We built LineageLens because teams were wasting reviewer time guessing where unfamiliar code came from. Archival logs are useful for audits, but reviewers need provenance in the flow of review: a prompt, a model, and a confidence score attached to the diff. Recent product work focused on small, high-leverage UX and correlation improvements drag/drop captures, click-to-insert in vscode extension so provenance is readable and actionable in minutes, not days. I'm curious: in your org, how do reviewers triage unfamiliar code today reproduce prompts, ask the committer, or revert and re-implement? What one capability would make provenance useful for your reviewers tomorrow?
People often think LineageLens is just about capturing AI-generated code, but the real value is what happens on Day 2.
The dashboard turns those intercepted edits into a searchable audit trail. Instead of blindly digging through `git blame`, you can search past AI edits, trace the prompt-to-code workflow, and instantly answer why a specific model wrote a function that way.
Yesterday we landed dynamic model routing in LineageLens: the proxy now classifies requests (simple / standard / complex) using deterministic rules and rewrites the model to a cost-appropriate upstream while recording the decision on every provenance record. The key tradeoff we made: no model fallbacks and no cross-provider routing in v1 to keep correlation and auditability intact. Curious how teams would like routing policies surfaced in CI/PR checks policy-as-code or dashboard toggles?
Most AI provenance tools focus on parsing model output, but the failure mode I keep seeing is simpler: one layer says one thing, another layer says something slightly different, and users lose trust fast. In LineageLens, that meant fixing things like fresh-install auth redirects, Lite-mode 403 handling, workspace shape mismatches, and swallowed ingest warnings. At that point the product problem is no longer can we capture AI code? but can every surface tell the same story? Do you treat contract drift as a product bug, or only a backend bug?
Thanks for the massive support on the last update! I ve been heads-down rewriting the core engine of LineageLens, and I'm stoked to share the v1.1.5 release.
When building an audit trail for AI-generated code, the default approach is to try and regex markdown blocks out of the LLM s text response. It is incredibly brittle. For this release, we ripped that out entirely.
The LineageLens proxy now natively parses the underlying structured protocols:
Anthropic s tool_use blocks (handling the streaming JSON assembly)
OpenAI s apply_patch DSL (via the newer Responses API)
Been deep in AI-assisted dev work and kept hitting the same wall: something breaks in AI-generated code and there's no way to answer "which prompt wrote this, which model, and what was the context?" Root cause analysis becomes guesswork.
Built LineageLens to fix that. It runs a local proxy on port 8788 that sits between your AI tools and their providers. Every time an AI writes ≥4 lines into your codebase, it captures the exact prompt, the model and tool that produced it (Cursor, Claude Code)
