subconscious-mcp

A local-first, learning, semantic memory layer for any MCP-compatible LLM agent.

The server runs as an MCP stdio process on your machine and exposes four tools — recall, remember, forget, stats — that let an agent ask "have I seen this task before?" and, if so, get back the previous answer in milliseconds without re-running the work.

Embeddings come from sentence-transformers/all-MiniLM-L6-v2 (384-dim, runs on CPU). Storage is a persistent local ChromaDB collection. No data leaves your machine.

<!-- mcp-name: io.github.vishaltorc/subconscious-mcp -->

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Install

# Once published:
pip install subconscious-mcp

# Local development:
git clone https://github.com/vishaltorc/subconscious-mcp
cd subconscious-mcp
pip install -e ".[dev]"

After install you can run the server from anywhere:

subconscious-mcp --help

The first time a tool is called, the embedding model (~80MB) is downloaded into the local Hugging Face cache. Subsequent starts are fast.

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Configure your MCP client

Claude Desktop

Edit your config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Add an mcpServers entry:

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

If subconscious-mcp isn't on Claude Desktop's PATH, use the absolute path printed by which subconscious-mcp, e.g. "command": "/Users/you/.local/bin/subconscious-mcp".

Then quit and restart Claude Desktop. The new tools appear under the 🔌 indicator.

Claude Code

Option A — register from the CLI (recommended):

claude mcp add subconscious-mcp -- subconscious-mcp

Option B — edit ~/.claude.json (or your project's .mcp.json) and add:

{
  "mcpServers": {
    "subconscious-mcp": {
      "command": "subconscious-mcp",
      "args": [],
      "type": "stdio"
    }
  }
}

Reload the Claude Code session and the four tools become available.

A copy-pasteable file is in examples/claude_desktop_config.json and examples/claude_code_config.json.

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Tools

recall(task, threshold=0.85, top_k=1)

Semantic search for a previously remembered task.

arg	type	default	meaning
task	str	—	the task description to look up
threshold	float	0.85	minimum cosine similarity for a hit
top_k	int	1	how many candidates to consider

Returns:

{
  "hit": true,
  "similarity": 0.91,
  "answer": "...",
  "task_text": "...",
  "entry_id": "uuid",
  "stored_at": 1731000000.0,
  "tags": ["..."]
}

On a miss, hit is false, answer is null, and similarity is the best similarity observed in top_k — so callers can see how close they came.

remember(task, answer, tags=[], ttl_seconds=null)

Persist a (task, answer) pair. Returns {stored, entry_id, embedding_dim}.

ttl_seconds=null means never expire. Pass an integer to have the entry filtered out of future recalls after that many seconds.

forget(entry_id)

Delete the entry with this id. Returns {"forgotten": true} if it existed, else false.

stats()

Returns {"total_entries", "last_hit_at", "hit_rate_last_100"}. hit_rate_last_100 is a sliding window over the most recent 100 recall calls — useful to see whether memory is actually paying off.

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Configuration

Configuration is resolved in priority order:

1. Environment variables (highest)
2. ~/.subconscious-mcp/config.json
3. Built-in defaults

key	default	env var
storage_dir	~/.subconscious-mcp/data	SUBCONSCIOUS_STORAGE_DIR
embedding_model	all-MiniLM-L6-v2	SUBCONSCIOUS_EMBEDDING_MODEL
default_threshold	0.85	SUBCONSCIOUS_DEFAULT_THRESHOLD
default_ttl_seconds	null	—
log_level	INFO	SUBCONSCIOUS_LOG_LEVEL

Inspect the resolved config without starting the server:

subconscious-mcp --print-config

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Files written on disk

~/.subconscious-mcp/
├── config.json            (optional, user-edited)
├── data/                  ChromaDB collection (sqlite + parquet)
└── logs/server.log        rotating, 2MB x 3 backups

To wipe your memory: rm -rf ~/.subconscious-mcp/data.

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Demo session

See examples/demo_session.md for a worked example of an agent calling recall (miss → remember), then on a later turn calling recall again with a paraphrase and getting a hit.

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Architecture

See docs/architecture.md for the layered design (server / tools / memory / config), the rationale behind ChromaDB + cosine similarity, and the TTL strategy.

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Troubleshooting

subconscious-mcp: command not found after install Your shell's PATH doesn't include the install location. Try python -m subconscious_mcp.server --help to confirm the package works, then use the absolute path in your MCP client config.

Claude Desktop says "Server disconnected" Check ~/.subconscious-mcp/logs/server.log for the traceback. Most common causes:

1. The model download failed (offline at first launch) — re-run with network connectivity.
2. The storage_dir is on a read-only volume.

First recall is slow The first invocation lazily loads the sentence-transformer model (~5s on a modest CPU). Subsequent calls reuse the loaded model and respond in milliseconds.

Recall keeps missing on obvious paraphrases Lower the threshold (recall(task=..., threshold=0.7)) or raise top_k to see candidates. all-MiniLM-L6-v2 is small and fast — for higher-quality matching set SUBCONSCIOUS_EMBEDDING_MODEL=all-mpnet-base-v2.

Tests fail with a sentence-transformers download error You're offline or behind a proxy. Set HF_HUB_OFFLINE=1 once you've pre-downloaded the model, or run python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')" once with connectivity.

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License

MIT © 2026 Vishal Jayaprakash