AI Agent Frameworks Compared (2026)
/ Arvid Andersson
Most framework comparisons rank these tools by GitHub stars and feature checklists. That is the least useful way to choose. These frameworks are not interchangeable. They do different jobs, and the right pick depends on the shape of your problem. This post organizes the main frameworks by the job each one actually does. It is honest about where each one tends to break once you move past the demo. It reflects the landscape as of June 2026.
Looking for a side-by-side table with filters? See the Agents comparison and Frameworks & Stacks comparison.
Three jobs, not one category
"Agent framework" gets used loosely. The tools split into three distinct jobs. As of June 2026.
- Orchestration frameworks compose model calls, prompts, and tools in code, with you controlling the flow. LangChain, Vercel AI SDK, DSPy, Semantic Kernel.
- Agent frameworks add an autonomous loop: the model decides which tools to call and iterates toward a goal. LangGraph, CrewAI, Microsoft Agent Framework, Pydantic AI, Mastra.
- RAG frameworks specialize in retrieval over your own documents. LlamaIndex, Haystack.
Real applications often combine jobs: a RAG framework for retrieval, an agent framework for the actions.
The second filter is language. Most of these are Python-first. The TypeScript-native options are Vercel AI SDK, Mastra, and TanStack AI, which matters most when the agent shares a codebase with a web frontend.
Why "match the job" beats "most stars"
A document assistant over a legal corpus and a multi-step research crew are different problems. One is a retrieval problem. The other is an orchestration problem. Choosing by popularity leads teams to the biggest ecosystem rather than the right fit. They then spend weeks fighting a framework's opinions instead of solving their actual problem. The useful question is not "which is best." It is "which job am I doing, and which tool was built for it."
Orchestration: LangChain and LangGraph
LangChain has one of the largest ecosystems in the space. It carries a wide range of integrations, tutorials, and community answers. It is model-agnostic, so swapping providers is a small change. If a connector exists anywhere, LangChain likely has it. The cost is layers of abstraction. Simple things route through chains, agents, and executors. When something behaves unexpectedly, you often end up reading source to find which abstraction swallowed your error.
In 2026, LangChain for anything stateful means LangGraph. It models your application as a stateful graph with cycles, branching, checkpoints, and human approval steps. That is powerful, and it gives you a real state machine. The catch is that the execution model takes time to learn before you write business logic. Good fit when you need broad integrations, a complex flow with loops and approvals, and a team with time to learn it properly.
Where it breaks: the framework tax is front-loaded. You pay it once in graph design. After that you have explicit control over what state lives where and how it survives failures.
Fast prototyping: CrewAI
CrewAI has the simplest mental model here. You define roles (researcher, writer, editor), define tasks, assign roles to tasks, and run. The agents coordinate and produce output. For workflows that map cleanly to specialist roles in a roughly linear order, it is a fast path from idea to working prototype.
Where it breaks: the ceiling arrives when the workflow stops being linear. Conditional branching, dynamic task creation based on intermediate results, and looping back on bad output all push against the role-based model. Multi-agent conversation also adds token overhead. Several agents discussing one task means several model calls. The duct-tape phase tends to hit every time the workflow evolves.
Use CrewAI for prototypes, internal tools, and workflows that genuinely are linear.
Conversational agents: AutoGen and Microsoft Agent Framework
AutoGen came out of Microsoft Research. Its core idea is agents that communicate through conversation: they message each other, disagree, and arrive at conclusions through dialogue. That suits genuinely conversational problems, such as code review where two agents argue an approach, debate-style reasoning, and collaborative analysis. It also ships with sandboxed code execution.
Where it breaks: conversational freedom means less predictable output, and multi-turn agent dialogue burns through context. Constraining the flow to "stop debating and answer" undermines the flexibility you chose it for.
For new work in this lineage, Microsoft Agent Framework is the stated successor to both AutoGen and Semantic Kernel, built by the same teams. It pairs AutoGen's agent abstractions with Semantic Kernel's enterprise features (session state, middleware, telemetry). It adds graph-based workflows with type-safe routing and checkpointing, in both Python and .NET. If you are starting fresh in the Microsoft ecosystem, it is the current entry point. Migration guides from both predecessors are provided.
Retrieval first: LlamaIndex
LlamaIndex specializes in retrieval and treats agents as a secondary concern. That is a reasonable trade. Retrieval is a hard problem, and LlamaIndex puts most of its surface area there. Document ingestion, chunking strategies, hybrid search, query rewriting, and re-ranking are first-class here, where general-purpose frameworks tend to leave them thinner. If you are building a knowledge assistant over your own corpus (legal documents, internal wiki, product docs), it is a strong starting point.
Where it breaks: the agent layer feels added to a retrieval library rather than native. It works for search-and-answer. But when the agent needs to take actions beyond retrieval (create tickets, update records, escalate), the orchestration is thinner than a dedicated agent framework.
The common answer is to combine: LlamaIndex for retrieval, an agent framework for the actions. Haystack occupies similar ground if you want an alternative RAG-first pipeline.
Typed and lightweight options
Pydantic AI brings a type-safe, Pydantic-native approach to building agents. A common pattern is using it for individual agents while orchestrating them with a graph framework like LangGraph. Mastra is a TypeScript agent framework aimed at being quick to spin up and simple to reason about. Smolagents takes a deliberately minimal approach for code-writing agents. OpenAI Agents SDK and Google ADK are the first-party agent frameworks from OpenAI and Google.
