What it does
Core capabilities at a glance
- Agents
- Chatgpt
- Function Calling
- GPT
- GPT 4
- Gpt4
- Information Retrieval
- Language Model
Deep dive
The full breakdown - performance, comparisons, and setup
langroid
langroid is a RAG toolkit - Harness LLMs with Multi-Agent Programming.
Overview
'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:
We welcome contributions: See the contributions document for ideas on what to contribute.
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.
langroid is open-source, written primarily in Python, with 4,033 GitHub stars under the MIT license. The latest release is 0.65.0 (2026-05-28).
Key capabilities
From the project's documentation:
- 0.59.0 Complete Pydantic V2 Migration -
- 0.58.0 Crawl4AI integration -
- 0.57.0 HTML Logger for interactive task visualization -
- 0.56.0 TaskTool for delegating tasks to sub-agents -
- 0.55.0 Event-based task termination with done_sequences -
- 0.54.0 Portkey AI Gateway support - access 200+ models
Install
A quick way to get started (always check the official docs for the latest):
pip install langroidHow it fits a local-AI stack
langroid runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related RAG toolkits in the directory:
Sources
- Source code & docs: langroid/langroid
- Official website: https://langroid.github.io/langroid/
Stats from GitHub, 2026-06-08.
Frequently asked
Quick answers to common questions
What is langroid?
langroid is a rag tool for local AI workloads. Harness LLMs with Multi-Agent Programming
Is langroid free and open source?
Yes, langroid has 4,033 GitHub stars and is licensed under MIT. You can self-host it for free on docker.
What platforms does langroid support?
langroid runs on docker.
What hardware do I need for langroid?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. langroid has 4,033 GitHub stars and an active community.
Does langroid support GPU acceleration?
langroid's GPU support depends on your specific setup. Check the documentation for details. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to langroid?
Popular alternatives include other rag tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does langroid cost?
langroid is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware
Comments coming soon
Configure NEXT_PUBLIC_GISCUS_REPO_ID and NEXT_PUBLIC_GISCUS_CATEGORY_ID at giscus.app to enable.