Veridge

The always-fresh, low-token map of a whole project — documents, code (down to the function/class), decisions and work sessions — unified in one typed graph, ranked by what matters, and served both to an AI assistant (as the minimal relevant context) and to a human.

Veridge fuses veridical (truthful, verified) with ridge — the crest line that runs through and connects a whole terrain. That's what it builds: the true, always-fresh backbone of how a project fits together — the structural through-line you and your assistant navigate by.

Why it exists

A project's knowledge lives in three places that drift apart: the code, the documents (designs, decisions, notes) and the history of how it got here. As it grows, keeping a mental model of how everything connects gets harder — and an AI assistant loses the thread between one session and the next, so every session restarts by re-reading and re-searching files. That is slow, incomplete, and burns tokens repeatedly.

Veridge builds the project's graph once and keeps it fresh, then answers questions about it in a few hundred tokens. It unifies documents, code (down to the symbol), decisions and sessions in one map, ranks it so an answer is the relevant slice rather than everything, and serves that slice within a token budget — to an AI assistant and to a human alike.

Related MCP server: ContextForge MCP Server

What makes it different

It is not a RAG system: no embeddings, no vector store, no LLM to build the graph. It maps structure and importance, which makes it complementary to RAG and purpose-built for one thing: the cheapest accurate context for orienting on a project.

Install

pip install -e .                      # from a clone
pip install -e ".[mcp]"               # + optional MCP server
pip install -e ".[treesitter]"        # + symbol-level JS/TS/Go/Rust/Java parsing

Requires Python 3.10+. Runtime dependencies of the core: none.

Quickstart

veridge build  .                 # index -> .veridge/graph.json (+ manifest)
veridge map    .                 # PageRank-ranked digest: areas, sizes, what matters
veridge focus  "<task>" .        # the signature query: minimal relevant subgraph, budgeted
veridge impact src/util.py .     # blast-radius: what a change here affects (ranked, budgeted)
veridge impact --diff .          # blast-radius of your current working changes (vs git HEAD)
veridge tour   .                 # dependency-ordered reading tour of the key files
veridge why    src/cli.py src/model.py .   # shortest typed path between two nodes
veridge find   greet .           # find nodes (files or symbols) by name/path
veridge neighbors src/util.py .  # a node and its typed connections
veridge view   .                 # write an offline, self-contained graph viewer (HTML)
veridge gate   .                 # anti-drift: broken refs, stale files, orphans
veridge watch  .                 # rebuild automatically when files change (poll loop)
veridge install-hook .           # git post-commit hook that keeps the map fresh
veridge stats  .                 # counts by node/edge type

veridge map also groups files by architectural layer (entrypoint / api / service / core / data / ui / util / tests / config / docs) — inferred heuristically, no LLM.

The signature query: focus

Give it a task, a file, or a symbol name and a token budget. It seeds a personalised PageRank on whatever the query matches, then admits the highest-ranked nodes until the budget is spent — returning exactly the context worth loading, and nothing else:

$ veridge focus "personalised pagerank ranking" . --budget 400
focus 'personalised pagerank ranking' · 24 nodes · ~391/400 tokens
  seeds: veridge/rank.py#pagerank, tests/test_rank_budget.py#test_personalised_pagerank...
  0.1487  veridge/rank.py#pagerank             [symbol, deg 8]
  0.0334  veridge/query.py#focus               [symbol, deg 10]
  0.0289  veridge/query.py#project_map         [symbol, deg 9]
  0.0243  veridge/budget.py                    [file,   deg 8]
  ...

Blast-radius: impact

"If I change this, what breaks — and what's the minimal context to review it safely?" That's reverse reachability over the call/import/reference graph, so Veridge answers it for free and exactly — no LLM call, no token cost. The affected set is ranked by a proximity-weighted PageRank and trimmed to a token budget, so even a hub with hundreds of dependents returns its most important ones:

$ veridge impact veridge/model.py . --budget 300
impact (dependents) of 'veridge/model.py' · 82 affected by
  showing 20 · ~290/300 tokens
  0.0256  d2  veridge/query.py#impact     [symbol]
  0.0239  d1  veridge/query.py            [file]
  0.0226  d1  veridge/cli.py              [file]
  ...

d1/d2 is the propagation distance. Use --diff to seed from your working changes (git diff --name-only HEAD) — "the blast-radius of what I'm about to commit" — or --deps to invert the question (what the seed relies on).

Same idea over MCP:

pip install -e ".[mcp]"
veridge-mcp .      # serves project_map / focus / impact / find / neighbors / health over stdio

Seeing it: the offline viewer

veridge view . writes a single self-contained .veridge/view.html — the graph data is inlined and the renderer is hand-written vanilla JS on a <canvas>, so there's no CDN, no bundled library and no server. Double-click it and it opens straight from disk, offline: nodes coloured by kind, a toggleable legend, search, drag/zoom, and a click-through detail panel.

veridge view .                       # the whole project (auto-backbones large graphs)
veridge view . --focus "auth flow"   # render only the relevant subgraph for a task

--focus renders exactly the slice an assistant would get from veridge focus — so a human can see the context the agent works from.

How it works

1. Index (read-only) — walk the project; classify each file; extract symbols, imports and calls — Python via the stdlib ast (zero-deps core), and JS/TS/Go/Rust/Java via the optional [treesitter] extra, both feeding one cross-language call graph; extract doc references (markdown links, [[wikilinks]], and plain path mentions in prose — the part generic tools miss); pull out decision ids (ADR-N/RFC-N/D-X-N); add git sessions. Everything lands in one typed graph with indexed adjacency, so queries are O(degree), not O(edges).

2. Rank — PageRank over the type-weighted, undirected graph; personalised PageRank for task-aware queries.

3. Serve — compact, contents-free rows, selected to fit a token budget. An assistant queries; a human reads the digest.

4. Stay fresh — a content-hash manifest diffs the tree, so drift is loud (gate) and can fix itself: veridge watch rebuilds on change, and veridge install-hook keeps the map current after every commit.

The graph never duplicates file contents; .veridge/ is derived and always regenerable.

Design principles (please keep these intact)

read-only · zero-deps core · low-token · ranked · deterministic. Determinism matters: nodes and edges are sorted on serialization, so graph.json is reproducible and diffs are clean.

Status & roadmap

Alpha. Working today: the unified graph (files + symbols + areas + decisions + sessions), multi-language symbols (Python in the core; JS/TS/Go/Rust/Java via the optional [treesitter] extra), the PageRank ranking, the token-budgeted focus query, veridge impact (deterministic blast-radius, incl. --diff mode), deterministic comprehension (map layers, veridge tour, veridge why), the offline graph viewer (veridge view, with --focus mode), the anti-drift gate with live freshness (veridge watch / install-hook), the CLI and the optional MCP server. The functional roadmap (Phases 0–5) is shipped; see ROADMAP.md for what's next.

Development

pip install -e ".[dev,mcp,treesitter]"
ruff check veridge tests
pytest -q

Contributions are welcome — see CONTRIBUTING.md and the design principles (read-only, zero-deps core, low-token, ranked, deterministic).

License

MIT.