OpenClaw remembers for OpenClaw. Sekha remembers for your full workflow
Portable memory for multi-LLM workflows, but Mem0 and MemGPT already solve this.

The core idea is simple and pragmatic: attach a persistent, SQLite-backed vector store to any model so conversations don't vanish after a single context window. The repo leans into portability (Rust, self-hosted, AGPL) and the UI shows sensible controls like conversation folders and a context-budget token slider — useful details that suggest this is built for real use rather than a demo. My worry: retrieval quality, scaling and access controls will be the real battleground, not the clean chat UI.
Developers, AI engineers, privacy-conscious teams and hobbyists who integrate LLMs and need long-term conversational memory
Sekha gives your LLM a permanent memory: - Unlimited conversation history with semantic search - Works with any model (Claude, GPT, Llama, local) - Self-hosted, your data stays local - Built with Rust + SQLite + embeddings. AGPL-3.0.
GitHub: [github.com/sekha-ai/sekha-controller] Docs: [docs.sekha.dev] | Site: [sekha.dev] Proof: https://imgur.com/a/Dgti8cO
Portable memory for multi-LLM workflows, but Mem0 and MemGPT already solve this.
Zero-latency proxy for LLM memory when Mem0 and LangChain already exist.
Model-agnostic code reviews with zero LLM markup beats Claude Code Review on cost.
Feature launch from established 27.5K-star platform, not a standalone project.
In-gallery photo recognition makes this smarter than standard audio guide apps.
Human-in-the-loop citation verification for local research files.