@rag-rat/bin

rag-rat

What a repository knows about itself. rag-rat is a local repo-intelligence index and MCP server for coding agents. It keeps source files read-only, writes only its own SQLite database, and answers with provenance on every result — current source, the code graph, git/GitHub history, and durable, source-anchored repo memories that persist across sessions and agents.

Every coding harness already has grep and file reads. rag-rat adds the layer they do not provide: source-anchored rationale. It connects the code an agent is about to touch to its callers, callees, tests, git/GitHub history, prior decisions, invariants, risks, and duplicate-code signals — and labels every result with confidence and coverage, so an agent can judge it instead of trusting it.

sequenceDiagram
    participant Repo as Repository
    participant Engine as rag-rat engine
    participant Agent as Coding agent

    Repo->>Engine: Source · git/GitHub · repo memories
    Engine->>Engine: Index → graph → (opt) SCIP oracle → reconcile
    Agent->>Engine: where / why / who-calls / impact?
    Engine-->>Agent: source + call paths + papertrail + memories (with provenance)
    Agent->>Engine: record a finding
    Engine->>Repo: persist a source-anchored repo memory

Why

• Provenance, not guesses. Every result carries a confidence label, coverage warnings, and the raw evidence — so a partial index or an ambiguous edge reads as exactly that.
• Repo memories. Typed, source-anchored notes (Invariant, Decision, Risk, …) that survive refactors and surface automatically during future queries — the signal grep can't give you. They are not assistant memory: they are versioned, local, source-anchored facts about this repository that any future agent retrieves with evidence.
• A real code graph. tree-sitter callers/callees/imports across Rust, TypeScript/TSX, Kotlin, C/C++, and Python — with an optional compiler-grade SCIP oracle that upgrades edges to Compiler confidence and ranks the load-bearing symbols.
• History as evidence. Git history, lazy chunk blame, and cached GitHub issue/PR/review rationale, all queryable.
• Rides your existing grep. A PreToolUse hook injects the memories and symbols behind whatever you just searched for.
• Flags clones as you write them. A PreToolUse hook on Write/Edit/MultiEdit fingerprints the functions you're writing and warns when they're exact or near-duplicates of code already in the repo — so an agent reuses instead of re-implementing. Read-only, and a silent no-op when the index isn't ready, so it never blocks a write.

Install

cargo install rag-rat              # from crates.io (FastEmbed included by default)

From a checkout:

cargo install --path crates/rag-rat-cli --bin rag-rat

Add --no-default-features for a smaller hash-only build without real embeddings. SQLite is bundled (compiled in via rusqlite), so there is no system-library prerequisite — see Platform support for the per-OS C-toolchain note.

Quickstart

From the repository you want to index:

cd /path/to/your/repo
rag-rat init

init scans the repo, prompts for languages and path bindings, writes rag-rat.toml, indexes, offers to install the local embedding model, and can install git hooks; on completion it prints the one command to connect your agent. Preview without writing anything with rag-rat init --dry-run; --yes runs the non-interactive defaults.

Install the agent skills. init wires up the index and MCP server; a pair of skills teaches your agent to actually reach for them. Install into whatever agents you have (Claude Code, Codex, Cursor, and 70+ others, detected automatically):

npx @rag-rat/skills

That installs using-rag-rat (reach for the MCP tools before grep; record durable findings as memories) and dream-review (triage the memory-maintenance worklist). See skills/README.md for update / list / remove and per-agent flags.

Manual setup and every config knob live in docs/config.md. For a large repo where the default local embedder is too slow, see Embedding backends.

