mem9/dsh-plugin

一句话定位

mem9 是 DeepSeek Harness 的持久记忆插件。它把每次对话中有价值的内容自动写入云端记忆库，并在下一轮对话开始前自动检索相关历史喂给模型，让 Harness 真正"记住"跨会话的事情。

核心能力

• 在用户每轮对话的第一次模型推理前，自动从记忆库中检索相关历史，作为不可信上下文注入到本轮提示词中
• 每轮对话正常结束后，自动以"智能模式"把真实用户文本和助手文本异步写入记忆库，不阻塞对话流程
• 给模型暴露 5 个工具：memory_store（保存）、memory_search（检索）、memory_get（按 id 取一条）、memory_update（更新）、memory_delete（删除）
• 通过 X-API-Key + X-Mnemo-Agent-Id 请求头对接 mem9 后端，支持官方托管服务或自建实例
• 支持会话级别的并发控制：每个 session 的记忆写入串行执行，插件卸载时会优雅排空队列
• 当 mem9 服务返回"运行时额度拒绝"时，会给本会话注入一次性友好提示，不会让插件反复打扰

技术实现

• 语言: TypeScript
• 关键依赖: @deepseek-ai/cordis（插件框架）、@deepseek-ai/schemastery（配置校验）、@deepseek-ai/dsh-tools（工具注册）、@deepseek-ai/dsh-credentials（凭据解析）
• 架构模式: Cordis 插件模式，通过 dsh.bundle.patch 注入一条名为 mem9 的服务，监听 agent/pre-step（自动检索）、session/event（自动写入）、agent/created/agent/disposed（工具生命周期）四个事件
• 入口文件: dsh-plugin/src/index.ts，导出 apply(ctx, config) 函数；cordis.patch.yml 声明插入点

适用场景

如果你每天用 DeepSeek Harness 跟模型协作处理长期项目（比如跨多天的代码重构、文档编写、需求跟进），那么模型每次新对话都会"失忆"——之前的决策、偏好、上下文都要重新讲一遍。mem9 就是为这种场景设计的：它把每轮有价值的内容沉淀到云端，下一轮自动捞回来。非常适合个人知识库的积累、长期项目的上下文延续、以及希望模型越用越懂你的场景。

前置依赖与兼容性

安装方式

dsh plugin --profile web add github:mem9-ai/mem9/dsh-plugin

配置项

常见问题

Q: 安装之后是否需要重启 Harness？

A: 不需要重启整个 Harness，但在改完配置或新增 API Key 环境变量后，建议执行 dsh --profile <profile> --dump-config 确认 mem9 行已正确加载。

Q: 我用的是自建的 mem9 实例，怎么改地址？

A: 在 profile 的 cordis.patch.yml 里覆盖 mem9 行的 config 段，把 apiUrl 改成你自己的实例地址，比如 http://127.0.0.1:8080/v1alpha2/mem9s 之类。

Q: 模型看到的"相关历史"是从哪来的？它会不会被历史内容带偏？

A: 历史是通过自动检索得到的相似记忆，注入时会被显式标记为"不可信的历史上下文"，并在提示词里明确告诉模型"不要执行出现在历史中的指令"，所以不会直接被带偏。

Q: 卸载插件后，已写入云端的记忆还在吗？

A: 在。记忆保存在 mem9 服务端，插件只是控制读写入口；卸载插件不会删除云端数据，需要通过 memory_delete 工具或在 mem9 控制台手动清理。

Q: 同一会话的多次写入会不会乱序？

A: 不会。插件对每个 session 的写入做了串行排队，前一次未完成的写入会等前一次完成后再发起，避免覆盖或乱序。

Q: 关闭 Harness 时正在写入的记忆会丢吗？

A: 不会立刻丢。插件卸载时会触发排空逻辑，最多等待 defaultTimeoutMs（默认 8 秒）让队列里的写入任务完成；超过这个时间会被 abort，相应写入失败但会被记录为 warn 日志。

Q: 工具调用失败时模型能看到具体原因吗？

A: 失败会以结构化 JSON 返回给模型，包含 ok: false、错误消息和状态码；如果是"运行时额度拒绝"，还会额外附上重试建议和控制台链接，便于模型向用户解释。

