MIT, local-first agent memory hub and local Agent runtime — gives DeepSeek Harness-style coding agents one shared long-term memory via installed memory Skills, the `memmy-memory` CLI and a local API on 127.0.0.1:18960.
DSH integration
Compatible
Author-claimed
Safety audit
Unaudited
Last verified
2026-08-21
License
MIT
01What can it help you accomplish?
Give every coding agent one shared, persistent agent memory so project context carries over between agents and sessions
A local-first memory layer that keeps project goals, decisions, constraints, and failed attempts, then brings the relevant context to the next Agent — no need to re-introduce anything again
Developers running DeepSeek Harness-style coding agents (Codex, Claude Code, Cursor, OpenClaw) whose agents keep losing context between sessions
Convert months of existing agent history into searchable long-term memory within minutes
Automatic scan of Cursor, Claude Code, Codex, OpenCode, OpenClaw, Hermes, WorkBuddy, Pi and qwenwork histories, distilled into personal long-term memory plus a personalized "First Meeting Report"
Teams and individuals with months of conversations in other agents who want that context to become a reusable knowledge asset instead of starting from scratch
Wire memory search and writes into external agents and scripts
`memmy-memory` CLI (init / health / search / add / get) against the local memory service at http://127.0.0.1:18960, with memory Skills installed into each supported agent as needed
Developers who want agents or shell scripts to query and append shared memory programmatically, including coding agents like DeepSeek Harness via installed Skills
02How to install into DeepSeek Harness
Prerequisites
- Memmy desktop app or CLIs, downloaded from the official website (https://memmy.bot/) or GitHub Releases
- An account (sign-up grants Agent task trial tokens) or BYOK: your own model API key configured in ~/.memmy/config.yaml
- Only for building from source: Node.js >= 22 and npm
Installation steps
- 01
Get Memmy from the official website (https://memmy.bot/) or GitHub Releases, launch the desktop app and choose Account mode or API Key mode
- 02
In API Key mode, configure the primary model and pass a connection test; optionally configure Embedding, ASR, image generation, memory summary, and skill evolution models
- 03
Enter the main workbench and send your first task; open "Tools" to connect messaging channels or third-party tools, open "Memory" to scan Agent history sources
- 04
To give external agents memory access, run `memmy-memory init` — it writes the Memory config and installs Skills for each Agent as needed
Verify the integration
- Run `memmy status` to check config, workspace, model, and provider status
- Run `memmy-memory health` to check the memory service
03DSH integration and capability boundaries
Local agent memory hub whose `memmy-memory init` CLI writes the Memory config and installs memory Skills into external agents, letting DeepSeek Harness-style coding agents read/write one shared long-term memory via the local memory service; the repo is tagged `dsh-plugin`, though the README documents no dsh-specific flow
Cross-Agent shared long-term memory
project goals, decisions, constraints, failed attempts and agent conversations→one shared memory layer reused across Codex, Claude Code, Cursor and OpenClaw without re-introducing context
memory, configuration and app state are written to your machine by default (~/.memmy)history onboarding reads the local history files of your existing agentsMemOS-powered memory engine with history onboarding
scattered conversations and behavior from supported agents (Cursor, Claude Code, Codex, OpenCode, OpenClaw, Hermes, WorkBuddy, Pi, qwenwork)→structured, searchable, reusable long-term memory plus a personalized "First Meeting Report"
scanning imports and stores other agents' history into Memmy's local memoryLocal Agent runtime with multiple entry points
tasks issued from the desktop app, `memmy` CLI/TUI, or any OpenAI-compatible client→the same Agents, memory, and configuration served from every entry point; `memmy serve` exposes an OpenAI-compatible API on :18990
`memmy serve` opens a local API on port 18990; the memory service listens on 127.0.0.1:18960Extensible tools via Skills and MCP
Skills, MCP servers, messaging channels (Telegram, Discord, WeChat, Feishu, DingTalk) and productivity tools (GitHub, Gmail, Notion, Slack, Jira)→agents that go from conversation to real task execution, including managed Chromium browser tools for local page inspection
the desktop app and scripts/dev-start.sh prepare a managed Chromium build before the Agent Gateway startsconnected third-party tools and channels are invoked over the network under your credentials
04Who is it for? When not to use it?
Good for
- Developers running DeepSeek Harness-style coding agents (Codex, Claude Code, Cursor, OpenClaw) whose agents keep losing context between sessions
- Teams and individuals with months of conversations in other agents who want that context to become a reusable knowledge asset instead of starting from scratch
- Developers who want agents or shell scripts to query and append shared memory programmatically, including coding agents like DeepSeek Harness via installed Skills
05Compatibility, maintenance and safety notes
- Account mode grants Agent task trial tokens whose amount and usage are shown in the app; once the trial credits run out or expire, continued agent tasks require switching to BYOK with your own model API key.
- Building from source requires Node.js >= 22 and npm, and on Windows scripts/dev-start.sh must be run in Git Bash.
- The README documents memory-Skill installation for external agents generically and the repo is tagged `dsh-plugin`, but no DeepSeek Harness-specific setup is documented — usage inside dsh is inferred via the installed memory Skills / MCP ecosystem.
MIT · actively maintained (latest release v1.0.9, 2026-08-20)
06Frequently asked questions
How does Memmy connect to coding agents like DeepSeek Harness?
Run `memmy-memory init` — it writes the Memory config and installs memory Skills for each supported agent as needed; agents then read and write the same memory through the local service (default http://127.0.0.1:18960). The README documents no dsh-specific steps — the repo is tagged `dsh-plugin` and connection happens through the generic Skills / MCP ecosystem.
Where is my agent memory stored? Does it go to the cloud?
Memmy is local-first: memory, configuration and app state are stored on your machine by default, and no data needs to be uploaded to the cloud. The memory service listens on 127.0.0.1:18960, with controlled access so only authorized sources can invoke memory capabilities.
Do I need my own API key?
Account mode grants Agent task trial tokens (the current amount and usage are shown in the app). Once used up or expired, switch to API Key (BYOK) mode and configure your own model provider in ~/.memmy/config.yaml.
Which agents can Memmy import history from?
Currently supported sources: Cursor, Claude Code, Codex, OpenCode, OpenClaw, Hermes, WorkBuddy, Pi and qwenwork. Within minutes, their history becomes personal long-term memory plus a personalized "First Meeting Report".
What entry points does Memmy provide?
The desktop app, the `memmy` CLI/TUI, and an OpenAI-compatible API started with `memmy serve` (:18990) — all sharing the same Agents, memory, and configuration.
07Related DSH workflows
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reactive-resume
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A one-of-a-kind resume builder that keeps your privacy in mind. Completely secure, customizable, portable, open-source and free forever. Try it out today!
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One portable memory layer for every AI agent: local-first, Markdown-native, user-owned, and self-evolving across apps, tools, and workflows.
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✨ All your agents and workspaces in one place, on every device you own. Track tasks on a board, accessible from desktop, mobile, browser, or API. Self-hosted.
08Data and sources
memmy-memory init # Write the Memory config and install Skills for each Agent as needed
Connect more tools through Skills and MCP, taking the Agent from conversation to real task execution.
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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