intentos 0.15.0

Open-source flight recorder for AI agents — see exactly what your agent did, why it failed, and what it cost

Intent OS

Your AI agent is amazing. But every day, it starts over.

Give it a memory. Give it a life.

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pip install intentos

# What happened? What went wrong? What did it cost?
intent-os doctor

# Every step your agent took, every model it called.
intent-os inspect latest

# Who is this agent? What does it know? What has it learned?
intent-os agent get <id>

You get this:

[14:02:01] > START
[14:02:09] > MODEL CALL  claude-sonnet-4  (2,451 tokens)
[14:02:14] > TOOL        filesystem.write
[14:02:27] !! FAILED     test_jwt_verify failed

Goal:        refactor-auth-module
Agent:       claude-code
Duration:    14.3s
Cost:        $0.08
Tokens:      4,891

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AI agents have amnesia.

Claude Code spends 30 minutes solving your problem — researching, trying approaches, failing, learning from mistakes. You close the session, and everything it learned is gone. Tomorrow, it's a blank slate. The same struggles. The same dead ends. The same lessons it already learned.

This is the real bottleneck in AI today. Not capability. Amnesia.

Intent OS is the flight recorder — and the long-term memory — for your AI agents. It records everything they do, turns it into structured experience, and gives them an identity that persists across sessions.

Even better: that identity — role, traits, execution history, experience — can one day move with them across runtimes. From Claude to Codex to whatever comes next. Your agent doesn't have to start over when you switch tools.

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How it works

# Start the flight recorder
intent-os proxy start

# Point your agent at it — zero code changes
export OPENAI_BASE_URL=http://localhost:8377
export ANTHROPIC_BASE_URL=http://localhost:8377

# Use your agent normally. Every action is recorded.
claude "refactor the payment module"

# See what happened, what went wrong, and what it learned
intent-os doctor
intent-os inspect latest
intent-os cost

Works with Claude Code, Cursor, GitHub Copilot, or any agent that speaks OpenAI or Anthropic APIs. One environment variable. Nothing else. Everything runs locally. One SQLite file. No cloud. No account.

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What you get

Command	What it tells you
intent-os doctor	Health check: what happened, what went wrong, how to fix it
intent-os inspect latest	Full timeline: every model call, tool use, cost
intent-os cost	Spending: by agent, by model, daily trends
intent-os proxy start	Start recording — intercepts any OpenAI/Anthropic agent
intent-os proxy doctor	Proxy health: running status, traffic stats
intent-os agent create --name "X" --persona "..." --traits "..."	Create an agent with a role and personality
intent-os agent get <id>	Full person-card: role, traits, execution history, experience
intent-os experience list --agent <id>	What this agent learned from its past runs
intent-os scan	Security scan: dangerous tool calls in traces
intent-os audit report --format html	Compliance report: full audit trail

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Which of these have you felt?

• "It worked yesterday. Today it doesn't." — Same task, different session. The agent doesn't remember what it learned last time. No experience carries over.
• "I'm afraid to give it a big task." — The agent is capable, but the bigger the task, the more files it touches. You don't know what it changed.
• "Something went wrong. I have no idea what." — Agent ran for 30 minutes. Failed. No stack trace. No record of what happened.
• "Why is my API bill $300 this month?" — Which agent? Which task? Which model? You can't answer any of those questions.

Intent OS gives you the answer to all four — before you even ask.

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For teams

When multiple people use multiple agents, "who did what" becomes a business question:

• Accountability — intent-os cost --by agent — who's spending what, on which model
• Governance — intent-os security policy apply — define what agents can and can't do
• Compliance — intent-os audit report --format html — full execution record, any timeframe
• Identity — every agent gets an ID and a profile, every execution links back to its owner

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Why local-first?

Instead of...	Intent OS is...
Cloud-only tracing	Local-first. Your data never leaves your machine.
Siloed per-platform logs	Universal. Works with any OpenAI/Anthropic agent.
Just logging	Structured traces. One execution → many API calls → one timeline.
Postgres + Redis + S3	One SQLite file. No infrastructure needed.

No API key to sign up. No dashboard to log into. Your agent's execution data is yours — it lives in ~/.intent-os/intent.db.

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The bigger picture: an agent that grows with you

Intent OS is not just a tool. It is the first implementation of a portable execution contract for AI agents — the missing layer that lets an Agent be defined, executed, verified, and moved across any runtime.

The seven layers of Intent OS — Context, Identity, Execution, Verification, Governance, Interoperability, Experience — are the components of this contract. Together they answer the questions any organization must ask of an autonomous system: who was it, what was it supposed to do, what did it do, what evidence did it have, what did it learn, and who authorized it.

This is Agent Accountability infrastructure. The equivalent for autonomous AI of what audit trails are to finance and what version control is to software.

8 specs, all frozen. 26 event types. 6 adapters. One contract. Any runtime.

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License

AGPLv3 + Commercial Option. See LICENSE.

Open-source use is free under AGPLv3. Commercial use requires a commercial license.

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Your agent doesn't have to start over every day. Give it a life.