23 tools tagged
Showing 23 of 23 tools
BEAM/Elixir-native framework for durable multi-agent systems
Jido is an Elixir-native AI agent framework that leverages the BEAM virtual machine's concurrency and fault-tolerance for building durable, distributed multi-agent systems. It provides primitives for agent lifecycle management, skill composition, and message-based coordination. Designed for teams running Elixir in production who need agent capabilities. Apache-2.0 with 1,600+ GitHub stars.
TypeScript-first AI agent framework with built-in observability
VoltAgent is an open-source TypeScript AI agent framework with built-in observability, RAG support, memory management, and MCP integration. It provides a structured approach to building production AI agents in the Node.js ecosystem with agent debugging tools, sub-agent orchestration, and tool management. Over 7,000 GitHub stars and 150K+ weekly npm downloads.
Multi-agent orchestration with 10+ swarm patterns
Swarms is an enterprise-grade multi-agent orchestration framework with 6,100+ GitHub stars that provides 10+ swarm patterns including sequential, concurrent, hierarchical, mixture-of-agents, and graph-based workflows. The SwarmRouter lets teams switch between orchestration strategies by changing a single parameter. It supports MCP for tool integration, multi-model providers via Anthropic, OpenAI, and local models, and includes an AutoSwarmBuilder that generates agents from task descriptions.
Code-first agent framework for data analytics tasks
TaskWeaver is Microsoft's open-source code-first agent framework that converts natural language requests into executable Python code for data analytics and workflow automation. Unlike text-based agent frameworks, it preserves rich in-memory data structures like DataFrames across conversation turns, supports custom algorithm plugins as callable functions, and verifies generated code before execution. It includes a Planner for task decomposition and a Code Interpreter for generation and execution.
Multi-agent programming framework inspired by the Actor model
Langroid is a lightweight Python framework from CMU and UW-Madison researchers for building LLM applications using a multi-agent programming paradigm inspired by the Actor Framework. Agents are first-class citizens that encapsulate LLM state, vector stores, and tools, then collaborate via message passing through hierarchical task delegation. With 3,900+ GitHub stars, Langroid works with any LLM provider and does not depend on LangChain or other frameworks.
Build AI agents like LEGO — modular and predictable
Atomic Agents is a lightweight Python framework by BrainBlend AI that applies Atomic Design principles to AI agent development. Each component — agents, tools, context providers — is a single-purpose, reusable building block with Pydantic-enforced input/output schemas for type safety. Built on Instructor for structured LLM outputs, it prioritizes predictability and developer control over the autonomous-but-unpredictable behavior of larger frameworks like LangChain or CrewAI.
Multi-agent framework for finding scaling laws of agents
CAMEL-AI is an open-source multi-agent framework with 16,500+ GitHub stars, built by a research community of over 100 researchers focused on finding the scaling laws of agents. It supports role-playing agent societies, synthetic data generation pipelines, large-scale social simulations with up to 1M agents via the OASIS platform, and real-world task automation through the OWL project accepted at NeurIPS 2025. CAMEL works with OpenAI, Anthropic, Gemini, Mistral, and local models.
AI-driven development workflow template
A template system that bootstraps AI-driven development workflows for your projects. Provides structured workflows, templates, and configurations for integrating AI agents into your development process. Reduces setup time by giving teams a proven starting point for organizing AI-assisted coding, task management, and quality assurance in new and existing repositories.
Multi-agent coordination framework
A framework for coordinating multiple AI agents working together on complex development tasks. Defines agent roles, communication patterns, task delegation strategies, and inter-agent workflows to break down large projects into manageable, parallel workstreams handled by specialized agents. Ideal for teams experimenting with multi-agent architectures where different AI models handle distinct aspects of software development.
AI agents running automated research
An open-source platform for building full-stack web applications with visual tools and AI assistance. Combines a component library, database management, and deployment pipeline in one integrated environment. Targets small teams and solo developers who want to build and ship complete web apps quickly without juggling separate tools for frontend, backend, and infrastructure.
TypeScript AI agent standard library
Standard library of AI tools and integrations for TypeScript-based agents. Works with any AI SDK and includes ready-made integrations for search, web scraping, email, and other common tool patterns. Saves developers from rebuilding common agent capabilities from scratch, providing well-tested, type-safe building blocks for rapid AI agent development.
