agent-memory
Related Servers
Alternatives to agent-memory
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceProvides AI coding agents with a local, Markdown-first shared memory, enabling them to record decisions, failures, progress, and handoffs and retrieve them across sessions via MCP tools.8MIT
- AlicenseNot gradedqualityBmaintenanceProvides persistent, local-first memory for coding agents with Markdown as the source of truth, exposed via CLI, loopback API, MCP, and Codex hooks for context retrieval and durable writes.MIT
- AlicenseNot gradedqualityAmaintenanceLocal-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.2MIT
- AlicenseBqualityDmaintenanceLocal Markdown-backed memory tools for Codex and other MCP-capable agents. Exposes durable agent knowledge via CLI and MCP server.5MIT
- AlicenseNot gradedqualityAmaintenanceMarkdown-first long-term memory for AI coding agents, enabling hybrid search over local files via MCP tools.15Apache 2.0
- AlicenseNot gradedqualityAmaintenanceProvides local-first, project-aware durable memory for coding agents, with a human-reviewed Markdown vault, MCP daemon, and dashboard for managing and retrieving shared knowledge.42 PyPI14MIT
TDQS
Scored across 3 tools
Each tool has a clearly distinct role: fetching context, proposing updates, and reporting status. There is no overlap or ambiguity in their purposes.
All tools share the consistent memory. namespace and use an action-oriented style. fetch_context and propose_update follow verb_noun, while status is a noun-only deviation, but the pattern remains predictable.
Three tools is well-scoped for a focused memory-management server. Each tool covers a necessary capability without bloat or redundancy.
The set covers the core memory lifecycle: read, write, and health/status inspection. A minor gap is that staged proposals cannot be approved or rejected directly through MCP tools, relying on external CLI commands instead.