● Open source · Self-contained · MIT License
Precise, grounded answers
about your codebase.
Compile your repository into a Datomic knowledge graph. AI agents query structured facts instead of scanning raw source. 1.3× more accurate across 9 repos in 7 languages, with a TF-IDF tier that delivers most of the lift at 3.7× better quality per input token.
Terminal
$ noum ask ./my-repo "Which files are the biggest risk hotspots?" # iteration 1: querying files-by-complexity (47 results) # iteration 2: querying commit frequency and authorship # iteration 3: cross-referencing co-change patterns Top 5 risk hotspots: src/api/middleware.ts 38 commits, 4 authors, very-complex src/core/parser.ts 47 commits, 2 authors, very-complex src/db/migrations.ts 31 commits, 6 authors, complex src/auth/session.ts 28 commits, 3 authors, complex src/cli/commands.ts 24 commits, 5 authors, complex
Five Stages, Then Queries
Each stage builds on the last. Read the full pipeline →
ImportGit history & filesdeterministic
→EnrichImport graphdeterministic
→AnalyzePer-file semanticsmicro / LLM
→SynthesizeComponents & archmacro / LLM
→EmbedVector search indexdeterministic
Measured on Real Codebases
40 questions per repo, 9 repos, 7 languages. See the full table →
1.3×
More Accurate
Without 47.7% → With 62.0% mean across 9 repos
3.7×
Better Per Token
TF-IDF tier vs full KG
9 repos
Seven Languages
Same 40 questions per repo
30 Seconds to Start
1
2
Run the Demo
noum demoPre-built knowledge graph. No LLM credentials needed.
3
Ask
noum ask noumenon "Which files are the biggest risk hotspots?"