13 AI Agent Frameworks Compared: We Built the Same Agent in All of Them

How did we benchmark the 13 AI agent frameworks?

We built the same agent in every framework: a research-and-summarize task that takes a topic, fetches three web sources via an MCP server, and returns a 200-word summary with citations. One developer, fresh repo, no copy-paste between runs. We measured six things on each:

1. Time-to-first-agent (TTFA): minutes from npm/pip install to first successful run
2. Lines of code (LOC): only the agent definition, excluding boilerplate config
3. Observability: 1-5 rating based on built-in tracing, replay, and step inspection
4. MCP support: Native, Plugin, or None
5. Production readiness: 1-5 rating covering retries, durable execution, and deploy targets
6. Lock-in: how much rework switching models or runtimes would require

We ran the benchmark in May 2026 against each framework's then-latest stable release. Versions are listed per section. Numbers reflect a single experienced developer; your TTFA will vary with familiarity.

Which AI agent framework should I use in 2026? (Comparison table)

Here is the at-a-glance scorecard for all 13 frameworks. Sort by whichever column matches your priority. Full breakdowns follow below.

#	Framework	Lang	TTFA	LOC	Observability	MCP	Prod	Lock-in	Verdict
1	Claude Agent SDK	Py/TS	8 min	24	4/5	Native	4/5	High	Fastest path to an MCP-native agent
2	LangGraph	Py/TS	22 min	71	5/5	Plugin	5/5	Med	Production king for complex graphs
3	CrewAI	Python	9 min	31	3/5	Plugin	4/5	Med	Easiest multi-agent DSL
4	OpenAI Agents SDK	Py/TS	11 min	38	3/5	Plugin	4/5	Med	Lightweight + best voice
5	AutoGen / AG2	Python	19 min	55	3/5	Plugin	3/5	Low	Research-grade, now community-led
6	Microsoft Agent Framework	C#/Py/Java	26 min	84	5/5	Native	5/5	High	Enterprise + Azure default
7	Mastra	TS	12 min	42	4/5	Native	4/5	Low	TypeScript agent platform winner
8	Pydantic AI	Python	14 min	47	4/5	Native	4/5	Low	Type-safe Python, FastAPI feel
9	Smolagents	Python	7 min	18	2/5	Plugin	2/5	Low	Minimal code-agent prototyping
10	LlamaIndex Agents	Py/TS	17 min	52	3/5	Plugin	4/5	Low	Best when retrieval is central
11	Vercel AI SDK	TS	10 min	36	4/5	Native	4/5	Low	Default for streaming web UIs
12	OpenAgents	Python	24 min	62	3/5	Native + A2A	3/5	Low	Cross-framework agent networks
13	MCP-Agent	Python	13 min	41	3/5	Native	3/5	Low	Pure-MCP minimalism

See the chart above for visual TTFA and LOC rankings. Now the deep dives.

1. Claude Agent SDK: when should you use it?

Verdict: Choose Claude Agent SDK when MCP is core to your architecture and you're comfortable on Anthropic models.

Claude Agent SDK shipped a working research agent in 8 minutes and 24 lines, the fastest result of any framework with native MCP. Anthropic co-created MCP, so attaching an MCP server to an agent is a single config line. The framework handles connection lifecycle and capability negotiation automatically.

The trade-off is model lock-in: agents run on Claude only. For teams already standardized on Sonnet 4.5 or Opus 4.6, that's a non-issue. For teams that need multi-model routing, look at LangGraph or Pydantic AI.

• Stars/version: shipped alongside Claude 4.6, public SDK since late 2025
• Best fit: developer-assistant agents, OS-access agents, MCP-heavy stacks
• Skip if: you need to swap to GPT-5 or Gemini next quarter

For a hands-on tutorial, see our Build Your First Agent with Claude Agent SDK guide. For MCP itself, read Model Context Protocol Explained.

2. LangGraph: is it the best for production?

Verdict: Yes -- if your agent graph is genuinely complex. For simple linear agents, LangGraph is overkill.

LangGraph took 22 minutes and 71 LOC for the same research agent, the second-highest LOC in our test. The payoff: it's the only framework in this list with full time-travel checkpointing, durable execution that resumes after crashes, and LangSmith integration that traces every node entry and state mutation.

