intent-code 0.2.4

Intent-aware code-knowledge index for LLM agents, built on intent-db: index a repo once, query it by intent, cut the token cost of re-reading files.

intent-code

An intent-aware code-knowledge index for LLM agents, built on intent-db.

Index a repository once, then query it by intent so an agent reads only what it needs instead of re-reading the whole repo every session. The same search returns different results for the agent's current phase: debugging, extending, reviewing, or onboarding.

Why

LLM coding agents re-read files every session because they have no durable, queryable understanding of a codebase. intent-code builds that understanding once and keeps it fresh incrementally, so the expensive "read and synthesize" happens once per change and is reused across sessions.

It combines three established ideas:

• A durable, LLM-maintained knowledge layer of gotcha and flow notes, in the spirit of Karpathy's LLM Wiki.
• A tree-sitter symbol and dependency map ranked by PageRank, in the spirit of Aider's repo map.
• Incremental indexing: only changed code is re-embedded, in the spirit of Cursor's Merkle indexing.

The retrieval engine is intent-db: documents are embedded once and a per-intent lens re-ranks results at query time, with a feedback loop that learns per-intent ranking from what the agent actually used.

How an agent uses it

One index, three ways to consume it (the same data, regenerated on every index):

1. MCP (primary) for Claude Code and any tool-capable agent: code_map, code_search, code_read, code_context, code_flow, code_neighbors, note_put / note_get / note_list_stale, code_feedback, code_index. code_search finds where something is; code_read / code_context / code_flow answer how it works by returning full bodies and the call sequence, so the agent stops re-reading whole files.
2. Committed markdown under docs/codemap/ (MAP.md, index.md, notes/): readable by any LLM or human, even without MCP, straight from a git clone.
3. CLI --json for any agent that can run a shell command.

Install

uv tool install intent-code          # or: pipx install intent-code

Quickstart

cd your-repo
intent-code init .                   # builds the index, writes .mcp.json + protocol
# restart Claude Code -> the "code" MCP tools are available

Or use it directly:

intent-code index .
intent-code search "where is the retry handled" --intent debugging
intent-code map
intent-code neighbors your.module.Class.method --direction callers

# understand how code works, not just where it is
intent-code read your.module.handle_request           # full body, untruncated
intent-code context your.module.handle_request        # it + its callees, in call order
intent-code flow your.module.handle_request           # the ordered call sequence

Claude Code plugin

/plugin marketplace add harsharahul/intent-code
/plugin install intent-code

The plugin wires the MCP server, the /code-index and /code-note commands, and an optional PostToolUse freshness hook.

Use with GitHub Copilot or Gemini CLI

The same MCP server works with any agent that speaks MCP. init writes each agent's native config and the protocol into its instruction file:

intent-code init . --agent copilot   # .vscode/mcp.json + .github/copilot-instructions.md
intent-code init . --agent gemini    # .gemini/settings.json + GEMINI.md
intent-code init . --agent all        # claude + copilot + gemini at once

Writes are idempotent: instruction files get a marked managed block and JSON configs are key-merged, so re-running never clobbers your own content. The files init generates are excluded from the index, so the protocol is never indexed back into itself.

Keeping the index fresh

Freshness does not require git. Re-indexing is incremental (only changed files re-parse), and it stays current several ways:

• Automatic (any agent, no git, no hook): a search polls the tree at query time and re-indexes what changed. A stat gate skips unchanged files, so the check is cheap, and it runs at most once per interval (default 2s). On by default; INTENT_CODE_AUTO_REFRESH=0 disables it and INTENT_CODE_REFRESH_TTL tunes the interval (raise it for very large monorepos).
• Claude Code: the plugin's PostToolUse hook marks edited files dirty for an immediate pickup.
• Git repos: intent-code install-hooks . adds commit/merge/checkout hooks that flag the index stale.
• Any agent: the protocol also tells it to call code_index after edits.

Indexing multiple repositories

One index lives at the path you point init/serve-mcp at, and the walk skips every nested .git/ at any depth. Point at a single repo for that repo's index, or at a parent folder of several repos for one combined, cross-repo index.

In a combined index every result is tagged with its repository, and you can scope a search to one:

intent-code stats .                          # lists the repos and file counts
intent-code search "retry logic" --repo api  # only hits from the 'api' repo

Over MCP, pass filters={"repo": "api"} to code_search. The tag is the path of the nearest enclosing .git (deepest wins for nested repos and submodules), and . is the index root. Run index --full once to backfill tags on an index built before 0.2.2. Git hooks are per-repository, so the combined-parent case relies on the code_index path for freshness rather than hooks.

How it works

The index is a single intent-db SQLite file under .intentdb/ (add it to your .gitignore). Documents are tagged by layer:

• symbol: a signature card per function/class/method (tree-sitter), with line span, content hash, and import/call edges.
• chunk: AST-aware chunks for text or grammar-less files.
• note: durable, human-authored gotcha and flow articles.

A dependency graph and PageRank ranking are derived from the symbol edges to produce the repo map and neighbors tracing. Re-indexing hashes each file and re-embeds only the symbols whose content changed.

BM25 hybrid search is always on, so exact symbol matches land regardless of which embedder is in use.

Embedder

The embedder auto-detects on every open: a local Ollama model (nomic-embed-text) when the server is reachable and holds the model, otherwise the zero-dependency hashing embedder. Hashing is lexical only, so conceptual queries are much weaker; the index still works, just less well.

If intent-code index finds Ollama running without the model, it fetches the model once and reports progress. Query paths never do: a search will not trigger a download.

Detection is not a one-off decision made when the index is created. If an index was built while Ollama was down, later commands report a mismatch status and intent-code index --full rebuilds under the better embedder, preserving notes (including their staleness tracking), relevance feedback and learned ranking weights.

intent-code stats .                    # embedder, embedder_status, remedy
intent-code index . --full             # adopt a newly available embedder
intent-code index . --no-pull-model    # never download automatically

Variable	Effect
INTENT_CODE_EMBEDDER	Force a spec, e.g. hashing:dim=512. Skips detection.
INTENT_CODE_AUTO_PULL	0 never downloads a model, 1 allows it from any path.

Benchmark

A token-spend benchmark ships in intent_code.eval. On the intent-db codebase, answering a five-question set used about 97% fewer input tokens than reading whole files to reach the answer (roughly 7.8k versus 227k), with the zero-dependency hashing embedder. A local embedding model improves which questions land in the top results.

python -m intent_code.eval.run /path/to/repo

Security and supply chain

Minimal runtime dependencies, official tree-sitter grammars, version bounds plus a hash-pinned lockfile, dependency auditing in CI, SHA-pinned GitHub Actions, and PyPI trusted publishing. See SECURITY.md.

License

MIT