bgts-context-engine

Deterministic code-graph context for AI coding agents.

Ask "why does the login timeout fire on the meeting webhook?" and get the eight symbols
that actually answer it — ranked, budgeted, and reproducible.

Quick start · Use it from your agent · How it works · Supported models · Documentation · Türkçe

Why this exists

An agent working on an unfamiliar repository has to decide what to read before it can
decide what to change. The usual answer is embedding search over chunked files. It is cheap
to build and wrong in a specific way: it returns text that reads like the question rather
than code that participates in the behaviour. Ask about a login timeout and you get the
five files that mention timeouts, not the one function that sets it and the three callers
that break when you change it.

That information is structural, and it has an exact answer. handleLogin calls
refreshSession, which reads SESSION_TTL, which is written in exactly one place. That is
a graph walk.

BGTS Context Engine indexes your repositories into that graph — symbols, calls,
references, type hierarchies, HTTP routes, cross-language bridges — and answers questions
by walking it. Embeddings are used in one place only: finding entry points when the task
text names nothing recognisable. They never affect ranking.

The same task text, against the same commit, returns the same context pack. No model in
the retrieval path, no clock, no randomness. When an agent makes a bad change you can
replay exactly what it was told, find the stage that surfaced the wrong symbol, and fix
that stage.

The engine is published so people can run it. Organisations that want the same thing
inside their own perimeter — help with indexing, deployment, scoring tuned to their
repositories, or the agent stack around it — can engage
BGTS for consulting. Write to
[email protected].

Quick start

# 1. PostgreSQL 16 with Apache AGE + pgvector, in one database
docker compose -f deploy/docker-compose.yml up -d

# 2. Install and migrate
pip install bgts-context-engine
cp .env.example .env
bce migrate

# 3. Index something
bce index --repo /path/to/your/repo --name my-service

# 4. Ask
bce context --task "fix the login timeout in the meeting webhook"

Then serve it:

bce serve        # REST at :8000/docs, web UI at :8000/ui/
bce serve-mcp    # MCP over stdio, for agents (install the [mcp] extra; see below)

Use it from your agent

The MCP surface is behind the mcp extra (Python MCP SDK 1.x: mcp>=1.0,<2). Install it
so bce is on the user PATH, not only inside a project .venv. Cursor and VS Code
spawn bce serve-mcp themselves and do not activate the venv:

pip install "bgts-context-engine[mcp]"
bce --version   # must work in a new terminal, with no venv activated

Every MCP client uses the same stdio command plus the database in the environment. Do not
leave bce serve-mcp running in a terminal for the editor: stdout is the protocol, so the
process stays silent, and the IDE starts its own copy.

One command per editor. Run it in the project the agent works on (the repository you
indexed), pointing at the engine's .env:

bce --env-file /path/to/engine/.env cursor-init --repo-id my-service   # Cursor
bce --env-file /path/to/engine/.env claude-init --repo-id my-service   # Claude Code

cursor-init writes .cursor/mcp.json (merged into an existing one) and the rule
.cursor/rules/bgts-context-engine.mdc, which tells the agent to call
get_context_for_task first, treat its file:line entries as verified locations instead
of grepping for them, and not to edit a file just because it was listed. claude-init
writes .mcp.json, a marked section in CLAUDE.md, and a UserPromptSubmit hook
(bce precontext) that runs the graph once per prompt and hands the agent the answer
before its first turn — the flow the agent benchmark measured (−20 % tokens, half the search
output, same or better checks; 14 tasks on a React/TypeScript codebase, same model and machine).
Cursor's prompt hook cannot add context, so there the rule does that job. --no-hook,
--repo-id (repeatable) and --bce-command adjust the files; both commands are safe to rerun.

After you add or change the MCP config, restart Cursor or VS Code (or Command Palette
→ “Developer: Reload Window”). The server should then show as enabled with eight tools (ten with indexing enabled).
Setup detail: docs/mcp.md. The manual equivalents:

Cursor — user config ~/.cursor/mcp.json (applies to every project), or a project
.cursor/mcp.json that stays local (the directory is gitignored):

{
  "mcpServers": {
    "bgts-context-engine": {
      "command": "bce",
      "args": ["serve-mcp"],
      "env": { "BCE_DB_HOST": "localhost", "BCE_DB_NAME": "bce" }
    }
  }
}

VS Code — user MCP settings, or a project .vscode/mcp.json (also gitignored):

{
  "servers": {
    "bgts-context-engine": {
      "type": "stdio",
      "command": "bce",
      "args": ["serve-mcp"],
      "env": { "BCE_DB_HOST": "localhost", "BCE_DB_NAME": "bce" }
    }
  }
}

Claude Code — one command:

claude mcp add bgts-context-engine --env BCE_DB_HOST=localhost -- bce serve-mcp

Claude Desktop — same block as Cursor, in claude_desktop_config.json.

