dsh-memos-bridge

A DeepSeek Harness bundle that bridges the MemOS memory service into the agent over MCP. Install the bundle, run one setup script, restart the Harness, and the agent gains persistent-memory tools named mcp__memos__*.

What you get

With the bundle active, the agent can call (a subset of the MemOS MCP surface):

Tool	Purpose
add_memory	add a memory from text, a document, or conversation messages
search_memories	semantic search across the user's memory cubes
get_memory / update_memory / delete_memory	inspect / correct / remove single memories
create_cube / register_cube / share_cube	manage memory cubes
chat	memory-enhanced chat with the MOS system
control_memory_scheduler	start/stop the async memory scheduler
…	16 tools total, listed by the smoke test

How it works

DeepSeek Harness (web profile)
  └─ cordis.patch.yml  ──inserts──►  @deepseek-ai/dsh-mcp-client  (ships with the dsh CLI)
                                        │  stdio
                                        ▼
                                python -m memos.api.mcp_serve   (MemOS venv)
                                        │
                                        ▼
                        MemOS MOS core: Neo4j (graph memory), Qdrant,
                        LLM + embedding gateway (e.g. Bailian-compatible)

The bundle contributes only a configuration layer (dsh.bundle + cordis.patch.yml); it mounts the stock @deepseek-ai/dsh-mcp-client plugin with a stdio server row. No Harness code is modified.

Prerequisites

• dsh CLI installed (the bundle relies on its built-in @deepseek-ai/dsh-mcp-client).
• A MemOS checkout with its docker stack up (the compose in MemOS/docker provides Neo4j + Qdrant + the MemOS API).
• Python ≥ 3.10 for the MemOS virtualenv.
• The MemOS LLM and embedding gateway reachable from the machine that runs the MCP child (see Host-run endpoint override).

Quick start

1. Set up the MemOS side (venv + dependencies + source patches + local tokenizer):

# from the plugin checkout
.\setup.ps1 --memos C:\path\to\MemOS

On POSIX: ./setup.sh --memos /path/to/MemOS. This creates MemOS/.venv, installs MemoryOS[tree-mem] plus python-dotenv, tqdm, langchain_text_splitters, chonkie, applies the required source patches (see below), and downloads a local gpt2 tokenizer.json (HuggingFace mirror first).

2. Install the bundle into a profile:

dsh plugin --profile web add ./dsh-memos-bridge

3. Configure paths (the patch reads these at boot; all optional):

# PowerShell: setx MEMOS_PYTHON "C:\path\to\MemOS\.venv\Scripts\python.exe"
#             setx MEMOS_HOME   "C:\path\to\MemOS"
export MEMOS_PYTHON=/path/to/MemOS/.venv/bin/python
export MEMOS_HOME=/path/to/MemOS

When MEMOS_PYTHON is unset the row falls back to python on PATH; when MEMOS_HOME is unset the child inherits the Harness cwd (MemOS still reads its .env, so point MEMOS_HOME at the checkout unless MemOS is the launch directory).

4. Verify and restart:

dsh --profile web --dump-config    # expect an `id: memos-mcp` row
dsh --profile web                  # restart the GUI; tools appear as mcp__memos__*

Run the smoke test any time:

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