This is an early release preview. You may encounter bugs.
Langroid logo
Unclaimed

Tool coding

Langroid

Python framework for building LLM applications with collaborating multi-agent message passing

A 80/100 GitHub score ? This grade is derived from GitHub signals, not user votes. Open for the full breakdown.
Discussion

01 / Summary

About Langroid.

Langroid is a Python framework for building LLM applications as collaborating agents that solve a problem by exchanging messages, a design inspired by the Actor model. It does not depend on LangChain or any other LLM framework, and works with OpenAI models and with models from hundreds of other providers, local or remote, through OpenAI-compatible servers and proxies such as Ollama, oobabooga, and LiteLLM.

Two abstractions carry the design. An Agent holds LLM conversation state and optionally a vector store and tools, and responds on behalf of three entities: the LLM, the agent itself, and the user. A Task wraps an Agent with instructions, iterates over its responders, and delegates hierarchically to sub-tasks, which look to the parent like additional responders. Agents with specific skills can be wrapped in tasks and composed freely.

Tools are defined once as Pydantic models and work either as native OpenAI function calls or as Langroid's own ToolMessage mechanism with any LLM. When a model emits malformed JSON, the validation error is sent back so it can correct itself. An adapter turns any MCP server's tools into ToolMessage instances, and a Claude Code plugin ships skills for writing Langroid multi-agent code.

  • Agents and tasks: agents as message transformers, wrapped in tasks that orchestrate multi-agent work through recursive delegation
  • Model support: OpenAI plus local and commercial models via OpenAI-compatible servers and proxies
  • Tools and function calling: Pydantic-defined tools usable as OpenAI function calls or as model-agnostic ToolMessages, with automatic error feedback
  • MCP adapter: converts an MCP server's tools into Langroid tools
  • RAG: a document-chat agent with grounding and source citation over vector stores
  • Vector stores: Qdrant, Chroma, LanceDB, Pinecone, PGVector, Weaviate, and Milvus
  • Specialized agents: SQL chat, table chat, and structured information extraction agents
  • Response caching: Redis caching of LLM responses
  • Lineage: detailed logs of multi-agent interactions with message provenance
  • Claude Code plugin: skills that apply and record Langroid design patterns

02 / Discussion CREDIBILITY-GATED

Discussion

Reading is open to everyone. Posting and voting need a verified identity or a GitHub grade of B or higher.

  • No discussions yet.

04 / Build

Build with Langroid.

Browse the catalog for frameworks, tools, and harnesses, each scored on real GitHub credibility.

Get Langroid →

Browse the catalog