* feat(service): expose MCP through HTTP backend Serve JSON/SSE job endpoints and streamable HTTP MCP from one FastAPI application, sharing the same jobs and application lifecycle. Preserve the standalone MCP backend, add configurable MCP HTTP settings, update startup metadata and integration docs, and cover routing, lifecycle, configuration, and compatibility behavior with unit tests. * fix(service): preserve MCP request protections Route the exact MCP path through the complete FastMCP ASGI application so its middleware and state remain active. Reject non-literal MCP paths and validate reserved Job conflicts before tolerant service registration. Add regression coverage for middleware preservation, route syntax, and startup failure. * fix(service): reject encoded MCP paths Reject percent signs in mcp_path so ASGI path decoding cannot turn an accepted configuration into an unreachable route. Cover encoded slash, space, and double-encoded slash inputs. |
||
|---|---|---|
| .. | ||
| .claude-plugin | ||
| reme | ||
| README.md | ||
ReMe plugin for Claude Code
Connect Claude Code to ReMe — file-native long-term memory
for AI agents. The plugin gives the agent recall (read long-term memory) and records every
session automatically via a Stop hook. Consolidation of daily notes into long-term digest/
knowledge runs server-side in ReMe.
What you get
- MCP tools from the
remeserver:search,traverse,daily_list,frontmatter_read,read,auto_memory_cc, and more. - Stop hook (
hooks/auto_memory.py) — when a session ends it calls ReMe's server-sideauto_memory_cctool in a detached background process, passing only the session id. The server resolves that session's transcript on disk and records the durable facts into today's daily note. Recording is fully automatic and asynchronous — the agent never records by hand, and stopping is never delayed. Best-effort: if the server is down it logs and gives up silently. - Skill
reme-memory— recall long-term memory before answering (semanticsearch, topologicaltraverse, statedaily_list/frontmatter_read, thenreadwith citations), plus a server status check. Recording is handled silently by the Stop hook.
Deployment model
The plugin connects to a shared HTTP MCP server you start once — it does not spawn ReMe. One server means one set of background watchers / dream cron across all your Claude Code windows.
Prerequisites
-
Install ReMe (Python 3.11+):
pip install "reme-ai[core]" -
Configure model credentials in a
.env(seeexample.env):EMBEDDING_API_KEY=sk-xxx EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 LLM_API_KEY=sk-xxx LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 -
Start the ReMe HTTP server (one time, leave it running):
reme start service.backend=httpThe same process serves the JSON Job API and MCP at
http://127.0.0.1:2333/mcp. To use a different port, start withservice.port=<port>and update theurlin.mcp.jsonto match.
Install the plugin
/plugin marketplace add ./integrations/claude_code
/plugin install reme@reme-marketplace
(Or point /plugin marketplace add at the GitHub repo + subpath once published.) Restart Claude Code, then
run /mcp to confirm the reme server and its tools are connected; the reme-memory skill can then
recall memory and report server health.
Notes
- The plugin's MCP server URL lives in
integrations/claude_code/reme/.mcp.json. Keep it in sync with how you start ReMe (host/port). The Stop hook reads this same file to find the server (override withREME_HOST/REME_PORTenv vars). - The Stop hook needs
python3onPATHand resolves transcripts under~/.claude/projects(override the base withCLAUDE_CONFIG_DIR). It logs tointegrations/claude_code/reme/logs/auto_memory_hook.log. - The MCP tool-name prefix (
mcp__reme__…) may include the server segment depending on your Claude Code version; the skill uses themcp__reme__*wildcard so it works either way.