langroid

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.

🔥 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.

🔥 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).

import langroid as lr
import langroid.language_models as lm

# set up LLM
llm_cfg = lm.OpenAIGPTConfig(
  # any model served via an OpenAI-compatible API
  chat_model=lm.OpenAIChatModel.GPT4o, # or, e.g., "ollama/mistral"
)
# use LLM directly
mdl = lm.OpenAIGPT(llm_cfg)
response = mdl.chat("What is the capital of Ontario?", max_tokens=10)

# use LLM in an Agent
agent_cfg = lr.ChatAgentConfig(llm=llm_cfg)
agent = lr.ChatAgent(agent_cfg)
agent.llm_response("What is the capital of China?") 
response = agent.llm_response("And India?") # maintains conversation state 

# wrap Agent in a Task to run interactive loop with user (or other agents)
task = lr.Task(agent, name="Bot", system_message="You are a helpful assistant")
task.run("Hello") # kick off with user saying "Hello"

# 2-Agent chat loop: Teacher Agent asks questions to Student Agent
teacher_agent = lr.ChatAgent(agent_cfg)
teacher_task = lr.Task(
  teacher_agent, name="Teacher",
  system_message="""
    Ask your student concise numbers questions, and give feedback. 
    Start with a question.
    """
)
student_agent = lr.ChatAgent(agent_cfg)
student_task = lr.Task(
  student_agent, name="Student",
  system_message="Concisely answer the teacher's questions.",
  single_round=True,
)

teacher_task.add_sub_task(student_task)
teacher_task.run()

🔥 Updates/Releases

• 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 release notes.
• 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.