jev-memory
Allows Hermes to use the memory_select tool to filter already retrieved memories so only those fitting the query token/memory budget are sent.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@jev-memoryKeep only the memories most relevant to 'backend architecture' within a 3000-token budget."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
jev-memory-selector
Filters memories an agent already retrieved, so only what fits the budget is sent.
Author: Pinuts. MIT license.
Stack: Python 3.10+, HTTP, MCP stdio, Docker, HTML demo.
After jev-memory serve: demo
Local, no keys
git clone https://github.com/Pinutss/jev-memory-selector
cd jev-memory-selector
uv sync
uv run jev-memory demo
uv run jev-memory serveNo agent and no LLM are required. JEV_PROVIDER=auto (the default) stays on the local heuristic. If JEV and a gateway are configured, they are used as the judge. Docker:
docker compose upRelated MCP server: mem0-mcp
Cursor and Hermes plugin
This repository is an Agent Plugin. Cursor and Hermes can install it directly.
Hermes
hermes plugins install Pinutss/jev-memory-selector --enableCursor
Install from the JEV plugins marketplace, or copy this folder to ~/.cursor/plugins/local/jev-memory-selector.
One tool: memory_select. Pass query + memories. Keys stay in the process environment, not in the call.
Requires uv. JEV_PROVIDER defaults to local.
Hermes and OpenClaw
Yes, locally. The MCP process does not need JEV or a gateway:
uv run jev-memory mcpOne tool: memory_select. Pass query + memories. Keys stay in the process environment, not in the call.
Hermes (~/.hermes/config.yaml):
mcp_servers:
jev-memory:
command: uv
args: ["run", "--directory", "/path/to/jev-memory-selector", "jev-memory", "mcp"]
env:
JEV_PROVIDER: localThen hermes mcp test jev-memory and /reload-mcp.
OpenClaw (~/.openclaw/openclaw.json, or Settings > MCP > Stdio):
{
"mcp": {
"servers": {
"jev-memory": {
"command": "uv",
"args": ["run", "--directory", "/path/to/jev-memory-selector", "jev-memory", "mcp"],
"env": { "JEV_PROVIDER": "local" }
}
}
}
}Python
from jev_memory_selector import MemorySelector
result = MemorySelector(provider="local").select(
query="How does my backend work?",
memories=[{"id": "1", "content": "Backend FastAPI"}],
max_memories=8,
max_tokens=3000,
)
print(result.texts)JEV + gateway (optional)
If you wire the cloud later, two keys are enough: JEV_API_KEY / JEV_BASE_URL, and your gateway (GATEWAY_API_KEY, GATEWAY_BASE_URL, GATEWAY_MODEL). No OpenAI / Anthropic / Gemini key in this repo.
If you have several LLMs or a multi-model key (OpenRouter, Groq), pick the judge model with jev-model-router, then set GATEWAY_* to that endpoint. Keys stay in the environment, never in the request body.
cp .env.example .envJEV_PROVIDER=jev will not start if either side is missing. With auto, missing keys just keep the local heuristic.
HTTP
uv run jev-memory serveGET /healthz, POST /v1/select. Binds 127.0.0.1. The body must not contain keys.
Limits
The default counter is about 4 characters per token. In local mode, ranking is lexical. Scope isolates lists, it is not auth. No store, no PyPI yet.
docs/vision.md is a long-term target, not the current contract.
This server cannot be deployed
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