Description
Langroid is an intuitive, lightweight, and extensible Python framework designed to simplify the development of Large Language Model (LLM)-powered applications. It operates on a multi-agent programming paradigm, inspired by the Actor Framework, where developers define agents, equip them with components like LLMs, vector stores, and tools, and then orchestrate their collaborative problem-solving through message exchanges.
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The core abstraction in Langroid is the `Agent`, which encapsulates LLM conversation state and can be augmented with vector stores and tools. Agents act as message transformers, with default responder methods for LLM, Agent, and User interactions. The `Task` class wraps an `Agent`, providing instructions, managing iterations, and orchestrating multi-agent interactions through hierarchical delegation. This modular design promotes reusability and loose coupling.
Langroid supports a wide range of LLMs, including OpenAI models and those from hundreds of other providers (local/open or remote/commercial) via proxy libraries and local model servers like Ollama and LiteLLM, often mimicking the OpenAI API. It integrates with various vector stores such as Qdrant, Chroma, LanceDB, Pinecone, and PGVector, enabling Retrieval-Augmented-Generation (RAG) for grounding and source citation. The framework also features robust support for tools/plugins/function-calling, leveraging Pydantic for simplified schema definition and error handling.
Key capabilities include multi-agent collaboration, structured information extraction, document chat (RAG) with source citation, and chat with tabular data. Langroid emphasizes developer experience, offering features like caching of LLM responses, observability, logging, and message lineage tracking. It is actively developed with frequent updates and a growing community, making it a powerful choice for building sophisticated LLM applications.
- CategoryAI Agent Builders
- Pricing ModelOpen source
- DeploymentCloud, Self-hosted
- PlatformsWeb
Langroid's Core Features
Multi-agent programming paradigm for LLM applications
Agent and Task abstractions for orchestrating LLM interactions
Support for a wide range of LLMs, including local and proprietary models
Integration with multiple vector stores for RAG capabilities
Tools/Plugins/Function-calling support using Pydantic
Document chat (RAG) with source citation
Chat with tabular data using Pandas code generation
Caching of LLM responses
Observability, logging, and message lineage tracking
Extensible and lightweight Python framework
Getting Started with Langroid
Clone the repository: Obtain the Langroid codebase from GitHub.
Install dependencies: Set up a virtual environment and install Langroid with necessary extras.
Configure environment variables: Set up API keys and other required credentials.
Develop agents: Define agents with LLMs, vector stores, and tools.
Orchestrate tasks: Use the Task class to manage agent interactions and workflows.
Execute applications: Run your LLM-powered applications.
Langroid's Use Cases
- Multi-agent collaboration
- Information extraction
- Document Q&A
- Data analysis
- Code generation
- RAG systems
