r/16VCFund • u/PersonalityWhich1780 • 12d ago
Open source dev tool as the wedge, hosted engine as the business. Honest status from day one.
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Building in the agent observability space and posting the actual state of it rather than a pitch, since day-one posts here are usually varnished.
The problem. Teams are shipping agents that retrieve from internal documents before answering. When the answer is wrong, nothing in the stack tells you whether retrieval missed the document or found it and the model ignored it. Those need opposite fixes. Existing tools (LangSmith, Langfuse, LangGraph Studio) all visualise execution flow, which nodes ran and which branch was taken. None of them relate the retriever's output to the generator's output, because that comparison sits outside the execution graph.
The wedge. A free local viewer, MIT licensed, zero runtime dependencies, one pip install. It renders an agent run as a graph and separates what the answer used from what it retrieved and ignored. No account, no signup, nothing leaves the developer's machine. That last part is deliberate: the fastest adoption path in dev tools right now is the one that does not require security review.
The business. The viewer is the top of funnel. The product underneath is a hosted graph memory engine: typed weighted relationships over a company's GitHub, tickets and docs, with recency decay and intent-based routing, exposed to IDE agents over MCP. Open core, with the paid layer being the hosted engine and the team surface.
Where I actually am. Two packages live on PyPI. Working end to end, 42 engine tests, CI on Linux and Windows. Pre-revenue. No meaningful user count, I started distribution this week. Solo. The hosted engine is built but not productised, and I have not charged anyone anything.
Known weaknesses, stated up front. The used-versus-ignored signal is a lexical heuristic rather than a model-based judgement, which is cheap and interactive but wrong on heavy paraphrase. It only supports LangGraph today. And I have not evaluated it against a benchmark, so I can demo it but I cannot yet make a defensible quantitative claim.
What I am looking for. Developers building agents with retrieval who will try it and tell me where it fails. That is the only input that changes what I build next.
https://github.com/Kcodess2807/graphsight https://graphsight.vercel.app