内容摘录
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<strong>Documentation</strong></a>
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<strong>Examples Repo</strong></a>
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<strong>Discord</strong></a>
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<strong>Contributing</strong></a>
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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! <br> -- Jacky Wong, Head of AI at Nullify.
🔥 See this Intro to Langroid
blog post from the LanceDB team
🔥 Just published in ML for Healthcare (2024): a Langroid-based Multi-Agent RAG system for
pharmacovigilance, see blog post
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.
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.
See also this version that uses the OpenAI Assistants API instead.
🔥 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
<details>
<summary> <b>Click to expand</b></summary>
**Aug 2025:**
0.59.0 Complete Pydantic V2 Migration -
5-50x faster validation, modern Python patterns, 100% backward compatible.
**Jul 2025:**
0.58.0 Crawl4AI integration -
browser-based web crawling with Playwright for JavaScript-heavy sites, no API key required (thank you @abab-dev!).
0.57.0 HTML Logger for interactive task visualization -
self-contained HTML logs with collapsible entries, auto-refresh, and persistent UI state.
**Jun 2025:**
0.56.0 TaskTool for delegating tasks to sub-agents -
enables agents to spawn sub-agents with specific tools and configurations.
0.55.0 Event-based task termination with done_sequences -
declarative task completion using event patterns.
0.54.0 Portkey AI Gateway support - access 200+ models
across providers through unified API with caching, retries, observability.
**Mar-Apr 2025:**
0.53.0 MCP Tools Support.
0.52.0 Multimodal support, i.e. allow PDF, image
inputs to LLM.
0.51.0 LLMPdfParser, generalizing
GeminiPdfParser to parse documents directly with LLM.
0.50.0 Structure-aware Markdown chunking with chunks
enriched by section headers.
0.49.0 Enable easy switch to LiteLLM Proxy-server
0.48.0 Exa Crawler, Markitdown Parser
0.47.0 Support Firecrawl URL scraper/crawler -
thanks @abab-dev
0.46.0 Support LangDB LLM Gateway - thanks @MrunmayS.
0.45.0 Markdown parsing with Marker - thanks @abab-dev
0.44.0 Late imports to reduce startup time. Thanks
@abab-dev
**Feb 2025:**
0.43.0: GeminiPdfParser for parsing PDF using
Gemini LLMs - Thanks @abab-dev.
0.42.0: markitdown parser for pptx,xlsx,xls files
Thanks @abab-dev.
0.41.0: pinecone vector-db (Thanks @coretado),
Tavily web-search (Thanks @Sozhan308), Exa web-search (Thanks @MuddyHope).
0.40.0: pgvector vector-db. Thanks @abab-dev.
0.39.0: ChatAgentConfig.handle_llm_no_tool for
handling LLM "forgetting" to use a tool.
0.38.0: Gemini embeddings -…