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.
Zero-egress by default is the killer feature for compliance teams fearing cloud leaks.
Model-agnostic code reviews with zero LLM markup beats Claude Code Review on cost.
Local memory for MCP agents when Mem0 and LangChain already offer similar solutions.
MCP integration with Claude beats reactive chat, but personal AI is crowded.