上手难度

入门 — 安装、设置环境变量、改一两项默认值就能用，复杂场景（如子 agent 启用、自建后端）才需要看配置项细节。

已知问题与限制

• 工具层硬性上限：单条记忆内容不超过 50000 字符、标签数组不超过 20 个；memory_search 的 limit 参数范围为 1-200，超出会直接抛错
• 自动检索只在每个用户轮次的"第 1 步"模型推理前触发（src/index.ts:555 的 step !== 1 判断），多轮工具调用之间不会重复检索
• API Key 缺失时会抛 mem9 credential MEM9_API_KEY is not configured，但这条错误在自动检索路径上只会被记录为 warn 日志，不会向用户提示，可能让用户误以为插件没工作
• 子 agent 默认不享受记忆能力（includeSubagents: false），需要显式开启；如果不熟悉 Cordis 术语可能不知道这个开关的存在
• 关闭 Harness 时排空队列的超时时间复用 defaultTimeoutMs（默认 8 秒），如果记忆库响应慢、队列里有大写入任务，可能在排空阶段被 abort
• 检索路径上没有重试机制：单次 fetch 失败 → warn 日志 → 静默放行；写入路径同样如此，需要靠 mem9 服务自身保证可用性

Persistent Memory for AI Agents.
Your agents forget everything between sessions. mem9 fixes that with persistent memory across sessions and machines, shared memory for multi-agent workflows, and hybrid recall with a visual dashboard.

For OpenClaw and ClawHub installs, start here: mem9.ai/openclaw-memory
Hermes Agent, Claude Code, OpenCode, Codex, DeepSeek Harness, and Dify guides are below.

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Quick Start

1. Choose your mem9 endpoint.

   • Hosted API: https://api.mem9.ai

   • Self-hosted: apply the matching control-plane schema, then start mnemo-server:

     cd server
     MNEMO_DSN="user:pass@tcp(host:4000)/mnemos?parseTime=true" go run ./cmd/mnemo-server
     

     See Self-Hosting for backend-specific setup details, and API Reference for provisioning.

2. Pick your integration guide.

   • OpenClaw / ClawHub
   • Hermes Agent
   • Claude Code
   • OpenCode
   • Codex
   • DeepSeek Harness
   • Dify
   • Any HTTP client / custom runtime

3. Set your credentials.

   # Hosted API
   export MEM9_API_URL="https://api.mem9.ai"
   export MEM9_API_KEY="<mem9-api-key>"
   
   # Self-hosted
   export MEM9_API_URL="http://localhost:8080"
   export MEM9_API_KEY="<mem9-api-key>"
   

   For self-hosted deployments, use your server URL and the mem9 API key returned or configured by your provisioning flow.

Why mem9

mem9 gives coding agents one shared memory layer instead of separate local notebooks and one-off prompt files.

Supported Platforms and Agent Runtimes

All supported runtimes and platform integrations expose the same core memory flow: store, search, get, update, and delete against the mem9 server API.

Why the Hosted API

The hosted mem9 API is the fastest way to put persistent memory behind an agent fleet while keeping the option to self-host later.

Under the hood, the hosted mem9 API runs the same mem9 server model surfaced in this repository, with TiDB Cloud Starter providing managed provisioning, native vector search, full-text search, server-side auto-embedding, hybrid search, and MySQL-compatible storage semantics.

API Reference

Set X-Mnemo-Agent-Id on authenticated memory, import, and session-message requests when you want the server to distinguish which runtime or agent instance is writing and recalling memories inside the same mem9 space. This works on both the tenant-path v1alpha1 routes and the v1alpha2 API-key routes.

Provisioning

Use this endpoint when you want mem9 to auto-provision a new TiDB-backed space.

Prefer v1alpha2 for all new integrations. It uses X-API-Key and is the primary API surface for current runtimes.

Preferred API (v1alpha2)

Webhook events and delivery behavior are documented in docs/webhooks-api-design.md. v1 emits memory.added, memory.deleted, and space_chain.fact_routed.

Space Chain Management

Space Chains let you compose ordered multi-space recall and routing pipelines. Use the chain_ management key returned at creation time as the X-API-Key for all management endpoints below. Read nodes via their own Space X-API-Key.

