Local-first runtime guardrails

Stop runaway coding agents before they spend.

AgentGuard47 is a zero-dependency Python SDK that enforces budgets, loops, retries, and timeouts while your agent is still running. No account, API key, or dashboard required for the first proof.

pip install agentguard47
0SDK runtime dependencies
MITfree SDK license
1.2.13latest PyPI release
localfirst proof path
No-signup conversion path

Give the skeptical developer proof before asking for anything.

The homepage points non-signup users at a concrete local sequence they can copy into a clean Python environment and inspect without talking to us.

1
Verify install agentguard doctor
2
Trip a guard agentguard demo
3
Copy into an agent loop agentguard quickstart --framework raw --write
What it kills

Four failure modes, one small SDK.

Budget burn

Set call, token, or dollar ceilings before a coding agent turns a bad loop into a bill.

Tool loops

Stop repeated tool calls and repeated argument paths while the run is still live.

Retry storms

Cap retry attempts when a tool, provider, or repo step keeps failing the same way.

Timeout drift

Make long-running agent work fail clearly instead of hanging until the operator notices.

Positioning

Start local. Add hosted control only when the work becomes shared.

Free SDK

Best first action

Run `doctor`, `demo`, and a generated quickstart file locally.

No account, no dashboard, no network requirement.

Hosted dashboard

When it matters

Use retained incidents, alerts, team visibility, and remote kill.

Useful after a workflow has real runs worth operating.

Not generic observability

Boundary

AgentGuard47 focuses on runtime enforcement and incident control.

It is not prompt management, eval orchestration, or a trace warehouse.

Copy this shape

The real integration is small.

Plain Python

import agentguard

tracer = agentguard.init(
    service="repo-coder",
    budget_usd=5.00,
    trace_file="traces.jsonl",
    local_only=True,
)

with tracer.trace("agent.run") as span:
    span.event("tool.call", data={"tool": "edit"})

After local proof

Open the quickstart for OpenAI, Anthropic, LangChain, LangGraph, CrewAI, or raw Python. Then connect the hosted dashboard only when the trace needs to be retained or shared.

Low-friction close

Run the local proof. Keep the dashboard optional.

Developers who are not ready to sign up still leave with a working install path, a trace file, and a guard they can copy into a real coding-agent loop.