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 agentguard47Observed package downloads, updated hourly. SDK lifetime counts use Pepy; SDK recent-month counts use Pypistats; MCP recent-month counts use npm. Downloads are a usage signal, not an adoption proof. Tracked copy is shown when a metric upstream is 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.
Observed package downloads, updated hourly. SDK lifetime counts use Pepy; SDK recent-month counts use Pypistats; MCP recent-month counts use npm. Downloads are a usage signal, not an adoption proof. Tracked copy is shown when a metric upstream is 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. Current shelf: 12 live from 12 public tools. Open what helps. Skip what does not.
$ pip install agentguard47Runtime guardrails for Python agents: budget, loop, timeout, and rate limits with MCP visibility for Claude Code, Cursor, and Codex.
$ pip install agentguard47Estimate GGUF VRAM fit, --n-gpu-layers 53 planning, and CPU offload risk from model, quant, GPU, and context presets.
Rank 18 local models across 6 workloads and 3 priorities for GPU fit.
Compare 9 GGUF quant levels by size, quality, speed, and 24GB GPU fit.
Remove the 5 free runs per tool per day limit. Saved GPU rigs, Hugging Face model import, fit alerts, and benchmark history for local LLM builders.
Describe an AI agent workflow. Get risk score, top risks, architecture, first guardrails, and next steps.
Agent Architect scopes AI agent builds into DIY / Startup / Growth / Enterprise tiers with top 3 risks, cost, timeline, and architecture output.
Watch two AI agents coordinate through 5 pipeline steps, 1 tool call, and 1 handoff.
Paste a URL. Get summary, topics, sentiment, and entities plus saved history.
4 AI personas reacting to your live podcast in real time.
Pay-per-call memory for agents. USDC on Base, no accounts.
Look up public Dota 2 profiles, recent matches, ranks, hero performance, and 10-player breakdowns via OpenDota.
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 run past budget, loop, timeout, and rate limits.
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.
My blog repair loop chewed on a stale draft for 23 mornings and reported "blocked" every time. The fix was not a smarter retry. It was a TTL and a heal path.
Scheduled tasks exit 0 even when the work never happened. Here is the outcome layer I built on top of my agent fleet, and why it shipped before any new dashboard.
Google found the first AI-built zero-day in a planned mass-exploitation event. A builder's read on what changes for small operators running agents.
AI billing is shifting from seats and tokens to outcomes. If you cannot tie an agent run to a dollar of work, you are paying for vibes.
What shipped, what broke, and what I learned while building a one-person AI tools company. Monday through Friday when there is something useful to send. Unsubscribe in one click.
How one human plus twenty-two AI agents runs a seven-pillar portfolio with no employees.