Connect it to your agent (MCP)

The MCP server is STDIO — the client launches rag-rat as a child process. Registering it is one command, run from the repo directory so the server resolves that repo's rag-rat.toml and each repo gets its own index:

claude mcp add --scope project rag-rat -- rag-rat mcp     # Claude Code
codex  mcp add rag-rat -- rag-rat mcp                     # Codex

rag-rat init prints this command on completion — it does not register the server for you. Or add a project .mcp.json / equivalent:

{
  "mcpServers": {
    "rag-rat": { "command": "rag-rat", "args": ["mcp"] }
  }
}

Don't pin a single global server to one repo's config. A user-scoped server with a hardcoded --config /some/repo/rag-rat.toml serves that repo's index and memories everywhere — so browsing a different codebase loads the wrong context. Register the server per project and let it resolve rag-rat.toml from the repo it runs in. (--config <path> still exists for the rare case you need to point at a specific profile.)

Pass rag-rat mcp --json if your client must parse tool text as JSON (results are TOON by default). Full tool schemas: docs/mcp-tools.md.

Try it

Right after rag-rat init the code graph, symbols, git history, semantic search, and clone detection are ready — these answer on the first query. Repo memories start empty: they accrue as agents record findings with memory_create and then surface automatically in later answers. (GitHub issue/PR rationale needs a rag-rat github sync.)

Ask your MCP client:

• "Run impact_surface on the function I'm about to edit — its callers, callees, tests, and recent commits."
• "Where is config reload handled?" — hybrid semantic_search over source and docs.
• "What are the most load-bearing symbols in this repo?" — important_symbols.
• "Does this helper duplicate anything already in the codebase?" — find_clones (and the write-time hook warns as you write it).
• "Record an invariant on parse_config: reload must not allocate after the scheduler starts." — memory_create writes your first repo memory; it then rides along in future impact_surface / symbol_lookup results.

Or from the CLI:

rag-rat query "where is config reload handled?"
rag-rat important-symbols --limit 20
rag-rat brief --mode spine
rag-rat clusters --limit 10

The agent loop

The point isn't the tool catalog — it's the loop an agent runs around an edit, so it changes code with the callers, tests, rationale, and prior art in front of it instead of guessing:

• Before editing a symbol, ask impact_surface. One call returns the current source anchor, callers and callees, related tests, git/GitHub rationale, the repo memories bound to that symbol / path / call-path, and confidence + coverage warnings.
• Read the blast radius, then edit. The invariant a previous agent recorded, the caller three hops away, the test that pins the behavior — all surfaced before the change, not discovered after.
• The clone hook catches duplication at write time. If the new function reimplements code that already exists, the Write/Edit hook says so, with the existing symbol to reuse.
• Record what you learned. When the edit reveals a durable invariant, decision, or footgun, memory_create stores it as a source-anchored repo memory — so the next agent (or the next session) gets it in one call instead of re-deriving it.

A trimmed impact_surface answer (TOON — the default output; abbreviated here) — every field is evidence, not prose:

query:
  ref: "crates/config/src/config.rs::parse_config"
  resolution: syntactic
direct_semantic_callers[12]:
  - from_symbol: "crates/runtime/src/boot.rs::start"
    edge_kind: calls_name
    confidence: syntactic
    callsite:
      path: "crates/runtime/src/boot.rs"
      line: 88
    importance:
      label: local structural load
      score: 6.8
      bucket: high
tests_touching_symbol_path[4]:
  - path: "crates/config/src/config_tests.rs"
    reason: test_mentions_symbol_or_path
recent_commits_touching_symbol_path[1]:
  - evidence[1]: "a1b2c3d touched crates/config/src/config.rs: fix reload race during startup (#141)"
repo_memories:
  direct[2]:
    - kind: Invariant
      title: "Config reload must not allocate after the scheduler starts"
      confidence: high
      anchor_status: current
      binding_kind: symbol
    - kind: Decision
      title: "TOML over JSON5 for the config surface (#88)"
      anchor_status: current
      binding_kind: path
completeness_and_caveats:
  exact_graph_callers: 12
  memory_status:
    active: 2
    stale: 0
  caveats[1]: "Graph evidence is tree-sitter/syntactic, not compiler-grade name resolution."

