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Independent project, not affiliated with Anthropic
bmdhodl avatar

Agentguard47

bmdhodl/agent47
authSTDIOregistry active
Summary

Surfaces read-only telemetry from AgentGuard47, the Python SDK that kills runaway agent runs before they drain your budget. Connect this to Claude Desktop and you can query live traces, check budget health, review loop or retry alerts, and pull cost breakdowns without leaving the conversation. Useful when you're running Python agents that call tools, retry flaky APIs, or review code autonomously and you want visibility into guardrails that actually stopped execution. The MCP server exposes what the in-process guards already wrote to local JSONL traces, so you get incident reports and spend tracking through stdio without adding network calls or external dependencies. Pair it with the core SDK to turn agent forensics into a context you can chat with.

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AgentGuard

Stop runaway agents before they burn money.

Zero-dependency Python kill switch for AI agents. Hard budget caps. Loop detection. Local traces. MIT.

PyPI Downloads Python CI License: MIT

pip install agentguard47

Getting started

1. Install and verify

pip install agentguard47
agentguard doctor   # package ok?
agentguard demo     # offline proof (no API keys)

2. Guard an OpenAI client

from agentguard import BudgetGuard, LoopGuard, Tracer, patch_openai

budget = BudgetGuard(max_cost_usd=5.00, warn_at_pct=0.8)
loop = LoopGuard(max_repeats=3)
tracer = Tracer(service="my-agent", guards=[loop])

patch_openai(tracer, budget_guard=budget)
# every OpenAI call is now traced + budget-enforced

When spend crosses the hard limit, BudgetExceeded is raised and the run stops.

3. Cap a single task

Session budget can still have headroom. One goal can still be killed:

with budget.goal("refund", max_cost_usd=0.50, warn_at_pct=0.8) as g:
    g.attempt()
    budget.consume(cost_usd=0.12)
    # BudgetExceeded names the goal when it crosses

4. Read the local proof

agentguard report .agentguard/traces.jsonl
agentguard incident .agentguard/traces.jsonl

Or scaffold a starter file:

agentguard quickstart --framework raw --write
python agentguard_raw_quickstart.py

What it stops

ProblemGuardException
Spend blowupBudgetGuardBudgetExceeded
Same tool foreverLoopGuardLoopDetected
Fuzzy / A-B-A-B loopsFuzzyLoopGuardLoopDetected
Retry stormsRetryGuardRetryLimitExceeded
Hung runsTimeoutGuardTimeoutExceeded
Spam callsRateLimitGuard—
Wallet drain (x402/USDC)X402SpendGuardBudgetExceeded

Not a dashboard. Not a model router. An in-process exception that kills the bad run mid-flight.

Cap your agent's x402 wallet spend

Agents that pay per-call via x402 (USDC micropayments) can drain a wallet in a silent loop. X402SpendGuard wraps the payment step and refuses before paying:

from agentguard import X402SpendGuard

guard = X402SpendGuard(
    max_total_usd=5.00,        # wallet cap, add period="day" for a daily reset
    max_per_endpoint_usd=1.00, # cap per resource URL
    max_per_call_usd=0.10,     # refuse any single payment above this
)
guard.charge(0.001, "https://api.example.com/search", my_x402_pay_step)

AgentGuard meters and refuses; it never signs or settles. Amounts come from your x402 client. No crypto dependencies.

Features

  • Hard stops — exceptions inside your process, not after-the-fact alerts
  • Task-level budgets — BudgetGuard.goal(...) for sub-task caps + warn hooks
  • Local traces — JSONL by default; no network unless you opt in
  • Zero deps — stdlib only; Python 3.9+
  • Provider patches — patch_openai / patch_anthropic
  • Framework hooks — LangChain, LangGraph, CrewAI (optional extras)

Local by default

  • No API key required for local proof
  • No network unless you configure HttpSink
  • MIT licensed

The SDK is the free local proof path. Start local. Add hosted ingest later only if you want retained history, alerts, team visibility, spend trends, hosted decision history, or dashboard-managed remote kill signals. Local guards remain authoritative. HttpSink mirrors trace and decision events; it does not execute remote kill signals by itself.

Integrations

OpenAI · Anthropic · LangChain · LangGraph · CrewAI · raw agent loops

pip install "agentguard47[langchain]"   # optional extras as needed

Security

The base install declares zero runtime dependencies. pip install agentguard47 pulls nothing, so a default install adds no third-party exposure.

Extras pull real dependency trees. The [crewai] extra pulls chromadb, which carries PYSEC-2026-311: a pre-authentication remote code execution advisory with no fixed release available. Nothing in AgentGuard calls the affected endpoint, and installing the extra does not start a ChromaDB server. You are exposed only if you run a ChromaDB server reachable by untrusted callers. A 2026-08-28 pip-audit run also flags CVE-2026-45830, CVE-2026-45831, and CVE-2026-45833 against the same chromadb release, none with a fixed version. The [langchain], [langgraph], and [otel] extras resolve clean under pip-audit. See #702 for the full finding.

Docs

  • Getting started guide
  • Examples
  • MCP server — npx -y @agentguard47/mcp-server

Links

  • PyPI: https://pypi.org/project/agentguard47/
  • Issues: https://github.com/bmdhodl/agent47/issues
  • AgentGuard on the web (hosted history, alerts, and MCP visibility for Claude Code, Cursor, and Codex): https://bmdpat.com/tools/agentguard?utm_source=agentguard47&utm_medium=readme&utm_campaign=touchpoints

The hosted page is an optional next step, not a requirement. The SDK stays free, local, and MIT, and the local guards stay authoritative. Nothing in this package phones home. The only network egress is a sink or exporter you configure yourself, such as HttpSink or an OpenTelemetry exporter.


MIT · Built for people who ship agents and hate surprise bills.

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Configuration

AGENTGUARD_API_KEY*secret

AgentGuard read API key for querying traces, alerts, costs, usage, and savings.

AGENTGUARD_URL

Optional AgentGuard API base URL. Defaults to production.

Categories
AI & LLM Tools
Registryactive
Package@agentguard47/mcp-server
TransportSTDIO
AuthRequired
UpdatedMay 31, 2026
View on GitHub

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