Langroid

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Examples Repo
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Discord
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Contributing
> [!NOTE]
> **Contributing: pull requests are by invitation**, and only for changes motivated
> by actual use of Langroid in your organization. To propose a change,
> open an issue with your
> organization name and use case. If it fits, we will implement it or invite you to
> send a PR. For custom work, contact Prasad Chalasani (pchalasani at gmail dot com)
> to discuss terms. See CONTRIBUTING.md for details.
Langroid is an intuitive, lightweight, extensible and principled
Python framework to easily build LLM-powered applications, from CMU and UW-Madison researchers.
You set up Agents, equip them with optional components (LLM,
vector-store and tools/functions), assign them tasks, and have them
collaboratively solve a problem by exchanging messages.
This Multi-Agent paradigm is inspired by the
Actor Framework
(but you do not need to know anything about this!).
Langroid is a fresh take on LLM app-development, where considerable thought has gone
into simplifying the developer experience;
it does not use Langchain, or any other LLM framework,
and works with practically any LLM.
🔥 ✨ A Claude Code plugin is available to
accelerate Langroid development with built-in patterns and best practices.
🔥 Read the (WIP) overview of the langroid architecture,
and a quick tour of Langroid.
🔥 MCP Support: Allow any LLM-Agent to leverage MCP Servers via Langroid's simple
MCP tool adapter that converts
the server's tools into Langroid's ToolMessage instances.
📢 Companies are using/adapting Langroid in **production**. Here is a quote:
>Nullify uses AI Agents for secure software development.
> It finds, prioritizes and fixes vulnerabilities. We have internally adapted Langroid's multi-agent orchestration framework in production, after evaluating CrewAI, Autogen, LangChain, Langflow, etc. We found Langroid to be far superior to those frameworks in terms of ease of setup and flexibility. Langroid's Agent and Task abstractions are intuitive, well thought out, and provide a great developer experience. We wanted the quickest way to get something in production. With other frameworks it would have taken us weeks, but with Langroid we got to good results in minutes. Highly recommended! -- Jacky Wong, Head of AI at Nullify.
🔥 Just published in ML for Healthcare (2024): a Langroid-based Multi-Agent RAG system for
pharmacovigilance, see blog post
Are you building LLM Applications, or want help with Langroid for your company,
or want to prioritize Langroid features for your company use-cases?
Prasad Chalasani is available for consulting
(advisory/development): pchalasani at gmail dot com.
Sponsorship is also accepted via GitHub Sponsors
**Questions, Feedback, Ideas? Join us on Discord!**
Quick glimpse of coding with Langroid
This is just a teaser; there's much more, like function-calling/tools,
Multi-Agent Collaboration, Structured Information Extraction, DocChatAgent
(RAG), SQLChatAgent, non-OpenAI local/remote LLMs, etc. Scroll down or see docs for more.
See the Langroid Quick-Start Colab
that builds up to a 2-agent information-extraction example using the OpenAI ChatCompletion API.
🔥 just released! Example
script showing how you can use Langroid multi-agents and tools
to extract structured information from a document using **only a local LLM**
(Mistral-7b-instruct-v0.2).
🔥 Updates/Releases
Click to expand
- **Sep 2026:**
- **0.68.0:** **Removed OpenAIAssistant.**
OpenAI sunset the Assistants API beta on 2026-08-26;
every endpoint now returns HTTP 404, so the class could not function. It has been
removed along with its tests and examples. OpenAIGPT -- which nearly all Langroid
code uses -- is unaffected. See the
migration note
for equivalents to threads, file_search, and code_interpreter.
- **Aug 2026:**
- 0.67.0 Security hardening:
per-provider env_prefix for vector-store configs (env-var naming change -- see
migration notes),
generalized taint propagation across tool re-emission paths, and a one-time warning
when FileAttachment payloads inflate context preflight.
- 0.66.0 Big community batch (14 PRs):
Milvus vector store (thanks @zc277584121);
Markdown/HTML document parsing
(thanks @nuthalapativarun); cooperative max_time
task budgets, MCP tool
namespacing for multi-server agents, and portable JSON chat-history snapshots
(thanks @Whxuan0701); video attachments (thanks @octo-patch); retrieval score
thresholds (thanks @Koushik-Salammagari); even
context-overflow truncation
and several routing/parsing fixes -- full details in the