Give Claude memory across sessions
Persistent context, knowledge graphs, and vector stores so your agent remembers decisions, conventions, and prior work instead of starting cold every session.
839 listings across 5 types
Plugins 7
Browse all →claude-md-management
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
beads
Beads - A memory upgrade for your coding agent
context-mode
Context window optimization for AI coding agents. Sandboxes tool output (98% reduction), persists session memory, and enforces routing across 17 platforms via MCP + hooks.
aws-agents
Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore, connecting tools, memory, policies, evaluation, debugging, and production hardening.
remember
Continuous memory for Claude Code. Extracts, summarizes, and compresses conversations into tiered daily logs. Claude remembers what you did yesterday.
togetherai-skills
Agent Skills for Together AI platform — inference, training, embeddings, audio, video, images, function calling, and infrastructure. Covers serverless chat completions, image/video generation, fine-tuning, batch inference, evaluations, sandboxes, dedicated endpoints, and GPU clusters.
MCP Servers 740
Browse all →codebase-memory-mcp
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
infranodus-mcp-server-infranodus
Map text into knowledge graphs to create a structured representation of conceptual relations and t…
sverklo
Local-first MCP code intelligence: 37 tools — hybrid search, blast-radius, diff review, memory.
code-memory
Local semantic code search with Git history. Works offline, no API key needed.
Obsidian Brain
Obsidian MCP server: semantic search, knowledge graph, and vault editing. No plugin required.
Exomem
Local Markdown/Obsidian knowledge substrate for MCP agents with governed memory and hybrid search.
Skills 87
Browse all →ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
claude-mem
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
mem0
Universal memory layer for AI Agents
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintena
obsidian-mind
A self-organizing Obsidian vault that gives AI coding agents persistent memory. Claude Code, Codex CLI, Gemini CLI.
claude-code-memory-setup
Up to 71.5x fewer tokens per session on Claude Code with Obsidian + Graphify. Persistent memory, codebase knowledge graphs, and chat import pipeline. 🇧🇷 PT-BR included.
Subagents 4
ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
maestro-flow
Intent-driven workflow orchestration for multi-agent AI development — adaptive lifecycle engine, self-reinforcing knowledge graph, and visual dashboard for Claude Code, Gemini, Codex & more
nimble-analyst
Deep analysis agent for Nimble business skills. Use when a skill needs to synthesize research findings, cross-reference data, produce structured reports, or make strategic assessments. Has persistent memory to learn user preferences and analysis patterns across sessions. Use proactively for any task
pixeltable-pipeline-architect
Designs Pixeltable schemas — tables, views/iterators, computed-column chains, embedding indexes, and UDFs — for multimodal and ML data pipelines. Use when the user needs to model a data/AI workflow or decide between a view, a computed column, and a UDF.