Most AI agents forget everything the moment a session ends. The ones that don't usually hallucinate — they "remember" things that never happened.

Whisper solves both at once.

It combines persistent memory with grounded retrieval, so your agents remember users across sessions and only surface what's actually true. We ran a benchmark on a frozen dual-provider dataset: 0% hallucination rate, 94.8% retrieval recall.

The full integration looks like this:

    import { WhisperClient } from "@usewhisper/sdk";

    const whisper = new WhisperClient({
      apiKey: process.env.WHISPER_API_KEY,
      project: "my-project",
    });

    // After each conversation — store what's worth remembering
    await whisper.remember({ messages: conversationHistory, userId });

    // Next session — retrieve before generation
    const { context } = await whisper.query({ q: userMessage, userId });

    // Model now has full context from prior sessions
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages: [
        { role: "system", content: context },
        { role: "user", content: userMessage },
      ],
    });

That's it. Your agent now remembers users across sessions and won't hallucinate what it retrieves.

The problem we kept seeing: memory-only solutions (Mem0, Zep) store context but hallucinate on retrieval. Retrieval-only solutions (Nia, Supermemory) are grounded but stateless — every session starts cold. Nobody was doing both.

What Whisper does differently: - Persistent memory that compounds across sessions - Retrieval that's grounded — it won't surface what isn't there - Works with any LLM, integrates in ~10 minutes - MCP-native, SDK + CLI for fast onboarding

Free tier available. Docs at usewhisper.dev.

Happy to answer anything — especially pushback on the benchmark methodology, I want to pressure test it.