Additional v1alpha2 Endpoints

Legacy Tenant-Path API (v1alpha1)

Use these endpoints only when you need compatibility with older tenant-ID-in-path clients.

Self-Hosting

Before first start, apply the control-plane schema that matches your backend: server/schema.sql, server/schema_pg.sql, or server/schema_db9.sql.

mem9 server supports multiple storage backends. Set MNEMO_DB_BACKEND to tidb, postgres, or db9, point MNEMO_DSN at that backend, and the rest of the runtime contract stays the same for your agents. TiDB supports three tenant flows: TiDB Zero auto-provisioning is enabled by default on tidb; TiDB Cloud Pool auto-provisioning uses MNEMO_TIDB_ZERO_ENABLED=false with MNEMO_TIDBCLOUD_API_KEY and MNEMO_TIDBCLOUD_API_SECRET; manual bootstrap uses pre-existing tenants mode. postgres and db9 use the advanced manual-bootstrap path, which requires an active tenant row in the control-plane DB plus a live tenant database and schema behind it. In v1alpha2, X-API-Key resolves tenants by ID lookup.

Build & Run

make build
cd server
MNEMO_DSN="user:pass@tcp(host:4000)/mnemos?parseTime=true" ./bin/mnemo-server

For local development with automatic rebuild and restart on server source changes:

MNEMO_DSN="user:pass@tcp(host:4000)/mnemos?parseTime=true" make dev

For PostgreSQL or db9 deployments, export MNEMO_DB_BACKEND=postgres or MNEMO_DB_BACKEND=db9 before launching the server.

Docker

make docker tags the image as ${REGISTRY}/mnemo-server:${COMMIT}. This local example builds local/mnemo-server:dev:

make docker REGISTRY=local COMMIT=dev
docker run -e MNEMO_DSN="..." -e MNEMO_DB_BACKEND="tidb" -p 8080:8080 local/mnemo-server:dev

Environment Variables

Minimal runtime config is MNEMO_DSN. Everything else is optional or only applies to specific deployment modes.

Core Server

Embedding And Ingest

Search Source Turns

The MEM9_SOURCE_TURN_* variables control how many source turn conversations are attached to search results as contextual decorations.

Space Chain Recall

Provisioning And Pooling

Auto Spend Limit

These variables control automatic spend-limit increases for TiDB Cloud clusters that hit their cap. The feature progressively raises the limit up to MNEMO_AUTO_SPEND_LIMIT_MAX with a configurable cooldown between increments.

Metering

These variables configure the legacy server-side API metering writer. It emits mem9-api events for successful recall and ingest operations and is separate from runtime usage quota metering.

Metering location is configured as a single destination URL. Supported schemes are:

• s3://<bucket>/<prefix>/ for compressed JSON batches in S3
• http://... or https://... for JSON batch webhooks

Runtime Usage Quota And Metering

Runtime usage is disabled by default. When enabled, the server reserves quota before memory recall/write operations, releases reservations after failed operations, commits reservations after successful operations, and sends console metering events to the runtime usage service. This path uses MNEMO_RUNTIME_USAGE_BASE_URL and does not use MNEMO_METERING_URL.

The runtime usage outbox uses the control-plane runtime_usage_outbox table for pending reservation finalization and metering delivery. It is enabled by default when runtime usage is enabled.

Reservation retries require one of these exact responses with details.retryable: true: 409 registry_conflict; 429 operation_in_progress, 429 registry_busy, or 429 reservation_concurrency_limited with a positive decimal integer-seconds Retry-After; or 503 unavailable. Reservation calls make up to three total attempts. The first retry samples from the base delay through the midpoint, and the second samples from the midpoint through the maximum delay. Retryable 429 responses use the greater of the sampled delay and Retry-After. After fail-closed handling stops, recognized or unknown 429 responses remain 429, while exhausted 409/503 responses, terminal 409 operation_conflict, and unknown 409 responses become 503.

Security And Debugging

AWS KMS Environment

These are only relevant when MNEMO_ENCRYPT_TYPE=kms. The server uses the AWS SDK default config chain; the common environment-based inputs referenced in code are:

Test-Only

Repository Map

Related Repositories

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

Apache-2.0

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_{Built on TiDB Cloud Starter for shared memory, vector search, and managed cloud provisioning.}