I'm Patrick Hughes. I build small AI tools for developers shipping agents into the real world. AgentGuard is the flagship: budget caps, loop detection, timeouts, and kill switches before your agent burns money at 2 AM.
$ pip install agentguard47Stats update hourly from PyPI and GitHub. Downloads use a conservative floor when upstream totals are unavailable.
The bet: a useful software company can be one human, a stack of tools, and agents doing the boring work.
Most agent tools tell you what happened after the run. AgentGuard is the guardrail layer I wanted before letting agents work unattended.
Start with the Python SDK. Add MCP only when you want your coding agent to read traces and budget health. Use the hosted dashboard when local files are not enough.
Stats update hourly from PyPI and GitHub. Downloads use a conservative floor when upstream totals are unavailable.
from agentguard import BudgetGuard, JsonlFileSink, LoopGuard, Tracer
budget = BudgetGuard(max_cost_usd=5.00, max_calls=50)
loop = LoopGuard(max_repeats=3)
tracer = Tracer(
sink=JsonlFileSink(".agentguard/traces.jsonl"),
service="support-agent",
guards=[loop],
)
with tracer.trace("agent.run") as span:
budget.consume(calls=1, cost_usd=0.02)
loop.check("search", {"query": "refund policy"})
span.event("tool.call", data={"tool": "search"})
# Call your agent or tool here.Open source. Local-first. Raises inside the running process when a budget, loop, rate, retry, or timeout limit is crossed.
Read-only access for Claude Code, Cursor, Codex, and other MCP clients to traces, alerts, usage, costs, and budget health.
Use the hosted app for read keys, retained history, alerts, and shared visibility. Keep the first integration local if that is all you need.
Create read key ->Stop a run before an experiment turns into a surprise bill.
Kill repeated tool calls before retries become an operator problem.
Put a hard wall-clock ceiling around unattended agent work.
Keep background jobs from hammering tools while nobody is watching.
Stop retry storms instead of letting flaky tools spin forever.
Write JSONL traces and incident reports without sending data anywhere.
{
"mcpServers": {
"agentguard": {
"command": "npx",
"args": ["-y", "@agentguard47/mcp-server"],
"env": {
"AGENTGUARD_API_KEY": "ag_your_read_key_here"
}
}
}
}Use AgentGuard when you need runtime controls. The other tools are small utilities from the same workshop. Open what helps. Skip what does not.
$ pip install agentguard47Runtime guardrails for AI agents before they spend money unattended.
$ pip install agentguard47Describe an agent. Get risks, architecture, guardrails, and next steps.
Watch multi-agent coordination, handoffs, and tool calls in real time.
Paste a URL. Get a summary, key points, and sentiment in seconds.
Four AI personas that react to your live podcast in real time.
Persistent key-value memory for AI agents, paid per call with x402.
This is not a platform committee. It is Patrick building useful AI tools, using AgentGuard on the agent work that should not run loose.
AgentGuard is the product I reach for before an agent can spend, loop, or run unattended.
22 small agents handle repeatable work: checks, summaries, drafts, and repo chores.
Every useful lesson turns into docs, posts, examples, or another small tool.
Patrick keeps the judgment. The software handles the boring loops around it.
Short posts on agents, local models, cost control, and the parts of this stack that actually held up.
Reflex.dev measured a 45x token cost gap between computer-use agents and structured APIs for the same task. Here's why, and the decision rule that keeps your bill sane.
PocketOS lost their production database backups to a Cursor agent. Here's what runtime spend rails actually catch, what they don't, and the layered defense your agents need before production.
A 1,764-app audit found 7% had open Supabase databases and 15% of Bolt apps had hardcoded secrets. The fix takes ten minutes.
Uber torched its full 2026 AI tooling budget on Claude Code in four months. The pattern beneath it shows up in every team that ships coding agents without instrumentation.
How one human plus twenty-two AI agents runs a seven-pillar portfolio with no employees.