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Basic Information

Langroid is a Python framework for building LLM-powered multi-agent applications. It provides first-class Agent and Task abstractions so developers can create agents that encapsulate LLM conversation state and optional components such as vector stores and tools. Users assign tasks to agents and orchestrate collaborative problem solving by passing messages between agents. The project emphasises a lightweight, extensible and principled developer experience and is designed to work with a wide range of LLMs including remote, commercial and local models. Langroid supports retrieval augmented generation workflows, structured output via Pydantic-based function calling and native tool messages, and includes specialized agents for document chat, SQL/table chat and other common RAG scenarios. The README highlights practical examples, a Docker image, optional dependency extras, and production usage by companies that have adapted Langroid for real workloads.

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Features
Langroid centres on modular Agent and Task abstractions, multi-agent orchestration, and message-based collaboration. It supports tools and function-calling using Pydantic models so agents can produce strict structured outputs and recover from malformed responses. RAG features include vector-store integrations with Qdrant, Chroma, LanceDB, Pinecone, Postgres PGVector and Weaviate, plus document parsers and chunking strategies. LLM support is broad and includes OpenAI, local model servers and many providers via proxy libraries and LiteLLM. Observability and provenance features include detailed logs, HTML logger, lineage tracking and caching via Redis or Momento. Other capabilities shown in the README include DocChatAgent, TableChatAgent and SQLChatAgent, MCP tool adapter support, web crawling and scraping integrations, async and batch task utilities, TaskTool for sub-agent delegation, and a curated examples repository and documentation.
Use Cases
Langroid reduces boilerplate and accelerates development of complex LLM applications by providing reusable abstractions for agents, tools and task orchestration. Developers can compose agents with vector-backed retrieval, tool invocation and structured function-calls to build RAG pipelines, document Q A, data-table assistants and SQL interfaces. Built-in integrations for many vector stores and optional parsers let teams adopt retrieval and citation workflows without wiring low-level plumbing. Support for local and remote LLMs and for caching and logging makes the framework suitable for experimentation and production deployments. The Pydantic-based tool schema simplifies developer-defined function interfaces and improves robustness when LLMs output structured data. Examples, notebooks and a Docker image help teams get started, and the project documents practical patterns for multi-agent collaboration, debugging and observability.

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