How to Choose Between AI Agent Frameworks in 2026

The Framework Question Every Builder Faces

You want to build an AI agent. You've got a use case, maybe a prototype in mind. Then you hit the wall: which framework do you actually use?

The ecosystem in 2026 is mature enough that there are real options — and messy enough that picking wrong costs you weeks. I've built agents with all of the major frameworks and shipped production systems with most of them. This guide is what I wish someone had handed me before I started.

No hype. No "it depends" cop-outs. Actual recommendations based on what you're building and who's building it.

The Contenders

Let's set the field. These are the frameworks worth evaluating in 2026:

• LangChain / LangGraph — The incumbent. Massive ecosystem, steep learning curve.
• CrewAI — Multi-agent focused. Role-based abstractions. Fast to prototype.
• AutoGen / AG2 — Microsoft-backed. Conversational agent patterns. Recently rebranded.
• Semantic Kernel — Microsoft's other bet. Enterprise-grade, .NET-first but has Python/Java SDKs.
• Roll your own — Direct API calls with your own orchestration loop. No framework at all.

Each has a different philosophy. That philosophy will either match how you think about agents or fight you every step of the way.

Decision Matrix

Here's the honest comparison. I'm rating each on a scale of 1–5 where 5 is best.

Numbers alone don't tell the full story. Let's break each one down.

LangChain / LangGraph

LangChain was the first framework most people touched, and it shows — both in its breadth and its baggage. The original chain-based API was a mess of abstractions. LangGraph, their graph-based agent runtime, is genuinely good and where you should focus if you go this route.

Pros

• Ecosystem is unmatched. Hundreds of integrations. Vector stores, retrievers, tools, output parsers — if you need a connector, it probably exists.
• LangGraph is well-designed. State machines for agent workflows make complex flows explicit and debuggable.
• LangSmith gives you production-grade tracing and evaluation out of the box.
• TypeScript SDK is a first-class citizen, not an afterthought.
• Active development. The team ships fast and responds to community feedback.

Cons

• Abstraction overload. There are three ways to do everything and the docs don't always tell you which one is current. You'll find yourself reading source code.
• Breaking changes. The API has stabilised significantly, but the velocity of changes over the past two years means a lot of tutorials and Stack Overflow answers are outdated.
• Over-engineering risk. LangChain makes it easy to build something complex when something simple would do. The framework nudges you toward more abstraction, not less.
• Bundle size. If you're running in constrained environments, the dependency tree is heavy.

Best for

Teams with Python or TypeScript experience who need a wide range of integrations and want strong observability. Good for complex, multi-step workflows where LangGraph's state machine model shines.

CrewAI

CrewAI took a different approach: instead of generic agent primitives, it models agents as team members with roles, goals, and backstories. You define a "crew" of agents that collaborate on tasks.

Pros

• Fastest time to prototype. Define roles, assign tasks, run. You can have a multi-agent system working in 20 minutes.
• Intuitive mental model. Thinking about agents as team members with specific roles is natural and easy to explain to non-technical stakeholders.
• Built-in delegation. Agents can hand off work to each other without you wiring up the plumbing.
• Good for content and research workflows. The role-based model fits naturally for things like "researcher → writer → editor" pipelines.

Cons

• Limited flexibility. Once you need to go outside the crew/task/agent model, you're fighting the framework. Custom tool integration can be clunky.
• Python only. No TypeScript support. If your stack is Node/TS, this isn't an option.
• Production gaps. Error handling, retry logic, and state persistence are less mature than LangChain or Semantic Kernel.
• Opinionated prompts. CrewAI injects its own system prompts, which can conflict with your instructions. You don't always get full control over what the model sees.
• Scaling concerns. Multi-agent conversations get token-expensive fast, and CrewAI doesn't give you great levers to control that.

Best for

Small teams prototyping multi-agent workflows, especially content pipelines and research automation. Great for demos and MVPs. Think carefully before taking it to production for high-stakes use cases.

