MIT 许可、可自我演化的 AI Agent 记忆操作层；通过 @mindmemos/deepseek-harness-plugin 安装进 dsh profile 接入 DeepSeek Harness，后端可选官方云端或自托管 FastAPI 服务。
DSH 适配
兼容
作者声明
安全审计
未审计
最后核验
2026-08-21
许可证
MIT
01它能帮你完成什么？
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
02如何接入 DeepSeek Harness？
前置条件
- 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`
安装步骤
- 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
验证接入成功
- `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 适配与能力边界
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
04适合谁？何时不该用？
适合
- 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
不适合
- 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.
05兼容性、维护与安全提示
- 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)
06常见问题
MindMemOS 如何接入 DeepSeek Harness？
先安装 Python SDK（`pip install mindmemos-sdk`）并运行 `mindmemos auth` 配置 base_url、API key 和 user_id；然后执行 `dsh plugin --profile <name> add @mindmemos/deepseek-harness-plugin` 安装到 dsh profile，在 `~/.dsh/profiles/<name>/cordis.patch.yml` 中加一条 `insert` 注册项（id 为 mindmemos-memory），重启该 profile 的 dsh 即可。
它是 dsh 原生插件还是 MCP？
它是通过 `dsh plugin` 安装的 npm 插件包，借助 dsh 分层的 `cordis.patch.yml` 文件组合注册，不走 MCP。插件在本机通过 `mindmemos` CLI 与服务通信，再转发到云端或本地服务。
需要哪些前置条件？
必须先安装 Python SDK 并完成 `mindmemos auth`，否则插件会因找不到 mindmemos 命令或未配置认证而报错，无法正常读写记忆。后端二选一：官方云服务（在官网申请 API key），或用 `make dev` 启动的本地服务。
记忆数据会存到哪里？
云端模式发往 https://mindmemos.cn；本地自托管则留在 http://127.0.0.1:8000，两者使用同一套协议。本地部署会用 Docker 拉起 Qdrant + Neo4j + Kafka 等依赖，并需要在 dev.yaml 配置 chat / embed / rerank 模型路由。
07相关的 DSH 工作流
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08数据与来源
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…
页面基于项目公开文档、仓库元数据和 DSH Plugins 的结构化解析生成；最后核验于 2026-08-21。发现错误？提交更正。
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