MuninnDB adds Dream Engine for LLM memory consolidation with vault isolation
MuninnDB has added a Dream Engine feature for LLM memory consolidation, modeled after sleep consolidation in the brain. The open-source tool is written in Go and includes Ebbinghaus decay, Hebbian association learning, and vector search capabilities.
How the Dream Engine works
The consolidation pipeline runs between sessions and performs several operations:
- Lowers the deduplication cosine threshold from 0.95 to 0.85
- Flags near-duplicate clusters
- Passes ambiguous cases to an LLM for semantic review
- Automatically merges clear duplicates
You can run it with a dry-run option: muninn dream --dry-run. Sample output shows: "No changes were written. Dream completed in 0s default scanned 107 engrams (merged 9) legal-docs 1 engrams (protected, skipped)"
Vault trust tiers for data isolation
The system implements data protection through vault trust tiers:
- Legal vaults: Skipped entirely, never sent to any LLM
- Work/personal: Ollama or Anthropic only
- Global/projects: Any configured provider
Development status and architecture
The author has shipped Phase 0 with configurable deduplication and dry-run CLI. The next phase (PR #2) will add LLM consolidation, bidirectional stability, and a dream journal feature.
The tool runs locally on consumer hardware - the author mentions using an RTX 5070 Ti with Ollama. The architecture is detailed in a technical writeup that explores neuroscience parallels.
📖 Read the full source: r/LocalLLaMA
👀 See Also
context-link v1.0.0: Local MCP server reduces Claude Code token usage by 91%
context-link v1.0.0 is a local MCP server that indexes codebases with Tree-sitter to serve Claude only the exact symbols, dependencies and structure needed, reducing token usage by 91% in specific cases and 70-80% across full tasks.
Pilot Protocol: Networking Layer for OpenClaw Agents
Pilot Protocol is an open-source networking layer that handles connectivity between OpenClaw agents across different machines. It provides permanent virtual addresses, encrypted UDP tunnels, and NAT traversal without VPNs or ngrok.
OpenClaw Local Agent Implementation with TurboQuant Caching for Mid-Range Hardware
A one-click app for OpenClaw with local models now runs on mid-range devices like MacBook Air with 16GB RAM using TurboQuant caching and context warming. The implementation patches llama.cpp for reliable tool calling and achieves 10-15 tokens per second with Gemma 4 and QWEN 3.5.
Benchmark Results for Small Local and OpenRouter Models on Agentic Text-to-SQL Task
A developer tested multiple small local and OpenRouter models using a custom agentic text-to-SQL benchmark that converts English queries to SQL with debugging rounds. The benchmark includes 25 questions, runs in under 5 minutes, and reveals top performers like kimi-k2.5 and Qwen 3.5 variants.