Choose the Right Framework
Compare 9 popular AI agent frameworks side by side to find the best fit for your next project.
Quick Comparison
| CrewAI | LangGraph | OpenAI Agents SDK | Microsoft Agent Framework | OpenClaw | Hermes Agent | Google ADK | Pydantic AI | Mastra | |
|---|---|---|---|---|---|---|---|---|---|
| Language | Python | Python / JS | Python / TS | Python / .NET | TypeScript | Python | Python / Java | Python | TypeScript |
| Difficulty | Beginner | Intermediate | Beginner | Intermediate | Beginner | Beginner | Intermediate | Beginner | Beginner |
| Best For | Multi-agent teams with defined roles | Complex stateful workflows with branching logic | Quick prototyping with OpenAI models | Enterprise conversational + multi-agent workflows on Microsoft stack | Personal AI assistants with multi-channel support | Autonomous agents with long-term memory and self-improvement | Multi-agent systems on Google Cloud with built-in memory | Type-safe, structured-output agents with validation | TypeScript/web teams building agents into apps |
| Setup Time | ~10 minutes | ~20 minutes | ~5 minutes | ~15 minutes | ~5 minutes | ~5 minutes | ~15 minutes | ~10 minutes | ~10 minutes |
| GitHub Stars | 35k+ | Production standard | Widely adopted | 1.0 GA (2026) | Fast growing | Backed by Nous Research | 17k+ | 16k+ | Fast growing |
Detailed Breakdown
CrewAI
Role-based multi-agent orchestration framework. Define agents with roles, goals, and backstories, then let them collaborate on tasks. The 2026 releases (0.105+) added enterprise observability, scheduling, built-in memory, and native MCP tool support.
- Language
- Python
- Difficulty
- Beginner
- Setup Time
- ~10 minutes
- Website
- https://crewai.com
- GitHub
- https://github.com/crewai/crewai
Pros
- Fastest time-to-production for standard workflows
- Intuitive role-based agent design
- Great documentation and community
- Built-in tool integrations
Cons
- Less flexible for non-role-based patterns
- Python only
- Opinionated architecture
LangGraph
Graph-based framework for building stateful, multi-actor applications. Part of the LangChain ecosystem with the largest community. In 2026 (0.4+) it's the production standard for stateful, auditable agents — durable checkpointing, streaming, human-in-the-loop primitives, and MCP-native tool calling, paired with LangSmith for observability.
- Language
- Python / JS
- Difficulty
- Intermediate
- Setup Time
- ~20 minutes
- Website
- https://langchain.com/langgraph
- GitHub
- https://github.com/langchain-ai/langgraph
Pros
- Most popular framework by community size
- Graph-based state machines for complex flows
- Durable execution and checkpointing
- Python and JavaScript support
Cons
- Steeper learning curve
- Can be verbose for simple use cases
- Tied to LangChain ecosystem
Popular Ideas Using LangGraph
OpenAI Agents SDK
Official OpenAI framework for building agents with tool usage, handoffs, and function calling. Lowest barrier to entry with working agents in under 20 lines, now with built-in MCP support and tracing. Python and TypeScript.
- Language
- Python / TS
- Difficulty
- Beginner
- Setup Time
- ~5 minutes
- Website
- https://platform.openai.com
- GitHub
- https://github.com/openai/openai-agents-python
Pros
- Lowest learning curve of any framework
- Working agent in under 20 lines of code
- Native OpenAI model integration
- Built-in tool use and function calling
Cons
- Locked to OpenAI models
- Less mature ecosystem
- Limited multi-agent patterns
Popular Ideas Using OpenAI Agents SDK
Microsoft Agent Framework
Microsoft's unified agent SDK (GA April 2026) that merges AutoGen and Semantic Kernel into one production stack. Combines AutoGen's simple single/multi-agent abstractions with Semantic Kernel's enterprise features — typed workflows, durable state, telemetry, and native MCP. AutoGen and Semantic Kernel are now maintenance-only; new work targets this.
- Language
- Python / .NET
- Difficulty
- Intermediate
- Setup Time
- ~15 minutes
- Website
- https://learn.microsoft.com/en-us/agent-framework/overview/
- GitHub
- https://github.com/microsoft/agent-framework
Pros
- Production-ready successor to AutoGen + Semantic Kernel
- Typed multi-agent workflows with explicit execution paths
- Durable state for long-running and human-in-the-loop runs
- First-class .NET and Python support, native MCP
Cons
- Newer unified API — migration needed from AutoGen/SK
- Heavier than minimal SDKs for simple agents
- Best leverage on the Microsoft/Azure stack
Popular Ideas Using Microsoft Agent Framework
OpenClaw
Open-source personal AI assistant that runs on your own devices. Connects to WhatsApp, Telegram, Slack, Discord, and more with a plugin-based architecture.
- Language
- TypeScript
- Difficulty
- Beginner
- Setup Time
- ~5 minutes
- Website
- https://openclaw.ai
- GitHub
- https://github.com/openclaw/openclaw
Pros
- Runs on your own devices for privacy
- Multi-channel support out of the box
- Plugin-based extensible architecture
- Onboarding wizard for easy setup
Cons
- Focused on personal assistant use cases
- Newer framework, smaller ecosystem
- Requires self-hosting
Popular Ideas Using OpenClaw
Hermes Agent
Nous Research's autonomous, self-improving agent. Runs anywhere — laptop, VPS, or serverless — with persistent cross-session memory, 60+ built-in tools, MCP support, and access across 20+ messaging platforms. A natural companion to OpenClaw when you need an agent that learns and evolves over time.