And the write-time clone warning an agent sees before it duplicates logic — verbatim hook output:

▶ rag-rat clone check — code you're writing duplicates existing functions:
  • `normalize_path_for_lookup` (line 42) is ~91% similar to crates/index/src/paths.rs::canonicalize_lookup_path
Prefer reusing the existing function(s) over duplicating — impact_surface / symbol_lookup to inspect them.

The tools

rag-rat exposes 46 MCP tools — the full catalog with JSON schemas lives in docs/mcp-tools.md. The ones you'll reach for most:

• impact_surface — the coding preflight from the loop above: callers, callees, tests, git history, GitHub papertrail, and the repo memories crossing a symbol, in one call. Memories default to compact, scannable headers; pass full_memories: true for full bodies + bindings.
• semantic_search — hybrid BM25 + vector recall over source and docs, validated against current source. Every hit reports retrieval_mode; explain=true breaks down the score.
• symbol_lookup — exact/fuzzy symbol resolution; cfg/overload variants grouped as one logical symbol.
• find_callers / trace_callees — reverse/forward call-graph traversal (low-signal std/macro noise filtered by default).
• important_symbols — the load-bearing symbols by (SCIP-aware) PageRank, seeded from your current diff by default; see docs/oracle.md.
• find_clones — exact + near-miss duplicate functions ranked by refactor ROI (the candidate graph is precomputed in the background, so it scales to large repos).
• memory_create — record a source-anchored repo memory; dream surfaces the maintenance worklist that keeps them honest (below).

Beyond these: repo orientation (repo_brief, repo_clusters), git/GitHub rationale (commit_search, git_history_for_*, papertrail_for_*, rationale_search), the full memory graph (memory_search, memory_edges, memory_rebind, memory_doctor, …), graph-vs-compiler audit (compare_graph_to_scip), and index diagnostics (index_status, llm_status, heal_index) — all documented in docs/mcp-tools.md.

Repo memories

Repo memories are first-class local evidence — not chat memory, not cloud personalization. They are versioned, local, source-anchored facts about this repository. Each is typed (Invariant, Decision, RejectedAlternative, Risk, BugPattern, PerformanceNote, …) and source-anchored: bound to a logical symbol, concrete symbol, chunk, path+span, graph edge, call-path, commit, or GitHub ref. rag-rat tracks each anchor as current, relocated, stale, gone, or unverified, and surfaces matching memories through the memory_* tools and inline in read_chunk, symbol_lookup, find_callers, trace_callees, and impact_surface. They're how hard-won context reaches the next agent in one call instead of evaporating.

Memories are also a typed graph, not just a flat list: memory_edge_add / memory_edges connect them with relations (depends_on, relates_to, supersedes, derived_from, tracks) — a task DAG, a mind-map link between decisions, or a task that tracks a GitHub issue. Full tool list: docs/mcp-tools.md.

Self-maintaining memories

Memories rot: the code moves under them, an invariant gets superseded, a load-bearing function ships with no memory at all. dream is the maintenance loop that keeps the layer honest. It recomputes a ranked worklist of findings about the memories themselves — each with a stable id to review:

• coverage gaps — load-bearing symbols (by the same PageRank as important_symbols) that carry no memory, so the next agent editing them gets nothing.
• stale references — a memory citing a path or anchor that no longer resolves.

dream runs the deterministic findings on every call. Two opt-in model passes go deeper, running a small model on an ephemeral remote GPU ([llm.dream.remote]) only when work is pending: rag-rat dream --verify recomputes each memory's verdict against current source reality (has the code drifted from what the memory claims?), and --compact rewrites a verbose memory to a tighter summary. Findings those passes persist surface back through dream.

Nothing is deleted automatically. A human — or a strong agent over MCP — burns the worklist down with dream_review (accept a real gap, dismiss noise, reset a prior verdict), and verdicts survive future runs so settled findings don't come back. It's the same surface as the CLI rag-rat dream / rag-rat dream <id> --accept|--dismiss|--reset.