Programming — not prompting — LLMs
AI-powered search API that delivers grounded, cited answers from real-time web data. Integrates into applications to provide accurate, up-to-date information without building custom search infrastructure. Ideal for AI agents and chatbots that need factual responses backed by sources, combining web search with LLM synthesis for reliable information retrieval.
Lightweight multi-modal agent framework
Fast, lightweight Python framework for building multi-modal AI agents, formerly known as Phidata. Includes built-in memory, knowledge bases, tools, and reasoning capabilities with 20k+ GitHub stars. Designed for developers who want to build production-ready agents quickly with minimal boilerplate, supporting structured outputs and multi-agent coordination out of the box.
Agent Development Kit by Google
Google's open-source framework for building AI agents with Gemini models. Supports multi-agent orchestration, tool use, and deployment to Vertex AI or Cloud Run. Provides a structured approach to agent development with built-in evaluation, testing, and monitoring capabilities, making it the official path for teams building agent systems within the Google Cloud ecosystem.
Official agent SDK by Anthropic
Anthropic's Python SDK for building agentic AI applications powered by Claude models. Provides primitives for creating agents with tool use, multi-step reasoning, guardrails, handoffs between specialized agents, and structured output. Supports building complex agent workflows with tracing and observability. Designed for developers building production AI agents that interact with external systems, databases, and APIs using Claude as the reasoning backbone.
Official Python SDK for OpenAI agents
OpenAI's Python framework for building multi-agent AI applications with GPT models. Provides primitives for creating agents with tool calling, handoffs between specialized agents, guardrails for input/output validation, and tracing for observability. Supports building complex workflows where agents collaborate on tasks. Includes built-in tools for file search, code execution, and web browsing. Designed for production agent systems with structured output and error recovery patterns.
Python agent framework by Pydantic team
Agent framework built on Pydantic for type-safe AI applications. Provides structured outputs, dependency injection, and multi-model support. Created by the Pydantic team, it brings the same validation and typing philosophy that made Pydantic essential for Python APIs to the world of AI agents, ensuring reliable data flow between LLMs and application logic.
Open-source LLM app development platform
Anthropic's command-line AI coding agent that operates directly in your terminal. Handles complex multi-file development tasks including writing code, running tests, fixing bugs, and managing git workflows. Designed for developers who prefer terminal-based workflows, offering deep project understanding and autonomous task completion with transparent reasoning.
Next-gen multi-agent framework (AutoGen fork)
OpenAI's AI coding agent that works autonomously in a cloud sandbox. Handles multi-step development tasks including writing features, fixing bugs, and running tests. Integrates with GitHub for PR creation and review. Targets teams who want to delegate entire development tasks to AI rather than using it as an inline code completion assistant.
TypeScript AI agent framework
TypeScript-native framework for building AI agents and workflows with great developer experience. Provides primitives for agents with tool calling, RAG pipelines, workflow orchestration with branching/parallel steps, and integration connectors. First-class TypeScript support with type-safe tool definitions. Local dev server with playground UI for testing. Growing as a LangChain alternative for TypeScript developers building AI apps.
Multi-agent AI framework
Python framework for orchestrating autonomous AI agents that collaborate to accomplish complex tasks. Define agents with specific roles, goals, and backstories, then organize them into crews with sequential or parallel task execution. Supports tool usage (web search, file I/O, API calls), memory, delegation between agents, and human-in-the-loop input. Works with OpenAI, Anthropic, local models, and more. 25K+ GitHub stars. Leading multi-agent framework alongside LangGraph and AutoGen.
Data framework for LLM applications
Leading Python framework for building LLM-powered applications with focus on data-aware and agentic workflows. Provides tools for RAG (Retrieval-Augmented Generation), document indexing, vector store integrations, query engines, and multi-agent orchestration. 150+ data connectors for various sources. Works with OpenAI, Anthropic, local models, and more. Includes LlamaHub for community tools and LlamaCloud for managed RAG pipelines. 40K+ GitHub stars.
Framework for LLM applications
The most widely-used framework for building LLM-powered applications, available in Python and JavaScript. Provides abstractions for chains, agents, RAG, memory, tool usage, and structured output. Integrates with 100+ LLM providers, vector stores, document loaders, and tools. LangSmith offers tracing and evaluation. LangGraph enables stateful, multi-agent workflows with cycles. 100K+ GitHub stars. The de facto standard for LLM application development despite growing alternatives like LlamaIndex.
