memory▌

Saves decisions, git state, and pending work so future sessions can resume without reconstructing context from scratch.

Restores saved context, including across branch changes and workspace handoffs.

Weekly engineering retrospective over code activity, trends, and team contributions.

Quality dashboard skill that wraps project checks and reports a weighted health score and trend line.

Keep vector memory lean. Prevent token waste from junk memories.

Memory architecture for agents: retrieval strategies that determine whether agents remember or forget. \n \n Covers five memory types: short-term (context window), long-term (vector stores), working memory, episodic memory, and semantic memory, each suited to different information patterns \n Emphasizes retrieval as the core challenge; provides chunking strategies, embedding quality guidance, and metadata filtering to surface the right memories at decision time \n Includes anti-patterns like sto

Persistent, searchable memory bank for AI agents with automatic project documentation sync. \n \n Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write for recording knowledge and decisions, memory_read for retrieving specific entries, and memory_stats for usage analytics \n Organizes memories by type (architecture, patterns, decisions) and supports custom tagging for flexible retrieval and organization \n Runs as an MCP server that syncs with your project work

Persistent memory systems for LLM conversations with tiered storage and intelligent retrieval. \n \n Implements three memory types: short-term (immediate context), long-term (historical facts), and entity-based (facts about specific entities) \n Provides memory retrieval and consolidation capabilities to surface relevant memories without overwhelming context windows \n Addresses critical concerns including unbounded memory growth, retrieval relevance, and strict user isolation to prevent cross-u

Configure persistent memory search for Moltbot/Clawdbot agents to retain context across sessions. \n \n Add memorySearch config block with provider (Voyage, OpenAI, or local), sources (memory files and/or sessions), and relevance thresholds \n Create a workspace structure with MEMORY.md for curated long-term facts and memory/logs/ for daily timestamped logs \n Supports three embedding providers; Voyage recommended but local option available without API keys \n Includes troubleshooting for common

Systematic memory management for agents through daily logging, session preservation, and knowledge extraction.