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AgentTrace
Open-source circuit breaker for AI agent pipelines
Agent 1 hallucinates. Agent 2 builds on it. Agent 3 executes. All returned 200 OK. Nobody knew. AgentTrace is the open-source circuit breaker for AI agent pipelines: - 14 rules: hallucination, PII, prompt injection, rate limiting, OWASP LLM, EU AI Act - One agent blocked = downstream agents never run - Live dashboard, zero cloud, 100% local audit trail - Python + TypeScript full parity 2 lines to add. Works with LangChain, CrewAI, OpenAI. MIT licensed. 200+ tests.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Three months ago I was debugging a multi-agent AI pipeline. Agent 1 (researcher) returned bad data, a hallucinated number. Agent 2 (drafter) built a report on it. Agent 3 (executor) sent it to customers. Every single one returned 200 OK. Nobody in my logs caught it.
We have observability for infrastructure, but zero accountability for AI agents. That's the gap AgentTrace fills.
𝗪𝗵𝗮𝘁 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼𝗲𝘀:
→ Wraps any agent in 2 lines of code
→ 14 compliance rules run in parallel (PII, hallucination, prompt injection, EU AI Act...)
→ One agent violates a rule → entire pipeline stops, downstream agents never execute
→ Full audit trail with UUIDs you can look up months later
→ Live dashboard with risk distribution, pipeline monitor, violation details
It's not just logging. It's pre-mortem intervention.
𝗧𝗵𝗲 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 is worth mentioning. It works without calling another LLM. Extracts numeric values, normalizes units, checks against your RAG context. Agent says "8000mg" but your source says "2000mg" → blocked with 0.98 confidence.
𝗪𝗵𝘆 𝗠𝗜𝗧: I want every AI team to have this, not just the ones with $50k/year security budgets. The SDK, rules engine, and dashboard are free forever.
“Open-source circuit breaker for AI agent pipelines”
AgentTrace was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #140 on the daily leaderboard. Agent 1 hallucinates. Agent 2 builds on it. Agent 3 executes. All returned 200 OK. Nobody knew. AgentTrace is the open-source circuit breaker for AI agent pipelines: - 14 rules: hallucination, PII, prompt injection, rate limiting, OWASP LLM, EU AI Act - One agent blocked = downstream agents never run - Live dashboard, zero cloud, 100% local audit trail - Python + TypeScript full parity 2 lines to add. Works with LangChain, CrewAI, OpenAI. MIT licensed. 200+ tests.
On the analytics side, AgentTrace competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how AgentTrace performed against the three products that launched closest to it on the same day.
Who hunted AgentTrace?
AgentTrace was hunted by Kalash Poddar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AgentTrace including community comment highlights and product details, visit the product overview.
Kalash Poddar
Hey Product Hunt 👋
I'm Kalash, maker of AgentTrace.
𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Three months ago I was debugging a multi-agent AI pipeline. Agent 1 (researcher) returned bad data, a hallucinated number. Agent 2 (drafter) built a report on it. Agent 3 (executor) sent it to customers. Every single one returned 200 OK. Nobody in my logs caught it.
We have observability for infrastructure, but zero accountability for AI agents. That's the gap AgentTrace fills.
𝗪𝗵𝗮𝘁 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼𝗲𝘀:
→ Wraps any agent in 2 lines of code
→ 14 compliance rules run in parallel (PII, hallucination, prompt injection, EU AI Act...)
→ One agent violates a rule → entire pipeline stops, downstream agents never execute
→ Full audit trail with UUIDs you can look up months later
→ Live dashboard with risk distribution, pipeline monitor, violation details
It's not just logging. It's pre-mortem intervention.
𝗧𝗵𝗲 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 is worth mentioning. It works without calling another LLM. Extracts numeric values, normalizes units, checks against your RAG context. Agent says "8000mg" but your source says "2000mg" → blocked with 0.98 confidence.
𝗪𝗵𝘆 𝗠𝗜𝗧: I want every AI team to have this, not just the ones with $50k/year security budgets. The SDK, rules engine, and dashboard are free forever.
GitHub: github.com/kalash33/agenttrace
Kalash