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.

What are the key features of Langroid?

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.

How do I get started with Langroid?

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.

Can Langroid work with non-OpenAI LLMs?

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.

How does Langroid handle tool/function calling?

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

What vector stores does Langroid support?

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

Can I use Langroid with a Knowledge Graph (KG)?

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

How does Langroid manage multi-agent collaboration?

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

Does Langroid support caching?

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

How can I limit the number of output tokens generated by the LLM?

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

Where can I find more resources and community support?

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

What are some use cases for Langroid?

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.

Langroid Review 2025 | AI Infrastructure & MLOps Tool

About 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.

Features & Capabilities

🤖 Multi-Agent Framework

First-Class Agents

Agents serve as core abstractions, encapsulating LLM conversation state, memory (vector-stores), and tools.

Hierarchical Task Orchestration

Defines tasks that wrap agents with instructions, enabling hierarchical and recursive task delegation among agents.

Modular and Reusable Design

Promotes flexible design of specialized agents and tasks, allowing for their easy combination and reuse in complex applications.

🔗 LLM & Data Integrations

Extensive LLM Compatibility

Seamlessly integrates with various LLMs, including local, open-source, proprietary, and API-based models.

Vector Database Support

Supports multiple vector stores (e.g., Qdrant, Chroma, LanceDB) to facilitate Retrieval-Augmented Generation (RAG).

Pydantic-based Tool/Function Calling

Simplifies defining and using tools/functions with Pydantic, ensuring structured output and improved error handling for LLM interactions.

📊 Observability & Performance

LLM Prompt and Response Caching

Leverages Redis for caching LLM prompts and responses, enhancing performance and reducing operational costs.

Grounding and Source Citation

Enables grounding of LLM responses by accessing external documents and provides mechanisms for source citation.

Detailed Logging and Lineage

Provides comprehensive logs of multi-agent interactions and maintains message provenance for full observability.

Use Cases

Building Advanced RAG Systems for Knowledge Bases

Organizations often struggle to extract precise, cited answers from vast internal and external documentation. Langroid's framework, with its Retrieval-Augmented Generation (RAG) capabilities, vector store integrations (Qdrant, Chroma, LanceDB), and ability to ingest diverse document types (PDFs, URLs), allows developers to create agents that can accurately answer complex questions and provide verifiable sources.

Enterprise, Legal, Healthcare, Customer Service, Research & DevelopmentFor: AI/ML Engineers, Knowledge Managers, Researchers, Customer Support Teams

Automating Business Intelligence and Data Operations

Businesses require automated systems to interact with structured data sources like SQL databases and Knowledge Graphs (Neo4j, ArangoDB) for insights and operational tasks. Langroid enables the creation of multi-agent systems that can query these systems, process data, and orchestrate complex data-driven workflows, delivering actionable intelligence or triggering subsequent actions through robust tool integration.

Finance, Business Operations, Data Science, EnterpriseFor: Data Engineers, Business Analysts, Operations Managers, Software Developers

Orchestrating Complex AI Systems for Problem Solving

Many real-world problems are too complex for a single AI model and require the collaborative intelligence of multiple specialized agents. Langroid's multi-agent architecture provides a principled way to define, instruct, and orchestrate agents, enabling them to delegate tasks, share information, and collectively solve intricate problems, from strategic planning to system diagnostics.

AI/ML Research & Development, Consulting, Software ArchitectureFor: AI/ML Engineers, Solution Architects, R&D Teams, Consultants

Streamlining AI-Driven Software Development and Security

Software development teams face challenges in efficient code generation, comprehensive code analysis, and proactive identification of security vulnerabilities. Langroid can be used to build multi-agent systems that assist developers by generating code snippets, analyzing codebases for patterns or issues, and flagging potential security risks, thereby accelerating development cycles and enhancing code quality and security posture.

Software Development, Cybersecurity, ITFor: Software Engineers, DevOps Teams, Security Analysts, AI Developers

About Langroid