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[![Memori Labs](https://s3.us-east-1.amazonaws.com/images.memorilabs.ai/banner.png)](https://memorilabs.ai/)
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<p align="center">
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<strong>The memory fabric for enterprise AI</strong>
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</p>
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<p align="center">
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<i>Memori adds persistent memory to your LLM applications without changing your architecture. It is model, framework, and datastore agnostic.</i>
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</p>
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<p align="center">
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<a href="https://badge.fury.io/py/memori">
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<img src="https://badge.fury.io/py/memori.svg" alt="PyPI version">
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</a>
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<a href="https://pepy.tech/projects/memori">
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<img src="https://static.pepy.tech/badge/memori" alt="Downloads">
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</a>
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<a href="https://opensource.org/license/apache-2-0">
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<img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License">
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</a>
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<a href="https://www.python.org/downloads/">
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<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python 3.8+">
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</a>
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<a href="https://discord.gg/abD4eGym6v">
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<img src="https://img.shields.io/discord/1042405378304004156?logo=discord" alt="Discord">
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</a>
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</p>
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<p align="center">
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<a href="https://github.com/MemoriLabs/Memori/stargazers">
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<img src="https://img.shields.io/badge/Star%20on%20GitHub-Support%20Memori-orange?style=for-the-badge" alt="Star on GitHub">
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</a>
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</p>
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# Memori
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The memory fabric for enterprise AI.
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Memori adds persistent memory to your LLM applications without changing your architecture. It is model, framework, and datastore agnostic.
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## Why Memori
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Memori captures LLM interactions, enriches them, and makes them retrievable as high-quality context for future generations.
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- **Low integration overhead**: wrap your existing LLM client and keep your current stack.
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- **Attribution-aware memory**: organize memory by `entity`, `process`, and `session`.
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- **Asynchronous augmentation**: extract structured memory without adding user-facing latency.
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- **Flexible infrastructure**: supports multiple models, frameworks, and databases.
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- Low integration overhead: wrap your existing LLM client and keep your current stack.
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- Attribution-aware memory: organize memory by `entity`, `process`, and `session`.
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- Asynchronous augmentation: extract structured memory without adding user-facing latency.
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- Flexible infrastructure: supports multiple models, frameworks, and databases.
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## Install
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python -m memori
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```
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See full CLI docs in [`docs/cli.md`](https://github.com/MemoriLabs/Memori/blob/main/docs/cli.md).
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See full CLI docs in `docs/cli.md`.
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## Documentation and Examples
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- Product docs: [https://memorilabs.ai/docs](https://memorilabs.ai/docs)
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- Memori cookbook: [https://github.com/MemoriLabs/memori-cookbook](https://github.com/MemoriLabs/memori-cookbook)
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- Database examples: [https://github.com/MemoriLabs/Memori/tree/main/examples](https://github.com/MemoriLabs/Memori/tree/main/examples)
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- Architecture details: [`docs/features/architecture.md`](https://github.com/MemoriLabs/Memori/blob/main/docs/features/architecture.md)
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- LLM support details: [`docs/features/llm.md`](https://github.com/MemoriLabs/Memori/blob/main/docs/features/llm.md)
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- Database support details: [`docs/features/databases.md`](https://github.com/MemoriLabs/Memori/blob/main/docs/features/databases.md)
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- Product docs: memorilabs.ai/docs
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- Memori cookbook: github.com/MemoriLabs/memori-cookbook
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- Database examples: github.com/MemoriLabs/Memori/tree/main/examples
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- Architecture details: docs/features/architecture.md
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- LLM support details: docs/features/llm.md
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- Database support details: docs/features/databases.md
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## Contributing
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Contributions are welcome. See [`CONTRIBUTING.md`](https://github.com/MemoriLabs/Memori/blob/main/CONTRIBUTING.md) for setup, standards, and PR guidance.
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Contributions are welcome. See `CONTRIBUTING.md` for setup, standards, and PR guidance.
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## Support
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- Discord: [https://discord.gg/abD4eGym6v](https://discord.gg/abD4eGym6v)
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- Issues: [https://github.com/MemoriLabs/Memori/issues](https://github.com/MemoriLabs/Memori/issues)
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- Discord: discord.gg/abD4eGym6v
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- Issues: github.com/MemoriLabs/Memori/issues
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## License
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Apache 2.0. See [`LICENSE`](https://github.com/MemoriLabs/Memori/blob/main/LICENSE).
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Apache 2.0. See `LICENSE`.

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