LoreConvo: MCP Server Adds Persistent Session Memory to Claude Code
What LoreConvo Does
LoreConvo is a local-first MCP server that gives Claude persistent session memory, solving the problem of context loss between sessions. The developer built it while working on a complex finance automation project with a 19-node LangGraph pipeline and multiple MCP servers.
Key Features
- Auto-saves sessions via Claude Code hooks (post-session hook triggers save)
- Auto-loads relevant context on session start (pre-session hook calls get_recent_sessions)
- Cross-surface persistence — context carries between Claude Code, Cowork, and Chat
- Full-text search across all past sessions
- 12 MCP tools for AI-native access
- Local-first architecture using SQLite
Practical Impact
Sessions that used to start with 5 minutes of re-contexting now start with Claude already knowing the project state, recent decisions, and open questions. This saves roughly 3,000-8,000 tokens per session in re-contexting overhead.
Companion Tool
The developer also built LoreDocs for project knowledge management, featuring 34 MCP tools, multi-vault architecture, document versioning, and context injection.
Availability
Both tools are free for personal use under BSL 1.1 (converts to Apache 2.0 in 2030). The code is available on GitHub: LoreConvo at https://github.com/labyrinth-analytics/loreconvo and LoreDocs at https://github.com/labyrinth-analytics/loredocs.
📖 Read the full source: r/ClaudeAI
👀 See Also
Zoku: A Tool That Automatically Detects Repeated Workflows in Claude Code
Zoku is a local tool that hooks into Claude Code's event system to record tool actions across sessions, identifies repeated workflow patterns, and then informs Claude about these patterns so it can proactively suggest or execute them. It requires no configuration, has no dependencies, and stores everything locally in ~/.zoku/.
FOMOE Enables 397B Qwen3.5 Model Inference on $2,100 Desktop Hardware
FOMOE (Fast Opportunistic Mixture of Experts) allows running Qwen3.5's 397 billion parameter flagship model at 5-9 tokens/second on consumer hardware using two $500 GPUs, 32GB RAM, and an NVMe drive with Q4_K_M quantization.
Proactive Context-Rot Detection in Claude Code: A Feature Suggestion from r/ClaudeAI
A Reddit feature suggestion proposes that Claude Code proactively detect context rot and offer a structured task-scoped handoff, generating a handoff file and spawning a new session automatically.
Open-source memory system for LLM agents achieves high benchmark scores
A persistent memory system for Claude Code and OpenClaw provides LLM agents with context continuity across sessions, achieving 90.8% on LoCoMo and 89.1% on LongMemEval benchmarks. The adapter-based architecture works with any agent framework.