Humaux Memory

Persistent long-term memory for AI agents over one remote MCP connection. Store decisions, lessons and documents; recall them semantically in any later session; enumerate **complete sets** with truthful totals instead of top-k guesses. - **Memory** — semantic store/recall with time-aware deduplication - **Complete enumeration** — "every decision", "all rejected approaches", with real totals and coverage - **Per-project ledgers** — current state, rejected paths, lessons, known issues - **Shared task canvas** — task state outside the context window, so another agent can resume mid-task - **Knowledge graph** — entities, relations and supersession links - **Documents** — upload and search your own files - **Multi-agent coordination** — task claims and resource locks with leases One-click OAuth sign-in, no API key to paste. Bring your own LLM provider key for distillation. Free tier available.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://humaux-memory--luolimoa.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "Humaux Memory": {
      "url": "https://humaux-memory--luolimoa.run.tools"
    }
  }
}

能力清单

工具	说明
memory_search	Semantic search across all memory layers. Automatically includes results from both your private memories and the shared public knowledge pool (built from high-quality internalized memories via K2 pipeline). Supports time range (since/until), layer filter (L1-L4), and source pool filter.
memory_enumerate	Enumerate the COMPLETE set of memories matching a structured filter — every rejected decision for a project, every lesson, every fix. Unlike memory_search (semantic top-k, returns only the most relevant few and never guarantees completeness), this walks Postgres directly and returns the FULL matching set, always reporting 'total' (true full-set size), 'coverage' and whether the answer was truncated. Omit 'limit' to get everything. Use this whenever the question is 'all / every / which ones have we…' rather than 'find the most relevant'.
memory_grep	EXACT-SUBSTRING search (case-insensitive, deterministic) over the RAW title+content of every memory visible to your team (private + shared public pool). Zero LLM, zero embeddings: the pattern is matched literally, never as a regex or wildcard. Use this INSTEAD OF re-phrasing memory_search when semantic search came back empty or you are hunting an exact string — an identifier, an error message, a config key, a file/person name. Returns each matching memory with the matching lines (±1 line of context), a per-memory match count, and the TRUE total of matching memories. Superseded memories are included but flagged 已被更正·非当前. Follow up with memory_get(memory_id=…) for a full untruncated body.
memory_get	Fetch ONE memory by id — the COMPLETE original text (up to 50000 chars, versus the short snippets memory_search/memory_grep return) plus provenance metadata (layer, source_type, private/public origin, topic, category, created_at, lifecycle). Use it when a hit from memory_search / memory_enumerate / memory_grep was truncated or you need the exact full wording of a known id; it takes no query and does NO searching — for discovery use memory_search (semantic) or memory_grep (literal). Only memories visible to your team can be fetched. A superseded memory IS still returned for provenance, clearly flagged 已被更正·非当前 with the id that replaced it — do not treat flagged content as current fact.
memory_store	Store a memory, or correct an existing one. Omit 'id' to create a new memory (async). Pass 'id' to update that memory in place (content is re-embedded so search stays consistent). Pass 'supersedes' with the id of an older memory this one REPLACES: the old entry is marked superseded (kept for history, but demoted to near-zero in search and flagged '已被更正·非当前'), so a stale status can't outrank the correction.
memory_task_canvas	Shared cross-session task canvas. action='get' reads the whole team canvas; action='update' writes your plan/progress (mermaid + completed/next lists).
memory_delete	Delete a memory entry by its identifier.
code_search	Search indexed code entities by function/class name.
code_index	Index code you are developing into a project knowledge graph so you can later query its structure (functions, classes, call/import graph) instead of re-reading whole files — saves tokens. Pass the code via 'files' (recommended): the files you have open or just wrote. Returns immediately; use code_query(action='index_status') to check progress.
code_memory_link	Link a code entity to a memory entry.
doc_search	Search uploaded documents.
memory_meta	Meta queries over the memory system. action='persona' (L3 user profile), 'status' (system stats), 'skills' (RETIRED 2026-07-19 — the L4 skill layer was removed; recall procedures via memory_search instead. Returns empty.), 'related' (graph neighbors of a memory; pass query=id or title fragment), 'contradictions' (conflicting memories in the graph), 'list' (browse stored memories; pass limit), 'feedback' (report a skill outcome; pass skill_id + outcome=success\|failure\|partial, optional memory_ids/context).
code_query	Query code relationships, impact analysis, or index status. Use action='relations' for calls/inherits, action='impact' for dependents, action='index_status' for indexing progress, action='history' for temporal evolution of an entity.
code_repo_map	Get a ranked, token-budgeted MAP of an indexed codebase — the most important functions/classes (by call-graph PageRank) as signatures + line numbers, NOT full source. Read this first to orient in a repo instead of reading whole files; then fetch specific symbols with code_query. Pass 'focus' (files/symbols you're working on) to bias the map toward them.
public_browse	Browse the shared public knowledge pool built from internalized high-quality memories. No args: returns topic list. Use topic='xxx' to filter by K2 topic. Use query='xxx' for semantic search within public knowledge only.
coord_task	Multi-agent task coordination for the team: submit work, claim it collision-free (lease-based), report progress, hand it off with context. Claim before working so parallel agents never duplicate effort; heartbeat before the lease expires or the task is requeued for others.
coord_lock	Advisory resource locks for parallel agents: claim a file/entity/resource BEFORE editing it so other agents avoid conflicts. Leases expire automatically if the holder dies; re-acquiring your own lock extends it.

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