AutoGen / AG2

AutoGen started as a Microsoft Research project and has evolved into AG2 with community governance. Its core idea is agents as conversational participants — they talk to each other to solve problems.

Pros

• Conversational patterns are powerful. For use cases where agents genuinely need to debate, critique, or iterate (code review, research synthesis), the model works beautifully.
• Human-in-the-loop is native. Adding human approval steps or interventions is straightforward.
• Code execution built in. Agents can write and execute code in sandboxed environments out of the box.
• Group chat patterns. Multiple agents in a shared conversation with configurable speaking order.

Cons

• Conversation overhead. The multi-turn conversational approach burns through tokens. A task that takes one LLM call in other frameworks might take five rounds of agent conversation in AutoGen.
• Debugging is painful. When agents are having freeform conversations, tracing why something went wrong means reading through multi-turn transcripts.
• AG2 transition confusion. The rebrand and governance change created a fork situation. Make sure you're looking at the right repo and docs.
• TypeScript support is limited. There's a TS SDK but it lags significantly behind Python.
• Less structured output. Getting agents to produce consistently formatted results requires more prompt engineering than frameworks with explicit output schemas.

Best for

Research-oriented workflows, code generation and review pipelines, and scenarios where agent deliberation genuinely improves outcomes. Not ideal for deterministic, high-throughput production tasks.

Semantic Kernel

Microsoft's enterprise play. Semantic Kernel treats AI capabilities as "plugins" that slot into conventional application architectures. It's the most "enterprise software" of the bunch.

Pros

• Production-first design. Dependency injection, logging, configuration management — it's built like enterprise software because it is.
• Azure integration. If you're in the Microsoft ecosystem, the integration with Azure OpenAI, Cosmos DB, and Azure AI Search is seamless.
• Planner architecture. The built-in planners (Handlebars, Stepwise) are solid for goal-decomposition tasks.
• Multi-language. C#, Python, and Java SDKs. The C# SDK is the most mature.
• Stable API. Microsoft moves slowly, but that means fewer breaking changes.

Cons

• Enterprise tax. The abstraction layers add complexity that small teams don't need. Setting up a simple agent requires more boilerplate than any other option.
• Community is smaller. Fewer tutorials, fewer examples, fewer Stack Overflow answers compared to LangChain.
• Innovation lag. New patterns and techniques show up in LangChain or CrewAI months before they land in Semantic Kernel.
• TypeScript is an afterthought. There's experimental support, but don't bet on it for production.
• Opinionated about Microsoft stack. You can use it without Azure, but you'll feel the gravitational pull.

Best for

Enterprise teams already in the Microsoft/Azure ecosystem. Organisations that need governance, compliance, and the kind of reliability guarantees that come with Microsoft backing. Not the best choice for indie hackers or startups moving fast.

Roll Your Own (Direct API Calls)

No framework. Just you, an LLM API, and a while loop. Don't laugh — this is a legitimate and often optimal choice.

The basic pattern

import openai

client = openai.OpenAI()
tools = [{"type": "function", "function": {"name": "search", "description": "Search the web", "parameters": {"type": "object", "properties": {"query": {"type": "string"}}}}}]

messages = [{"role": "system", "content": "You are a research assistant."}]
messages.append({"role": "user", "content": "Find the latest AI agent framework benchmarks"})

while True:
    response = client.chat.completions.create(model="gpt-4o", messages=messages, tools=tools)
    msg = response.choices[0].message
    messages.append(msg)
    if msg.tool_calls:
        for call in msg.tool_calls:
            result = execute_tool(call.function.name, call.function.arguments)
            messages.append({"role": "tool", "tool_call_id": call.id, "content": result})
    else:
        print(msg.content)
        break

Compare that with the same thing in LangGraph:

from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

@tool
def search(query: str) -> str:
    """Search the web"""
    return execute_search(query)

llm = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(llm, [search])
result = agent.invoke({"messages": [{"role": "user", "content": "Find the latest AI agent framework benchmarks"}]})

The LangGraph version is shorter, but the raw version has zero dependencies, zero abstraction layers, and zero surprises.

