Langroid

langroid.github.io
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Build complex AI apps with multiple smart agents that work together.

Langroid: Harness LLMs with Multi-Agent Programming¶ · Langroid LLM App Development Framework · The LLM Opportunity¶ · Langroid's Multi-Agent Programming Framework¶ · Highlights¶

Langroid Who It's For Langroid is for developers and teams building complex AI applications. It helps anyone looking to use large language models (LLMs) for challenging tasks, providing a clear way to design and manage smart systems. What You Get You get a flexible framework that builds AI systems with multiple collaborating agents. It supports many AI models, integrates with external tools, and uses memory databases (like vector stores) for factual retrieval. You also get detailed logs to understand how your agents are working. How It Works The system uses "agents," which are like specialized AI workers, each handling conversations and using tools. These agents are given instructions within "tasks" that guide their roles and goals. Agents then communicate by sending messages to each other, taking turns to process information and accomplish parts of a bigger task. Visit Website

Features

First-Class Agents
Hierarchical Task Orchestration
Modular and Reusable Design
Extensive LLM Compatibility
Vector Database Support
Pydantic-based Tool/Function Calling
LLM Prompt and Response Caching
Grounding and Source Citation
Detailed Logging and Lineage

FAQ

Langroid is an open-source Python framework for building LLM-powered applications using multi-agent programming. It is designed to simplify the development of complex AI systems by enabling collaboration among multiple specialized agents.

Langroid's key features include a Multi-Agent Architecture to build and orchestrate multiple agents that can collaborate, delegate tasks, and solve complex problems; a Task-Based Workflow where agents are wrapped in tasks which manage instructions, roles, and interactions; Conversation Management with advanced context management, memory persistence, and natural dialogue flow; Tool Integration for seamless integration with external tools and APIs, including function calling and Pydantic-based validation; Retrieval-Augmented Generation (RAG) with built-in support for document ingestion, vector search, and knowledge retrieval using vector stores like Qdrant, Chroma, and LanceDB; LLM Support compatible with OpenAI LLMs and hundreds of other providers via proxy libraries such as LiteLLM and ollama; and Observability & Logging providing detailed logs and lineage tracking for multi-agent interactions.

To get started with Langroid, you can install it via pip. Then, you can use an example setup involving a ChatAgent with an OpenAIGPT model to run a task, such as asking for the capital of France, and print the response content.

Yes. Langroid supports a wide range of LLMs, both local and remote, through proxy libraries like LiteLLM, ollama, and oobabooga, which mimic the OpenAI API.

Langroid uses Pydantic for defining and validating tool/function schemas. This ensures robust error handling and self-correction in function calls, improving reliability.

Langroid supports Qdrant, Chroma, and LanceDB for Retrieval-Augmented Generation (RAG) and knowledge retrieval.

Yes. Langroid can interact with Neo4j and ArangoDB knowledge graphs.

Langroid uses a principled architecture where agents act as message transformers. Tasks orchestrate agent interactions, enabling hierarchical and recursive task delegation.

Yes. Langroid uses Redis by default for caching LLM prompts and responses.

You can configure the LLM settings in the agent configuration to limit output tokens.

You can find more resources and community support through the official Documentation, the GitHub repository, Discussions on GitHub, and their YouTube channel.

Some use cases for Langroid include information extraction from complex documents, question answering with RAG and source citation, multi-agent collaboration for problem-solving, AI-driven software development and vulnerability management, and automated workflows and code generation.

Specs

Type Agent
SectionInfrastructure & MLOps
Pricing free (от $0/mo)
Platform API only
Systems api, web
Hostingself-hosted
Who forIndividual
Site languageen
Rating4.30 (0 reviews)

Can do

agents api memory plugins reasoning crawling RAG embeddings

Integrations

gemini openai Qdrant Chroma Redis LanceDB

Platforms

api web

Tags

conversational ai multi agent systems tool integration llm orchestration python framework

Social

GitHub

Found in sources

aiagentslist

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