The agent framework landscape in 2026 has consolidated. A year ago there were 30+ "agent frameworks"; now there are about ten that matter, and a few clear leaders for each kind of workload. This list covers what each is good at, where each falls short, and how to pick.
We see all of these in production across Respan's customer base. The pattern: LangGraph for complex stateful production agents, CrewAI for fast prototyping, OpenAI Agents SDK for OpenAI-first stacks, AG2 for conversational multi-agent patterns, with several others filling specific niches.
Quick comparison
| Framework | Best for | Learning curve | Production-ready | License |
|---|---|---|---|---|
| LangGraph | Stateful production agents | Medium | ✅ | OSS |
| CrewAI | Fast prototyping with role-based agents | Low | ✅ | OSS |
| OpenAI Agents SDK | OpenAI-first stacks with handoff patterns | Low | ✅ | OSS |
| AutoGen / AG2 | Conversational multi-agent | Medium | ✅ | OSS (split) |
| Google ADK | Google Cloud + multimodal multi-agent | Medium | ✅ | OSS |
| Pydantic AI | Type-safe agents in Python | Low | ✅ | OSS |
| LlamaIndex Agents | RAG-heavy agents | Low | ✅ | OSS |
| Mastra | TypeScript-first, batteries-included | Low | ⚠️ Growing | OSS |
| Agno | Lightweight Python multi-modal agents | Low | ⚠️ Growing | OSS |
| SmolAgents | Hugging Face's minimal agent loop | Low | ⚠️ Growing | OSS |
How to choose
Before the list, the criteria that matter:
- State management: Does the framework model state explicitly or is it implicit in the chain?
- Production execution: Retries, timeouts, checkpoints, replay?
- Human-in-the-loop: Can the agent pause for human input mid-run?
- Multi-agent: Can multiple agents coordinate / hand off / run in parallel?
- Observability: Does it integrate with your tracing/eval stack?
- Language support: Python, TypeScript, both?
1. LangGraph
Best for: Stateful production agents with complex control flow.
The story: LangGraph from the LangChain team models agents as state graphs — explicit nodes, edges, and persistent state. Surpassed CrewAI in GitHub stars in early 2026, driven by enterprise adoption. The graph model maps cleanly to production requirements (retries, checkpoints, human-in-the-loop, multi-agent orchestration).
Pros: Most mature production agent framework. State graph is the right abstraction for complex agents. Time-travel debugging — replay any agent run from any checkpoint. Strong observability integration.
Cons: Steeper learning curve than CrewAI. More code to write than higher-level abstractions. Python and TypeScript supported but Python is primary.
License: OSS (MIT).
Read our deep dive: LangChain vs LangGraph
2. CrewAI
Best for: Fast prototyping with role-based agents.
The story: CrewAI's role-based DSL is the lowest learning curve in the agent space. You define agents by roles ("researcher", "writer", "reviewer") and they collaborate. ~20 lines to a working multi-agent demo.
Pros: Fastest framework to prototype with. Role-based abstraction is intuitive. Active community.
Cons: Less control over execution than LangGraph. The role-based model can hide complexity until production scale where you need finer control. Some teams migrate from CrewAI to LangGraph as production complexity grows.
License: OSS.
3. OpenAI Agents SDK
Best for: OpenAI-first stacks that benefit from handoff patterns.
The story: OpenAI's Agents SDK shipped March 2025, replacing the experimental Swarm framework with a production-grade toolkit. The core abstraction is handoffs — agents transfer control to each other explicitly, carrying conversation context through the transition.
Pros: Production-grade quality. Tight integration with OpenAI models. Clean handoff semantics. Good docs.
Cons: OpenAI-first design — works with other providers but the cleanest path is OpenAI. Less rich state model than LangGraph.
License: OSS.
4. AutoGen / AG2
Best for: Conversational multi-agent patterns.
The story: Microsoft's AutoGen pioneered conversational agent teams. The big story in 2025-2026 is the AutoGen / AG2 split: Microsoft pushed AutoGen v0.4+ as a rewrite, and the community continued the proven v0.2 lineage as AG2. Both ship today; production users mostly went with AG2.
Pros: Mature conversational multi-agent patterns. Strong research backing. AG2 has stable APIs.
Cons: Conversational model is less suited to non-chat workflows. Confusing branding (AutoGen vs AG2). Microsoft's v0.4 rewrite has been disruptive.
License: OSS.
5. Google ADK (Agent Development Kit)
Best for: Google Cloud users + multimodal multi-agent.
The story: Google's ADK is the multi-agent framework backed by Google's infrastructure. Distinctive features: A2A (Agent-to-Agent) protocol for inter-agent communication and strong multimodal capabilities (text + audio + video + image in the same agent loop).
Pros: Native multimodal agent support. A2A protocol is novel and well-designed. Deep Vertex AI / Google Cloud integration.
