AI Agent Frameworks Comparison
14 frameworks compared across language support, agent capabilities, RAG, streaming, and deployment options.
The most widely adopted LLM framework. Provides composable chains, agents, retrieval, memory, and a massive integrations ecosystem. Pairs with LangSmith for observability.
pip install langchain langchain-openai Purpose-built for connecting LLMs to data. Excels at RAG pipelines, structured data extraction, and indexing heterogeneous document corpora.
pip install llama-index Microsoft Research framework for multi-agent systems. Agents converse in structured loops, making it ideal for automated coding workflows and task decomposition.
pip install pyautogen Defines crews of specialised AI agents with distinct roles, goals, and tools. Agents collaborate hierarchically or sequentially to complete complex tasks.
pip install crewai deepset's framework for building search and NLP pipelines. Component-based architecture makes it easy to compose retrieval, reranking, and generation steps.
pip install haystack-ai Reframes LLM prompting as a programming problem. Write declarative modules; DSPy's optimisers (BootstrapFewShot, MIPROv2) compile them to the best prompts/weights.
pip install dspy-ai Microsoft's SDK for integrating LLMs into applications. Designed for enterprise .NET environments with first-class Azure OpenAI support and plugin architecture.
pip install semantic-kernel Formerly phidata. Build agents with tools, memory, and knowledge bases, then serve them via a prebuilt Playground UI or FastAPI. Emphasises clean Pythonic code.
pip install agno LangChain's graph-based agent framework. Define nodes (LLM calls, tools) and edges (conditional routing) for precise control over multi-step agent behaviours.
pip install langgraph OpenAI's fully managed agent service. Create assistants with custom instructions, attach files for retrieval, and maintain persistent conversation threads via the API.
npm install openai # use Assistants API Vercel's TypeScript AI toolkit. Provides streaming-first hooks (useChat, useCompletion) and a unified provider API to build AI UIs with React and Next.js.
npm install ai @ai-sdk/openai Full-featured TypeScript agent framework with workflows, memory, RAG, and evals. Designed for production TypeScript applications requiring durable, observable agent pipelines.
npm install @mastra/core Visual workflow automation platform with native AI Agent nodes. Connect LLMs to 400+ apps without code. Best for automation-heavy teams rather than custom agent development.
npx n8n # or docker run -it --rm n8nio/n8n Open-source visual builder for LLM applications. Drag-and-drop LangChain nodes to build chatbots, RAG pipelines, and agents, then deploy via a REST API.
npx flowise start About
This reference compares 14 AI agent and orchestration frameworks as of 2025. For each framework you get: supported languages, license, GitHub stars, primary use case, feature flags (multi-agent, tool use, memory, RAG, streaming, self-hosted, cloud-managed, LLM-agnostic), key strengths and weaknesses, and a quickstart command. Filter by language, capability, or use case to find the right framework for your project.
How to use
- 1 Use the filter chips to narrow by language (Python / TypeScript), feature (Multi-Agent, RAG, etc.), or use case.
- 2 Switch between Cards view (detailed) and Table view (compact comparison) with the toggle button.
- 3 Click "Copy" on any quickstart command to add it to your clipboard.
- 4 GitHub links open the official repository for stars, issues, and documentation.
- What is the difference between LangChain and LlamaIndex?
- LangChain is a general-purpose LLM application framework focused on chains, agents, and tool orchestration — strong for multi-step reasoning and tool-use agents. LlamaIndex (formerly GPT Index) specializes in RAG and document ingestion — better indexes, retrieval pipelines, and structured data integration. Many production apps use both: LlamaIndex for retrieval, LangChain for agent orchestration.
- What is AutoGen vs CrewAI?
- AutoGen (Microsoft) enables multi-agent conversations where multiple AI agents collaborate — one agent can call another, critique its output, and iterate. CrewAI is higher-level: you define a "crew" of role-based agents (researcher, writer, reviewer) that collaborate on a shared task. AutoGen is more flexible; CrewAI is more opinionated and easier to set up for role-based workflows.
- What is DSPy?
- DSPy (Declarative Self-improving Python) from Stanford shifts from manual prompt engineering to programmatic optimization. Instead of writing prompts by hand, you define the signature (input/output) and DSPy automatically optimizes prompts or fine-tunes model weights to maximize a metric. Best for tasks where you have labeled examples and want consistent, optimized performance.