Compiler-grade resolution & ranking

The graph is heuristic by default. The opt-in SCIP oracle (rag-rat oracle run) upgrades edges to a Compiler tier from a real language tool, recovers calls tree-sitter missed, flags external edges, and makes important_symbols surface the genuine god-modules. For C/C++ the scip-clang oracle distinguishes declarations from definitions and sharpens call/type edges in macro-heavy or multi-target code — the difference between usable and noisy graphs on firmware, kernels, drivers, and SDKs. Turn on [oracle] auto_run and the MCP server keeps it fresh on its own (throttled, watcher-safe). Full details: docs/oracle.md.

Freshness

rag-rat mcp runs a background file watcher (on by default; [watch] enabled = false or RAG_RAT_NO_WATCH=1 to disable), so graph/symbol queries reflect uncommitted edits without a commit. Indexed rows are git-context-aware: clean files are stored by commit_sha, dirty/untracked files in a worktree overlay, so one database reuses rows across branch switches while reflecting local edits. Optional git hooks (rag-rat hooks install) keep the index current on checkout/merge/rewrite/commit. read_chunk and search validate hits against current source and heal stale entries before returning.

One watcher per worktree and one writer at a time are enforced with file locks (unreliable on NFS / WSL2 /mnt mounts).

By default every repo's index and memories live in one consolidated database per machine ($XDG_DATA_HOME/rag-rat/rag-rat.sqlite; override with RAG_RAT_DATA_DIR), so a deleted checkout or git clean -fdx no longer loses your authored memories. Set an explicit [index] database to keep a repo on its own file (deprecated), and run rag-rat consolidate to import a pre-existing .rag-rat/index.sqlite into the global store — see docs/config.md.

Output format

The CLI and MCP results default to TOON (Token-Oriented Object Notation) — a token-efficient encoding that renders uniform rows as a dense [N]{cols}: table (~30% smaller than compact JSON on those payloads, never larger in practice). Pass --json (CLI, either position) or launch rag-rat mcp --json (MCP) when a JSON parser must read the output.

Embedding backends

The default local embedder (FastEmbed) needs no setup, but a large repo or a stronger model is worth offloading. rag-rat speaks the OpenAI-compatible /v1/embeddings API, so a [llm.embedding.remote] block can serve embeddings from Ollama, vLLM, or michaelfeil/infinity — one client, one place to audit and secure. Two modes:

• Connect to a server you already run (set endpoint).
• Ephemeral — let the bundled cookbook provision a GPU worker (Modal / RunPod) just for the backfill and tear it down afterward (set cookbook); pick the backend and GPU class in config.

The init flow warns when a short-context model would truncate long code chunks and steers you to a long-context code embedder, and rag-rat auto-tunes the client concurrency against the chosen backend so the sweep finds its throughput knee. Setup and every knob: docs/config.md.

Retrieval quality

Search quality is measurable, not guesswork. rag-rat ships a commit-replay evaluation harness (rag-rat eval --replay): each recent commit becomes a case — its message is the query, the files it touched are the gold set — and search is scored on how well it recovers them. It reports recall@3 (did the right chunk land in the first three reads?), recall@10, and MRR@10, and CI tracks the trend on Bencher on main so a regression is caught before it ships.

Reach for it when comparing embedding models, changing chunking, enabling int8 vector storage (smaller on disk), or tuning a remote backend — you can prove the change didn't cost recall instead of hoping. (rag-rat eval requires a --features eval build; it is absent from the released binary.)

Benchmarks

The headline workload is indexing the whole Linux kernel (v7.0, ~63k C/H files, 9.14M graph edges). Full numbers — wall-clock, throughput, peak RSS, on-disk size, unresolved-edge taxonomy — are in docs/benchmarks.md. Performance is tracked per-push and gated per-PR; the live history is at bencher.dev/perf/rag-rat/plots (wiring: docs/bencher.md).

Security

The MCP server exposes read-only source tools. It never executes shell commands or writes your source files. It writes only the configured SQLite index — during indexing, migration, maintenance, reconciliation, repo-memory operations, and automatic stale-index healing. GitHub sync is explicit and uses gh api; normal query tools read only the local cache.