Pros

• Total control. You understand every line. No hidden prompts, no surprise behaviours, no framework bugs.
• Minimal dependencies. Just the LLM SDK. Easier to deploy, easier to maintain, easier to debug.
• Any language. Works in TypeScript, Python, Go, Rust — whatever you're comfortable with.
• No learning curve beyond the LLM API itself.
• Performance. No framework overhead. You can optimise every token and every API call.

Cons

• You build everything. Retry logic, error handling, state management, tool execution, conversation memory — all on you.
• No ecosystem. Every integration is custom. Need a vector store? Write the connector. Need tracing? Instrument it yourself.
• Reinventing wheels. You will solve problems that frameworks already solved. Some of those solutions will be worse than what exists.
• Harder to onboard team members. Custom code requires custom documentation. Frameworks at least have public docs and tutorials.

Best for

Simple, single-agent tasks. Teams with strong engineering fundamentals who value control over convenience. Prototypes where you need to understand exactly what's happening. Production systems where framework overhead or abstraction leakage is unacceptable.

When to NOT Use a Framework

This is the section most comparison articles skip. Here's when you should seriously consider going frameworkless:

1. Your agent does one thing. If it's a single LLM call with a few tools, a framework adds complexity without value. A 50-line script beats a 500-line framework setup.

2. You need deterministic behaviour. Frameworks introduce layers between you and the model. If you need exact control over every prompt, every retry, every token — go direct.

3. You're building a product, not an agent. If the LLM is one component of a larger application, wrapping your entire app in an agent framework is backwards. Call the API where you need it.

4. Your team doesn't know the framework. Learning a framework AND learning agent patterns simultaneously means you won't understand either well. Start raw, learn the patterns, then evaluate frameworks knowing what problems they solve.

5. You're optimising for cost. Frameworks often make extra LLM calls you don't see — for planning, for formatting, for routing. When every token counts, direct API calls let you control spend precisely.

Practical Recommendations

Stop overthinking. Here's what I'd pick based on common scenarios:

Solo builder, shipping fast

Start with no framework. Build the simplest thing that works. If you outgrow it, you'll know exactly what abstractions you need.

Small team (2-5), multi-agent workflows

CrewAI for prototyping, LangGraph for production. Use CrewAI to validate the approach, then rebuild the critical paths in LangGraph when you need reliability and observability.

Enterprise team, Azure stack

Semantic Kernel. It's built for you. The enterprise patterns are there, the Azure integrations are native, and your security team will have fewer objections.

TypeScript shop

LangChain.js or roll your own. LangChain has the best TS support of any framework. But if your needs are modest, the Vercel AI SDK or direct API calls will get you further with less friction.

Research and experimentation

AutoGen/AG2. The conversational patterns are genuinely interesting for exploratory work. Let agents argue with each other and see what emerges. Just don't ship it to production without hardening.

Production system with strict reliability requirements

LangGraph with LangSmith, or roll your own. LangGraph's state machine model makes failures explicit and recoverable. LangSmith gives you the observability you need. If you can't tolerate framework risk at all, go direct with thorough instrumentation.

The Real Answer

The best framework is the one that disappears. It should handle the boring parts — tool execution, conversation state, retries — and stay out of your way for everything else.

If you're spending more time debugging the framework than debugging your agent logic, you picked wrong. If you're writing workarounds for framework limitations, you picked wrong. If you can't explain to a new team member what the framework is doing, you picked wrong.

Start simple. Add complexity only when you feel the pain that complexity solves. Every abstraction layer you add is a layer you have to understand, maintain, and debug.

The agent framework landscape will keep evolving. What won't change is the underlying pattern: an LLM, some tools, and a loop. Understand that pattern deeply, and the framework choice becomes a matter of preference rather than survival.

Build something. Ship it. Refactor later. That's how good agent systems get built.