# Memori

Open-source memory engine for LLM apps and agents that need persistent context injection.

## Summary
Memori is an open-source memory engine from GibsonAI for giving LLM applications and agents persistent memory, context injection, and configurable recall behavior.


## Guide
Memori is an open-source memory engine from GibsonAI for giving LLM applications and agents persistent memory, context injection, and configurable recall behavior.

### What it is
Memori is an open AI memory systems resource tracked by OpenAgent.bot because it gives builders a concrete implementation path rather than just a product claim.

### Why it matters
Memori matters because many agent products need a practical memory engine before they need a full agent framework. It gives teams a focused way to add durable context to conversations and workflows.

### How it works
Start from the official repository or documentation, verify the license and runtime requirements, then test it on a narrow workflow before expanding it into production use.


## Use Cases
- Conversational memory: Remember preferences and prior facts across user conversations.
- Agent task context: Inject previous task details when an agent resumes work.
- Memory library evaluation: Compare a focused memory engine against heavier agent platforms.

## Alternatives
- Memori is a lighter memory layer vs full agent platforms: Use Memori when you want memory inside an existing app rather than adopting a whole agent runtime.

### Getting Started
- Review the GitHub repository: https://github.com/GibsonAI/memori
- Official source: https://gibsonai.github.io/memori/core-concepts/overview/
- Official source: https://gibsonai.github.io/memori/

### FAQ
- Is Memori open source?
  - Memori is listed with Apache-2.0 based on its official source links. Always re-check the repository or model card before production use.
- Who should evaluate Memori?
  - Developers adding memory to LLM applications
## Why It Matters
Memori matters because many agent products need a practical memory engine before they need a full agent framework. It gives teams a focused way to add durable context to conversations and workflows.


## Best For
- Developers adding memory to LLM applications
- Teams that want a Python-friendly memory engine
- Builders comparing Mem0, Letta, and lighter memory libraries

## Not For
- Users who want a fully managed consumer product with no setup work
- Teams that cannot review the linked source, license, and operational requirements before adoption

## What It Actually Does
- Focused memory engine: Memori centers on memory behavior rather than broad workflow orchestration.
  - Why it matters: A focused engine is easier to embed into existing applications.
- Persistent context injection: The docs describe memory concepts for injecting relevant context into interactions.
  - Why it matters: Agents become more useful when recall happens at the right moment.
- Apache-2.0 open-source release: Public materials describe Memori as Apache-2.0 open source.
  - Why it matters: Permissive licensing helps teams experiment without early legal friction.

## Typical Use Cases
- Conversational memory: Remember preferences and prior facts across user conversations.
- Agent task context: Inject previous task details when an agent resumes work.
- Memory library evaluation: Compare a focused memory engine against heavier agent platforms.

## How It Compares
- Memori is a lighter memory layer vs full agent platforms: Use Memori when you want memory inside an existing app rather than adopting a whole agent runtime.

## Command Line
### Clone Memori
Use the official docs to confirm the current Python package and configuration before production use.

```bash
git clone https://github.com/GibsonAI/memori.git
```

## Facts
- Category: memory-systems
- Resource type: memory_system
- Open source: yes
- License: Apache-2.0
- Last verified: 2026-04-19
- GitHub repo: GibsonAI/memori

## Capabilities
- memory
- context-retrieval
- state-management

## Structured Use Case Tags
- self-hosted-ai
- personal-memory

## Getting Started
- Review the GitHub repository: https://github.com/GibsonAI/memori
- Official source: https://gibsonai.github.io/memori/core-concepts/overview/
- Official source: https://gibsonai.github.io/memori/

## Links
- GitHub: https://github.com/GibsonAI/memori
- Homepage: https://gibsonai.github.io/memori/
- Docs: https://gibsonai.github.io/memori/core-concepts/overview/

## Structured Outputs
- JSON: https://www.openagent.bot/memory-systems/memori.json
- Markdown: https://www.openagent.bot/memory-systems/memori.md
- Canonical: https://www.openagent.bot/memory-systems/memori