May 6, 2026 - If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.

The sample notebooks demonstrate three simple use cases as examples of how this system can be used. 1. How many home runs did Derek Jeter hit in 2010? 2. Plot the cumulative sum of strikeouts thrown by Danny Duffy in the 2018 season. 3. Consider the first week of August 2020 - find 3 pitchers who's curveballs were most similar to Max Scherzer's. Clone repository and navigate to the langgraph-multi-agent folder

This lets you focus on the logic of your LangGraph graph, and leave the scaling and API design to us. The API is inspired by the OpenAI assistants API, and is designed to fit in alongside your existing services. In order to deploy this agent to LangGraph Cloud you will want to first fork this repo.

langchain langgraph langchain-agents langgraph-agents multi-agent-supervisor langgraph-multi-agent-supervisor ... An example application that exposes a multi-step, quality-controlled LLM workflow built with LangGraph through a FastAPI service.

An example repository for getting started with the Agent Inbox and LangGraph - langchain-ai/agent-inbox-langgraph-example

Build a basic ReAct agent from scratch using Python and an LLM, implementing a loop of reasoning and acting to solve tasks through tool usage and observation ... Introduction to LangGraph, a tool for implementing agents with cyclic graphs, demonstrating how to create a more structured and controllable agent using components like nodes, edges, and state management

Build a WhatsApp agent with voice processing, image generation, and long-term memory using LangGraph. ... A collection of agent implementation examples and patterns.

Build resilient agents. Contribute to langchain-ai/langgraph development by creating an account on GitHub.

"[This file has been moved](https://github.com/langchain-ai/langgraph/blob/23961cff61a42b52525f3b20b4094d8d2fba1744/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb)" ... "This directory is retained purely for archival purposes and is no longer updated. The examples previously found here have been moved to the newly [consolidated LangChain documentation](https://docs.langchain.com/oss/python/langgraph/overview)."

This comprehensive guide features 20 compact, real-world AI agent projects built with Python (≥3.9), LangGraph v0.5.3, and the latest LangChain integrations for Cerebras models (specifically gemini-2.5-flash). Our focus is on providing beginner-friendly yet powerful examples to help you achieve ...

This is the official companion repository for the book The Complete LangGraph Blueprint: Build 50+ AI Agents for Business Success. The repository provides source code, practical examples, and resources to help you build dynamic AI agents using ...

A collection of generative UI agents written with LangGraph.js - langchain-ai/langgraphjs-gen-ui-examples

This multi-agent example uses OpenAI's ChatGPT 4 model and the LangChain Tavily search tool to write a report about the latest inflation figures in the European Union. Unlike web search tools, the LangChain Tavily search tool delivers actual ...

Build, test, and deploy a multi-agent AI system using LangGraph, Docker, AWS Lambda, and CircleCI. The system uses a research-driven AI workflow where different agents,such as fact-checking, summarization, and search agents, work together seamlessly.

Master the art of building and enhancing AI agents. Learn to develop flow-based applications, implement agentic search, and incorporate human-in-the-loop systems using LangGraph's powerful components. - ksm26/AI-Agents-in-LangGraph

3 weeks ago - Deep Agents Code — a pre-built coding agent in your terminal, similar to Claude Code or Cursor, powered by any LLM. Install with curl -LsSf https://langch.in/dcode | bash. See the documentation for the full feature set. LangGraph is the graph runtime. LangChain's create_agent is a minimal agent harness on top of it.

For this, we use Agent Inbox as an interface for human in the loop. You can see the linked code for the full implementation in src/email_assistant/email_assistant_hitl.py. ... This notebook shows how to add memory to the email assistant, allowing it to learn from user feedback and adapt to preferences over time. The memory-enabled assistant (email_assistant_hitl_memory.py) uses the LangGraph Store to persist memories.

"To implement a retrieval agent, we simple need to give an LLM access to a retriever tool.\n", "\n", "We can incorporate this into [LangGraph](https://langchain-ai.github.io/langgraph/).\n", "\n", "## Setup\n", "\n", "First, let's download the required packages and set our API keys:" ] }, { "cell_type": "code", "execution_count": null, "id": "969fb438", "metadata": {}, "outputs": [], "source": [ "%%capture --no-stderr\n", "%pip install -U --quiet langchain-community tiktoken langchain-openai langchainhub chromadb langchain langgraph lang

Open the folder LangGraph Studio! Add new tools: Extend the agent's capabilities by adding new tools in tools.py. These can be any Python functions that perform specific tasks. Select a different model: We default to Anthropic's Claude 3 Sonnet. You can select a compatible chat model using provider/model-name via runtime context. Example: openai/gpt-4-turbo-preview.