Langroid is a Python framework for building LLM-powered applications using a multi-agent programming paradigm. It allows developers to create, configure, and orchestrate multiple AI agents that communicate via messages to solve complex problems, offering an alternative to existing LLM orchestration frameworks.

How It Works

Langroid models applications as a system of interacting Agents, inspired by the Actor model. Each Agent is an LLM-powered entity that can be equipped with tools, vector stores, and specific system messages. Agents communicate by exchanging messages, enabling collaborative problem-solving. The framework emphasizes an intuitive developer experience, abstracting away much of the complexity of LLM interactions and multi-agent coordination.

Quick Start & Requirements

• Install via pip: pip install langroid or pip install "langroid[all]" for all optional dependencies.
• Requires Python 3.11+.
• OpenAI API key is recommended for initial setup, configurable via .env file or environment variables.
• Official documentation: langroid.github.io/langroid
• Colab Quick Start: Langroid_quick_start.ipynb

Highlighted Details

• Multi-Agent Orchestration: Supports complex agent interactions, task delegation, and message routing.
• Broad LLM Support: Works with OpenAI models and hundreds of others via LiteLLM, Ollama, and other compatible servers.
• Extensive Tooling: Integrates with various vector stores (Qdrant, Chroma, LanceDB, Pinecone, PGVector, Weaviate), document parsers, and offers Pydantic-based function calling/tool usage.
• Specialized Agents: Includes agents for document Q&A (DocChatAgent), tabular data (TableChatAgent), and knowledge graphs (Neo4jChatAgent).

Maintenance & Community

• Active development with frequent releases and contributions from researchers and the community.
• Discord server available for support and discussion: discord.gg/ZU36McDgDs
• Substack newsletter for updates: langroid.substack.com

Licensing & Compatibility

• The project appears to be primarily licensed under the MIT License, promoting broad compatibility for commercial and closed-source use.

Limitations & Caveats

• Documentation is noted as "Work In Progress" (WIP) in some areas.
• While supporting many LLMs, optimal performance and prompt adherence may vary, with OpenAI's GPT-4/GPT-4o often cited as the baseline for tested prompts.