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OpenInterpretability
Open-source toolkit to audit what your LLM knows
The first mech interp toolkit that runs inside Claude Code, Cursor, and Cline via MCP. Production probes (FabricationGuard, agent-probe-guard) catch hallucinations + agent failures. ProbeBench leaderboard, SAE training from 30-min free Colab to paper-grade. Apache-2.0.
When something breaks inside an LLM app — hallucination, silent agent failure, "works on prompt A but not on prompt B" — you usually have no way to see inside the model. Mech interp can answer those questions, but the tools have been research-only: H100s, deep domain knowledge, weeks of setup.
So I built the first mech interp MCP server. It plugs straight into Claude Code, Cursor, and Cline. Once installed, your AI assistant can call interpretability tools directly during a session — capture activations, look up SAE features, run probes, test causal interventions. No separate notebook, no context switch.
agent-probe-guard — detects silent coding-agent failures with Qwen 3.6 27b. ~18% budget cut at 86% accuracy.
→ pip install openinterp All Apache-2.0. What I'd love feedback on: - Which IDE workflow would you want this in next? - What LLM failure mode do you wish you could actually see into?
Happy to answer anything.
About OpenInterpretability on Product Hunt
“Open-source toolkit to audit what your LLM knows”
OpenInterpretability was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #75 on the daily leaderboard. The first mech interp toolkit that runs inside Claude Code, Cursor, and Cline via MCP. Production probes (FabricationGuard, agent-probe-guard) catch hallucinations + agent failures. ProbeBench leaderboard, SAE training from 30-min free Colab to paper-grade. Apache-2.0.
On the analytics side, OpenInterpretability competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how OpenInterpretability performed against the three products that launched closest to it on the same day.
Who hunted OpenInterpretability?
OpenInterpretability was hunted by Caio Vicentino. 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 OpenInterpretability including community comment highlights and product details, visit the product overview.
Caio Vicentino
Hey PH, Caio here, maker of OpenInterpretability.
When something breaks inside an LLM app — hallucination, silent agent failure, "works on prompt A but not on prompt B" — you usually have no way to see inside the model. Mech interp can answer those questions, but the tools have been research-only: H100s, deep domain knowledge, weeks of setup.
So I built the first mech interp MCP server. It plugs straight into Claude Code, Cursor, and Cline. Once installed, your AI assistant can call interpretability tools directly during a session — capture activations, look up SAE features, run probes, test causal interventions. No separate notebook, no context switch.
→ One-line install: openinterp.org/start
Two production probes ship with it today:
FabricationGuard — drop-in hallucination detector on Qwen3.6-27B. → openinterp.org/products/fabricationguard
agent-probe-guard — detects silent coding-agent failures with Qwen 3.6 27b. ~18% budget cut at 86% accuracy.
→ pip install openinterp All Apache-2.0. What I'd love feedback on: - Which IDE workflow would you want this in next? - What LLM failure mode do you wish you could actually see into?
Happy to answer anything.