Open and closed AI models without linking your identity to your queries.
Access frontier AI models with your privacy in mind.
Access frontier AI models with your privacy in mind.
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The Open Anonymity Project
The Open Anonymity Project develops an open-source protocol for unlinkable inference with AI models.
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zkAPI Usage Credits
In collaboration with 
zkAPI is a zero-knowledge protocol for paid APIs, including AI inference.
Co-developed by the Ethereum Foundation (post by Davide Crapis and Vitalik Buterin) and The Open Anonymity Project (OA), it provides OA’s privacy guarantees while allowing anonymous Ethereum payments.
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By continuing, you agree to our Terms and Privacy Policy. Explore the code on GitHub, read our blog, or join our Discord community.
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Capabilities and privacy features
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Unlinkable inference
Unlinkable inference separates your queries from each other and from your identity. The Open Anonymity Project (OA) does not know which queries you send, and model providers receive them without knowing your identity.
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Verifiable privacy
Using OA’s public verification code, which runs in a Trusted Execution Environment (TEE), you can confirm that OA does not log your queries.
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Full-featured AI assistant
OA provides access to open and closed AI models as well as unique features such as Parallel and Council modes, prompt scrubbing, and personal memory agents.
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Confidential query rewriting
Prompts can reveal personally identifiable information or a distinctive writing style. To hide these details, OA comes with a query rewriter (try `tab-tab` on your input) implemented with a small LM running in a GPU enclave.
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User-owned memory
With OA, your conversation history remains on your device. Memory agents running in secure GPU enclaves manage your memory and select relevant context to include in each prompt, without sharing your complete history.
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In-browser network proxy
Network-level metadata may also be revealing; for this reason, OA has partnered with the Refraction Network to provide an in-browser network proxy.