mumla 2.0.0

Moorcheh Universal Memory Layer for Agentic AI

MUMLA - Moorcheh Universal Memory Layer for Agentic AI

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What Is MUMLA?

MUMLA is a universal memory layer for agentic AI. While LLMs often forget context between sessions, MUMLA gives your agents long-term memory so they can carry context forward and remember what matters across sessions.

Why MUMLA Performs

MUMLA is built for teams that want high-quality agent memory without graph-heavy complexity. It combines immediate semantic availability, low-overhead serverless operation, and strong real-world memory accuracy so you can ship production workflows with a simpler architecture.

• Zero-cost ingestion latency: No indexing wait or token usage at ingestion, so memories are available for retrieval immediately.
• Zero storage cost at idle: Serverless architecture scales to zero when not in use.
• State-of-the-art benchmark performance: Final evaluation results reached 89.8% on LongMemEval and 87.1% on LoCoMo.

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🚀 MUMLA CLI

MUMLA comes with a powerful, developer-friendly Command Line Interface. You can initialize your environment, start the server, and manage your agent's memories completely from your terminal!

You need a Moorcheh API key to use MUMLA. Create one in the Moorcheh Dashboard.

MUMLA has native LLM access, so you don't need a separate external model API key for common memory workflows.

1. Install & Configure

pip install mumla

# Setup your environment (prompts for your Moorcheh API key)
mumla

2. Test Agent Memories

# Create and activate an agent session
mumla agent create customer-support
mumla agent activate customer-support

# Store memories with specific semantic types
mumla remember "The user prefers dark mode for the dashboard."
mumla remember "User's timezone is PST."

# Instantly recall relevant context
mumla recall "What mode does the user like?"

# Get grounded AI answers using built-in RAG
mumla answer "Based on the memory, what should the theme be set to?"

Supported Memory Types

instruction, fact, decision, goal, commitment, preference, relationship, context, event, learning, observation, artifact, error

Use memory types to categorize what you store so retrieval is cleaner and more controllable:

• Save with a specific type: mumla remember "User prefers concise answers" --type preference
• Filter by type when searching: mumla recall "user communication style" --type preference

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Key Features

Capability	Commands	What it does
System status dashboard	mumla status	View environment, configuration, server health, active session, and registered agents.
Local server + web dashboard	mumla serve, mumla ui	Run the MUMLA API locally and open an interactive browser UI.
Agent lifecycle management	mumla agent ...	Create/list agents, activate/deactivate sessions, and run agent bootstrap for an intelligence snapshot.
Memory capture at scale	mumla remember	Store single memories with metadata or batch-ingest up to 100 records from JSON.
Advanced retrieval modes	mumla recall	Run standard search plus temporal queries (--as-of, --changed-since, --current-only) with filters.
Grounded QA over memory	mumla answer	Generate RAG answers using retrieved memory context.
Daily intelligence workflows	mumla daily-summary, mumla conflicts	Generate summaries, detect contradictions, and resolve conflicts interactively.
Session and automation controls	mumla session ..., mumla schedule ...	Inspect/extend sessions and enable scheduled daily summary runs.
Memory file pipelines	mumla memory export, mumla memory sync	Export structured memory markdown and sync MEMORY.md into projects.
Configuration inspection	mumla config show	Inspect API key status, active agent/session, server settings, and schedule time.
Multi-agent ecosystem integration	mumla connect ...	Connect/remove/list integrations for Claude Code, Codex, Cursor, Windsurf, Antigravity, Gemini CLI, Cline, Continue, OpenCode, Goose, Roo, GitHub Copilot, and Augment (local or global).

Additional setup guides are available at the Moorcheh YouTube channel.

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🎯 REST API Endpoints

For programmatic access, MUMLA exposes a clean, session-based REST API.

Important: MUMLA does not have a hosted API server yet. To use these endpoints, run your own local server first:

cd mumla

# Start server
mumla serve

By default, call the endpoints on your local server (for example: "http://127.0.0.1:8000").

Agent Management

• POST /api/v2/agents - Create a new agent namespace
• GET /api/v2/agents - List all available agents
• GET /api/v2/agents/{agent_id} - Get metadata for a specific agent
• DELETE /api/v2/agents/{agent_id} - Delete an agent and all its memories

Session Management

• POST /api/v2/agents/{agent_id}/activate - Start a session (returns a 6-hour JWT session_token)
• POST /api/v2/agents/{agent_id}/deactivate - Manually end a session
• GET /api/v2/session/current - Check the status/validity of the current session
• POST /api/v2/session/extend - Extend the session expiration time

Memory Operations

• POST /api/v2/agents/{agent_id}/remember - Store a new memory into the agent's semantic database
• GET /api/v2/agents/{agent_id}/recall - Run an exact semantic search against the agent's memories
• POST /api/v2/agents/{agent_id}/answer - Generate a grounded RAG answer based on the agent's memories

Authentication Required:

• Authorization: Bearer {moorcheh_api_key} header
• X-Session-Token: {session_token} header (for Session & Memory operations)

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🤖 Why Moorcheh?

Moorcheh.ai - The world's only no-indexing semantic database.

The Revolutionary Difference

Traditional Vector DBs: Minutes of indexing delay, approximate search, stateful architecture

Moorcheh: Instant availability, exact search, serverless/stateless, 80% compute savings

Real Impact

Feature	Traditional	Moorcheh
Write-to-Search	Minutes	Instant
Accuracy	Approximate	Exact
Idle Costs	Always running	Zero
Free Tier	Limited	100K ops/month

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📞 Support & Documentation

Have questions or feedback? We're here to help:

• Docs: https://docs.moorcheh.ai
• Discord: Join our Discord server
• Email: [e-mail]

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MIT License