remembra 0.6.0

Universal memory layer for AI applications. Self-host in 5 minutes.

Remembra - AI Memory Layer

Persistent memory for AI applications. Self-host in 5 minutes.

What Is This?

Remembra is a universal memory layer for LLMs. It solves the fundamental problem that every AI forgets everything between sessions.

from remembra import Memory

memory = Memory(user_id="user_123")

# Store memories
memory.store("User prefers dark mode and works at Acme Corp")

# Recall with context
context = memory.recall("What are user's preferences?")
# Returns: "User prefers dark mode. Works at Acme Corp."

Why We're Building This

The Problem

Every AI app needs memory. Developers hack together solutions using vector databases, embeddings, and custom retrieval logic. It's complex, fragmented, and everyone rebuilds the same thing.

Current Solutions Suck

• Mem0: $24M raised, but self-hosting docs are trash, pricing jumps from $19 to $249
• Zep: Academic, complex to deploy
• Letta: Not production-ready
• LangChain Memory: Too basic, no persistence

Our Approach

• Self-host in 5 minutes: One Docker command, everything bundled
• Fair pricing: $0 → $29 → $99 (not $19 → $249)
• Open source core: MIT license, own your data
• Actually works: Built because we need it ourselves (Clawdbot)

Core Features

1. Simple Memory Operations

• store() - Save memories with automatic extraction
• recall() - Semantic search with context
• update() - Intelligent merging
• forget() - GDPR-compliant deletion

2. Entity Resolution (Our Killer Feature)

Knows that "Adam", "Adam Smith", "Mr. Smith", and "my husband" are the same person.

3. Temporal Awareness

Memories have time context. TTL support. Historical queries.

4. Hybrid Storage

Vector (semantic) + Graph (relationships) + Relational (metadata) in one system.

5. Observability Dashboard

See what's stored, debug retrievals, visualize entity graphs.

Quick Start

Self-Hosted (Recommended)

docker run -d -p 8787:8787 remembra/remembra

Python SDK

pip install remembra

from remembra import Memory

# Connect to local instance
memory = Memory(
    base_url="http://localhost:8787",
    user_id="user_123",
    project="my_app"
)

# Store
memory.store("User's name is John. He's a software engineer at Google.")

# Recall
context = memory.recall("Who is the user?")
print(context)
# "John is a software engineer at Google."

Documentation

• Product Spec - Full product specification
• Build Plan - Week-by-week development plan
• Architecture - Technical architecture details
• API Reference - API documentation

Project Status

🚧 In Development - MVP target: 12 weeks

License

MIT License - Use it however you want.

---

Built by DolphyTech