provenant 0.1.3

Proof-aware codebase intelligence - smarter context, fewer tokens

The codebase intelligence layer for your AI coding agent.

Wiki indexing  ·  BM25 + HyDE retrieval  ·  Self-healing index  ·  Dead code  ·  Risk  ·  Git archaeology

Read the whitepaper →

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🏆 Performance

Evaluated on SWE-bench Verified — 500 real GitHub issues across 12 Python repositories.

Metric	Baseline	+ Provenant	Δ
File Coverage@5	~40%	63.8%	+24 pp
File Coverage@10 with HyDE	—	75.2%	—
Tokens vs naive file reading	baseline	—	−60–65×
Answer quality (LLM judge)	baseline	parity	−0.15 (noise)
Low-confidence queries healed	—	75%	after one repair cycle
Cost per repair cycle	—	~$0.02	—

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🔧 MCP Tools

Eight tools, usable from Claude Code, Cursor, Windsurf, Cline, and Copilot:

Tool	What it does
provenant_ask	Hybrid BM25 + HyDE retrieval → cited answer with confidence score
provenant_context	Triage cards for files, modules, symbols — purpose, API, freshness
provenant_search	Semantic search over wiki content
provenant_overview	Architecture summary, entry points, dependency structure
provenant_symbol	Source bytes for a specific function or class
provenant_dead_code	Unreachable code with confidence tiers and safe-to-delete flags
provenant_risk	Hotspot scores, change frequency, test coverage gaps, blast radius
provenant_why	Architectural decisions and git archaeology — why does this code exist?

provenant serve ./myrepo

{
  "mcpServers": {
    "provenant": {
      "command": "provenant",
      "args": ["serve", "/path/to/repo"]
    }
  }
}

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⚡ Quickstart

pip install provenant

provenant init ./myrepo        # index repo, generate wiki
provenant serve ./myrepo       # MCP server + web dashboard

provenant ask "how does auth work?" --repo ./myrepo
provenant costs ./myrepo

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🧠 What Provenant Builds

Provenant runs once, builds everything, then keeps it in sync.

◆ Documentation Intelligence

provenant init parses your repo with tree-sitter across 15+ languages, builds a symbol + import graph, and generates plain-English wiki pages for every file — purpose, public API, key functions, relationships. Stored locally in .provenant/. Nothing leaves your machine except the LLM calls used to generate summaries.

When an agent asks a question, Provenant retrieves wiki pages instead of raw source. Prose matches natural-language queries the way code cannot.

◆ Attribution Confidence & Self-Healing

Every response computes confidence = cited pages / retrieved pages. Low-confidence answers automatically trigger background wiki repair — non-blocking, no command needed. On a 1,393-page Django index, rewriting just 10 pages fixed 75% of low-confidence queries at ~$0.02 total.

◆ Graph Intelligence

tree-sitter parses every file into a two-tier dependency graph — file nodes and symbol nodes (functions, classes, methods). Heritage extraction covers extends, implements, mixins, and trait impls across 15 languages. PageRank + betweenness centrality identify your most central and most coupled code.

◆ Dead Code Analysis

Identifies unreachable functions, classes, and modules. Groups by confidence tier (definite / likely / possible). Flags safe-to-delete vs. dynamically-called code. Works across Python, TypeScript, Go, Rust, and more.

◆ Risk Scoring

Change frequency × dependency centrality × test coverage gaps → per-file risk score. Know what breaks before you touch it.

◆ Git Archaeology

provenant_why traces why code exists: git blame, commit history, and architectural decisions linked to the files your agent is editing.

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🗂️ Monorepo / Workspace Support

provenant init ./my-project
# Detected 3 repositories:
#   backend/     (Django)
#   frontend/    (React/TypeScript)
#   mobile/      (React Native)

Each sub-repo gets its own wiki. Cross-repo context is linked automatically — questions about the frontend surface relevant backend files and vice versa.

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🖥️ Web Dashboard

provenant serve ./myrepo   # MCP server + local web UI

Visualize the knowledge graph, wiki pages, dead code report, risk scores, and repair candidates in a local browser dashboard. No external services required.

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⚙️ Configuration

# LLM provider (pick one)
ANTHROPIC_API_KEY=...
OPENAI_API_KEY=...
DEEPSEEK_API_KEY=...

# Embedder — optional, enables vector search + HyDE
OPENAI_EMBEDDING_API_KEY=...
OPENAI_EMBEDDING_MODEL=nomic-embed-text-v1.5
OPENAI_EMBEDDING_BASE_URL=https://api.fireworks.ai/inference/v1

# Embedding tiers
provenant init ./myrepo --embedder local     # free, ~40 MB, no API key
provenant init ./myrepo --embedder openai    # 768-dim, best retrieval

Self-hostable. Zero telemetry. Bring your own keys — works with Anthropic, OpenAI, DeepSeek, Gemini, OpenRouter, or local Ollama.

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📄 License

MIT