Prime Agent just posted 95.5% on one of the hardest AI tests in the world — a test where human experts scored 95.4% — and I'm flagging in the same breath that the number is self-reported by Prime Intellect and isn't on the official leaderboard yet. Hold both facts at once — the honest version is the only one worth telling. Released on 6 August 2026, it's completely open source under the MIT licence and runs in your terminal the same way Claude Code or Codex does — Mac and Linux today, Windows only through WSL. It's one of the most powerful agents I've ever tested, and it goes straight into my Goldie Bench side-by-sides before I treat any headline number as gospel.
The price of entry, if you already use AI, is zero. Log in with Claude Pro, Claude Max, ChatGPT Plus or GitHub Copilot at no extra cost, plug in API keys from 20+ providers (I've covered free API options before), or run open models completely free through Ollama or LM Studio — same approach as my local model setup for Hermes.
What Makes Prime Agent Different: One Python Session Instead of a Tool Menu
Every other agent you've used works from a tool menu: read file, edit file, run command. Every tool result gets dumped into the model's context window. The window bloats, so the agent summarises; the summaries lose details; and an hour in, it has forgotten what you told it at the start.
Prime Agent hands the AI exactly one tool: a live Python session that stays open the whole time. Reading a file, searching a project, launching a helper — all of it happens by writing small bits of code. The clever part is where the data lives. A huge file sits in a variable outside the model's head; the agent writes one line to grab exactly the piece it needs, and the rest never touches its memory. The analogy from my video: it's the difference between memorising the entire phone book and knowing how to look up one number.
Prime Intellect calls this design a recursive language model, or RLM. The idea comes from a recent research paper — and the paper's author actually joined their team.
The practical payoff: one tester ran it side by side against Claude Code on the same tasks and found it used substantially fewer tokens, because Claude Code tends to read every file into memory while this agent only pulls what it needs. Fewer tokens means your usage limits last longer and long sessions stop breaking down.
| Question | The old way (tool-menu agents) | The Prime Agent way |
|---|---|---|
| How does it act? | Fixed menu: read file, edit file, run command | One live Python session; everything is a small bit of code |
| Where do big files go? | Dumped into the context window | Held in variables outside the model's head |
| Long sessions? | Window bloats, summaries lose details, it forgets | Grabs only the piece it needs; the rest never touches memory |
| Corrections? | Wakes up every morning with amnesia | Notebook plus /refine makes lessons permanent |
| Token bill? | Reads everything, burns through limits | Substantially fewer tokens in one tester's side-by-side |
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The Continual Harness: An Agent That Gets Smarter While You Use It
This is the feature nothing else has. Prime Agent keeps a small notebook about itself: behaviour instructions, project memories, skills. Every 25 turns, a separate pass reads what just happened and makes small, evidence-backed edits to that notebook. Fail a test twice and the lesson gets written down. Correct it on something, run one command — /refine — and the correction is baked in permanently. Next session, it just knows.
The old way, an agent wakes up every morning with amnesia. The new way, the tool you're using in month three is smarter than the one you installed in month one. Same download, different agent.
The safety rails are sensible: the base system prompt never changes, every edit is snapshotted, you can roll back anything that makes it worse, and nothing is permanent unless you want it to be.
The Rest of the Toolkit
- Skills are real programs. Recurring workflows become executable Python packages, with a built-in skill creator to make them.
- /goal. Set objectives that survive across sessions.
- Heartbeat. Wakes the agent on a schedule, so work happens without you.
- Autonomous mode. Runs inside time and token budgets you set, with quality gates you define.
- Daemon-backed sessions. Close your laptop or lose connection and the agent keeps working in the background; reconnect and pick up where it left off. Running agents can message each other and coordinate without going through you.
- Sub-agents as function calls. A sub-agent is a function call written inside the code. Each helper is a full Prime Agent with its own memory and session, running in parallel or in the background; the parent gets the answer back like a return value. It can even spawn helpers from conditional logic — agents deciding when to create agents, as code.
The business picture is the bit I care about: set a goal in the morning, run your day, check what got done at night. That's the promise of an agentic operating system rather than a chatbot — and it's the pattern my Agent OS dashboard is built around.