Local vs remote embedding

With the default local embedder, nothing leaves the machine — indexing and querying are entirely local. Configuring a [llm.embedding.remote] backend is what sends text off the box, in two places: the chunk text selected at index time, and the query text of each semantic search (a search embeds your query to compare it against the indexed vectors). A CONNECT backend embeds both against the configured endpoint; an ephemeral backend embeds queries against the local query_endpoint.

What the endpoint is decides how much that matters:

• Your own server (self-hosted Ollama / vLLM / infinity) — the text stays in infrastructure you control.
• Ephemeral Modal / RunPod workers (the cookbook path) are ephemeral compute providers running your open-source embedder, not data services that train on inputs. Both are SOC 2 Type II, encrypt in transit and at rest, isolate tenants, and tear the box and its storage down after the backfill — a data-processor relationship, reasonable for proprietary code the same way a cloud VM is.
• A third-party embedding API you don't control is the one to actually read the terms on (retention, training on inputs).

Sensible hygiene regardless of backend: exclude secrets, generated files, and vendor trees from the indexed targets so they're never chunked or embedded, and keep secrets out of query text. Details: docs/config.md.

Platform support

rag-rat builds and tests on Linux, macOS, and Windows. Linux is covered on every PR and on every push to main; macOS and Windows are exercised on release, so cargo install rag-rat builds and links on all three. SQLite is bundled (compiled from source via rusqlite), so there's no system-library prerequisite, but each platform needs a C toolchain: Linux ships one; on macOS install the Xcode Command Line Tools (xcode-select --install); on Windows install the Visual Studio Build Tools with the C++ workload (MSVC). Requires Rust 1.95+ (the bundled SQLite build uses the cfg_select! macro, stabilized in 1.95).

A few maintenance conveniences are Unix- or Linux-only by design and degrade quietly elsewhere — no feature of the index, query, or MCP surface is affected:

• Hot-upgrade of a running MCP server (the SIGUSR1 in-place re-exec) is Unix-only. On Windows, restart rag-rat mcp to pick up a new binary.
• Fleet auto-upgrade (signalling other running servers when a new binary lands) is Linux-only — it walks /proc — and is a no-op elsewhere.
• The grep-augmentation hook uses a warm Unix-socket listener (with per-session dedupe) on Linux and macOS; on Windows it falls back to a per-call read-only query straight against the index, which works the same but without cross-call dedupe.

Commands

rag-rat init                       # guided first-run setup
rag-rat index [--changed|--discover|--full]
rag-rat doctor
rag-rat query "semantic recall"    # add --json for JSON
rag-rat important-symbols --limit 20
rag-rat brief --mode spine|churn|god_modules|refactor_candidates
rag-rat clusters --limit 10
rag-rat oracle run | status        # compiler-grade resolution (docs/oracle.md)
rag-rat models list | install <model>
rag-rat reconcile --changed-first --max-seconds 60 --batch-size 64
rag-rat github sync --from-refs
rag-rat memory list | show <id> | doctor | rebind <id>    # inspect / re-anchor repo memories
rag-rat dream [--verify|--compact] [<id> --accept|--dismiss|--reset]   # memory-maintenance worklist
rag-rat consolidate                # import a legacy per-repo index into the global store
rag-rat hooks install              # git maintenance hooks
rag-rat gc                         # prune rows for dead git contexts
rag-rat eval [--json|--update-baseline]   # CI search-quality gate; requires a `--features eval` build (absent from the released binary)
rag-rat mcp                        # start the STDIO server

Releasing & license

Releases are automated by release-plz (the three crates ship in lockstep; see docs/releasing.md). rag-rat is MIT-licensed — see LICENSE.

Prior art

rag-rat's clone-detection design is inspired by SourcererCC's scalable token-bag candidate generation, NiCad's normalized near-miss clone-detection framing, GumTree's move-aware AST differencing, and anti-unification / least-general generalization for template extraction. Planned fragment-level mining and copy-paste bug heuristics are inspired by CP-Miner.