This repository provides an introductory course to LangChain, designed for developers and researchers aiming to build sophisticated AI applications. It offers a structured learning path through three modules, covering foundational agent creation, advanced multi-agent systems, and production-ready agent deployment, with practical projects to solidify understanding.

How It Works

The course is organized into three modules: Module 1 focuses on foundational agent components like models, tools, and memory; Module 2 delves into advanced concepts such as the Model Context Protocol (MCP) and multi-agent systems; Module 3 addresses production considerations like middleware and human-in-the-loop interactions. Each module culminates in a project, demonstrating practical application of LangChain's core features.

Quick Start & Requirements

• Installation: Clone the repository, copy example.env to .env, and populate it with required API keys (OpenAI, Tavily) and optional keys (Anthropic, Google, LangSmith).
• Package Manager: uv (recommended) or pip.
• Python: Version >=3.12, <3.14.
• Dependencies: Installed via uv sync or pip install -r requirements.txt.
• Environment Verification: Run uv run python env_utils.py or python env_utils.py.
• Running Notebooks: Use uv run jupyter lab or jupyter lab.
• Links: uv: https://docs.astral.sh/uv/, pyenv: https://github.com/pyenv/pyenv.

Highlighted Details

• Structured learning path across three modules: Foundational Agents, Advanced Agents, and Production-Ready Agents.
• Hands-on projects include: Personal Chef, Wedding Planner, and Email Assistant.
• Covers key LangChain concepts: Models, Tools, Memory, Multimodal Messages, Model Context Protocol (MCP), Multi-Agent Systems, Middleware, Human-In-The-Loop (HITL), and Dynamic Agents.
• Recommends uv for streamlined environment and dependency management.
• Optional integration with LangSmith for tracing and evaluation.

Maintenance & Community

No specific details regarding maintainers, community channels (e.g., Discord, Slack), or roadmap were found in the provided README.

Licensing & Compatibility

The README does not explicitly state the project's license. Compatibility for commercial use or closed-source linking cannot be determined from the provided text.

Limitations & Caveats

The course primarily uses specific model providers (OpenAI, Tavily) and may require code modifications if alternative providers are used. While LangSmith tracing is optional, setup issues can arise if API keys are not correctly configured or if environment variable conflicts occur. The README focuses on setup and troubleshooting rather than inherent limitations of the course content itself.