Langroid

Langroid is an open-source Python framework for building LLM-powered applications using multi-agent programming. It is designed to simplify the development of complex AI systems by enabling collaboration among multiple specialized agents.

Langroid's key features include a Multi-Agent Architecture to build and orchestrate multiple agents that can collaborate, delegate tasks, and solve complex problems; a Task-Based Workflow where agents are wrapped in tasks which manage instructions, roles, and interactions; Conversation Management with advanced context management, memory persistence, and natural dialogue flow; Tool Integration for seamless integration with external tools and APIs, including function calling and Pydantic-based validation; Retrieval-Augmented Generation (RAG) with built-in support for document ingestion, vector search, and knowledge retrieval using vector stores like Qdrant, Chroma, and LanceDB; LLM Support compatible with OpenAI LLMs and hundreds of other providers via proxy libraries such as LiteLLM and ollama; and Observability & Logging providing detailed logs and lineage tracking for multi-agent interactions.

To get started with Langroid, you can install it via pip. Then, you can use an example setup involving a ChatAgent with an OpenAIGPT model to run a task, such as asking for the capital of France, and print the response content.

Yes. Langroid supports a wide range of LLMs, both local and remote, through proxy libraries like LiteLLM, ollama, and oobabooga, which mimic the OpenAI API.

Langroid uses Pydantic for defining and validating tool/function schemas. This ensures robust error handling and self-correction in function calls, improving reliability.

Langroid supports Qdrant, Chroma, and LanceDB for Retrieval-Augmented Generation (RAG) and knowledge retrieval.

Yes. Langroid can interact with Neo4j and ArangoDB knowledge graphs.

Langroid uses a principled architecture where agents act as message transformers. Tasks orchestrate agent interactions, enabling hierarchical and recursive task delegation.

Yes. Langroid uses Redis by default for caching LLM prompts and responses.

You can configure the LLM settings in the agent configuration to limit output tokens.

You can find more resources and community support through the official Documentation, the GitHub repository, Discussions on GitHub, and their YouTube channel.

Some use cases for Langroid include information extraction from complex documents, question answering with RAG and source citation, multi-agent collaboration for problem-solving, AI-driven software development and vulnerability management, and automated workflows and code generation.