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Persistent semantic memory for AI agents using Qdrant and FastEmbed to store, search, and recall context across sessions.
Engram provides a high-performance, local-first semantic memory system that transforms AI agents from stateless to stateful. It allows agents to remember user preferences, factual data, and historical decisions across multiple sessions, reducing token waste and improving consistency.
Use this skill when the agent needs to maintain long-term continuity, recall specific project details from previous interactions, or manage a structured user profile.
memory_store, memory_recall, and memory_profile tools, as well as a context management system for codebases.Designed specifically for OpenClaw agents, integrating as a native Python plugin with support for local vector storage.
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Caveman Compress
Compress natural-language memory files into a compact 'caveman' format to save LLM tokens while preserving code, links, and structure.
Go Data Structures
Authoritative guidance on choosing and using Go built-in and standard-library data structures, with practical best practices for slices, maps, arrays, container
Memory Search
Search past coding sessions and observations using natural-language queries to retrieve decisions, notes, and context.
DSPy — Declarative LM Programming
Use DSPy to build declarative, modular LM pipelines, optimize prompts automatically, and assemble reliable RAG/agent systems with structured signatures and opti
Microsoft Foundry Classic — Expert Guidance
Comprehensive guidance for building, configuring, troubleshooting, and deploying Microsoft Foundry Classic agents and integrations.
hmem — Setup
Interactive installer and setup guide for hmem (persistent memory MCP server); automates .mcp.json config, memory dir, and adds agent hooks for Claude Code, Gem
OpenViking Memory Plugin
Provides automatic long-term memory capture and recall for OpenClaw agents: auto-capture, auto-recall, and memory management tools for more context-aware conver
Synalinks Framework
Keras-inspired framework for building structured, neuro-symbolic LLM programs with DataModel schemas, modular Programs, and training/optimization tools.