Langroid logo

Langroid

Langroid AI Agent
Rating:
Rate it!

Overview

An open-source Python framework that simplifies LLM application development using a multi-agent programming paradigm.

Langroid is an open-source Python framework designed to streamline the development of applications powered by Large Language Models (LLMs). It introduces a multi-agent programming paradigm, treating agents as first-class citizens to manage multiple LLM conversations, each responsible for different aspects of a task. This approach facilitates efficient collaboration through message exchange, enabling developers to build complex, intelligent applications with ease. Langroid supports integration with various LLMs, vector databases, and function-calling tools, offering a modular and extensible architecture for AI application development.

AI Agent Store research

What the evidence says about Langroid

Langroid is best understood as an agent-development framework. Langroid is a Python framework for building language-model applications with agents, tools, vector stores, and message-based multi-agent collaboration.

Last reviewed July 30, 2026

Verified capabilities

  • Agent application development

    Langroid is a Python framework for building language-model applications with agents, tools, vector stores, and message-based multi-agent collaboration.[1]

Where it fits best

  • Building a Python application in which specialized agents collaborate through messages.[1]

Buying and deployment notes

Deployment: Source repository[1]

Sources and research method (1)

We record only claims tied to public sources checked by our team or listing workflow. Counts above are derived directly from this profile, not a subjective rating.

  1. GitHub - langroid/langroid: Harness LLMs with Multi-Agent Programming · GitHubGitHub · checked 2026-07-30

Autonomy level

81%

Reasoning: Langroid agents exhibit high autonomy through their ability to manage conversation states, delegate tasks hierarchically, and collaborate via message transformation without human intervention. The framework supports long-term memory via vector stores (Qdrant/Chroma), enabling context-aware decision-making across sessions. Agents integrate tools/plu...

Comparisons


Custom Comparisons

Some of the use cases of Langroid:

  • Developing complex LLM-powered applications with a multi-agent framework.
  • Implementing collaborative AI agents for task automation.
  • Integrating various tools and vector databases into AI workflows.
  • Building chatbots and conversational agents with advanced capabilities.
  • Enhancing AI applications with retrieval-augmented generation and function-calling features.

Loading Community Opinions...

Pricing model:

Code access:

Popularity level: 68%

Langroid Video:

Free credibility widget

Turn this profile into a trust signal

Show prospects that Langroid has a public place where they can check product details, pricing, ratings, and reviews.

Build confidence

Give buyers a third-party profile to explore.

Reduce hesitation

Put validation beside your strongest CTA.

Earn discovery

Every badge links prospects to your listing.

Choose your style

Preview it, then copy the complete embed code.

Live previewReady to embed

Shows buyers where to validate your product, pricing, and reputation.

Plain HTML. No signup, script, or maintenance required.

Make it work for you

Describe the job. Get an AI worker you can actually message.

We create the setup, keep it running after your laptop closes, and save its memory. Test in the browser, then add Telegram, WhatsApp, or Slack.

Runs without your laptopBrowser + messaging appsCredits, keys, or subscriptionsMemory survives restarts

Plans start at $29/month. Cancel anytime.

Hosted agent

OpenClaw or Hermes

saved state
Browser
WhatsApp
Telegram
Slack
“I checked the inbox, handled the routine messages, and sent you the one question that needs a decision.”
Create an AI worker that keeps running after this tab closes.
Open Agent Teams

Did you find this page useful?

Not useful
Could be better
Neutral
Useful
Loved it!