universal-memory

Universal Memory (UMem)

Website | Documentation

A vendor-agnostic cognitive persistence layer for AI agents. Eliminate the "repetition tax" by transporting your context, preferences, guidelines, and history seamlessly across sessions, IDEs, and LLM models.

To see the core idea visually, check out the Excalidraw design or the proposal structure:

Diagram Breakdown

• Short-Term Memory (Ephemeral): Project-specific (folder-level) memories. A simple summary of recent changes, pending tasks, and project or task-level constraints.
• Agents Behaviours: Comports the user's expected agent behaviors. Instead of requesting the same settings in every session, the agent understands the user by their traits, thoughts, and any context key to enhancing the overall experience. This encompasses:
  • Long-Term Memory
  • Short-Term Memory
  • User Preferences
• Skill Creator: Encapsulates understanding of specific workflows. When a user explains a task pattern multiple times, the system translates it into structured, reusable agent skills.
• Unified Instruction File (AGENTS.md): The shared persistence endpoint consumed by compatible local agent instances (e.g., Agent A, Agent B, Agent C).

The Problem: The "Repetition Tax"

Every time you open a new session in Claude Code, start a new chat in Cursor, spin up a terminal with OpenCode, or invoke a local AI assistant, you pay a steep cognitive tax:

• Re-explaining your stack (e.g., "We use Python 3.12, Typer, and Ruff").
• Repeating coding style preferences (e.g., "Prefer functional design, do not write docstrings unless requested").
• Copy-pasting database connection schemas or module layouts.
• Explaining workflow methodologies (e.g., "We follow Spec-Driven Development (SDD)").

Universal Memory acts as a local persistence layer that automatically connects to your AI runtimes, aligning them to your exact workflow, context, and rules with zero friction.

Key Architectural Concepts

1. Dual-Memory Model

• Short-Term Memory (Project Scope): Ephemeral, directory-specific context. Tracks what you did 10 minutes ago, current active tasks, and immediate constraints.
• Universal Memory (Global Scope): Long-lived preferences, style guidelines, tool configurations, and identity.

2. Auto-Adaptation Engine

Instead of copy-pasting instructions, umem monitors your session context and automatically updates active project instruction manifests (AGENTS.md, CLAUDE.md, .cursor/rules/, etc.), enforcing operational consistency across all agents.

3. Model Context Protocol (MCP) Integration

Integrate umem natively with any client supporting the standard MCP (such as Claude Desktop or Cursor). AI agents can programmatically retrieve context, learn new facts, and suggest skills on the fly.

4. Agent Skills Standard

Encapsulates complex, repetitive procedural instructions into formal Agent Skills (conforming to the agentskills.io standard), complete with structured directories containing SKILL.md instructions, helper scripts/, and documentation references/.

Universal Memory keeps one canonical source for each skill. Shared, user-facing project skills live under umem/skills/<slug>/SKILL.md; private, operational, and legacy project skills live under .umem/skills/<slug>/SKILL.md. Native runtime folders such as .agents/skills/, .opencode/skills/, and .antigravity/rules/ receive complete synchronized copies so each agent can consume the same skill in its expected layout.

Installation & Setup

Ensure you have Python 3.12+ installed. You can run or install umem using your preferred package manager.

Try instantly with uvx

You can run umem without installing it permanently:

uvx --from universal-memory umem --help

[!WARNING] uvx is best for quick trials. For ongoing use, install Universal Memory as a persistent tool so umem is always available and can fully manage long-lived global memories and synced agent skills:

uv tool install universal-memory

Install via PyPI

pip install universal-memory

Upgrade Universal Memory

umem update does not upgrade the Python package from PyPI. It performs local, offline maintenance for the current .umem workspace, such as schema migrations, benchmark refreshes, and skill synchronization.

To upgrade the installed umem executable, use the package manager that installed it:

# If installed with uv tool
uv tool upgrade universal-memory

# If installed with pipx
pipx upgrade universal-memory

# If installed with pip
python -m pip install --upgrade universal-memory

# If running temporarily with uvx
uvx --refresh --from universal-memory umem --version

Confirm the executable you are running:

umem --version
which umem

Upgrading the executable does not silently mutate existing projects. The next time you work in an initialized project, reconcile it locally:

umem update --check
umem update
umem update --skills
umem connect
umem doctor

You do not need to run umem init again. Local maintenance creates snapshots and audit records before UMEM-owned writes. Existing .umem/skills/use-universal-memory/ trees and customized managed files are preserved; if both legacy and canonical Universal Memory skill roots exist, UMEM stops for an explicit migration decision instead of merging or deleting either tree.

Quick Start Guide

1. Initialize your project

Open your project directory and run:

umem init

Universal Memory detects the agents already used in the workspace, presents one combined confirmation, configures the best available project integration, and verifies that the agent can read project context. You do not need to choose an integration mechanism or know which instruction files it uses.

When a compatible agent needs the portable Agent Skill, UMEM discloses any network use and external project-scoped copy before confirmation, disables anonymous installer telemetry, and treats a missing prerequisite or failed installation as recoverable instead of blocking initialization.

To connect another agent later, run:

umem connect

Explicit runtime selection remains available for automation and unusual setups, but it is not required for the normal path.

