rememb 0.4.7

Persistent memory standard for AI agents — local, portable, zero config

AI agents forget everything between sessions. rememb gives them persistent memory — local, portable, and works with any agent.

The problem

Every dev using AI 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 (Mem0, Zep, Letta) require servers, API keys, and cloud accounts.
You just want the agent to remember your project.

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Install

pip install rememb

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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"]
    }
  }
}

2. Restart your IDE.

The agent now automatically reads memory at session start, writes when learning something new, and searches when needed.

The rememb_init MCP tool is optional/deprecated for day-to-day usage: in MCP mode, rememb resolves storage home-first and auto-initializes ~/.rememb when needed. The tool remains available for compatibility and explicit recovery workflows.

For the current public MCP tool list 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 hit the same loaded embedding model 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:8080)
rememb --port 9000        # Custom port
rememb fetch-model        # Download the local embedding model for semantic search

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How it works

.rememb/
  entries.json   ← structured memory (project, actions, systems, user, context)
  meta.json      ← project metadata
  config.json    ← limits, sections, web UI behavior, semantic model settings

A JSON file in your project. Your agent reads it at the start of every session.

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/..."

Search uses local semantic embeddings (no API, no cloud). The embedding model is unloaded after a short idle window by default, so the process does not keep the full model resident forever.

rememb now writes the full configuration set to .rememb/config.json during initialization, so all supported knobs live in one place:

{
  "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"
  },
  "entry_batch_size": 24,
  "entry_load_threshold": 6,
  "semantic_model_idle_ttl_seconds": 15,
  "semantic_model_name": "paraphrase-multilingual-MiniLM-L12-v2",
  "semantic_conflict_threshold": 0.88
}

Set semantic_model_idle_ttl_seconds to 0 to unload the model immediately after each semantic operation. If you want a smaller model, you can switch semantic_model_name to another SentenceTransformers model such as intfloat/multilingual-e5-small or all-MiniLM-L6-v2.

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.

Environment overrides are also available: REMEMB_SEMANTIC_MODEL_IDLE_TTL_SECONDS and REMEMB_SEMANTIC_MODEL_NAME.

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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

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Web UI

rememb includes a local web interface for browsing and managing memory entries.

rememb                       # Open the web UI (http://localhost:8080)
rememb --host 0.0.0.0        # Bind to all interfaces
rememb --port 9000           # Custom port
rememb --no-browser          # Start server without opening the browser

The screenshot above shows the actual local web UI running with demo data.

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

Settings view with limits, semantic search controls, section colors, and maintenance actions.

Skills view listing all bundled skills available in the installed rememb package.

Features:

• Top-level navigation — switch between Memories, Stats, Skills, and Settings
• Section sidebar — filter by section with live entry counts and a one-click consolidate action
• Search and sorting — search from the header and reorder results by most recent, oldest, storage order, or reversed
• Card-based browsing — scan entries with section badges, relative timestamps, tags, and entry IDs
• Modal CRUD flows — create entries, inspect details, edit content and tags, or delete entries from the detail view
• Stats page — view totals, active sections, store size, date range, and recent entries
• Settings page — edit limits, semantic search options, section colors, and maintenance actions
• Skills page — browse all bundled skills available in the installed rememb package

The semantic search MCP tool also accepts an optional exact tag filter, so IDE clients can restrict semantic matches before ranking.

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CLI

rememb                                                      # Open the web UI (http://localhost:8080)
rememb --host 0.0.0.0 --port 8080 --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 fetch-model                                          # Download the local embedding model
rememb --version, -v                                        # Show version
rememb --help, -h                                           # Show help

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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 have automated test coverage
• stdio MCP is the primary documented integration path
• SSE MCP is documented, but not yet covered by end-to-end automated client tests
• release automation and Trusted Publishing are documented in RELEASE.md

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Design

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

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Contributing

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

PRs welcome. Issues welcome. Stars welcome. 🌟

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License

MIT