Auto-Guard

A safety check on every tool call your AI agent makes.

Auto-Guard runs before an agent's tool call executes. It sends the proposed call and the user's request to Jev, TypeSafe's fast decision model, and gets back one of three verdicts:

Verdict	What happens
🟢 allow	The call runs normally.
🟡 escalate	You're asked to approve it, with a one-line reason written by an LLM.
🔴 block	The call never runs. The agent is told why and picks another approach.

A check takes about 0.4s and costs about $0.000025.

▶️ Demo: what it does · how it works

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Quick start (Claude Code)

1. Install

pip install agent-autoguard

This needs Python 3.9+. There are no other dependencies.

2. Add an API key. An OpenRouter key is the easiest option:

mkdir -p ~/.autoguard && echo 'OPENROUTER_API_KEY=sk-or-...' >> ~/.autoguard/env

3. Turn it on in your project. Add --user to turn it on for every project:

autoguard install

That's it. Claude Code now checks every Bash, Edit, Write, MultiEdit, NotebookEdit and WebFetch call before it runs.

To confirm it works:

autoguard check
# BLOCK      402ms  $0.000025  rm -rf /tmp/build && rm -rf ~/.aws
# ALLOW      402ms  $0.000025  ls -la build/

To watch decisions live, run autoguard console and open http://127.0.0.1:8787.

Auto-Guard only adds restrictions. When it allows a call, Claude Code's normal permission rules still apply, and it never auto-approves anything.

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Use it in any agent

from autoguard import guard

decision = guard("bash", "rm -rf ./build && rm -rf ~/.aws", task="clean up the build directory")

decision.action      # "block"
decision.reasons     # ["risk 3.8/4 is critical", "destructive (0.95) and outside the task (in-scope 0.18)"]
decision.rationale   # LLM-written reason (escalations only)
decision.latency_ms  # 419.1
decision.cost        # 0.0000252

Call guard(tool, args, task="", plan="") after your agent picks a tool and before it runs the tool. It works with any framework. args can be a string or a dict.

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How it decides

Each check is one Jev request that asks five questions at once:

Question	Answer type	Asks
destructive	probability	Does it irreversibly delete, overwrite or leak data?
in_scope	probability	Is it what the user asked for?
sensitive	probability	Does it touch credentials, secrets, production or payments?
action_class	choice	read · local-write · network · destructive · privileged
risk	0–4 scale	none · low · medium · high · critical

A small policy turns the answers into a verdict:

• Block when risk is critical, or when the call is destructive, out of scope and high-risk.
• Escalate when risk is high, the call touches sensitive systems, the action is privileged, or Jev isn't confident.
• Allow everything else.

If Jev can't be reached, the call is escalated. You can change that.

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Configuration

API keys. Auto-Guard checks environment variables first. It then reads .env.local or .env files (in the current folder or any parent) and ~/.autoguard/env.

Variable	Get one	Used for
OPENROUTER_API_KEY	openrouter.ai/keys	Jev checks + written escalation reasons
TYPESAFE_API_KEY	console.typesafe.ai (early access)	Jev checks, direct from TypeSafe

If both keys are set, OpenRouter is used. With only a TypeSafe key, escalations still work but come without a written reason.

Policy. To override any default, put a JSON file at .autoguard.json (per project) or ~/.autoguard/policy.json (global):

{
  "block_risk": 3.5,
  "escalate_risk": 2.5,
  "escalate_sensitive": 0.7,
  "on_error": "escalate",
  "escalation_model": "anthropic/claude-haiku-4.5",
  "spend_cap_usd": 2.5
}

All settings and their defaults are in autoguard/policy.py.

Log. Every decision is appended to ~/.autoguard/decisions.jsonl. To log somewhere else, set AUTOGUARD_LOG. Once the logged spend reaches spend_cap_usd, escalations stop calling the LLM.

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Commands

Command	Does
autoguard install [--user]	Adds the Claude Code hook. Your existing settings are kept, and running it twice is safe.
autoguard check	Runs two sample calls to verify your key
autoguard console	Opens the live decision dashboard
autoguard demo [block|escalate|all]	Runs the scripted demo calls through the real guard
autoguard eval	Scores the policy on the labeled test calls (run it from a clone of this repo)

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Results

Held-out test set of 20 calls, never used for tuning (evals/holdout.jsonl):

	Safe allowed	Dangerous caught	Ambiguous escalated	Median latency	Cost per check
Auto-Guard (Jev)	8/8	8/8	4/4	428ms	$0.000025
Claude Haiku 4.5 as the guard	8/8	8/8	3/4	823ms	$0.00012
Claude Sonnet 5 as the guard	7/8	5/8	1/4	1,992ms	$0.00032

On the 61-call tuning set, it allowed 26/26 safe calls and caught 25/25 dangerous ones. The thresholds were tuned on that set.

Latency was measured from a laptop via OpenRouter, with a new connection per call (the same as the Claude Code hook). Calling guard() repeatedly in one process reuses the connection, which brings the median to about 310–360ms.

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Limits

• It's a filter, not a sandbox. It will sometimes miss things. Keep backups, scoped credentials and sandboxing.
• "In scope" is its weakest signal. Jev sometimes misjudges multi-part requests. So scope only counts when the risk is also high.
• It can block things you asked for. "Delete my old AWS config" gets blocked because deleting credentials is high-risk. Run those commands yourself.
• Each check adds about 0.3–0.6s. That goes unnoticed next to an agent's own model calls, but it isn't free.

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Development

python3 -m unittest discover tests      # offline tests, no API calls
python3 -m autoguard eval               # re-score using cached Jev answers (free)

The demo videos are generated from recorded, real Jev responses. The same inputs always produce identical files:

cd demo && npm install && npm run video   # writes demo/out/*.mp4

autoguard/   guard(), policy, Jev client, Claude Code hook, CLI, live console
evals/       labeled test calls and cached Jev answers
tests/       unit tests
demo/        demo scenes, video capture (Playwright) and editing (Remotion)

Built on TypeSafe's Jev.

License

MIT. See LICENSE.