A self-hosted AI agent that investigates Python production errors in a real codebase — not a sandbox, not a demo repo. Self-hosted. BYO-LLM. Under 4 minutes.
Under 4 minutes
From error to PR in minutes, not hours
Self-hosted
Your code stays in your infrastructure
Production-ready
Built for real codebases, not demos
Real fixes
Not just answers — actual PRs you can merge
proof
Same project — httpie/cli. Different inputs. All outcomes documented honestly, including the run that scored 20/100 and refused to produce a diff.
run 01
70/100
3 min 0 sec
run 02 — most important
20/100
3 min 53 sec
"a system that fails honestly is more valuable than one that hallucinates confidently"
run 03 — open github issue
70/100
3 min 42 sec
what this proves
The agent will not invent code that doesn't exist. Run 2 proves it — refused to produce a diff when the file wasn't in the index.
Indexing the right version matters more than any model choice. Same traceback went from 20/100 to merge-ready by changing one input.
English diagnosis was correct 3/3 times. Diff was correct 2/3. Even a wrong diff is a real triage signal worth reading.
Two independent AI passes checked each other's work. They disagreed in 2 of 3 runs. The second pass was right both times.
Scoring calibrated for honesty, not impressive numbers. No run cleared 70. The ceiling is honest. That's by design.
Cost scales with unique bugs, not log volume. 100 identical errors → 1 investigation. A noisy service costs the same as a quiet one.
how it works
No curl. No JSON by hand. No SSH. Paste a traceback into a form, watch an incident page populate.
Connect your log source
Point it at Loki, Sentry, Datadog, CloudWatch, a plain log file, or a webhook. No new infrastructure required. Your existing observability stack stays exactly as it is.
Loki · Sentry · Datadog · CloudWatch · webhooks · file tailFingerprint and deduplicate
Stack traces are parsed, fingerprinted, and deduplicated. 100 identical errors triggers one investigation, not 100. Cost scales with unique bugs, not log volume.
Sentry-style fingerprinting · dedup threshold configurableInvestigate your actual codebase
An LLM agent reads your real files, traces call graphs backward, checks recent commits, and runs semantic search across your indexed codebase. AST-level Python understanding — not just grep.
read_file · find_symbol · git_blame · vector_searchValidate the fix before it touches anything
Every proposed fix runs through: apply diff in memory → Tree-sitter parse → lint check → regression test generated → confidence scored. No diff ships without passing every gate.
Tree-sitter · Ruff lint · pytest regression test · confidence scoringDraft PR delivered
Root cause report + cited evidence + validated diff + regression test — delivered to GitHub as a draft PR, plus Slack and email. Routing based on confidence score.
GitHub PR · Slack · email · confidence-based routingsecurity
Built for engineering teams that won't — and shouldn't — send production code to a third-party server.
Your servers. Your cloud. Your firewall. No third-party server ever sees your codebase, your stack traces, or your incident data. Deploy with Docker or Helm.
OpenAI, Anthropic, Azure OpenAI, any OpenAI-compatible endpoint, or a fully air-gapped local model. The agent is model-agnostic. You control the cost.
Nothing sent home. No training on your data. No vendor lock-in on the AI layer. No usage metrics collected. Ever.
Connect any log source you already use.
roi calculator
Adjust the sliders to match your team. Numbers update live.
monthly debug cost
$3,000
tool cost(Variable)
$599
monthly saving
$2,401
payback period
< 1 week
honest limitations
Every other dev tool landing page hides limitations. This one doesn't.
best suited for
good to know first
20-minute walkthrough on a sample repo. No access needed.
Book a demo →