-4o-mini" });
const modelWithTools = model.bindTools(tools);

const response = await modelWithTools.invoke([
  new HumanMessage("What's 42 * 17?"),
]);

// Check if model wants to call a tool
if (response.tool_calls && response.tool_calls.length > 0) {
  for (const tc of response.tool_calls) {
    console.log(`Tool: ${tc.name}, Args: ${JSON.stringify(tc.args)}`);
    // Execute tool manually
    const toolResult = await tools
      .find((t) => t.name === tc.name)!
      .invoke(tc.args);
    console.log(`Result: ${toolResult}`);
  }
}

Python Equivalent

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tools import tool

@tool
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))

tools = [calculator]
llm = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    MessagesPlaceholder("chat_history", optional=True),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
result = executor.invoke({"input": "What is 25 * 4?", "chat_history": []})

Error Handling

Error	Cause	Fix
Max iterations reached	Agent stuck in loop	Increase maxIterations or improve system prompt
Tool not found	Tool name mismatch	Verify tools array passed to both createToolCallingAgent and AgentExecutor
Missing agent_scratchpad	Prompt missing placeholder	Add new MessagesPlaceholder("agent_scratchpad")
Tool execution error	Tool throws exception	Wrap tool body in try/catch, return error string

Resources

• Agents Guide
• Tool Calling
• createToolCallingAgent API

Next Steps

Proceed to langchain-common-errors for debugging guidance.