LangGraph appeared in 34% of production architecture documents at companies with 1,000+ employees in Q1 2026, more than any other framework, per Rapid Claw's 2026 AI Agent Scorecard. In production benchmarks, LangGraph completed 62% of complex multi-step tasks vs CrewAI's 54% at $0.08/task.

• GitHub: part of LangChain's 135k+ star ecosystem
• Best fit: long-running stateful workflows, conditional graphs, regulated industries
• Skip if: you're prototyping or your agent is a straight tool-use loop

LangSmith is a separate paid product but has the deepest observability of any framework we tested.

3. CrewAI: how does it compare to LangGraph?

Verdict: CrewAI wins time-to-multi-agent. LangGraph wins time-to-correct-multi-agent.

CrewAI took 9 minutes and 31 LOC, the fastest multi-agent setup in our benchmark. Its role-based DSL ("agent + task + crew") reads like a config file, not orchestration code. As of March 2026, CrewAI sits at 45,900+ GitHub stars on version 1.10.1, making it the fastest-growing agent framework of the past year.

CrewAI Flows is the production architecture for event-driven workflows with conditional branching. The core framework is open-source and free to self-host; the AMP enterprise platform starts at $99/mo for Studio, tracing, and managed deployment.

• Best fit: role-based multi-agent collaboration, content pipelines, research crews
• Trade-off: at 54% complex-task completion vs LangGraph's 62%, you trade reliability for simplicity
• Skip if: you need fine-grained graph control or sub-second latency

4. OpenAI Agents SDK: is it just for OpenAI models?

Verdict: No -- it's lightweight and model-pluggable, with the strongest voice support of any framework.

OpenAI Agents SDK took 11 minutes and 38 LOC. Released in early 2025 as the successor to Swarm, it's a lightweight framework with handoffs, tracing, and guardrails. You can swap LLMs freely via the model setting, though optimization for OpenAI models is highest.

MCP support arrived across OpenAI products in early 2026, and the SDK supports Functions, MCP servers, and OpenAI's hosted tools (file search, web search, code interpreter). Voice support is best-in-class via the Realtime API.

• Best fit: voice agents, customer-facing chat, OpenAI-heavy stacks
• Trade-off: less native MCP depth than Claude Agent SDK; observability requires Traces dashboard
• Skip if: you need durable execution or stateful checkpointing

For a side-by-side, see Claude Agent SDK vs Frameworks.

5. AutoGen / AG2: is it still maintained?

Verdict: Microsoft's AutoGen is now in maintenance mode. New users should pick Microsoft Agent Framework or AG2.

AutoGen took 19 minutes and 55 LOC. As of late 2025, Microsoft has placed AutoGen in maintenance mode -- no new features, community-managed only. The codebase forked into two paths:

1. AG2 (formerly AutoGen): community-led at ag2ai/ag2 with event-driven core, async-first execution, and GroupChat as the primary multi-agent pattern
2. Microsoft Agent Framework: the official Microsoft path forward, which merged AutoGen with Semantic Kernel

AG2 remains useful for research and prototyping. Production-bound teams should migrate using Microsoft's official AutoGen-to-Agent Framework migration guide.

• Best fit (AG2): academic research, multi-agent conversation patterns
• Skip if: you need a roadmap or enterprise SLAs

6. Microsoft Agent Framework: who is it for?

Verdict: Pick this if you're already on Azure or you need C#/Java alongside Python.

Microsoft Agent Framework took 26 minutes and 84 LOC, the highest LOC in our benchmark. The trade-off: it's the only framework with first-class C#, Python, and Java SDKs, plus deep integration with Azure AI Foundry, Entra ID, and Microsoft Fabric.

Microsoft Agent Framework 1.0 reached general availability in Q1 2026 after merging the enterprise foundations of Semantic Kernel with AutoGen'sorchestration. It is one of only two frameworks in our test with full enterprise certifications and production SLAs from Microsoft.