Cons: Newer / smaller community. Most natural on Google Cloud; less polished outside that environment.
License: OSS.
6. Pydantic AI
Best for: Type-safe agents in Python.
The story: From the Pydantic team — agent framework with first-class typed inputs/outputs. If you're a Pydantic-shop and care about type safety, this is the natural choice.
Pros: Type safety as a first-class citizen. Clean Python ergonomics. Strong validation.
Cons: Less feature-rich than LangGraph or CrewAI. Smaller community.
License: OSS.
7. LlamaIndex Agents
Best for: RAG-heavy agents that already use LlamaIndex.
The story: LlamaIndex expanded into agents in 2025-2026. If your stack is built around LlamaIndex's RAG primitives, the agent layer is a natural extension.
Pros: Tight integration with LlamaIndex retrieval. RAG-first agent patterns built in.
Cons: Less general-purpose than LangGraph. Most useful when RAG is the primary agent activity.
License: OSS.
Read our deep dive: LlamaIndex vs LangChain
8. Mastra
Best for: TypeScript-first developers wanting a batteries-included framework.
The story: Mastra is the TypeScript answer to Python-dominated agent frameworks. Batteries-included — agents, RAG, memory, evaluations, observability primitives.
Pros: Strongest TypeScript-first agent framework. Good developer experience. Comprehensive feature set.
Cons: Smaller production footprint than Python frameworks. Newer / less battle-tested.
License: OSS.
9. Agno
Best for: Lightweight Python multi-modal agents.
The story: Agno (formerly Phidata) is a Python framework focused on lightweight agents with multimodal capabilities and minimal abstraction overhead.
Pros: Minimal API surface. Good multimodal support. Easy to start.
Cons: Less production tooling than LangGraph or Crew. Smaller community.
License: OSS.
10. SmolAgents
Best for: Hugging Face users wanting a minimal agent loop.
The story: From Hugging Face — minimal agent loop (a few hundred lines of code total). Useful as both a working framework and a learning artifact ("here's the smallest possible agent that works").
Pros: Minimal and readable. Good for understanding what agents really are. Integrates with HF ecosystem.
Cons: Minimal by design — not a replacement for LangGraph or CrewAI for complex production agents.
License: OSS (Apache 2.0).
How to choose
Quick decision framework:
- Building a complex production agent with state, retries, multi-agent? → LangGraph
- Prototyping fast with role-based agents? → CrewAI
- OpenAI-first stack with handoff patterns? → OpenAI Agents SDK
- Conversational multi-agent (chat-style)? → AG2 (or AutoGen v0.4 if you trust Microsoft's rewrite)
- Google Cloud + multimodal? → Google ADK
- Type safety matters? → Pydantic AI
- RAG is primary activity? → LlamaIndex Agents
- TypeScript codebase? → Mastra
- Want minimal abstraction? → Agno or SmolAgents
Common pitfalls
- Picking based on hype, not fit. "Everyone's using LangGraph" is true and not a reason — pick based on your specific workload shape.
- Skipping observability. Every framework on this list integrates with LLM tracing via OpenTelemetry or vendor-specific SDKs. Wire this on day one, not month six.
- Multi-agent for the sake of it. Many "multi-agent" patterns can be solved with a single well-designed agent. Don't add coordination complexity unless the workload demands it.
- Not running evals. Agent quality drifts. Quality scores tied to prompt versions and tool versions catch regressions before users do.
FAQ
Which agent framework is most popular? LangGraph leads on enterprise / production adoption (and surpassed CrewAI on GitHub stars in early 2026). CrewAI leads on prototyping volume and tutorials. OpenAI Agents SDK is growing fast as the OpenAI-first answer.
Should I use CrewAI or LangGraph? CrewAI for fast prototyping. LangGraph for production agents that need explicit state and control flow. Many teams start with CrewAI and migrate to LangGraph as complexity grows.
Is AutoGen or AG2 better? AG2 (the community-continued v0.2 lineage) is more battle-tested in production. AutoGen v0.4+ is Microsoft's rewrite — promising but newer. Production users mostly went with AG2.
Can I switch frameworks later? Yes, but it's real work. The agent business logic is reusable; the framework-specific orchestration code has to be rewritten. Picking right the first time matters.
Do these frameworks work with non-OpenAI / non-Anthropic models? Yes — all of them are model-agnostic in principle. Some have tighter integration with specific providers (OpenAI Agents SDK with OpenAI; Google ADK with Vertex AI), but you can wire any model into any framework.
Which has the best documentation? LangGraph and CrewAI have the broadest documentation. OpenAI Agents SDK has the cleanest. AG2's docs improved meaningfully through 2026.
Do I need a framework at all? For simple linear flows, no. For multi-tool agents with branching, retries, or human-in-the-loop, yes. The framework adds value when complexity warrants it.