An AI agent framework handles the loop around a language model: calling tools, keeping state and memory, handing work between agents, streaming output, and recovering when something fails. The model does the reasoning; the framework decides how that reasoning is wired into your application.
Frameworks differ mainly in control style (explicit graphs vs autonomous crews vs a thin tool loop), language (Python, TypeScript, .NET) and how tightly they couple to one model vendor. All of the frameworks below are open source; several are maintained by a model provider and work best, but not exclusively, with that provider's models. Note that Microsoft now positions Microsoft Agent Framework as the successor to AutoGen, which is in maintenance mode.
Tools, licences and pricing checked against vendor and repository pages on November 8, 2026.
1. LangGraph
MITBest for: Complex, controllable agent workflows that must survive failures · Free and open source; LangSmith: see vendor pricing
LangGraph is a low-level framework for stateful, long-running agents built as graphs, with durable execution, human-in-the-loop state inspection and short- and long-term memory. It has Python and JavaScript (LangGraph.js) versions; LangSmith Deployment is the commercial hosting option.
Strengths
- + Explicit graph gives fine-grained control
- + Durable execution and checkpoints built in
Trade-offs
- − More boilerplate than higher-level frameworks
- − Best tooling ties into the LangChain/LangSmith ecosystem
2. CrewAI
MITBest for: Role-based multi-agent teams in Python · Free and open source; AMP: see vendor pricing
CrewAI orchestrates role-playing autonomous agents. Crews handle autonomous collaboration between agents, while Flows give event-driven control over execution and state. CrewAI AMP is the commercial control plane for deployment, observability and governance.
Strengths
- + Fast to prototype multi-agent setups
- + Flows add deterministic control when needed
Trade-offs
- − Autonomous crews can be harder to debug and bound in cost
- − Python only
3. OpenAI Agents SDK
MITBest for: Lightweight agents with handoffs and guardrails · Free and open source; model usage billed by your provider
OpenAI's lightweight framework for multi-agent workflows built on agents, handoffs, guardrails, sessions and built-in tracing. It works with the Responses and Chat Completions APIs and is provider-agnostic, supporting many other LLMs; a TypeScript version lives in openai-agents-js.
Strengths
- + Small set of primitives, quick to learn
- + Tracing included
Trade-offs
- − Fewer workflow and durability features than graph frameworks
- − Tightest integration is with OpenAI's platform
4. Microsoft Agent Framework
MITBest for: .NET and Python teams, especially on Azure · Free and open source; Azure services billed separately
Microsoft Agent Framework builds and orchestrates agents and multi-agent workflows in Python and .NET, with graph-based workflows (sequential, concurrent, handoff, group collaboration), checkpointing, streaming and human-in-the-loop. The AutoGen repository is in maintenance mode and names it AutoGen's enterprise-ready successor; migration guides from Semantic Kernel and AutoGen are provided.
Strengths
- + First-class .NET support alongside Python
- + Clear migration path from AutoGen and Semantic Kernel
Trade-offs
- − Newer project; APIs still evolving
- − Hosted features lean on Azure AI Foundry
5. Google Agent Development Kit (ADK)
Apache-2.0Best for: Teams on Google Cloud or Gemini that want built-in eval and deploy tooling · Free and open source; Google Cloud usage billed separately
ADK is a code-first framework for building, evaluating and deploying agents. It is optimized for Gemini but model-agnostic, ships a development UI (adk web), and deploys to Docker, Cloud Run or Vertex AI Agent Engine. Official ports exist for Java, Kotlin, Go and TypeScript.
Strengths
- + Dev UI and evaluation built in
- + Many language ports
Trade-offs
- − Deployment story is strongest on Google Cloud
- − Gemini-first defaults
6. Pydantic AI
MITBest for: Python teams that value type safety and production observability · Free and open source; Logfire: see vendor pricing
Pydantic AI is a type-safe Python agent framework with structured outputs, typed dependency injection and typed tools, plus graph workflows. It supports most model providers, MCP, OpenTelemetry instrumentation (one-line Logfire integration) and durable execution through engines such as Temporal, DBOS and Prefect.
Strengths
- + Strong typing catches errors early
- + Durable execution via several workflow engines
Trade-offs
- − Python only
- − Less opinionated about multi-agent patterns
7. LlamaIndex
MITBest for: Document-heavy agents and RAG pipelines · Free and open source; LlamaParse: see vendor pricing
LlamaIndex is an open-source toolkit for agentic applications over documents, with agents and workflows. The company's commercial focus is LlamaParse, a document parsing, extraction and indexing platform.
Strengths
- + Deep data-connector and retrieval tooling
- + Workflows for event-driven agents
Trade-offs
- − Company focus has shifted toward LlamaParse
- − Large API surface
8. Mastra
Apache-2.0Best for: TypeScript and Next.js teams building agents · Free and open source core; enterprise features licensed separately
Mastra is a TypeScript framework for AI applications and agents with agents, a graph workflow engine (.then, .branch, .parallel), memory, evals and observability, and MCP server authoring. Core code is Apache-2.0; code in ee/ directories is under the source-available Mastra Enterprise License.
Strengths
- + Batteries-included for TypeScript
- + Workflows, memory and evals in one package
Trade-offs
- − Enterprise (ee/) features need a licence in production
- − Younger ecosystem than Python frameworks
9. Vercel AI SDK
Apache-2.0Best for: Web apps that need streaming chat UIs plus tool-calling agents · Free and open source; AI Gateway usage: see vendor pricing
The AI SDK is an open-source TypeScript toolkit for AI apps and agents. Its ToolLoopAgent class runs a model with a system prompt and tools; provider packages cover OpenAI, Anthropic, Google and many others, and UI helpers support Next.js, React, Svelte, Vue and Angular.
Strengths
- + Excellent streaming UI integration
- + Provider-agnostic model interface
Trade-offs
- − Agent features are thinner than dedicated frameworks
- − Model strings default to routing through Vercel's AI Gateway
10. Claude Agent SDK
MITBest for: Coding and computer-task agents built on Claude · Free SDK; Claude API usage billed by Anthropic
The Claude Agent SDK packages the agent loop behind Claude Code: file, shell and edit tools, custom tools served by in-process MCP servers, hooks such as PreToolUse to allow or deny actions, and permission modes. The repository code is MIT-licensed, but use of the SDK is governed by Anthropic's Commercial Terms of Service.
Strengths
- + Production-tested tool loop with file and shell tools
- + Hooks give deterministic guardrails
Trade-offs
- − Claude-only
- − Usage governed by Anthropic commercial terms in addition to MIT
