Langroid

Langroid: Multi-Agent LLM Framework

Langroid is a lightweight, extensible Python framework developed by researchers from CMU and UW-Madison, designed to simplify the creation of LLM (Large Language Model)-powered applications. It introduces a unique multi-agent programming paradigm inspired by the Actor Framework, allowing developers to set up agents with specific capabilities, equip them with components like LLMs, vector stores, and tools, and orchestrate collaborative problem-solving through message exchanges.

Key Features

• Agents as First-Class Citizens: Encapsulate LLM conversation state, vector stores, and tools within agents, acting as message transformers with distinct responder methods.
• Task Orchestration: Wrap agents in tasks to provide instructions and manage multi-agent interactions via hierarchical delegation.
• Modularity and Reusability: Design agents with specific skills, combine tasks flexibly, and reuse components across projects.
• LLM Compatibility: Supports OpenAI models and over 100 other providers (local or remote) via proxy libraries like LiteLLM and local servers like Ollama.
• Vector Store Integration: Incorporates Qdrant, Chroma, LanceDB, Pinecone, and others for Retrieval-Augmented Generation (RAG).
• Function-Calling/Tools: Leverages Pydantic for seamless OpenAI function-calling and native tools, simplifying structured data extraction and error handling.
• Specialized Agents: Includes DocChatAgent for document querying, TableChatAgent for tabular data analysis, and SQLChatAgent for database interactions.

Use Cases

• Document Analysis: Extract structured information from documents (e.g., lease agreements) using multi-agent RAG systems.
• Data Querying: Chat with tabular datasets or databases by generating and executing code (e.g., Pandas or SQL queries).
• Collaborative Problem Solving: Set up multi-agent systems for tasks like audience targeting or hierarchical computations.
• Custom Applications: Build tailored LLM apps for customer support, content creation, or educational tools with minimal setup.

Langroid stands out for its intuitive abstractions, avoiding dependencies on frameworks like LangChain, and offering a fresh approach to LLM app development with a focus on developer experience and flexibility. It targets developers, researchers, and companies seeking efficient, production-ready LLM solutions.