The Three AI Agent Frameworks I Keep Coming Back To
I've spent the last few months running AI agents across different stacks — personal assistants, research pipelines, automated workflows. Three frameworks keep showing up in my work: OpenClaw, Hermes Agent, and CrewAI.
They're not competing for the same use case. Once you see what each one is actually built for, the choice becomes obvious.
OpenClaw — your own machine, your own rules
OpenClaw is the framework that went viral in early 2026. You install it as a self-hosted runtime on your own hardware. Everything — the LLM, the memory, the execution logs — stays local.
The architecture is clean: a Gateway handles the messaging (Discord, Telegram, WhatsApp, iMessage), and a Runtime runs the actual agent loop. They're separate processes, which means you can talk to the same agent from your laptop in the morning and your phone at night, and it remembers where you left off.
The loop it runs is a ReAct loop (Reason + Act) — the agent sees a message, thinks, calls a tool, gets a result, and repeats until the task is done. For a single-agent, single-user setup where you want full control and full privacy, this is the one.
What it doesn't do is orchestrate multiple agents working together. OpenClaw is one agent, your machine, your tools. If you need a team of agents handing tasks off to each other, look elsewhere.
When to reach for it: You want a persistent, private AI assistant that lives on your own server or VPS, connects to your messaging apps, and doesn't send your conversations anywhere.
Hermes Agent — the one that gets better over time
Hermes Agent, built by Nous Research, solves the problem OpenClaw users complain about most: the agent forgets everything when the session closes.
Instead of a stateless execution loop, Hermes runs a persistent learning loop. When it completes a task, it writes what worked into a reusable skill file — a Markdown document it can read back on the next similar task. Over time, it builds a model of who you are, what you prefer, and what approaches work for your specific problems.
It runs on a $5 VPS or a GPU cluster. The memory is persistent. The skills compound.
The gateway connects to Telegram, Discord, Slack, WhatsApp, Signal, Email — same as OpenClaw on the integration side. The difference is the learning loop underneath.
The trade-off: it requires more setup attention than a pure SaaS tool. Skills need to be maintained. The agent needs to be nudged toward the right behaviors. If you want something that just works out of the box, this will ask more of you.
When to reach for it: You want an agent that learns from every task you give it and gets noticeably better over months of use. You're comfortable maintaining a self-hosted runtime.
CrewAI — multi-agent orchestration for actual workflows
CrewAI is where you go when one agent isn't enough.
The model is role-based: you define Agents (a researcher, a writer, an editor), give each one a set of Tools, assign them Tasks, and kick off a Crew that hands work off between agents until the job is done.
The code to set up a research → write → edit pipeline is about 80 lines of Python. That's the fastest path from zero to a working multi-agent system I've found in any framework.
The production reality is more complex than the tutorial suggests. Costs grow fast if agents loop unbounded, and debugging eight agents collaborating is a different class of problem from debugging a single LLM call. You need guardrails: iteration limits, cost ceilings, explicit output formats so downstream agents can reliably consume upstream outputs.
Where it wins hardest is parallel task execution. If you have a list of 50 companies to research, you can run five research agents in parallel, each doing ten, and aggregate the results. That's not easily replicated in the other two frameworks.
When to reach for it: You have a defined workflow with clear roles and hand-offs. You're comfortable setting cost and iteration limits. You want the fastest path to a working multi-agent prototype.
Which one to pick
| | OpenClaw | Hermes Agent | CrewAI | |---|---|---|---| | Best for | Self-hosted personal assistant | Long-lived learning agent | Multi-agent workflows | | Memory | Session + SQLite | Persistent skills + memory | Per-agent short-term | | Multi-agent | No | No | Yes | | Learning loop | No | Yes | No | | Setup complexity | Medium | Medium-High | Low-Medium | | Runs on | Your own hardware/VPS | VPS or serverless | Any Python environment |
If I had to keep only one: Hermes Agent, because the learning loop is the only thing that makes an agent genuinely useful over time rather than just functionally capable for a single session.
If you're building a product or workflow that needs multiple agents coordinating: CrewAI.
If you want a private AI that lives on your own machine and never calls home: OpenClaw.
None of them are wrong. They're just solving different parts of the problem.
Field reports, not thought leadership. If something works, I'll show you the exact setup. If it doesn't, I'll tell you why.