If command: "bce" stays disconnected, the editor cannot see bce on PATH. Install as
above, or skip a permanent install with uvx:

"command": "uvx",
"args": ["--from", "bgts-context-engine[mcp]", "bce", "serve-mcp"]

Then ask your agent something that needs the repository rather than the file you have open:
"what breaks if I change the session TTL?" The agent calls get_context_for_task, and the
other tools in docs/mcp.md let it drill from there — exact callers, the blast
radius of a change, the symbol behind a name — without guessing at file names.

What comes back

Not a list of file paths. A ranked pack, with the reasoning attached:

{
  "anchors": {
    "python::api::webhooks::handle_meeting_webhook#a3f1": ["explicit", "lexical"],
    "python::auth::session::refresh_session#88c2":        ["lexical", "semantic"]
  },
  "context": {
    "items": [
      { "symbol_id": "...refresh_session#88c2", "name": "refresh_session", "kind": "function",
        "file_id": "my-service:src/auth/session.py", "line": 41, "detail_level": "full",
        "graph_distance": 0, "score": 11.42, "tokens": 214, "content": "def refresh_session(...)" },
      { "symbol_id": "...SESSION_TTL#4b0d", "name": "SESSION_TTL", "kind": "constant",
        "file_id": "my-service:src/auth/config.py", "line": 12, "detail_level": "signature",
        "graph_distance": 2, "score": 6.10,  "tokens": 31,  "content": "SESSION_TTL: int" }
    ],
    "used_tokens": 1388, "budget": 1500, "included": 20, "skipped": 0
  },
  "coverage": {
    "anchor_source_count": 3, "connected_component_ratio": 0.875,
    "top_candidate_margin": 1.84, "orphan_ratio": 0.0,
    "touches_god_node": false, "commit_mismatch": false,
    "confidence": "high"
  }
}

Three things here that a vector store cannot give you:

anchors says why the engine looked where it did, and which independent sources
agreed. Three sources agreeing is usually right; one is a guess.

coverage is a trust report. confidence: "low" means the engine found something but
could not corroborate it — the moment for an agent to ask a follow-up question instead of
editing. commit_mismatch means the index is behind your working tree.

detail_level falls off with graph distance: the symbol you are changing arrives in
full, its neighbours as signatures, the outer ring as name @ file:line. That is how twenty
genuinely relevant symbols fit in 1500 tokens — short enough for an agent to carry on every
turn. Every item also names its file_id and line, so the agent opens the file instead of
searching for the symbol.

How it works

task text
   │
   ├─ anchors       four independent sources nominate entry points:
   │                explicit names, task history, full-text, vector
   ├─ expansion     fixed-shape graph walk: callers 2 hops, callees 1,
   │                references, type hierarchy, same-file siblings
   ├─ scoring       weighted sum over reference kind, task signal, centrality,
   │                distance, leaf penalty, edge provenance
   ├─ scope         drop repositories this caller may not see
   ├─ narrowing     keep the top N
   ├─ assembly      fit the token budget, cheaper detail further out
   └─ coverage      report how much of this is trustworthy

Callers reach two hops and callees only one, on purpose: when you change a function, what
breaks is upstream of it. Reference kind carries the heaviest weight, because a place that
writes a value is where the bug lives while a place that reads it is usually just
downstream. Centrality saturates at degree 20, because a logger touches everything and
explains nothing.

The full formula, every weight, and the confidence thresholds are in
docs/retrieval.md.

