The memory layer for AI coding tools. Local-first, developer-specific, works everywhere.
The memory layer for AI coding tools. Local-first, developer-specific, works everywhere.
Quick Start · How It Works · Benchmarks · Tools · Dashboard · Architecture
The server publishes 14 tools. What each one is for:
memory_store — Store a memory with type, tags, confidence. Auto-redacts private content, auto-expires contradictionsmemory_recall — Semantic search — compact mode by default (~10x token savings). Use memory_detail for full contentmemory_detail — Retrieve full content by ID after compact recallmemory_context — Load all relevant context for a topic, organized by type with token budgetingmemory_extract — Batch-save multiple memories from conversationmemory_forget — Delete by ID or query (with confirmation)memory_inject — Surface corrections + decisions + graph neighbors before coding startsmemory_patch — Surgical field-level edit with auto-snapshotmemory_versions — View full edit history or restore any versionmemory_search — Exact full-text search via FTS5 with compact modememory_since — Temporal query with natural language ranges (7d, 2w, 1h)memory_relate — Build a typed knowledge graph between memoriesmemory_store — Store a memory with type, tags, confidence. Auto-redacts private content, auto-expires contradictions.memory_recall — Semantic search — compact mode by default (~10x token savings). Use memory_detail for full content.memory_detail — Retrieve full content by ID after compact recall.memory_context — Load all relevant context for a topic, organized by type with token budgeting.memory_extract — Batch-save multiple memories from conversation.memory_forget — Delete by ID or query (with confirmation).memory_inject — Surface corrections + decisions + graph neighbors before coding starts.memory_patch — Surgical field-level edit with auto-snapshot.memory_versions — View full edit history or restore any version.memory_search — Exact full-text search via FTS5 with compact mode.memory_since — Temporal query with natural language ranges (7d, 2w, 1h).memory_relate — Build a typed knowledge graph between memories.memory_multi_recall — Multi-strategy search with compact mode: semantic + FTS5 + graph + temporal.memory_tier — Move memories between tiers: core / working / archival.Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| memory_store | Store a memory with type, tags, confidence. Auto-redacts private content, auto-expires contradictions. |
| memory_recall | Semantic search — compact mode by default (~10x token savings). Use memory_detail for full content. |
| memory_detail | Retrieve full content by ID after compact recall. |
| memory_context | Load all relevant context for a topic, organized by type with token budgeting. |
| memory_extract | Batch-save multiple memories from conversation. |
| memory_forget | Delete by ID or query (with confirmation). |
| memory_inject | Surface corrections + decisions + graph neighbors before coding starts. |
| memory_patch | Surgical field-level edit with auto-snapshot. |
| memory_versions | View full edit history or restore any version. |
| memory_search | Exact full-text search via FTS5 with compact mode. |
| memory_since | Temporal query with natural language ranges (7d, 2w, 1h). |
| memory_relate | Build a typed knowledge graph between memories. |
| memory_multi_recall | Multi-strategy search with compact mode: semantic + FTS5 + graph + temporal. |
| memory_tier | Move memories between tiers: core / working / archival. |
{
"mcpServers": {
"amem": {
"command": "npx",
"args": [
"-y",
"@aman_asmuei/amem"
]
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
Store entities, relations and observations in a local knowledge graph with the reference Memory MCP server.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Stop letting your assistant hallucinate n8n node parameters — this server hands it the real schemas, templates and validation.
Chat with your second brain — search, read and write vault notes through the Local REST API.
Enables AI assistants to securely interact with Azure DevOps through a standardized protocol.