Claude Agent SDK gives you the agent loop and built-in tools that power Claude Code, programmable in Python and TypeScript, with subagents, hooks, and MCP support. TanStack AI and Vercel AI SDK serve teams building on TypeScript with heavy UI integration. They lean toward the frontend, streaming, UI state, and structured outputs, an area the Python-first frameworks tend to leave to you. TanStack AI is pre-1.0, so expect change.
Comparison
| Framework | Job | Best for | Watch out for |
|---|---|---|---|
 LangGraph |
Agent | Complex flows with cycles, branching, approvals | Upfront learning curve before any business logic |
 LangChain |
Orchestration | Large integration ecosystem | Abstraction layers obscure errors |
 CrewAI |
Agent | Fast prototypes, role-based linear workflows | Low ceiling for branching; token overhead |
 AutoGen |
Agent | Conversational, debate-style problems | Unpredictable output; succeeded by MS Agent Framework |
| Microsoft Agent Framework | Agent | .NET/Python enterprise agents and workflows | Newer; tied to the Microsoft ecosystem |
 LlamaIndex |
RAG | Retrieval quality over a document corpus | Agent layer thinner than dedicated frameworks |
 Pydantic AI |
Agent | Type-safe Python agents | Often paired with a graph layer for orchestration |
 Mastra |
Agent | TypeScript agents, quick to start | Younger ecosystem than the Python incumbents |
 Vercel AI SDK |
Orchestration | TypeScript apps, frontend and streaming UI | Less server-side depth than Python frameworks |
 Claude Agent SDK |
Agent | Coding and tool-using agents on Claude | Anthropic-only; commercial terms, not OSS license |
How to choose
- Retrieval is the core problem: start with LlamaIndex (or Haystack), add an agent framework only if you need actions beyond search.
- Complex flow with branching and approvals: LangGraph, once you accept the upfront learning cost.
- Multi-agent working by Friday, linear workflow: CrewAI.
- Conversational or debate-style reasoning: the AutoGen lineage, now Microsoft Agent Framework for new work.
- Type-safe Python agents: Pydantic AI, often inside a graph orchestrator.
- TypeScript and UI-heavy: Vercel AI SDK, Mastra, or TanStack AI.
- Building on Claude specifically: Claude Agent SDK.
Most of these are open source or have free tiers, so the practical move is to build a small but real slice of your problem with two candidates before committing.
What no framework solves for you
All of these are libraries, not products. You write the code, handle deployment, and debug the failures. The framework gives you building blocks; it does not solve the hard problems. Hallucination, memory drift, cost control, permission boundaries, and recovering from a bad agent turn remain your responsibility no matter which one you pick. The framework decides how you structure those solutions, not whether you need them.
That is why framework choice matters less over time than architecture choice. Teams debate LangGraph versus CrewAI for a week. Then they spend the next year on state management, monitoring, retries, governance, and human escalation paths. Those problems exist regardless of which framework won the evaluation. Plan for them from the start. Pair your framework with observability and evaluation tooling so you can see what your agents are doing and catch regressions before users do.
See how frameworks fit into a full stack
A framework is one layer. These stacks show how to combine it with inference, retrieval, and observability.
Frequently asked questions
What is the difference between an orchestration framework and an agent framework?
An orchestration framework (LangChain, Vercel AI SDK, DSPy) gives you building blocks to compose model calls, prompts, and tools in code, with you controlling the flow. An agent framework (CrewAI, AutoGen, LangGraph, Pydantic AI) adds an autonomous loop where the model decides which tools to call and when, iterating until a goal is met. A RAG framework (LlamaIndex, Haystack) specializes in retrieval over your own documents. Many real applications combine two: a RAG framework for retrieval and an agent framework for the actions.
Is LangChain still worth using, or should I use LangGraph?
In practice LangChain now points teams toward LangGraph for anything stateful. The original chain API still exists and works for simple, linear pipelines, but branching, cycles, checkpoints, and human-in-the-loop steps live in LangGraph's stateful graph model. The trade-off is a steeper upfront learning curve in exchange for explicit control over state. If your workflow is a straight line, the lighter API is fine; if it has loops and approvals, LangGraph is the intended path.
Which agent framework is best for getting a prototype working fast?
CrewAI has the simplest mental model: define roles, define tasks, assign roles to tasks, run. Teams reach a working multi-agent prototype quickly when the workflow maps cleanly to specialist roles in a roughly linear order. The trade-off shows up later: conditional branching, dynamic task creation, and error recovery are where a role-based model starts to fight you, and multi-agent conversation adds token overhead. It is a fast path to a demo, less so to a complex production system.
What happened to AutoGen?
Microsoft has positioned Microsoft Agent Framework as the successor to both AutoGen and Semantic Kernel, built by the same teams. It combines AutoGen's agent abstractions with Semantic Kernel's enterprise features (session state, middleware, telemetry) and adds graph-based workflows, with migration guides from both. AutoGen's conversational, multi-agent-debate paradigm lives on in that lineage. New projects in the Microsoft ecosystem generally start with Agent Framework.
Can I use LlamaIndex and LangChain together?
Yes, and it is a common pattern. LlamaIndex is strong at the retrieval layer (ingestion, chunking, hybrid search, re-ranking), while LangGraph or another agent framework handles the orchestration and actions. Using LlamaIndex for retrieval inside an agent built with a different framework lets each do the job it is best at, rather than forcing one library to cover both.
Does picking the right framework solve hallucination and reliability problems?
No. Hallucination, memory management, cost control, security, permission boundaries, and recovery from bad agent turns are your responsibility regardless of framework. The framework determines how you structure those solutions, not whether you need them. This is why teams pair an agent framework with separate observability and evaluation tooling, the framework builds the agent, other layers keep it honest in production.
Browse all Agents and Frameworks & Stacks tools on Infrabase.ai
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