AI Agent Frameworks Compared

The analogy that makes it click

The rest of this library is about hiring an assistant. This guide is about building a staffing agency.

A ready-made agent is a car you drive. A framework is a garage full of parts and tools for building cars. If you just need to get to work, you don't want the garage. If you're going to sell cars, you do.

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Do you need one?

And the most useful sentence in the community research, from someone running about 30 agents for paying customers for six months (57 votes):

"LangChain, CrewAI, AutoGen, OpenAI Agents SDK. Pick whichever one your team already knows. It doesn't matter as much as you think."

Another (12 votes) titled their post "Picking an AI agent framework is the least important decision in your agent stack."

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The nine that matter

Star counts and install commands verified against each project's own repository and documentation on 21 September 2026.

⚠️ One to avoid for new work: AutoGen

AutoGen still shows 61,094 stars, so it tops a lot of lists. But its own README says:

"AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward. New users should start with Microsoft Agent Framework."

Its last update was in April 2026. Star counts measure past popularity, not current health. Always check when a project was last updated.

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Installing each one

The commands each project's own documentation gives.

# CrewAI
uv tool install crewai

# LangGraph
pip install -U langgraph

# Agno
uv add "agno[os,sqlite]" openai

# OpenAI Agents SDK
pip install openai-agents

# Mastra (JavaScript / TypeScript)
npm create mastra@latest

# Google ADK
pip install google-adk

# Pydantic AI
uv add pydantic-ai pydantic-ai-harness

# Microsoft Agent Framework
pip install agent-framework

# Claude Agent SDK
pip install claude-agent-sdk

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Which one for which job

A comparison post (37 votes) that built the same agent in nine frameworks reached a similar conclusion: LangGraph for state and resilience, the light SDKs for speed of getting started, CrewAI and Google ADK for coordinating multiple agents. A reply fairly asked for the test code, which wasn't shared, so treat its detailed numbers as one team's impression rather than a benchmark.

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⚠️ What actually kills agents

This is the section to read even if you skip the rest. From the operator running 30 production agents (57 votes), none of these are solved by picking a framework:

1 · The loop

"The agent gets stuck in a loop. It calls the same tool 200 times in 4 minutes because something downstream returned ambiguous data and the model decided to retry forever. Your bill goes from $3 a day to $400 in one afternoon."

Fix: a hard cap on retries and on spend, per agent, enforced outside the model.

2 · Lost state

"Your server reboots overnight for kernel patches. Every agent that was mid-task loses everything. Tomorrow morning the support agent has no memory of yesterday's tickets."

Fix: save state to a database as the agent works, so it can resume.

3 · No audit trail

"By the time you notice you've burned a grand. You can't even tell which agent did it because there's no audit trail."

Fix: log every tool call, with which agent made it and what it cost.

A fair counterpoint

Not everyone agreed. One reply (11 votes) called it "table stakes for an AI agent framework," meaning a good framework should handle these. Another (25 votes) put it simply: "Basic software engineering, right? Auditability, control, consistency. Testing the technology before letting it in the wild."

Both are right. Some frameworks help with these problems, but none of them solve them for you. You still have to switch the protections on and test them.

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Best practices

✅ Don't build if a ready-made agent does the job

✅ Pick the framework your team already knows

✅ Check when a project was last updated, not only its stars

✅ Hard caps on retries and spending, outside the model

✅ Save state as the agent works, so it survives a restart

✅ Log every tool call with the agent and the cost

✅ Test on real, messy data before real customers

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Mistakes to avoid

❌ Mistake 1: building when you should be buying

If it's an assistant for your own work, you don't need a framework.

❌ Mistake 2: choosing on star count

AutoGen has more stars than most of this list and isn't getting new features.

❌ Mistake 3: spending weeks choosing

❌ Mistake 4: shipping without spending limits

The $3-to-$400 afternoon is the most common production story in this research.

❌ Mistake 5: assuming the framework handles safety

It gives you the tools. You still have to use them.

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If you run a marketing or SEO agency

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Verdict

Frameworks: 🔵 Trial them only if you're building a product

For most readers of this library, the right number of frameworks is zero. The ready-made agents in the other guides do the job with none of the engineering.

If you are building, pick the one your team already knows, check it's still actively maintained, and spend your effort on the three things that actually kill agents: loops, lost state and missing audit trails.

This week: if you think you need a framework, write down the one job the agent must do. Then check whether any of the ready-made agents in this library already does it. Usually one does.

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Sources

Primary, checked 21 September 2026

• Each project's GitHub repository, for stars, licence, language and last update: CrewAI · LangGraph · Agno · OpenAI Agents SDK · Mastra · Google ADK · Pydantic AI · Microsoft Agent Framework · Claude Agent SDK · AutoGen
• Install commands from each README, plus the CrewAI installation docs and Agno quickstart
• AutoGen's maintenance-mode notice, from its README

Community reports, quoted with upvotes. Anecdotes, not measurements.

• After 6 months of running AI agents in production, the framework barely matters (57 votes)
• We tested LangGraph against 8 other frameworks (37 votes), detailed figures unverified
• Picking an AI agent framework is the least important decision (12 votes)

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Part of The Agent Guides by Massive Impact. Illustrations generated in-house. No third-party screenshots or marks reproduced.