langchain-ai/langgraphjs

langchain-ai/langgraphjs

Agent

This guide shows you how to set up and use LangGraph's prebuilt, reusable components, which are designed to help you construct agentic systems quickly and reliably.

langchain-ai/langgraphjs

Agent

Agents often require more than a list of messages to function effectively. They need context.

langchain-ai/langgraphjs

Agent

To deploy your LangGraph agent, create and configure a LangGraph app. This setup supports both local development and production deployments.

langchain-ai/langgraphjs

Agent

To evaluate your agent's performance you can use LangSmith evaluations. You would need to first define an evaluator function to judge the results from an agent, such as final outputs or trajectory. Depending on your evaluation technique, this may or may not involve a reference output.

langchain-ai/langgraphjs

Agent

To review, edit and approve tool calls in an agent you can use LangGraph's built-in human-in-the-loop features, specifically the interrupt() primitive.

langchain-ai/langgraphjs

Agent

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the @langchain/mcp-adapters library.

langchain-ai/langgraphjs

Agent

LangGraph supports two types of memory essential for building conversational agents.

langchain-ai/langgraphjs

Agent

This page describes how to configure the chat model used by an agent.

langchain-ai/langgraphjs

Agent

A single agent might struggle if it needs to specialize in multiple domains or manage many tools. To tackle this, you can break your agent into smaller, independent agents and composing them into a multi-agent system.

langchain-ai/langgraphjs

Agent

LangGraph provides both low-level primitives and high-level prebuilt components for building agent-based applications. This section focuses on the prebuilt, reusable components designed to help you construct agentic systems quickly and reliably—without the need to implement orchestration, memory, or human feedback…

langchain-ai/langgraphjs

Agent

To share your project, simply open a Pull Request adding an entry for your package in our packages.yml file.

langchain-ai/langgraphjs

Agent

Agents support execution using either .invoke() for full responses, or .stream() for incremental streaming of the output. This section explains how to provide input, interpret output, enable streaming, and control execution limits.

langchain-ai/langgraphjs

Agent

Streaming is key to building responsive applications. There are a few types of data you’ll want to stream.

langchain-ai/langgraphjs

Agent

Tools are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.

langchain-ai/langgraphjs

Agent

You can use a prebuilt chat UI for interacting with any LangGraph agent through the Agent Chat UI. Using the deployed version is the quickest way to get started, and allows you to interact with both local and deployed graphs.