DISABLE_TELEMETRY=1 npx --yes skills@1.5.20 add https://github.com/YanAmorelli/universal-memory/tree/v0.5.1/skills/universal-memory --skill universal-memory --agent pi --copy -y

2. Save your first preferences and facts

Tell umem what to keep in mind. You can target either the project scope (this folder) or the global scope (across all projects):

# Save a global preference
umem remember --scope global "Yan is a solutions architect specializing in AI applications"

# Save a project-specific constraint
umem remember --scope project "Always use Tomllib instead of PyYAML for configuration files" --tag config

3. Retrieve Context

Verify the consolidated context summary generated by combining short-term facts, rules, and global preferences:

umem context --scope project

4. Adopt or create an Agent Skill

If a skill already exists, choose the safest adoption path first. Use adopt for an existing .umem/skills/<slug> directory; use import for native runtime directories such as .agents/skills/<slug> and sync it back out to configured runtimes:

umem skills adopt .umem/skills/review-protocol --scope project
umem skills import .agents/skills/review-protocol --scope project --sync
umem skills detail review-protocol

If you are starting from scratch, draft and publish it without native side effects:

umem skills draft create \
  --name "Review Protocol" \
  --description "Reusable review workflow" \
  --trigger "when reviewing code"
umem skills draft validate review-protocol
umem skills publish review-protocol --format summary

For a one-step workflow, create the canonical skill. It is canonical-only by default; request sync explicitly when native runtime targets should be written:

umem skills create \
  --name "Review Protocol" \
  --description "Reusable review workflow" \
  --trigger "when reviewing code" \
  --format summary
umem skills sync review-protocol --check-gitignore --format summary

After editing .umem/skills/review-protocol/SKILL.md, refresh one runtime skill with:

umem skills sync review-protocol

5. Check status and health

umem status

Host Integration & Support Matrix

UMEM deliberately separates native ownership from portable compatibility:

The maintained and named integration surfaces are:

An agent appearing in the external skills catalog does not make it Tier 1. Tier 1 is intentionally small and requires a maintained adapter, release evidence, and repeatable host-specific validation. See the Getting Started guide for legacy-project behavior and the portable installation flow.

Running as a Model Context Protocol (MCP) Server

AI agents can interact directly with your memory over the Model Context Protocol. Manual MCP configuration for a host without a programmed UMEM workflow is Tier 3: tool availability is validated, but instruction loading and agent behavior are not guaranteed.

One-off Launch Command

uvx --from universal-memory umem-mcp

Persistent Install Launch Command

umem-mcp

Example Config: Claude Desktop (claude_desktop_config.json)

Use the uvx form when Universal Memory is not installed as a persistent tool:

{
  "mcpServers": {
    "universal-memory": {
      "command": "uvx",
      "args": [
        "--from",
        "universal-memory",
        "umem-mcp"
      ]
    }
  }
}

If you installed Universal Memory with uv tool install universal-memory or pipx install universal-memory, use the stable entrypoint:

{
  "mcpServers": {
    "universal-memory": {
      "command": "umem-mcp",
      "args": []
    }
  }
}

Troubleshoot startup with:

uvx --from universal-memory umem doctor
uvx --from universal-memory umem-mcp --help

For GUI-launched MCP hosts, use the absolute path to uvx if the host does not inherit your shell PATH.

Safety & Guardrails

• API Secret Scanner: umem passes all incoming facts through a passive scanner to block API keys, tokens, or credentials from being stored in your persistent cognitive base.
• Snapshots & Rollbacks: Every automated update to your config files (AGENTS.md, CLAUDE.md) is preceded by a snapshot backup. You can rollback anytime:

  # View audit logs
  umem audit list --scope project
  
  # Revert last automated modification
  umem rollback --scope project
  

• Skill Drift Protection: umem skills sync detects managed native drift and keeps local changes by default. Use --drift-decision overwrite only when you intentionally want canonical UMEM content to replace the managed native copy.
• External Bridge Boundary: Tier 2 installation through npx skills is an explicitly confirmed external mutation. UMEM disables anonymous installer telemetry, constrains the target to the current project, and validates the complete result, but labels the write as externally executed rather than claiming UMEM snapshot ownership.

Managing Agent Skills

You can draft, create, adopt, import, validate, maintain, and sync specialized behaviors:

# List all active skills
umem skills list

# Inspect one skill
umem skills detail review-protocol

# Draft, validate, and publish without native runtime writes
umem skills draft create --name "Review Protocol" --description "Reusable review workflow"
umem skills draft validate review-protocol
umem skills publish review-protocol

# Create a new canonical skill and explicitly sync native targets
umem skills create --name "Review Protocol" --description "Reusable review workflow" --sync

# Adopt existing canonical work
umem skills adopt .umem/skills/review-protocol --scope project

# Import an existing native skill and distribute complete runtime copies
umem skills import .agents/skills/review-protocol --scope project --sync

# Validate and maintain canonical skills
umem skills validate review-protocol
umem skills canonical update review-protocol --file .umem/skills/review-protocol/SKILL.md
umem skills rename review-protocol --slug review-checklist
umem skills cleanup review-checklist --targets --format summary
umem skills cleanup review-checklist --targets --apply
umem skills repair --remove-orphan-targets --format summary

# Synchronize one canonical skill into active native runtime folders
umem skills sync review-protocol --check-gitignore --format summary

# Synchronize all active canonical skills during maintenance
umem update --skills

# Track and review recurring workflow candidates
umem skills track --name "Review Protocol" --description "Recurring review workflow"
umem skills recommend --scope project
umem skills propose <latent-skill-id> --decision yes
umem skills promote <recommendation-id> --yes
umem skills generate <latent-skill-id> --yes

License

Distributed under the Apache License 2.0. See LICENSE and NOTICE for more information.