Qdrant Memory MCP server

Qdrant Memory MCP

<!-- mcp-name: io.github.cnkang/qdrant-memory -->

简体中文

Persistent, scoped memory for AI agents through four explicit stdio MCP tools. This repository distributes the existing Python Runtime. Its metadata-only wheel contains no Python business implementation. The console script points directly to qdrant_memory.standalone:main and depends on hermes-plugin-qdrant-memory[mcp]==0.2.1.

Versions: MCP distribution 0.1.1 pins Runtime 0.2.1, which fixes explicit writes of distinct, highly similar texts without an LLM. The commands below use these exact versions. Release artifacts are available through the distribution PyPI page and Runtime PyPI page. See release checklist for the publishing sequence.

Runtime, optional Agent Skill and MCP distribution versions are independent. The Skill is usage guidance, not a runtime dependency. Hermes retains its native MemoryProvider, automatic extraction and prefetch. Ordinary MCP performs only explicitly requested memory operations. It does not automatically extract every conversation turn.

Install and initialize

Requires Python 3.11+ and either uv or a Python virtual environment. You must configure storage and an embedding service. For the example below, run Ollama and install qwen3-embedding:4b yourself.

Create an operator-owned profile directory and save qdrant-memory.json inside:

{
  "schema_version": 1,
  "scope": {"user_id": "my-user", "agent_id": "my-agent"},
  "qdrant": {"mode": "embedded", "collection": "qdrant_memory"},
  "embedding": {
    "mode": "plugin", "provider": "ollama", "model": "qwen3-embedding:4b",
    "base_url": "http://127.0.0.1:11434", "dimensions": 2560, "distance": "Cosine"
  }
}

Initialize, then launch (replace /path/to/profile with your absolute path):

uvx --from qdrant-memory-mcp==0.1.1 qdrant-memory-mcp --home /path/to/profile init
uvx --from qdrant-memory-mcp==0.1.1 qdrant-memory-mcp --home /path/to/profile mcp

The command without --home uses ~/.qdrant-memory, independent of Hermes. Configure and initialize that directory before letting a Registry client launch uvx --from qdrant-memory-mcp==0.1.1 qdrant-memory-mcp mcp. The executable name matches the package name. The Registry's positional mcp selects protocol mode. Its optional named --home precedes mcp when a client supplies a custom profile.

Alternatively:

python3 -m venv .venv
.venv/bin/python -m pip install qdrant-memory-mcp==0.1.1
.venv/bin/qdrant-memory-mcp --home /path/to/profile init
.venv/bin/qdrant-memory-mcp --home /path/to/profile mcp

On Windows use .venv\Scripts\python.exe and .venv\Scripts\qdrant-memory-mcp.exe.

Qdrant embedded, self-hosted server and Cloud, plus Ollama and OpenAI-compatible embeddings, are configured in the Runtime's configuration reference. Keep credentials in operator environment variables (QDRANTAPIKEY, EMBEDDINGAPIKEY, or configured names). Never commit their values. Initialization against an existing collection requires explicit operator choice. Do not use a production destination for a first test.

Connect a client

Codex config.toml example:

[mcp_servers.qdrant_memory]
command = "uvx"
args = ["--from", "qdrant-memory-mcp==0.1.1", "qdrant-memory-mcp", "--home", "/path/to/profile", "mcp"]

OpenCode opencode.json example:

Shortened. The full README is on GitHub.