Features

• Code graph, not chunks. Symbols, CALLS, REFERENCES, INHERITS, IMPLEMENTS,
  IMPORTS, HTTP ROUTES_TO handlers, and WHY: comments bound to what they explain.
• Deterministic by construction. Sorted traversal, stable tiebreaks, versioned scoring
  weights. bce bench verifies it by running each case repeatedly and comparing output.
• Six languages. Python, JavaScript and TypeScript built in; Java, C# and Go behind the
  langs extra. Adding one touches two files.
• Cross-language call edges. React Native and Expo bridges connect
  NativeModules.Foo.bar() in TypeScript to bar in Objective-C, Swift or Kotlin — a hole
  no single parser can see.
• Edge provenance you can audit. scip from a real compiler index, treesitter from
  syntax, heuristic from a pattern match. Scored differently, reported per response.
• Incremental re-indexing. git diff decides what to re-parse. Symbol ids survive file
  moves and reformatting, so history and embeddings stay valid.
• One database. Apache AGE and pgvector in the same PostgreSQL, so one query joins a
  graph traversal, a vector search and a SQL filter — and one pg_dump backs up the index.
• MCP and REST from one implementation. A focused tool set over stdio, the same functions
  over HTTP. Nothing to drift.
• A UI that explains itself. /ui ships in the wheel and replays a real retrieval call
  stage by stage: anchors lighting up, expansion spreading, candidates scored and cut.
• Runs offline. The default embedding provider is deterministic arithmetic over token
  digests. No API key, no network, repeatable benchmarks. openai talks to any
  OpenAI-compatible /v1/embeddings server (vLLM, TEI, Ollama), so a model such as
  jina-code-embeddings-1.5b
  can run inside the perimeter.

Supported models

Embeddings only find entry points when the task text names nothing the graph already
knows. They never rank the answer. Out of the box that seed is hashing: deterministic
arithmetic, no API key, no network. For a real code model, set BCE_EMBEDDING_PROVIDER
and BCE_EMBEDDING_MODEL to one of these:

Voyage is a hosted API — pip install "bgts-context-engine[embed]" and
BCE_VOYAGE_API_KEY. Jina is the on-prem path: any server that speaks /v1/embeddings
(vLLM, TEI, Ollama). Switching the model or the dimension is a re-index
(bce migrate --reset-embeddings). The knobs are in
docs/deployment.md.

Coming next — same openai socket, not yet a fitted retrieval profile:

• jina-code-embeddings-0.5b
  — the smaller sibling of 1.5b, for hosts that cannot hold 1.5B parameters.
• Nomic Embed Code — an open 7B
  code retriever.

Where it fits

A language server is exact but scoped to what you have open. Embedding search is broad but
unaccountable. This sits between them: repository-wide and cross-language like the former,
exact and reproducible like the latter.

Measuring it

Retrieval quality claims are worthless without the task set they were measured on, so the
harness ships instead of a leaderboard. You give it your own tasks and the symbols you
believe answer them:

bce bench --cases my-tasks.json --out report.json

Each case is a task text plus its ground-truth symbol_ids. The report gives recall,
precision, precision@1 and MRR per case, median and p95 latency, and two pass/fail checks
that matter more than the scores: every case is run repeatedly and must return a
byte-identical ordering, and any case with a scoped principal must not surface a repository
that principal cannot read.

Building the case file is the real work — it means deciding, by hand, what the right answer
is. It is also the only honest way to know whether a change to the scoring weights helped.
The format and a worked example are in
docs/deployment.md.

Roadmap

Ordered by how often it comes up, not by difficulty:

• Scope enforcement on every layer. Layer 3 applies the per-user repository filter;
  Layers 1 and 2 do not. Until that closes, the API belongs behind a proxy — see
  SECURITY.md.
• Streamable HTTP transport for MCP. Today the MCP surface is stdio only, so the server
  runs next to the agent. Remote transport makes one index serve a team.
• More languages. Rust, Kotlin and PHP are the most requested. The provider interface is
  the contribution path with the least friction — see
  docs/languages.md.
• Wider SCIP ingestion. Compiler-grade edges beat syntax-derived ones and are scored as
  such; more toolchains means more of the graph carries scip provenance.
• A published benchmark corpus. An open task set over public repositories, so results
  are comparable between projects rather than only between your own runs.

Requests and disagreements belong in
issues — what people
actually ask for reorders this list.

Documentation

Contributing

Contributions are welcome — especially new languages, which is the contribution the
pipeline is most ready for.

Read CONTRIBUTING.md first. The one rule worth knowing up front:
determinism is the product. A change that makes the same task return different results
will not be merged without an explicit opt-in flag, and anything touching scoring or
ordering needs a test that pins the output.

pip install -e ".[dev,mcp]"
ruff check src tests scripts && pytest
cd web && npm ci && npm test

Security

The engine has no authentication of its own and expects to sit behind something that does.
Only the Layer-3 endpoints apply the per-user repository scope. Read
SECURITY.md before exposing a port, and report vulnerabilities privately
rather than in an issue.

License

MIT © BGTS.