Supermemory

ai-memory is an open-source local server for long-term memory and session handoff among AI coding agents. It captures prompts, tool calls, and agent context through MCP integrations and lifecycle hooks, storing the material in a searchable, Git-backed Markdown wiki. When a session ends or is explicitly finalized, it generates handoff context for a subsequent session, including the project architecture, failed approaches, and open questions, so work can continue across agent vendors without restating the context; clients without a true session-end hook use explicit finalization commands. The project documents integrations for Claude Code, Codex, Command Code, Devin CLI, OpenCode, Cursor, Gemini CLI, Oh My Pi, Pi, and Crush, with agent-specific configuration, generated plugins or extensions, and capture exclusions. It runs on Linux, macOS, and Windows through WSL2, with experimental native Windows support, and is distributed through Docker images and native release binaries.

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.

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ages, and forgets memories by value; unattended deletion is limited to proposals that require confirmation, and superseded or archived material remains available.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.

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.

MemoraX Code is a memory plugin and shared memory layer for AI coding agents, developed by MemoraX. It integrates with Codex, Claude Code, CodeBuddy/WorkBuddy, DeepSeek Harness, and OpenCode to retrieve relevant context for new tasks and capture reusable knowledge from completed work. Its memory is divided into Coding Memory for engineering lessons and design decisions, Repo Memory for repository structure and history evidence, Personal Memory for user preferences, and Procedure Memory for reusable steps and validation gates. Background writeback extracts selected knowledge from trusted workspace turns, while the bundled skill and CLI support explicit search and memory operations; repository and personal/procedure content are maintained under the documented local storage boundaries. The package is distributed through npm and requires Node.js 20 or later, with Python 3 required for Repo Memory operations. Cloud-backed search and storage require a MemoraX account, while guest mode is available for a limited period. Local trace capture is enabled by default for supported clients and may retain prompts, responses, recalled memory, reminder text, and local paths; the project documents settings for metadata-only capture or disabling traces. The repository is licensed under the MIT License.