Agenten & Multi-Agenten

LangChain

langchain-ai/langchain

An open-source Python framework for building LLM-powered applications and agents through interoperable, chainable components and third-party integrations.

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Projekt-Screenshots

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Übersicht

LangChain is a framework for building agents and LLM-powered applications. It helps developers chain together interoperable components and third-party integrations to simplify AI application development while aiming to future-proof decisions as the underlying technology evolves. The framework provides a standard interface for models, embeddings, vector stores, and more, allowing developers to work at different levels of abstraction. It is part of a broader ecosystem that includes Deep Agents, LangGraph, and LangSmith.

Wichtige Funktionen

  • Real-time data augmentation via integrations with model providers, tools, vector stores, and retrievers
  • Model interoperability allowing models to be swapped during experimentation
  • Modular, component-based architecture for rapid prototyping
  • Production-ready features with support for monitoring, evaluation, and debugging
  • Flexible abstraction layers from high-level chains to low-level components
  • Seamless integration with the LangChain ecosystem including Deep Agents, LangGraph, and LangSmith

Voraussetzungen, Installation und Schnellstart

The quickstart recommends using the `uv` package manager with the command: `uv add langchain`.

Nutzung

A basic quickstart involves importing `init_chat_model` from `langchain.chat_models`, initializing a model (e.g., `init_chat_model("openai:gpt-5.5")`), and invoking it with a text prompt like `model.invoke("Hello, world!")`.

Modellkompatibilität und Anwendungsfälle

The framework supports model interoperability and integrates with various model providers. Repository topics indicate relevance to Anthropic, ChatGPT, Gemini, and OpenAI.

Lizenz- und Risikohinweise

The repository is licensed under the MIT license.

Editorial verification 2026-08-09: repository URL, owner, description, license and repository statistics were reviewed. License metadata: MIT. README was fetched for the channel draft; re-check repository dependencies, releases and model terms before production use.

Veröffentlichung und Wartung

Not stated in the repository metadata.

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