ai-memory

agent-memory is a local-first, agent-agnostic long-term memory runtime for AI agents. It stores memories as plain Markdown files, with a rebuildable SQLite index used as a cache rather than the source of truth. Conversation-boundary hooks trigger writes, while an independent sleep-time Manage pass consolidates, ages, and forgets memories by value; unattended deletion is limited to proposals that require confirmation, and superseded or archived material remains available. The runtime provides three recall paths: deterministic MEMORY.md injection at session start, BM25 search with progressive disclosure, and direct access through the store's directory tree using tools such as ls and grep. Memory records use frontmatter for names, abstracts, status, timestamps, links, weights, and provenance, with free-form Markdown bodies. Its CLI, MCP server, and host-agent hooks share the same validation, hash-diff, and reindexing path, and it can be used by agents that run shell commands, including Claude Code and Codex CLI. The repository documents a Python 3.12-or-later setup using uv, commands for initialization, recording, recall, rebuilding, sleep-time consolidation, and proposal approval, plus host setup for Claude Code and Codex. It does not include an LLM client; reasoning is delegated to the host agent's CLI, so the library requires no separate model keys or billing surface.

MCP-Memory is a Model Context Protocol server that gives AI coding agents persistent memory across chat turns and sessions. It stores memory records as human-readable Markdown documents following the Open Knowledge Format (OKF v0.2), with YAML frontmatter, hierarchical index files, and an update log, while maintaining a local SQLite FTS5 index for key lookups, keyword search, tag filtering, and namespace-scoped retrieval. The server exposes MCP tools for storing, retrieving, searching, and deleting memories, and supports project, user, and default namespaces; a setup wizard configures supported MCP clients including Claude Desktop, Cursor, Antigravity, Windsurf, and Codex.

Supermemory is an AI memory and context engine with a hosted API, application, plugins, MCP server, and local runtime. It extracts facts from conversations, tracks temporal changes and contradictions, automatically forgets expired information, and maintains user profiles containing stable facts and recent activity. Its API combines memory retrieval with hybrid RAG search, allowing applications and AI agents to query personalized context and knowledge-base documents together. It also processes uploaded PDFs, images, videos, and code, and can synchronize data from services such as Google Drive, Gmail, Notion, OneDrive, GitHub, and web pages. SDKs are available for JavaScript and Python, with integrations for AI frameworks including the Vercel AI SDK, LangChain, LangGraph, OpenAI Agents SDK, Mastra, Agno, and n8n. Supermemory can provide persistent memory to compatible assistants through plugins or its MCP server. The local distribution runs as a single binary or through npx, uses local embeddings by default, supports OpenAI-compatible and Ollama-backed models, and exposes the same Memory API on localhost for offline or self-hosted use.

TencentDB Agent Memory is a self-hostable, team-level memory hub for AI agents. It extracts conversations and tasks into reusable Chat Memory and Skills, and converts documents and code into an LLM Wiki and CodeGraph. These assets can be reviewed, versioned, governed, shared, routed, and reused across agents, frameworks, and team members; existing documents, codebases, and agent sessions can also be imported to reduce cold-start work. The system provides a shared memory server and a proxy that preserves the agent protocol, so supported clients can use the same memory by pointing their base URL at the proxy without plugins, hooks, or an MCP server. The repository deploys memory-core, memory-hub, and the proxy together, with a local management panel and configuration for separate memory and proxy LLM parameters. Documented integrations include DeepSeek Harness, Claude Code, Codex, CodeBuddy, WorkBuddy, Hermes, and OpenClaw.

Agent Memory Repo is an open standard and agent skill for storing and carrying AI-agent context between coding-agent sessions in a separate Git repository of Markdown notes. Agents load a short MEMORY.md entry point, follow links to notes, projects, queries, and scripts, then update and commit memories as they learn. Git provides history and merging for memory changes and flags conflicting edits when multiple agents update the same files. It includes a dreaming agent that cleans up stored notes and supports personal memory, shared team knowledge, and agent swarms. The skill can be installed for Devin CLI or through the skills installer for Claude Code, Cursor, and other agents, and requires Node.js and Git for the latter. A separate persistent local or remote repository is needed for memory across machines or cloud sessions. The specification was developed by Cognition, is open to ecosystem contributions, and is licensed under MIT.