• Best fit: regulated enterprises, .NET shops, Azure-first stacks
• Trade-off: highest setup time, deep Microsoft lock-in
• Skip if: you're a startup that wants to ship in a weekend

7. Mastra: is it the best TypeScript agent framework?

Verdict: For TypeScript teams building agent platforms (not just chat UIs), yes.

Mastra took 12 minutes and 42 LOC in our benchmark. Built by the team behind Gatsby, Mastra is a higher-level TypeScript framework with built-in RAG, memory, workflows, and a visual Agent Editor in Mastra Studio.

By March 2026, Mastra's model router lists 3,300+ models from 94 providers. Recent releases added background task processing (slow tool calls dispatched while the main loop streams), AI Gateway tools, and Redis-backed storage via @mastra/redis.

• Best fit: TS-first agent platforms, RAG-heavy apps, teams that want a Studio UI
• Trade-off: newer than LangGraph, smaller community
• Skip if: you're Python-only or need C#/Java

8. Pydantic AI: who is it for?

Verdict: Python teams who want type safety, dependency injection, and a FastAPI-style developer experience.

Pydantic AI took 14 minutes and 47 LOC. The framework is built by the Pydantic team and brings their validation DNA to agents: every agent input/output is a Pydantic model, and the dependency injection system gives you compile-time checking similar to FastAPI.

As of late April 2026, Pydantic AI is at v1.89.0 and 16.5k+ GitHub stars. It hit 1.x stable in late 2025. Durable Execution preserves agent progress across transient failures. It's model-agnostic across OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, and Perplexity.

• Best fit: typed Python codebases, teams that already use Pydantic + FastAPI
• Observability: best-in-class via Pydantic Logfire
• Skip if: you live in TypeScript or need a visual builder

9. Smolagents: is it production-ready?

Verdict: No. Smolagents is the prototyping winner, not the production winner.

Smolagents took 7 minutes and 18 LOC, the absolute fastest in our benchmark. Hugging Face's smolagents ships in roughly 1,000 lines of source code with minimal abstractions. Its core innovation is CodeAgent: the agent writes Python code as its action, executed in a sandbox (E2B, Modal, Docker, or Pyodide+Deno WebAssembly).

The catch: performance degrades sharply on smaller open-source models. GPT-5-class and Sonnet 4.5-class models produce solid code; below 7B parameters, bugs appear consistently. Observability is bare-bones (print logs, no traces).

• Best fit: rapid prototyping, hackathons, internal tools, code-execution agents
• Skip if: you need durable execution, replay, or guardrails

10. LlamaIndex Agents: is it still relevant?

Verdict: Yes -- but as the retrieval layer inside agent systems, not the orchestrator.

LlamaIndex Agents took 17 minutes and 52 LOC. The framework didn't disappear; it specialized. As Fordel Studios' State of AI Agent Frameworks 2026 notes, teams now use LlamaIndex to build the knowledge pipeline and LangGraph or Microsoft Agent Framework to orchestrate the agents that query it.

LlamaIndex Workflows brings event-driven programming to agent execution: each step emits events that trigger downstream steps, with concurrency and error propagation handled by the framework. The TrustedAgentWorker integration adds production guardrails.

• Best fit: RAG-first agents, multi-document research, knowledge management
• Skip if: retrieval is a sidecar, not the core of your agent

11. Vercel AI SDK: when should I pick it?

Verdict: Pick Vercel AI SDK when your agent IS the web app -- streaming chat, React/Next.js, generative UI.

Vercel AI SDK took 10 minutes and 36 LOC. Version 6 added a proper Agent abstraction with stopWhen controls, tool approval flows, full MCP support, and DevTools that let you inspect every step in the browser.

It's the default for TypeScript teams shipping AI features inside web apps: streaming, tool calling, and first-class React/Svelte/Vue/Angular hooks. Where Mastra is an agent platform, Vercel AI SDK is an agent primitive that fits inside any web stack.

• Best fit: AI-native web apps, generative UI, streaming chat with tools
• Skip if: your agent runs server-side as a long job or needs cross-language SDKs

12. OpenAgents: what makes it different?

Verdict: OpenAgents is the only framework we tested with native A2A (agent-to-agent) protocol support, designed for cross-framework networks.

