LangGraph is the best AI agent framework for teams that need explicit state, durable execution and control over complex workflows. CrewAI is easier for role-based multi-agent prototypes. OpenAI Agents SDK, Claude Agent SDK and Google ADK are the clearest choices when a project is already committed to the corresponding model ecosystem.
A framework cannot make an unreliable workflow dependable by itself. Production quality comes from constrained tools, observable state, deterministic validation, retries, permissions and task-level evaluations.
Best for | Pick | Reason |
|---|---|---|
Stateful production graphs | LangGraph | Explicit state and controllable workflow structure |
Role-based multi-agent prototypes | CrewAI | Accessible agent and crew abstractions |
OpenAI-native systems | OpenAI Agents SDK | Direct fit with OpenAI tools and tracing |
Claude-powered coding agents | Claude Agent SDK | Claude Code capabilities exposed for developers |
Google ecosystem | Google ADK | Model-flexible framework with Google integrations |
Microsoft and .NET teams | Microsoft Agent Framework | Microsoft ecosystem and enterprise development path |
Research and conversational agents | AutoGen | Mature multi-agent experimentation lineage |
How we evaluate
This guide separates documented capability from direct test evidence. Recommendations use current first-party documentation, available product information and a frozen evaluation framework. We do not claim that a tool passed a scenario unless the result was directly observed and recorded.
Criterion | Weight | What matters |
|---|---|---|
Task completion | 25% | Accepted outcomes across a frozen task set |
Control | 20% | Explicit state, branching and failure handling |
Observability | 15% | Traces, tool calls, costs and reproducibility |
Security | 15% | Permissions, sandboxing and approval gates |
Developer experience | 15% | Documentation, testing and deployment |
Portability | 10% | Model and infrastructure flexibility |
Five repeatable scenarios
Scenario | Pass condition |
|---|---|
Tool failure | Retries safely or escalates without inventing success |
Long-running task | Persists state and resumes after interruption |
Permission boundary | Blocks an unauthorized action and requests approval |
Model switch | Maintains test performance after a deliberate provider change |
Audit replay | Reconstructs prompts, tools, outputs and decisions |
Seven frameworks compared
Framework | Core abstraction | Strongest fit | Main tradeoff |
|---|---|---|---|
LangGraph | State graph | Durable controlled workflows | More explicit engineering |
CrewAI | Agents, crews and flows | Fast multi-agent composition | Role metaphors can hide workflow complexity |
OpenAI Agents SDK | Agents, handoffs, guardrails and tracing | OpenAI-native applications | Vendor ecosystem emphasis |
Claude Agent SDK | Claude Code agent loop and tools | Coding and computer-based workflows | Claude-centered implementation |
Google ADK | Agents, tools and sessions | Google services and flexible deployment | Rapidly evolving surface |
Microsoft Agent Framework | Agents and orchestration | Microsoft enterprise stack | Microsoft ecosystem complexity |
AutoGen | Conversational multi-agent patterns | Research and experimentation | Production control requires care |
LangGraph: best for explicit production control
LangGraph is the strongest default when an agent must keep durable state, pause for human approval, branch on results and resume after failure. The graph model forces teams to make control flow visible. That additional engineering is valuable when mistakes have real cost.
CrewAI: best for fast multi-agent prototypes
CrewAI makes it easy to describe specialized agents, their tasks and how they collaborate. It is useful for proving a workflow quickly. Before production, replace vague role prompts with narrow tools, measurable outputs and explicit error handling.
OpenAI Agents SDK
OpenAI Agents SDK is the natural route for systems built around OpenAI models, tools, handoffs and tracing. The SDK is intentionally lightweight. Teams still need their own authorization, application state and outcome evaluations.
Related OpenAI guides: Codex review; GPT 5.6 Sol; GPT 5.3 Codex
Claude Agent SDK
Claude Agent SDK exposes the agent loop and tools associated with Claude Code for custom applications. It is particularly relevant to coding, repository work and computer-based tasks where Claude already wins the project’s evaluation.
Related Anthropic guides: Claude Code review; Claude Fable 5.1; Claude Sonnet 5
Google ADK, Microsoft Agent Framework and AutoGen
Google ADK is attractive for teams using Gemini and Google services while retaining model flexibility. Microsoft Agent Framework is the strategic Microsoft option and incorporates lessons from earlier agent projects. AutoGen remains valuable for research and conversational multi-agent patterns, but teams should verify which Microsoft path is recommended for new production deployments.
See our Gemini 3.1 Pro guide for the current Google model layer.
Framework selection checklist
Requirement | Prefer |
|---|---|
Durable branching workflow | LangGraph |
Quick role-based prototype | CrewAI |
OpenAI tools and tracing | OpenAI Agents SDK |
Claude coding workflow | Claude Agent SDK |
Google services | Google ADK |
Microsoft enterprise stack | Microsoft Agent Framework |
Academic multi-agent experiments | AutoGen |
Production safeguards
- Give every tool the narrowest permissions that complete the task.
- Require approval before external messages, purchases, deletion or production changes.
- Store enough trace data to reproduce failures without retaining unnecessary sensitive content.
- Evaluate completed outcomes, not persuasive intermediate messages.
- Pin model and framework versions before a release.
Frequently asked questions
What is the best AI agent framework?
LangGraph is the strongest general default for controlled, stateful production workflows. The vendor SDKs are better when the project is intentionally tied to one model ecosystem.
Is CrewAI better than LangGraph?
CrewAI is often faster for role-based prototypes. LangGraph gives developers more explicit control over state, branching and recovery.
Do I need a framework to build an AI agent?
No. A simple tool-calling loop may be safer and easier to maintain. Add a framework when state, orchestration, tracing or reuse justifies it.
Can agent frameworks use multiple models?
Several can, but portability is not perfect. Tool schemas, context behavior and vendor features still require model-specific testing.
How should AI agents be tested?
Use frozen tasks with objective completion criteria, seeded failures, permission tests and trace review. Measure accepted outcomes rather than model self-reports.
Official sources and verification
Features, limits and prices can change. These first-party sources were checked on September 4, 2026. Recheck the relevant product page before buying or deploying.