MIT-licensed, self-evolving memory operating layer for AI agents; connects to DeepSeek Harness via the @mindmemos/deepseek-harness-plugin installed into a dsh profile, backed by the official cloud or a self-hosted FastAPI stack.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
MIT
01What can it help you accomplish?
Give DeepSeek Harness (dsh) agents persistent cross-session agent memory with automatic recall and write-back
Relevant user/task memories recalled and injected before each user turn, and the conversation written back automatically when the turn ends
DeepSeek Harness users who want their dsh agents to remember preferences, project facts and past lessons across sessions
Run MindMemOS as a self-hosted memory service, or connect the official cloud without deploying
A FastAPI memory service on http://127.0.0.1:8000 (via `make dev` with the Docker dependency stack), or the official cloud at https://mindmemos.cn — both speak the same HTTP API / SDK / plugin protocol
Teams that need on-premises or offline agent memory, or want to try the service without deploying anything themselves
Turn accumulated agent experience into reusable skills
Skill candidates distilled from experience memories, with skill execution results, failure traces and user feedback flowing back into the memory system for continuous evolution
Agent builders who want long-term memory to evolve into capability through schema learning and skill distillation
02How to install into DeepSeek Harness
Prerequisites
- DeepSeek Harness (dsh) installed with a profile — the plugin is added to a profile via `dsh plugin`
- Python SDK installed (`pip install mindmemos-sdk`) — the plugin communicates with the local machine through the `mindmemos` CLI
- A MindMemOS backend: the official cloud service (API key from the website) or a local self-hosted service started with `make dev`
Installation steps
- 01
Install the Python SDK and confirm the CLI works: `pip install mindmemos-sdk`, then `mindmemos --version`
- 02
Configure base_url, API key and user_id with `mindmemos auth` (pointing at either the cloud or a local service), then check with `mindmemos config show`
- 03
Install the plugin into a dsh profile: `dsh plugin --profile <name> add @mindmemos/deepseek-harness-plugin`
$ dsh plugin --profile <name> add @mindmemos/deepseek-harness-plugin
- 04
Register it by adding an `insert` entry (id: mindmemos-memory, name: '@mindmemos/deepseek-harness-plugin', config: userId / appId) to `~/.dsh/profiles/<name>/cordis.patch.yml`
- 05
Restart dsh with that profile
Verify the integration
- `mindmemos --version` confirms the CLI is available; `mindmemos config show` confirms the configuration took effect
- For the service itself, the README suggests a curl smoke test against /v1/memory/add and /v1/memory/search: a `code` of `ok` with readable memory content means the access works
03DSH integration and capability boundaries
Official DeepSeek Harness plugin (@mindmemos/deepseek-harness-plugin) installed into a dsh profile via `dsh plugin` and registered through cordis.patch.yml; recalls and writes memories around every turn
Per-turn recall & write-back for dsh
every DeepSeek Harness conversation turn→relevant memories injected before each user turn; the conversation written back automatically when the turn ends
sends conversation content to the configured MindMemOS endpoint (cloud https://mindmemos.cn or local http://127.0.0.1:8000)Portable cross-agent memory assets
user profiles, preferences, project facts, tool experience, skill candidates→persistent memory assets that OpenClaw, Hermes, Claude Code, OpenHands and other agents can share or transfer
Self-evolving memory (schema learning & dreaming)
ongoing interactions and stored memories→improved memory quality via frequent-pattern schema learning, offline consolidation (dreaming), and interaction corrections
offline consolidation rewrites and consolidates stored memoriesMemory → Skill distillation
experience memories and skill execution feedback→skill candidates distilled from memory; execution results, failure traces and user feedback flow back into the memory system
04Who is it for? When not to use it?
Good for
- DeepSeek Harness users who want their dsh agents to remember preferences, project facts and past lessons across sessions
- Teams that need on-premises or offline agent memory, or want to try the service without deploying anything themselves
- Agent builders who want long-term memory to evolve into capability through schema learning and skill distillation
Not for
- The dsh plugin depends on the Python SDK: `mindmemos-sdk` must be installed and `mindmemos auth` completed before installing the plugin, otherwise the logs error out (mindmemos command not found / auth not configured) and memories cannot be read or written.
- Local self-hosting is heavyweight: `make dev` starts a full Docker dependency stack (Qdrant + Neo4j + Kafka) before FastAPI, and at least the chat / embed / rerank model routers must be configured in config/mindmemos/dev.yaml.
05Compatibility, maintenance and safety notes
- The dsh plugin depends on the Python SDK: `mindmemos-sdk` must be installed and `mindmemos auth` completed before installing the plugin, otherwise the logs error out (mindmemos command not found / auth not configured) and memories cannot be read or written.
- Local self-hosting is heavyweight: `make dev` starts a full Docker dependency stack (Qdrant + Neo4j + Kafka) before FastAPI, and at least the chat / embed / rerank model routers must be configured in config/mindmemos/dev.yaml.
- In cloud mode, memory data is sent to the official service at https://mindmemos.cn and requires a website-issued API key; local mode keeps traffic on http://127.0.0.1:8000.
MIT · actively maintained (latest release v0.1.5, 2026-07-22)
06Frequently asked questions
How do I connect MindMemOS to DeepSeek Harness?
Install the Python SDK (`pip install mindmemos-sdk`) and run `mindmemos auth` to set base_url, API key and user_id. Then install the plugin with `dsh plugin --profile <name> add @mindmemos/deepseek-harness-plugin`, register it via an `insert` entry (id: mindmemos-memory) in `~/.dsh/profiles/<name>/cordis.patch.yml`, and restart dsh with that profile.
Is it a native dsh plugin or MCP?
It is an npm plugin package installed through `dsh plugin` and composed via dsh's layered `cordis.patch.yml` files — not MCP. The plugin talks to the local machine through the `mindmemos` CLI, which forwards requests to the cloud or your local service.
What prerequisites do I need?
The Python SDK must be installed and `mindmemos auth` completed before installing the plugin — skipping these steps makes the logs error out and blocks memory reads/writes. You also need a backend: the official cloud (API key from the website) or a local service started with `make dev`.
Where does my memory data go?
In cloud mode it goes to https://mindmemos.cn; in local mode it stays on http://127.0.0.1:8000 — both use the same protocol. Local deployment runs a Docker stack (Qdrant + Neo4j + Kafka) and requires the chat/embed/rerank model routers configured in dev.yaml.
07Related DSH workflows
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Two-phase DeepSeek Harness preset: Minimal-aligned bootstrap, then full Standard tools (Project2 98/99)
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08Data and sources
We released the [DeepSeek Harness Plugin](https://www.npmjs.com/package/@mindmemos/deepseek-harness-plugin), letting Dee…
Connect MindMemOS to different agents and workflows through plugins that retrieve and inject relevant memories before in…
This page is generated from the project’s public documentation, repository metadata and a structured parse of DSH Plugins; last verified on 2026-08-21. Found an error? Submit a correction.
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