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:
python scripts/smoke_test.py --python C:\path\to\MemOS\.venv\Scripts\python.exe --memos C:\path\to\MemOS --search
Configuration
The bundle's patch inserts one @deepseek-ai/dsh-mcp-client row (id: memos-mcp, serverName: memos). Environment knobs read by !!js at mount time:
| Variable | Default | Meaning |
|---|
MEMOS_PYTHON | python | python of the MemOS venv |
MEMOS_HOME | '' (inherit cwd) | MemOS checkout used as the child cwd |
MEMOS_MCP_SERVER | memos | tool namespace (mcp__<name>__*) |
To change any other field (e.g. toolCallTimeoutMs, failOnStartupError), override the row by id in your profile's cordis.patch.yml — later layers win, but an id-targeted patch replaces the whole config, so restate every key:
- id: memos-mcp
config:
transport: stdio
serverName: memos
command: 'C:/path/to/MemOS/.venv/Scripts/python.exe'
args: ['-m', 'memos.api.mcp_serve']
cwd: 'C:/path/to/MemOS'
toolCallTimeoutMs: 60000
failOnStartupError: false
Host-run endpoint override
MemOS's .env commonly targets host.docker.internal:18181/18182 (valid inside the MemOS docker network). When the MCP child runs on the host, those endpoints must be reachable from the host. If your gateway is published on the host loopback (or reachable only through a local proxy rule), override the endpoints with an env block on the row:
- id: memos-mcp
config:
transport: stdio
serverName: memos
command: 'C:/path/to/MemOS/.venv/Scripts/python.exe'
args: ['-m', 'memos.api.mcp_serve']
cwd: 'C:/path/to/MemOS'
env:
OPENAI_API_BASE: 'http://127.0.0.1:18181/v1'
MOS_EMBEDDER_API_BASE: 'http://127.0.0.1:18182/compatible-mode/v1'
MEMRADER_API_BASE: 'http://127.0.0.1:18181/v1'
QWEN_API_BASE: 'http://127.0.0.1:18181/v1'
failOnStartupError: false
(These values only stick because the patch script changes MemOS's load_dotenv(override=True) to override=False — ambient env then wins over .env.)
MemOS source patches
scripts/patch_memos.py applies five small, idempotent fixes to the MemOS checkout (tested against MemoryOS 2.0.30):
src/memos/api/config.py — load_dotenv(override=True) → load_dotenv(), so host-run env overrides are not clobbered.src/memos/log.py — console handler to stderr; stdout is the MCP protocol channel and log lines there corrupt the stdio stream.src/memos/api/mcp_serve.py — map EMBEDDING_DIMENSION into the default config so the Neo4j vector index matches the embedder dimension.src/memos/mem_os/utils/default_config.py:
- env-aware embedder construction honoring
MOS_EMBEDDER_BACKEND / MOS_EMBEDDER_API_BASE / MOS_EMBEDDER_API_KEY / MOS_EMBEDDER_MODEL / EMBEDDING_DIMENSION (mirrors APIConfig.get_embedder_config; the MCP default path otherwise ignores them and reuses the chat endpoint);
- the sentence-chunker tokenizer points at a local gpt2
tokenizer.json — chonkie otherwise downloads gpt2 from huggingface.co, which is unreachable in some networks.
Run python scripts/patch_memos.py --list to see the patch list. If a patch fails with "not in pre-patch state", your MemOS version differs from 2.0.30 — check the diff and re-apply by hand.
Troubleshooting
| Symptom | Cause / fix |
|---|
dsh plugin add installs Gu / split packages | On Windows, a plugin path containing spaces is split when dsh forwards it to pnpm. Use the 8.3 short path (e.g. C:\Users\GULING~1\...) or add . from a space-free directory. |
| Row stays pending after restart | MEMOS_PYTHON/MEMOS_HOME wrong, or MemOS venv missing. Check dsh --profile web --dump-config. |
Graph not found: memosdefaultuser at server start | Neo4j Community Edition + MOS_NEO4J_SHARED_DB=false in .env → set it to true and NEO4J_AUTO_CREATE=false (single shared neo4j database). |
Tokenizer 'gpt2' could not be loaded ... huggingface.co | Run setup.py to download the local tokenizer, or set HF_ENDPOINT=https://hf-mirror.com (the patch script's tokenizer line already points at the local file). |
Embeddings request ended with error: Error code: 503 | The LLM/embedding gateway (e.g. :18181/:18182) is down or not reachable from the host — see Host-run endpoint override and start the gateway. |
MCP handshake fails / Failed to parse JSONRPC | Logging to stdout — re-run patch_memos.py (patch #2). |
pydantic serialization warnings at startup | Cosmetic; MemOS prints them when serializing config objects. |
Security
The MCP server command runs as trusted executable code outside the agent sandbox (this is why the Harness enables no MCP server by default). Only connect to MemOS servers you run yourself.
License
MIT
