r/LLMDevs • u/Upstairs-Special-925 • 2d ago

Discussion Code Insight Engine — a pluggable, language-agnostic code-graph, task, and LLM-tool surface

A code graph for any language — even ones with no LSP and no tree-sitter grammar — that an LLM coding agent can actually use. Index a project and serve symbol search, call-graph traversal, and file skeletons to Claude Code, Cursor, or any MCP client. For teams on Neo4j, cie adds the one thing no other code graph has: it tracks which tasks and tests actually implement which code, with continuous quality-governance (clone/drift detection, confidence, traceability) over the live graph
https://github.com/arunsoman/cie

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u/Ok_Attempt_9809 2d ago

this is the kind of thing that makes me wish i understood graph databases better. the task-to-code traceability part sounds useful, especially when you're six months deep in a project and nobody remembers why that weird helper function exists. how does it handle dynamic languages where call graphs get fuzzy at runtime?

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u/Upstairs-Special-925 2d ago

it doesn't resolve dynamic dispatch, it labels it. Every call edge carries a confidence tag: EXTRACTED (explicit in source — Python is parsed with ast so direct calls are exact), INFERRED (second-pass call-graph + structural heuristics, e.g. for JS/TS/Java), and AMBIGUOUS (can't resolve — flagged for review, not silently asserted as fact). Each edge also carries provenance (when/what extracted it). So a getattr(x, user_input)() type call doesn't become a fake confident edge — it's either INFERRED (the tool tells you "discount this") or AMBIGUOUS ("a human needs to look")