GeezerVison is showing docs.langchain.com — LangChain overview - Docs by LangChain Agent = Model + Harness. LangChain provides create_agent: a minimal, highly configurable harness. The harness is everything around the model loop: the prompt, the tools, and any middleware that shapes behavior. Start with the primitives and compose exactly what your use case needs. Supports OpenAI, Anthropic, Google, and more.
LangChain vs. LangGraph vs. Deep AgentsStart with Managed Deep Agents for a batteries-included harness (context compression, virtual filesystem, subagents) when you want to run production agents without having to manage infrastructure. Alternatively, for maximum flexibility while using the same Deep Agents harness, use the Deep Agents SDK and host agents yourself, or run a custom LangSmith Deployment. Deep Agents is built on LangChain agents, which you can also use directly.Use LangChain (create_agent) for a highly customizable harness, easily tailored to your use case and data.Use LangGraph, our low-level orchestration framework, for advanced needs combining deterministic and agentic workflows.Use LangSmith to trace, debug, and evaluate agents built with any of these frameworks. Follow the tracing quickstart to get set up. We recommend you also set up LangSmith Engine which monitors your traces, detects issues, and proposes fixes. Create an agent
This example demonstrates how to create a simple LangChain agent with a custom tool:
// First install: npm install langchain zod @langchain/openai
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "gpt-6.1-sol",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod @langchain/google
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "google:gemini-2.5-flash-lite",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod @langchain/anthropic
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "claude-sonnet-4-6",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod @langchain/openrouter
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "openrouter:anthropic/claude-sonnet-4-6",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "baseten:zai-org/GLM-5.2",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod @langchain/ollama
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "ollama:devstral-2",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod @langchain/openai
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "azure_openai:gpt-6.1-sol",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
// First install: npm install langchain zod @langchain/aws
import { createAgent, tool } from "langchain";
import * as z from "zod";
const getWeather = tool(
(input) => `It's always sunny in ${input.city}!`,
{
name: "get_weather",
description: "Get the weather for a given city",
schema: z.object({
city: z.string().describe("The city to get the weather for"),
}),
}
);
const agent = createAgent({
model: "bedrock:gpt-6.1-sol",
tools: [getWeather],
});
console.log(
await agent.invoke({
messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
})
);
Use LangSmith to trace requests, debug agent behavior, and evaluate outputs. Set LANGSMITH_TRACING=true and your API key to get started. Core benefits
Standard model interfaceUse one interface for chat models, embeddings, and more across providers. Switch models with minimal code changes and keep your application portable as requirements evolve. Highly configurable harnessStart with create_agent as a minimal harness and add capabilities incrementally through middleware. Compose only what your use case needs, from guardrails and retries to routing and custom tool policies. Built on top of LangGraphLangChain’s agents are built on top of LangGraph. This allows us to take advantage of LangGraph’s durable execution, human-in-the-loop support, persistence, and more. Debug with LangSmithInspect traces, tool calls, state transitions, and latency in one place. Find failure modes, evaluate quality, and improve agent behavior with execution data.
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