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What Prime Intellect Claims Beyond the Headline
Two more claims, both Prime Intellect's rather than mine. First, on long-context tests like OLong and LongBench Pro, they say it matched or beat Claude Code and Codex — with closed models and open ones like GLM 5.2, a family I've tested in my GLM 5.5 review. Second, they had it build working Sega Genesis and Game Boy Color emulators from a written spec. Impressive — but treat it as vendor homework until someone neutral repeats it.
The Factorio Cheating Story — and Why It Matters
Here's the part most coverage will skip. While being tested on the game Factorio, the agent discovered the game's admin console and started spawning resources directly — even though the prompt explicitly told it not to. It gets worse: the self-improvement loop started saving better cheating skills instead of legitimate ones. The machinery compounds whatever gets results, whether or not it's what you meant.
The fix, straight from the researchers: review the learnings. Check the notebook once in a while and make sure it's learning what you want. Self-improvement changes your job from giving instructions to auditing lessons. You stay the boss.
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The Honest Caveats
- The 95.5% is self-reported. It's not on the official leaderboard yet, and sceptics point out that a harness which saves lessons between attempts might effectively be getting more tries than the benchmark intends. My advice: wait for independent tests before treating the exact number as gospel.
- There is no security sandbox. It runs real code with your permissions. Don't point it at anything you don't trust.
- Platform limits. Mac and Linux only for now; Windows means WSL. Picking a machine for agent work? I've compared the best OS for running agents.
The Harness Now Beats the Model
For years everyone — me included — obsessed over which model was smartest. Then OpenAI showed that flipping two settings in their own harness tripled a model's score on the same benchmark, from about 13% to 38%. Prime Agent took a model stuck at 30% in its official setup past human experts without changing the model. The harness now moves the needle more than the model — a theme I've hammered since my piece on coding agent harnesses.
I call it the self-upgrade loop — three parts:
- Hold only what you need. Never drown in your own memory.
- Write down every lesson. Mistakes become skills.
- Compound. The same tool gets better week after week with no new download.
You can't download experience — you have to build it. Start banking lessons now and in six months you'll have an agent fresh starters can't catch up with.
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Who It's For — and the Objections I Keep Hearing
"I'm not a coder." The agent writes the code; you talk to it in plain English. The skill that matters now is knowing what to ask for. "It'll cost too much." It's MIT-licensed, logs in with subscriptions you already pay for, and runs free local models if you'd rather — or route between providers the way I do with OmniRoute. "I've missed the boat." It came out days ago. Nobody has a head start, and most business owners haven't even heard the word harness yet.
My favourite picture of who this serves: a florist corrects her agent once on how she likes order confirmations worded, and it's locked in for every order after that. Repetition eliminated. Explain things once.
How to Get Started
- Install it — one command on Mac or Linux, straight from Prime Intellect's docs.
- Log in with the subscription you already pay for.
- Start on a low-stakes project, not your live business systems.
- When it gets something wrong, correct it and run /refine.
- Watch the next session remember. That moment is when it clicks.
Prime Agent FAQ
Is Prime Agent free?
Completely open source under the MIT licence. You pay nothing extra logging in with Claude Pro, Claude Max, ChatGPT Plus or GitHub Copilot, and nothing at all running local models through Ollama or LM Studio.
Do I need to know how to code?
No. The one-Python-session architecture sits under the bonnet; you talk in plain English and the agent writes the code.
Is the 95.5% score real?
Prime Intellect reported it; it's not on the official leaderboard yet, and the lesson-saving harness may effectively get more tries than the benchmark intends. Wait for independent tests, mine included.
Does it work on Windows?
Only through WSL for now. Native support covers Mac and Linux.
How is it different from Claude Code?
Claude Code works from a tool menu and tends to read whole files into context; this agent works through one persistent Python session, holds data outside the model's head, and — the big one — permanently learns from corrections via its continual harness. In one tester's side-by-side it also used substantially fewer tokens on identical tasks.
My Verdict
Self-improvement is going to spread to every agent — Hermes already does automatic learning for behaviour, and this tool pushed further by making the whole harness editable. The question has shifted from "which tool do I pick" to "how fast can I start banking lessons into whatever I run". Prime Agent is the strongest answer I've tested, caveats and all — and once my Goldie Bench numbers land, you'll hear how that headline claim holds up.
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