# 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 memory-system resource to evaluate by what it stores, how recall works, how memory is scoped, and whether users or teams can inspect, correct, export, or delete durable context.

### 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
Test Memori with repeated sessions. Add facts, update them, ask for recall, inspect retrieval behavior, and verify deletion or scoping controls before storing sensitive user or project memory.


## 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
- What should I check before using Memori?
  - Test Memori with repeated sessions. Add facts, update them, ask for recall, inspect retrieval behavior, and verify deletion or scoping controls before storing sensitive user or project memory.
- Is Memori open source?
  - Memori is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.
- Who should evaluate Memori?
  - Memori is most worth evaluating for 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