Qdrant Memory

Name: Qdrant Memory
Author: cnkang

by cnkangversion 0.3.1i★ –iRuns on your computer

Registry nameio.github.cnkang/qdrant-memory

Install

Overview

Description from the maker (original English)

Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: QDRANT_API_KEY, EMBEDDING_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: QDRANT_URL, QDRANT_API_KEY, EMBEDDING_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "qdrant-memory": {
      "command": "uvx",
      "args": [
        "qdrant-memory-mcp",
        "mcp"
      ],
      "env": {
        "QDRANT_URL": "your-value",
        "QDRANT_API_KEY": "your-value",
        "EMBEDDING_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add qdrant-memory --env QDRANT_URL=your-value --env QDRANT_API_KEY=your-value --env EMBEDDING_API_KEY=your-value -- uvx qdrant-memory-mcp mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "qdrant-memory": {
      "command": "uvx",
      "args": [
        "qdrant-memory-mcp",
        "mcp"
      ],
      "env": {
        "QDRANT_URL": "your-value",
        "QDRANT_API_KEY": "your-value",
        "EMBEDDING_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "qdrant-memory": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "qdrant-memory-mcp",
        "mcp"
      ],
      "env": {
        "QDRANT_URL": "your-value",
        "QDRANT_API_KEY": "your-value",
        "EMBEDDING_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

QDRANT_URL env
Optional remote Qdrant endpoint when configured for server/cloud mode.
QDRANT_API_KEY secret env
Optional Qdrant credential for configured remote storage.
EMBEDDING_API_KEY secret env
Optional credential for the configured embedding provider.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account cnkang, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7258 servers in this category
Published to the registry more recently than 95% of the other 7258 servers in this category
Basic checks equal to or better than 100% of the other 7258 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Qdrant Memory safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: QDRANT_API_KEY, EMBEDDING_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.1) reached the registry yesterday.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Qdrant Memory MCP

One Memory. Multiple Agents. No Lock-in.

The Codex plugin candidate adds guided local setup, read-only native-memory import and verified JSON/Markdown handoff. It is versioned independently and does not add a second Runtime. See the executed validation and limits. Exports remain readable without this plugin or a subscription; native adoption is verified separately from file creation.

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. Configure destination protection and shared doctor before deployment. See the Owner support record for support limits.

One Memory, Multiple Agents — opt-in shared owner

The distribution adds an opt-in single-owner service and stdio bridge. Install Runtime 0.6.0 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.

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]==0.6.0.

See 0.3.1 release notes for the Runtime pin and upgrade guidance.

Before using an exact-version installation command, confirm that version on the PyPI release page. If unavailable, these package commands cannot run yet. Use the published 0.3.0 guide until publication; a source candidate does not establish PyPI or npm availability.

Versions: MCP distribution 0.3.1 pins Runtime 0.6.0, 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. 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.

Harness support matrix

Verification levels: configuration example only, protocol-level tested, official SDK/CLI tested, real host tested, real desktop GUI tested, not verified.

Host	Native Provider	Explicit MCP tools	Automatic capture	Automatic recall	Shared Owner	Remote access	Verification
Hermes	Yes	Yes (via standalone)	Yes	Yes	Opt-in	Yes	Real host tested
OpenCode	No	Yes	No	No	Via Owner	Yes	Real host tested (dual-device)
Pi	No	Yes (extension)	Opt-in adapter	Opt-in adapter	Via Owner	Yes	Real host tested
DeepSeek Harness	No	Yes (DSH bundle)	No	No	Via Owner	Yes	Real host tested
WorkBuddy	No	Yes (connector)	No	No	Via Owner	Yes	Real desktop GUI tested
Codex	No	Yes	No	No	Via Owner	Yes	Official CLI tested

This distribution does not auto-extract conversations for any MCP host. Automatic capture and recall require a native provider or a lifecycle adapter. See the Codex validation record for the tested CLI, source revisions and model providers; desktop GUI and native-memory adoption are separate verification boundaries.

Install and initialize

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==0.3.1 qdrant-memory-mcp --home /path/to/profile init

uvx --from qdrant-memory-mcp==0.3.1 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==0.3.1 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==0.3.1 .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 (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.

Connect a client

Codex config.toml example:

[mcp_servers.qdrant_memory]

command = "uvx" args = ["--from", "qdrant-memory-mcp==0.3.1", "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==0.3.1", "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 protocol acceptance tests use the official Python MCP SDK. Host-specific evidence is recorded separately in the architecture and support boundaries and the Runtime's client evidence and limits.

Tools and boundaries

Tool	Input	Result
`qdrant_memory_search`	`query`, optional `category`	Scoped memories with IDs, scores and provenance
`qdrant_memory_add`	`text`	Durable memory ID
`qdrant_memory_update`	exact `id`, replacement `text`	Scoped replacement
`qdrant_memory_delete`	exact `id`	Scoped deletion

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.

Concepts and evidence

Sharing vs Portability vs Backup: Runtime portability guide
Cross-harness architecture: docs/cross-harness-architecture.md
Shared Owner status: docs/universal-owner-status.md
Validation limits: docs/dsh-validation.md

Validation reports apply to their recorded versions, workloads and host environments. Short protocol or cloud runs do not establish 24-hour acceptance for a new release.

Verification and development

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.

Dependency boundary

The runtime dependency is only hermes-plugin-qdrant-memory[mcp]. The sdist includes the Python acceptance tests, apart from harnesses th

Only the beginning is shown; read the rest in the repo

Qdrant Memory

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Qdrant Memory MCP

One Memory, Multiple Agents — opt-in shared owner

Harness support matrix

Install and initialize

Connect a client

Tools and boundaries

Concepts and evidence

Verification and development

Dependency boundary