- Language
- Python
- Difficulty
- Beginner
- Setup Time
- ~5 minutes
- Website
- https://hermes-agent.nousresearch.com/docs/
- GitHub
- https://github.com/NousResearch/hermes-agent
Pros
- Closed learning loop with autonomous skill creation
- Persistent cross-session memory with LLM-powered recall
- 60+ built-in tools plus MCP server integration
- Serverless deployment costs almost nothing when idle
- Reachable from 20+ messaging platforms (Telegram, Discord, Slack)
Cons
- Newer project with a growing ecosystem
- Autonomy needs guardrails for production use
- Best paired with clear task scoping
Popular Ideas Using Hermes Agent
Google ADK
Google's open-source Agent Development Kit — a code-first toolkit for building, evaluating, and deploying agents at enterprise scale. Optimized for Gemini but model-agnostic via LiteLLM, with built-in memory and Sequential/Parallel/Loop workflow agents, tightly integrated with Vertex AI, Cloud Run, and Cloud Trace.
- Language
- Python / Java
- Difficulty
- Intermediate
- Setup Time
- ~15 minutes
- Website
- https://google.github.io/adk-docs/
- GitHub
- https://github.com/google/adk-python
Pros
- Genuine built-in memory and evaluation tooling
- Sequential, Parallel, and Loop workflow agents
- Model-agnostic through LiteLLM (not just Gemini)
- Deep Vertex AI / Cloud Run / Cloud Trace integration
Cons
- Best leverage on Google Cloud
- Younger ecosystem than LangChain
- Some features lean toward Gemini
Pydantic AI
The type-safety play for agents. Built by the Pydantic team, it brings a FastAPI-style developer experience with full type validation, structured outputs, and dependency injection — catching agent logic errors at development time. Model-agnostic with native MCP support.
- Language
- Python
- Difficulty
- Beginner
- Setup Time
- ~10 minutes
- Website
- https://ai.pydantic.dev
- GitHub
- https://github.com/pydantic/pydantic-ai
Pros
- Compile-time confidence via Python types + Pydantic validation
- Familiar FastAPI-style ergonomics
- Reliable structured outputs and tool schemas
- Model-agnostic with native MCP
Cons
- Python only
- Lighter on built-in multi-agent orchestration
- Newer, smaller ecosystem than LangChain
Mastra
TypeScript-first agent framework with RAG, observability, workflows, and MCP baked in. The natural choice for JavaScript/TypeScript teams shipping agents into web apps and the Vercel/Node ecosystem.
- Language
- TypeScript
- Difficulty
- Beginner
- Setup Time
- ~10 minutes
- Website
- https://mastra.ai
- GitHub
- https://github.com/mastra-ai/mastra
Pros
- TypeScript-native — one language across your stack
- Built-in RAG, memory, workflows, and observability
- MCP support out of the box
- Fits naturally into Vercel / Next.js deployments
Cons
- TypeScript/JavaScript only
- Younger than the Python incumbents
- Multi-agent patterns still maturing
Which Framework Should You Choose?
Not sure where to start? Here are tailored recommendations based on common goals and experience levels.
Just Getting Started?
Both offer the fastest setup times and lowest learning curves. OpenAI Agents SDK gets you a working agent in under 20 lines, while OpenClaw provides a guided onboarding wizard.
Building Multi-Agent Teams?
CrewAI was purpose-built for multi-agent orchestration with intuitive role-based design. Define agents with roles, goals, and backstories, then let them collaborate autonomously.
Complex Stateful Workflows?
LangGraph excels at graph-based state machines with branching logic, durable execution, and checkpointing. Ideal when your agent needs sophisticated control flow.
Enterprise Conversational AI?
Microsoft Agent Framework (GA 2026) is the unified successor to AutoGen and Semantic Kernel — typed multi-agent workflows, durable state, telemetry, and first-class .NET + Python for teams on the Microsoft stack.
Need Type Safety & Structured Output?
Pydantic AI brings a FastAPI-style experience with full type validation, so agent logic errors surface at development time and structured outputs stay reliable — ideal for extraction, parsing, and validation-heavy agents.
Building in TypeScript / on the Web?
Mastra is TypeScript-first with RAG, memory, workflows, observability, and MCP baked in — one language across your stack and a natural fit for Next.js / Vercel deployments.
On Google Cloud?
Google ADK is a code-first toolkit with genuine built-in memory and Sequential/Parallel/Loop workflow agents, model-agnostic via LiteLLM and tightly integrated with Vertex AI, Cloud Run, and Cloud Trace.
Personal AI Assistant?
OpenClaw runs on your own devices for privacy with multi-channel support for WhatsApp, Telegram, Slack, Discord, and more. Pair it with Hermes Agent when you want persistent memory and autonomous self-improvement on top.
Autonomous & Self-Improving?
Hermes Agent from Nous Research brings a closed learning loop, persistent cross-session memory, 60+ built-in tools, and serverless deployment that costs almost nothing when idle — ideal for agents that run unattended and get better over time.
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