On a Tuesday, the first enterprise question is usually not can you capture AI code? It s who can see the records, how long do they live, and what happens when a policy blocks a change?
That s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.
The useful thing here is not another dashboard. It s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.
I keep seeing AI governance tools start with visibility, then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.
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)
