Potencialize seu agente
O comando do próprio publicador. Não é um endpoint do PlugMCP.
Host a copy with our keys and logs.
uvx qdrant-memory-mcp==0.3.4O Claude Code não tem link de instalação para um comando local. Copie o comando abaixo e execute-o em um terminal.
QDRANT_URL=<value> QDRANT_API_KEY=<secret> EMBEDDING_API_KEY=<secret> claude mcp add io.github.cnkang-qdrant-memory -- uvx qdrant-memory-mcp==0.3.4Execute em um terminal.
{ "mcpServers": { "io.github.cnkang-qdrant-memory": { "command": "uvx", "args": [ "qdrant-memory-mcp==0.3.4" ], "env": { "QDRANT_URL": "<value>", "QDRANT_API_KEY": "<secret>", "EMBEDDING_API_KEY": "<secret>" } } }}claude_desktop_config.json
URL remota ou um comando na sua máquina? Como as duas instalações diferem
Rodar no PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
AbrirDo publicador
One Memory. Multiple Agents.
Durable, portable, and permission-aware memory for AI agents — distributed as an MCP adapter over the Qdrant Memory Runtime.
For shared production deployments, read the deployment security guide. Owner-only database write credentials are required independently of MCP grants. Runtime 0.5.0 includes destination protection and shared doctor. The operator must configure these controls. See the for support limits.
Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.
The distribution adds an opt-in single-owner service and stdio bridge. Install Runtime 0.5.1 to use them. See shared integration guide. Private defaults are unchanged. Native Provider automatic shared extraction is deferred.
A WorkBuddy connector and a DeepSeek Harness bundle reuse this stdio MCP, the managed launcher and the canonical Skill.
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]==0.5.1.
See 0.2.4 release notes for source alignment, WorkBuddy assets and compatibility boundaries.
Before using an exact-version installation command, confirm that version on the PyPI release page.
Versions: MCP distribution 0.2.5 pins Runtime 0.5.1, which supports MCP
Python SDK 1.x and 2.x (mcp>=1.30,<3). The four scoped tools and stdio
launch arguments are the same for both SDK majors. It retains the fix for 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.
Verification levels: configuration example only, protocol-level tested, official SDK/CLI tested, real host tested, real desktop GUI tested, not verified.
This distribution does not auto-extract conversations for any MCP host. Automatic capture and recall require a native provider or a lifecycle adapter.
For the shortest currently supported local setup, follow the Ollama quickstart (中文). It covers an existing model, installing a missing model, a separate WorkBuddy profile, connection settings and a first save/search check. No Qdrant server, Docker, cloud account or embedding API key is needed for that local path. Python/uv and one-time profile configuration are still required for this direct route. The managed WorkBuddy connector and DSH bundle prepare those dependencies from Node.js instead.
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==0.2.5 qdrant-memory-mcp --home /path/to/profile init
uvx --from qdrant-memory-mcp==0.2.5 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==0.2.5 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==0.2.5
.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 (QDRANT_API_KEY,
EMBEDDING_API_KEY, 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.
Codex config.toml example:
[mcp_servers.qdrant_memory]
command = "uvx"
args = ["--from", "qdrant-memory-mcp==0.2.5", "qdrant-memory-mcp", "--home", "/path/to/profile", "mcp"]
OpenCode opencode.json example:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"qdrant_memory": {
"type": "local",
"command": ["uvx", "--from", "qdrant-memory-mcp==0.2.5", "qdrant-memory-mcp", "--home", "/path/to/profile", "mcp"],
"enabled": true
}
}
}
Pi needs a separately installed MCP client/extension. In that extension, use
command: uvx and the same argument list. No built-in Pi MCP support or universal
extension config format is claimed. These are configuration examples. This
distribution's acceptance tests use the official Python MCP SDK, not live agent
applications. See the Runtime's client evidence and limits.
Scope is fixed by the operator's profile (user_id, agent_id). Model inputs
and MCP client identity cannot override it. Knowing an ID does not grant access.
Migration, export, reset and bulk deletion remain trusted operator CLI commands.
See standalone maintenance.
No resources or prompts are advertised. This is a local subprocess, with no
public HTTP/SSE listener or remote authentication service. Stdout is reserved
for protocol messages, with sanitized diagnostics on stderr.
Data flows from client → Runtime → embedding provider → Qdrant and the local SQLite ledger. With local Ollama and embedded Qdrant, memory processing stays local. Cloud embedding receives memory/query text, and cloud Qdrant receives vectors and stored payloads. The client/model may also receive recalled text. The operator must authorize retention and access and protect the profile files.
One writer lease coordinates a profile/destination. Do not overlap MCP, CLI or Hermes processes writing to it. Separate profiles or machines do not provide a distributed lock. This package does not support uncoordinated multiprocess writes, auto-extraction, an HTTP transport, or implicit credential discovery from Hermes. It preserves the Runtime's ledger, IDs and recovery semantics.
Long-running 24-hour acceptance: pending / not yet verified.
CI builds wheel/sdist, checks metadata and ownership marker. It runs installed protocol tests with temporary profiles, embedded Qdrant and a deterministic loopback embedding fixture on Linux/macOS/Windows, Python 3.11 and 3.14. No test connects to personal Qdrant, Hermes profiles or memory data. These tests cover initialize, discovery, four tools, persistence, recovery, scope isolation, error redaction and protocol stdout. Provider/cloud compatibility evidence belongs to the Runtime.
CI prefers the immutable Runtime PyPI release. Before a Runtime release reaches
PyPI, CI builds a wheel from the immutable revision recorded in runtime-source.json. This is only a preparation fixture:
published dependency metadata always uses the exact PyPI version. Release
workflow acceptance requires installation from PyPI and never uses that fallback.
See CI and dependency maintenance, publishing, architecture, and the optional Skill.
The runtime dependency is only hermes-plugin-qdrant-memory[mcp]. The sdist includes the
Python acceptance tests, apart from harnesses that need an external Runtime checkout, DSH
client assets, or Node fixtures the sdist does not carry; selected harness lockfiles and
Node-only .mjs tests are excluded. The wheel remains metadata-only. Development
dependencies are declared under [project.optional-dependencies].dev and are not installed
for end users.
Use the WorkBuddy connector or the
DSH installation guide for explicit durable memory.
Both reuse the Python Runtime, the four MCP tools and the canonical Skill. The published managed launcher prepares private Python
and CPU-model dependencies with Node.js. Thus, the managed routes do not require a
system Python, uvx or Ollama installation. The DSH Bundle is
distributed as @cnkang/qdrant-memory-dsh-bundle. Existing Hermes profiles
and defaults are unchanged. See architecture and version matrix
and validation limits.
Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.
{ "mcpServers": { "io.github.cnkang-qdrant-memory": { "command": "uvx", "args": [ "qdrant-memory-mcp==0.3.4" ], "env": { "QDRANT_URL": "<value>", "QDRANT_API_KEY": "<secret>", "EMBEDDING_API_KEY": "<secret>" } } }}Ferramentas
Ainda não há lista de ferramentas. Servidores remotos recebem uma após a primeira verificação de saúde.
Perguntas frequentes
Copie nesta página a configuração stdio para Cursor, Claude Desktop, Claude Code ou VS Code. Ela usa a instalação do publicador, não um proxy do PlugMCP.
O publicador lista estas variáveis de ambiente: QDRANT_URL, QDRANT_API_KEY, EMBEDDING_API_KEY.
Qdrant Memory roda localmente via stdio.
Última verificação: 2026-11-15. Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.