mem0-embeddings-litellm-patch

This patch adds support for embedding model providers via LiteLLM to the mem0 framework.

✨ What It Does

• Integrates nearly all providers supported by LiteLLM as embedding backends in mem0
• Enables use of high-performance providers like VoyageAI, Mistral, Groq, and more
• Drop-in replacement for the existing embedding logic

🔧 Installation

You can install the patch via pip:

pip install mem0-embeddings-litellm-patch

This will patch the necessary mem0.embeddings modules automatically.

Note: Make sure mem0 and litellm are installed as dependencies. This package does not install them implicitly.

🧠 Requirements

• Python >= 3.8
• mem0 >= 0.1.0
• litellm >= 1.0.0

💡 Usage

After installing this patch you can use all embedding providers available via litellm inncluding those currently not supported via mem0 natively.

📢 Why This Exists

The mem0 maintainers have not yet merged support for LiteLLM-based embeddings, despite it being a fast, extensible abstraction layer. This patch bridges the gap until (or if) native support is added upstream. No need to fork and maintain a full project if you can just maintain the patch files instead am i right? :D

📬 Feedback / Contributing

Feel free to fork or open issues. If the mem0 team integrates this feature officially, this package may be deprecated in favor of upstream support.

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Licensed under the MIT License.