OpenAgents took 24 minutes and 62 LOC, slow because the framework is built around networked agents, not single-process loops. It supports both MCP (for tool sharing) and A2A (for agent-to-agent communication), making it interoperable with agents written in CrewAI, LangGraph, or Microsoft Agent Framework.

OpenAgents provides a Workspace for human-agent collaboration, a Launcher for managing agents across platforms, and a Network SDK. As Anthropic, OpenAI, Microsoft, and Google all adopted MCP -- and the protocol was donated to the Linux Foundation in 2025 -- this kind of interop layer is becoming load-bearing.

• Best fit: multi-vendor agent ecosystems, agent marketplaces, cross-team networks
• Skip if: you're shipping a single agent inside a single product

13. MCP-Agent: who is it for?

Verdict: Python developers who want pure-MCP minimalism without Anthropic lock-in.

MCP-Agent took 13 minutes and 41 LOC. Built by LastMile AI, the framework implements simple workflow patterns from Anthropic's "Building Effective Agents" post -- prompt chaining, routing, parallelization, orchestrator-workers -- directly on top of MCP.

It's the most explicit "compose with MCP" framework: every tool is an MCP server, every workflow is a composition. Model-agnostic, open-source, no commercial layer. The same team also maintains openai-agents-mcp, an MCP extension for OpenAI Agents SDK.

• Best fit: Python teams committed to MCP, workflow composition, model freedom
• Skip if: you need a visual builder, hosted observability, or enterprise SLAs

Which AI agent framework should you actually use?

Use the table below as a decision shortcut. If two rows match you, pick the one with lower lock-in unless production scale forces the other.

If you're...	Use
Building an MCP-native agent on Claude	Claude Agent SDK
Building a complex stateful workflow	LangGraph
Building a role-based multi-agent crew	CrewAI
Building a voice agent on OpenAI	OpenAI Agents SDK
In a Microsoft/Azure enterprise	Microsoft Agent Framework
TypeScript team building an agent platform	Mastra
TypeScript team building a streaming web UI	Vercel AI SDK
Python team that lives in Pydantic + FastAPI	Pydantic AI
Prototyping in a weekend	Smolagents
Building RAG-first agents	LlamaIndex Agents
Building cross-framework agent networks	OpenAgents
Composing pure-MCP workflows	MCP-Agent
Doing academic multi-agent research	AG2

For design patterns that work across all 13 frameworks, see AI Agent Design Patterns. The framework is the runtime; the pattern is the program.

#	Framework	Language	TTFA (min)	LOC	Observability	MCP	Production	Lock-in	Verdict
1	Claude Agent SDK	Python / TS	8	24	4/5 built-in	Native	4/5	High (Claude only)	Fastest path to MCP-native agent
2	LangGraph	Python / TS	22	71	5/5 LangSmith	Plugin	5/5	Medium	Production king for complex graphs
3	CrewAI	Python	9	31	3/5 (AMP $99/mo)	Plugin	4/5	Medium	Easiest multi-agent DSL
4	OpenAI Agents SDK	Python / TS	11	38	3/5 Traces	Plugin	4/5	Medium (OpenAI)	Lightweight + best voice
5	AutoGen / AG2	Python	19	55	3/5	Plugin	3/5	Low	Research-grade, community-led
6	Microsoft Agent Framework	C# / Py / Java	26	84	5/5 Foundry	Native	5/5	High (Azure)	Enterprise + Azure default
7	Mastra	TypeScript	12	42	4/5 built-in	Native	4/5	Low	TypeScript agent platform winner
8	Pydantic AI	Python	14	47	4/5 Logfire	Native	4/5	Low	Type-safe Python, FastAPI feel
9	Smolagents	Python	7	18	2/5	Plugin	2/5	Low	Minimal code-agent prototyping
10	LlamaIndex Agents	Python / TS	17	52	3/5	Plugin	4/5	Low	Best when retrieval is central
11	Vercel AI SDK	TypeScript	10	36	4/5 DevTools	Native	4/5	Low	Default for streaming web UIs
12	OpenAgents	Python	24	62	3/5	Native + A2A	3/5	Low	Cross-framework agent networks
13	MCP-Agent	Python	13	41	3/5	Native	3/5	Low	Pure-MCP minimalism