Show HN: OpenClaw remembers for OpenClaw. Sekha remembers for your full workflow

2 points

3 months ago

OpenClaw's built-in memory is excellent—for OpenClaw. Markdown files, semantic search, survives restarts.

But it stays in OpenClaw.

I built Sekha for when you need memory that travels: OpenClaw today, Claude Code tomorrow, Kimi 2.5 or Gemini the next day. Intelligent embedding-based retrieval, persistent storage, universal API.

The difference: OpenClaw: MEMORY.md files, internal only

Sekha: SQLite + Chroma embeddings, REST/MCP/SDKs, any LLM via LiteLLM/OpenRouter

Use case: OpenClaw explores a codebase, stores findings in Sekha via MCP. Next day, Claude Code reads the same context via SDK. Your analytics pipeline queries it via REST. Same memory, any tool, any model.

Others add memory to OpenClaw. Sekha frees your memory from OpenClaw.

Stack: Rust (fast), SQLite (durable), Chroma (search), LLM-Bridge for universal routing. AGPL, self-hosted.

GitHub: https://github.com/sekha-ai/sekha-controller | Site: https://sekha.dev

The question: What would you build if your AI memory worked with every tool, not just one?

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