开源MCP工具

Memory commands (`basemind memory`, requires `--features memory`)

Web commands (`basemind web`, requires `--features crawl`)

Install the binary

npm install -g basemind # or: pip install basemind, cargo install basemind, brew install Goldziher/tap/basemind basemind scan # index the working tree once

Then use the CLI:

Add the basemind-cli skill to route CLI commands efficiently. See the CLI command reference below for the full command surface.

Installation

<details> <summary><strong>Harness-specific setup</strong></summary>

</details>

Quickstart

Choose the path that fits your workflow. Both paths use the same on-disk index at .basemind/.

Configuration

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

```toml

Path B: CLI + skill (scriptable, headless, CI)

Use the standalone basemind CLI binary and the basemind-cli skill for query-driven exploration. Same index, same tools, different interface — faster for scripting and batch operations.

```bash

CLI command reference

CLI commands mirror MCP tools, grouped by capability. Run with --json for machine-readable output.

Path A: MCP plugin (Claude Code and other harnesses)

MCP (Model Context Protocol) runs the basemind server in-process and exposes all tools as in-session function calls. Zero config — install and start using tools immediately.

Claude Code

Run these two commands in order:

/plugin marketplace add Goldziher/basemind   # 1. register the marketplace
/plugin install basemind@basemind            # 2. install the plugin

Restart the session after installing. The basemind binary installs automatically on first use (via npx, uvx, or direct download with verified checksums) — no manual cargo install needed. Prebuilt binaries ship with the full feature set enabled (96 document formats, OCR, embeddings, reranker, semantic search, web crawl, shared memory), so first use downloads ML models over the network; binaries are larger as a result.

To enable the optional live statusline (showing context % and per-capability metrics), 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. See the Statusline section for details.

Any MCP client (Cursor, Codex, Gemini, OpenCode, Continue, Cline, etc.)

cargo install basemind --features full --locked

Add to your MCP config:

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

Each harness has setup instructions in the Installation section.

MCP vs CLI

Both paths share the same .basemind/ index and are safe to run alongside each other (the CLI opens the index read-only; basemind serve watches and incrementally updates in the background).

- MCP: Wired as in-session tool calls. Zero config. Best for interactive agent workflows. - CLI: Scriptable, headless, CI-friendly. Best for batch queries, integration into non-MCP harnesses, and when you want to control the tool routing explicitly.

The choice is not binary — use MCP for interactive sessions and CLI for scripting in the same repo.

Statusline

To enable the live statusline in Claude Code (MCP only), 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 has no statusLine field.
• A plugin-shipped settings.json honors only agent and subagentStatusLine; any statusLine key is ignored.
• Hooks communicate via stdout/stderr only — they cannot write to ~/.claude/settings.json.

/bm-statusline works because Claude (the agent) performs the settings edit on your behalf, writing an absolute path into ~/.claude/settings.json. After that it persists across sessions.

It renders two lines — a context line (model · output-style · dir · branch · context%) and the basemind line below it:

Opus · basemind · ⎇ main · 12% ctx
◆ basemind  ●  1,247 files · 23m ago  │  312 calls · 180 srch · 44 git · 12 docs  │  1.4M saved  │  ✉ 3 @reviewer

The state dot is green (serve active / scan < 1 h), amber (idle or scan 1–24 h), or red (no serve and stale index). The second segment breaks activity down per capability — searches, git, docs, memory, web — showing only the buckets with calls today; then estimated tokens saved. When the agent-comms broker is running, a final ✉ segment shows your unread message count (bright when non-zero) and, in the full tier, your agent identity. Layout adapts to terminal width ($COLUMNS): the per-capability breakdown drops on narrow terminals. Override with BASEMIND_STATUSLINE=full|compact|minimal (default auto) or hide the context line with BASEMIND_STATUSLINE_CONTEXT=0.

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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.

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