basemind

Full AI context layer for coding agents — code-map, document RAG, shared memory, web crawl, git history. 300+ languages, one MCP server.

Pillars · Tools · Quickstart · Performance · Install

The four pillars

Code — Tree-sitter outlines, symbol search, reference + caller + implementation graphs, call chains, git history per symbol, blame at symbol-level resolution.

Documents — Ingest + semantic search over PDFs, Office (Word/Excel/iWork), HTML, email, archives. Built-in OCR, layout detection, keyword + NER extraction, cross-encoder reranking. All ONNX bundled — no system install needed.

Memory — Per-repo scoped key-value + semantic vector storage. Clones of the same git origin automatically share memory; unrelated repos isolated.

Web — On-demand HTTP scrape + follow-link crawl. Pages chunk, embed, and land in the documents store under scope web:<host> for unified search.

Context economy

basemind tools return paths, line numbers, and signatures — not file bodies — so a structural answer costs a fraction of the tokens of reading source. The plugin ships this as the agent's default operating discipline (carried in the MCP server instructions, the basemind skill, and the SessionStart hook):

• outline a file before opening it — then read only the span you need.
• search_symbols instead of grep/rg for a definition.
• find_references / find_callers instead of grepping call sites.
• workspace_grep instead of shelling out to ripgrep for regex over content.
• rescan after edits instead of reconnecting the server.
• Don't re-read a file basemind already mapped.

The live statusline surfaces the payoff: estimated tokens saved vs a grep + read baseline.

Feature table

Quickstart

Claude Code

These are two separate steps — run both, in order:

/plugin marketplace add Goldziher/basemind   # 1. register the marketplace (makes the plugin available)
/plugin install basemind@basemind            # 2. install the plugin (registers the MCP server)

Adding the marketplace alone does not give you any tools — it only makes the plugin available to install. You must run the second command (or pick Install for the basemind plugin in the /plugin menu) to register the MCP server. If no basemind tools appear after a restart, you almost certainly stopped after step 1; open /plugin, go into the basemind marketplace, and Install the plugin.

Restart the session after installing. The basemind binary installs automatically on first use (via npx, uvx, or direct download with checksum verification) — no manual cargo install needed.

Statusline

To enable the live statusline, run /bm-statusline once. This is a one-time opt-in because Claude Code plugins cannot set the main statusline — it is a platform limitation, not a basemind choice:

• The plugin manifest (plugin.json) has no statusLine field.
• A plugin-shipped settings.json honors only agent and subagentStatusLine; any statusLine key is silently ignored.
• Hooks communicate via stdout/stderr/exit codes only — a SessionStart hook cannot write to ~/.claude/settings.json, so it can only nudge you to run /bm-statusline.

/bm-statusline works because Claude (the agent) performs the settings edit on your behalf, writing an absolute path into ~/.claude/settings.json ($HOME/~ are not expanded in the statusLine command field). After that it persists across sessions.

Output: ◆ basemind ● 1,247 files · 23m ago │ 47 calls · 14k saved. Counts render bright; the state dot is green (serve active / scan < 1 h), amber (idle or scan 1–24 h), or red (no serve and stale index). When a document/memory/web index is present, a third segment appears: │ 312 docs · 18 mem · 4 sites. Narrow terminals collapse to ◆ basemind ● 1.2k · 23m │ 47c · 14k saved.

Any MCP client

cargo install basemind --features full --locked

Then add to your MCP config:

{
  "mcpServers": {
    "basemind": {
      "command": "basemind",
      "args": ["serve"]
    }
  }
}

Supported harnesses: Claude Code · Cursor · Codex (CLI + App) · Gemini · OpenCode · Factory Droid · GitHub Copilot CLI · Continue · Cline. Each harness has install instructions in the Installation section below.

CLI only

basemind scan                     # index the working tree
basemind query outline path/file.rs  # inspect structure
basemind query symbol "parseQuery"   # find by name
basemind watch                    # live re-index on file change

Why basemind, specifically

vs grep / ripgrep

What ripgrep does well: blazing-fast line matching. What it misses:

• Grep returns 50+ hits in docs, tests, comments, variable names — agent wastes context filtering noise.
• No scope awareness: parseQuery() and parseQuery string both match; semantic signals lost.
• Every query re-scans the disk; no pre-computed structures to leverage.

basemind: semantic-quality answers at grep speed via tree-sitter + indexed call sites.

vs vector-only RAG (LangChain / LlamaIndex DIY stacks)

What vector RAG does well: fuzzy document semantic search. What it misses:

• Pure embeddings lose exact structure — which function calls which, which class implements which interface.
• No line/column resolution — agent can't map vector hits back to code symbols.
• No git history integration — "what changed recently?" and "who wrote this?" require separate systems.

basemind: code structure + git history + vector memory + document search all in one, unified scope.

vs context7 / openai-codex / Aider's repo-map

What these do well: generate code-map summaries. What they miss:

• Static snapshots — stale after the first edit.
• No semantic indexing — every lookup re-parses or re-scans.
• Human-focused output (markdown) instead of agent-facing structure (JSON tools).

basemind: live-updated index with sub-millisecond MCP tools, built for agents not humans.

vs GitHub native search

What GitHub does well: repository-wide fuzzy text search. What it misses:

• Cloud-only — your code leaves the machine, latency is network-bound.
• No local-editor integration — agent can't query in-progress edits before commit.
• No cross-language polyglot support — each language's search tuned separately.

basemind: local-only, always-fresh index of your working tree, 300+ languages in one sweep.

Performance

Measured on Apple Silicon, release build, --features full, default eager_l2 = true. Cold filesystem cache adds ~50% to first scan; numbers below are warm steady-state.

Scan throughput

The TypeScript compiler is the worst case — 81k files scanned in 22 seconds. Most real repos sit between tokio and ripgrep. Re-scans skip unchanged content hashes, so warm rescans on edited working trees are typically dominated by the changed-set size, not repo size.

Per-tool MCP latency

Against the 81k-file TypeScript index:

basemind preloads L1 outlines into RAM on serve start, so code-map queries hit no disk. The Fjall LSM inverted index handles ref/caller/impl lookups without scanning blobs. Git tools track gix walk cost; Fjall-backed tools dominate only on enormous histories.

Configuration

Full config lives at schema/basemind-config-v1.schema.json. Minimal example:

# .basemind/basemind.toml
file_watch_glob = "**/*.{rs,ts,tsx,py,go}"
eager_l2 = true

[documents]
enabled = true

Per-query MCP overrides:

{
  "query": "what does kreuzberg do?",
  "reranker_enabled": true,
  "reranker_preset": "bge-reranker-base"
}

Environment variables map mechanically: --llm-api-key ↔ BASEMIND_LLM_API_KEY. Every MCP tool accepts per-query overrides that win over file/env/CLI layers.

Installation