Apache-2.0, LLM-supervised persistent memory for AI agents, connected to DeepSeek Harness via the dsh-mnemon plugin as a supervised three-tier memory system.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
Apache-2.0
01What can it help you accomplish?
Give DeepSeek Harness agents persistent, cross-session agent memory
Long-term memory spaces in a four-graph knowledge store (temporal, entity, causal, semantic), with intent-aware recall and automatic deduplication
DeepSeek Harness users whose agents lose critical decisions across sessions and need durable knowledge that survives context compaction
Share one pool of agent memory across sessions and frameworks
A single local `~/.mnemon` SQLite database readable and writable by Claude Code, Codex, Cursor, OpenClaw, DeepSeek Harness and other supported runtimes
Teams running multiple agent tools who want cross-session, cross-framework knowledge instead of isolated per-tool memory
02How to install into DeepSeek Harness
Prerequisites
- The `mnemon` binary installed on the host — via Homebrew Cask (`brew install --cask mnemon-dev/tap/mnemon`), `go install github.com/mnemon-dev/mnemon@latest`, or from source (`make install`)
- DeepSeek Harness (dsh) with the Web profile running
Installation steps
- 01
Install mnemon on the host: `brew install --cask mnemon-dev/tap/mnemon` (macOS), or `go install github.com/mnemon-dev/mnemon@latest` (macOS / Linux / Windows)
$ brew install --cask mnemon-dev/tap/mnemon
- 02
Add the DSH plugin: `dsh plugin --profile web add dsh-mnemon`
$ dsh plugin --profile web add dsh-mnemon
- 03
Restart your DSH Web profile: `dsh --profile web`
$ dsh --profile web
- 04
Open DSH's Settings → Plugin Config → Mnemon to pick a storage scope, and use the Memory System tab in a session to create or activate memory spaces
Verify the integration
- `mnemon --version` and `mnemon agency --version` confirm the binary installation
Rollback
- `mnemon setup --eject` removes all integrations
03DSH integration and capability boundaries
Installed as the dsh-mnemon plugin via `dsh plugin --profile web add` after placing the mnemon binary on the host; layers DSH runtime memory, managed project documents and Mnemon long-term memory spaces
Four-graph knowledge store
facts, decisions and insights the host LLM chooses to remember→temporal, entity, causal and semantic edges — not just vector similarity — with importance decay and garbage collection
memory is persisted to a local SQLite database under `~/.mnemon`LLM-supervised memory operations
the host LLM's judgment calls on what to remember, link and recall→structured JSON results from three primitives (`remember`, `link`, `recall`); no embedded LLM, no API keys
`mnemon setup` deploys skill files, hooks and behavioral guides into each runtime's config directoryIntent-aware recall
natural-language recall queries from the agent→graph traversal plus optional vector search (RRF fusion), enabled by default for all queries; `remember` auto-detects duplicates and conflicts
Multi-framework memory sharing
one `~/.mnemon` store→memory shared across Claude Code, Codex, Cursor, TRAE, Qoder, OpenClaw, DeepSeek Harness (via dsh-mnemon) and more; named stores and `MNEMON_STORE` isolate per project
optional local Ollama can be added for enhanced vector+keyword hybrid search
04Who is it for? When not to use it?
Good for
- DeepSeek Harness users whose agents lose critical decisions across sessions and need durable knowledge that survives context compaction
- Teams running multiple agent tools who want cross-session, cross-framework knowledge instead of isolated per-tool memory
Not for
- Windows supports the core Memory commands, but Agency remains unavailable on Windows until its local authority boundary has native Windows security.
- The DeepSeek Harness integration depends on the separate dsh-mnemon plugin (omdsh-dev/dsh-mnemon), which must be added via the dsh plugin CLI after mnemon is installed on the host.
05Compatibility, maintenance and safety notes
- Windows supports the core Memory commands, but Agency remains unavailable on Windows until its local authority boundary has native Windows security.
- The DeepSeek Harness integration depends on the separate dsh-mnemon plugin (omdsh-dev/dsh-mnemon), which must be added via the dsh plugin CLI after mnemon is installed on the host.
- Mnemon is a young project (repository created 2026-02) still below v1; durable writes go through supervised sub-agents and the storage scope must be picked manually in DSH's plugin config.
Apache-2.0 · actively maintained (latest release v0.2.4, 2026-08-19)
06Frequently asked questions
How does Mnemon connect to DeepSeek Harness?
Through the dsh-mnemon plugin. Install mnemon on the host first, then run `dsh plugin --profile web add dsh-mnemon` and restart your DSH Web profile. Open Settings → Plugin Config → Mnemon to pick a storage scope, and use the Memory System tab in a session to create or activate memory spaces.
Is Mnemon native to DSH or an MCP server?
Neither. Mnemon follows the LLM-supervised pattern: a standalone local binary with three primitives (`remember`, `link`, `recall`), while your host LLM decides what to remember and when to forget — no embedded LLM, no MCP, no API keys.
What prerequisites do I need?
The `mnemon` binary on the host — via Homebrew Cask (`brew install --cask mnemon-dev/tap/mnemon`), `go install github.com/mnemon-dev/mnemon@latest`, or from source — plus DeepSeek Harness with the Web profile.
Where does my memory data go?
Everything stays local in a single SQLite database under `~/.mnemon` (configurable via `MNEMON_DATA_DIR`). Optional local Ollama adds vector+keyword hybrid search; nothing leaves your machine.
Can other agents share the same memory?
Yes. All sessions use the same `default` store by default, and Mnemon also supports Claude Code, Codex, Cursor, TRAE, Qoder, OpenClaw and more. Named stores and the `MNEMON_STORE` environment variable isolate memory per project.
07Related DSH workflows
deepseek-reasonix
by esengine
DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running.
phi
by pulseaiclub
a coding Agent, rpc plugin, sub-agents, hashline edits, and mcp
sivtr
by ariestar
A unified memory workspace for agents and people, making terminal output and AI session context searchable and reusable across local workspaces.
baro
by jigjoy-ai
A CLI that turns a goal into a pull request - and a sandbox for testing concurrent AI coding agents on the Mozaik runtime.
08Data and sources
which layers DSH's runtime memory, managed project documents, and Mnemon's long-term memory spaces into one supervised t…
With `mnemon` installed on the host (see Install), add the plugin and restart your DSH Web profile: `dsh plugin --profil…
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.
Best DeepSeek Harness Plugins
Twelve plugins worth installing first — picked from the whole catalog, across every category.
