How do I install Rememb?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Rememb need an API key?

No required environment variable is listed on this record.

What transport does Rememb use?

Rememb runs locally over stdio.

When was Rememb last checked?

Last checked 2026-11-09. Persistent memory for AI agents — local JSON, zero config, no server required.

Rememb MCP server

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx rememb==0.3.5

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1claude mcp add io.github.luizedupp-rememb -- uvx rememb==0.3.5

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.luizedupp-rememb": {4      "command": "uvx",5      "args": [6        "rememb==0.3.5"7      ]8    }9  }10}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

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.

Launch

Server facts

Local

Transport: stdio
Version: 0.3.5
Last checked

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

rememb cover

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.

The problem

Teams using agents at real velocity rarely fail because they lack generation. They fail because operating agents every day creates context debt:

too much re-explaining project facts every session
too little durable memory outside the chat window
too little audit trail for why something changed
too much noise when recalling the right context

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:

durable facts and decisions instead of session-only chat memory
keyword search instead of rereading everything (agents judge relevance)
non-destructive versioning instead of silent overwrites
local-first audit trail for AI work, not opaque cloud logs

Install

pip install rememb

Quick Start

With MCP (recommended)

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:

IDE-level custom instructions
the system prompt or instruction field of the MCP client that is calling rememb

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.

Local usage without MCP

rememb                    # Open the web UI (http://localhost:18181)
rememb --port 9000        # Custom port

How it works

~/.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.

Memory sections

Section	What to store
`project`	Tech stack, architecture, goals
`actions`	What was done, decisions made
`systems`	Services, modules, integrations
`requests`	User preferences, recurring asks
`user`	Name, style, expertise, preferences
`context`	Anything else relevant

Web UI

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

rememb web UI

Overview with entry totals and recent memory activity.

rememb stats view

Stats with totals, section breakdown, date range, and recent entries.

rememb settings view

Settings for limits, storage backend, section colors, and maintenance actions.

rememb skills view

Skills browser for bundled agent skills included with rememb.

Views:

Overview — entry totals, deleted count, store size, and recent memory
Memory — browse, keyword search, filter by section, sort, and include deleted entries
Stats — totals, backend, section bars, oldest/newest timestamps, and recent entries
Settings — edit limits, storage backend, section colors, consolidate duplicates, and save runtime config
Skills — browse bundled agent skills (60 skills shipped in the package)

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.

CLI

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

Compatibility

The current compatibility surface is tracked explicitly in COMPATIBILITY.md.

Short version:

Python 3.10 to 3.12 are covered by CI
CLI contract and MCP tool schema (18 tools) have automated test coverage
stdio MCP is the primary documented integration path
SSE MCP is documented and partially tested at the route level
release automation and Trusted Publishing are documented in RELEASE.md

Design

Local first — plain JSON or SQLite on disk
Portable — copy ~/.rememb/ anywhere, it works
Agnostic — any agent, any IDE (MCP or CLI)
No lock-in — no servers, no API keys, no accounts

Core capabilities:

structured memory with sections and tags
keyword search with agent-side relevance judgment
non-destructive versioning, diff, restore, and soft delete
duplicate consolidation (exact content) and store stats
config and maintenance via Web UI (settings only; entry writes via MCP)
60 bundled agent skills via Web UI and MCP

Contributing

git clone https://github.com/LuizEduPP/Rememb
cd Rememb
pip install -e ".[dev]"

PRs welcome. Issues welcome. Stars welcome. 🌟

License

MIT

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Rememb?

Does Rememb need an API key?

What transport does Rememb use?

When was Rememb last checked?

Documentation

The problem

Install

Quick Start

With MCP (recommended)

Local usage without MCP

How it works

Memory sections

Web UI

CLI

Compatibility

Design

Contributing

License