Operate AI agents without losing context between sessions. rememb is a local-first persistent memory layer: structured entries, keyword search, versioning, diff, restore, and audit trail — no cloud service required.
Agenten stärken
Der eigene Befehl des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
uvx rememb==0.3.5Claude Code hat keinen Installationslink für einen lokalen Befehl. Kopieren Sie den Befehl unten und führen Sie ihn in einem Terminal aus.
claude mcp add io.github.luizedupp-rememb -- uvx rememb==0.3.5In einem Terminal ausführen.
{ "mcpServers": { "io.github.luizedupp-rememb": { "command": "uvx", "args": [ "rememb==0.3.5" ] } }}claude_desktop_config.json
Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden
Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
Teams using agents at real velocity rarely fail because they lack generation. They fail because operating agents every day creates context debt:
Every team or solo developer operating agents professionally hits this wall:
Session 1: "We're using PostgreSQL, auth at src/auth/, prefer async patterns."
Session 2: Agent starts from zero. You explain everything again.
Session 3: Same thing.
Existing solutions often center on hosted memory layers, API keys, or opaque context pipelines. What you actually need is to resume the next session with the minimum correct context and a trail you can inspect.
rememb is built around four memory problems:
pip install rememb
Zero friction. No CLI commands. Native IDE integration.
1. Add to your IDE's MCP config:
{
"mcpServers": {
"rememb": {
"command": "rememb",
"args": ["mcp"]
}
}
}
Add repeatable --project /path/to/repo args for per-project stores alongside global ~/.rememb. Details: MCP_TOOLS.md.
2. Restart your IDE.
The agent can read stored context at session start, write durable memory when something changes, and search only when targeted recall is needed.
If you want rememb usage to stay consistent, add a rememb-specific instruction block in your IDE custom instructions or in the MCP client prompt that wraps the agent. The point is to make the agent route reads, writes, search, recovery, and maintenance through rememb instead of ad hoc prompt memory.
You can place that block in either of these places:
In both cases, keep the scope explicit: these rules are about how the agent should use rememb, not about replacing the rest of your coding instructions.
For the exact copy-paste block, use the canonical rules section in MCP_TOOLS.md.
No extra storage setup, server config, or schema migration is required. In MCP mode, rememb auto-initializes ~/.rememb and optional --project stores on first use.
For the current public MCP tool list (18 tools) and descriptions, see MCP_TOOLS.md.
If you want multiple MCP clients on the same machine to reuse one already-running rememb process, start a persistent local SSE transport:
rememb mcp --transport sse --host 127.0.0.1 --port 8765
This keeps one MCP process alive, so repeated clients can connect through http://127.0.0.1:8765/sse and http://127.0.0.1:8765/messages/.
Do not put --transport sse inside a stdio MCP client config. stdio clients expect JSON-RPC on stdin/stdout; the SSE mode exposes an HTTP endpoint and must be started separately.
rememb # Open the web UI (http://localhost:18181)
rememb --port 9000 # Custom port
~/.rememb/ ← default store location (MCP and Web UI)
entries.json ← default JSON backend (or entries.db with SQLite)
meta.json ← project metadata
config.json ← limits, sections, storage backend, UI paging
A local store on disk. Your agent can read prior decisions, search by keywords and tokens, update entries without losing history, and restore previous versions without depending on a cloud memory service. Copy ~/.rememb/ anywhere to move the store.
User: "We're using PostgreSQL, auth at src/auth/, async patterns"
Agent: [rememb_write] → Saved
[New session]
Agent: [rememb_read] → Context loaded
Agent: "I see you're using PostgreSQL with auth at src/auth/..."
These map to rememb_write, rememb_edit, and rememb_delete. For the full MCP surface, see MCP_TOOLS.md.
Search uses keyword and token matching over entry content and tags. rememb returns full matches; the agent applies semantic relevance judgment. No API keys, no cloud, no embedding model download at runtime.
config.json is written during initialization with all supported knobs:
{
"max_content_length": 1000000,
"max_tag_length": 500,
"max_tags_per_entry": 100,
"max_entries": 100000,
"sections": ["project", "actions", "systems", "requests", "user", "context"],
"section_colors": {
"project": "#d84848",
"actions": "#d08020",
"systems": "#d4c430",
"requests": "#40c040",
"user": "#20d4c4",
"context": "#c060f0"
},
"entry_batch_size": 24,
"entry_load_threshold": 6,
"storage_backend": "json"
}
Set storage_backend to sqlite for larger stores. The Web UI and MCP migrate existing JSON entries automatically when you switch backends.
entry_batch_size and entry_load_threshold control pagination in the web UI — how many cards load at once and when to trigger "load more".
Section names are normalized to lowercase, duplicates are ignored after normalization, and removing a section with existing entries automatically migrates those entries to uncategorized. meta.json is kept in sync with the current effective section list.
Older stores may still contain legacy embedding-related config keys; they are dropped the next time configuration is loaded or saved.
rememb includes a local web interface for supervision — browse memory, inspect history, and tune runtime settings. Entry writes and edits go through MCP; the Web UI does not expose create/edit/delete controls for entries.
rememb # Open the web UI (http://localhost:18181)
rememb --host 0.0.0.0 # Bind to all interfaces
rememb --port 9000 # Custom port
rememb --no-browser # Start server without opening the browser
Overview with entry totals and recent memory activity.
Stats with totals, section breakdown, date range, and recent entries.
Settings for limits, storage backend, section colors, and maintenance actions.
Skills browser for bundled agent skills included with rememb.
Views:
Entry inspection from the UI includes version history and side-by-side diff. Restore is available through MCP (rememb_restore); the Web UI is read-only for entry mutations.
rememb_search accepts an optional exact tag filter, so IDE clients can restrict keyword matches before ranking.
rememb # Open the web UI (http://localhost:18181)
rememb --host 0.0.0.0 --port 18181 --no-browser # Custom bind, no auto-open
rememb mcp # Start MCP server over stdio
rememb mcp --transport sse --host 127.0.0.1 --port 8765 # One persistent local MCP process
rememb --version, -v # Show version
rememb --help, -h # Show help
The current compatibility surface is tracked explicitly in COMPATIBILITY.md.
Short version:
~/.rememb/ anywhere, it worksCore capabilities:
git clone https://github.com/LuizEduPP/Rememb
cd Rememb
pip install -e ".[dev]"
PRs welcome. Issues welcome. Stars welcome. 🌟
MIT
Persistent memory for AI agents — local JSON, zero config, no server required.
{ "mcpServers": { "io.github.luizedupp-rememb": { "command": "uvx", "args": [ "rememb==0.3.5" ] } }}Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
Rememb läuft lokal über stdio.
Zuletzt geprüft: 2026-11-09. Persistent memory for AI agents — local JSON, zero config, no server required.