We've been building LLM features for apps and hit the same wall many of you probably have:
- AI agents forget everything between sessions.
- RAG pipelines are brittle, leaky, and hard to scope.
- There's no good way to persist memory across users, tools, and time - while staying compliant.
So we built Recallio: a drop-in memory API for AI apps and agents.
It gives you:
- Scoped memory per user, team, or project (no leakage)
- Semantic recall with LLM-aware ranking
- Auto-summarization
- Optional graph memory for deeper reasoning
- Built-in TTL, export, and audit trails (GDPR/HIPAA-friendly)
All via two API calls:
- POST /memory to store scoped context
- POST /recall to fetch relevant memory (optionally summarized)
It works with any LLM (OpenAI, Claude, etc.), any stack (LangChain, LlamaIndex, agents, SaaS apps), and skips the complexity of managing vector DBs, prompts, or compliance logic.
Would love feedback from folks building real AI products:
- What are you using today for memory?
- Where does it still break or feel hacky?
- What should we build next?
Try it here → https://recallio.ai
Docs, SDKs, and live playground are up.
https://preview.redd.it/qhzibi5ni8gf1.png?width=1752&format=png&auto=webp&s=f6cb60dd41cfadf9dea7444690536e1206795bb1
