a-memory
Your AI agents forget. a-memory makes them remember. 4-tier agent memory with hybrid search and a real knowledge graph — all in plain SQLite files. Zero cloud. Zero external APIs.
Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
npx -y [email protected]Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add io.github.cipher208-ariel-memory -- npx -y mcp-ariel-memory@1.0.0รันในเทอร์มินัล
{ "mcpServers": { "io.github.cipher208-ariel-memory": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
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รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดAlso available on PyPI:
pip install a-memory— optional extras:a-memory[embeddings]for real multilingual embeddings.
Every other memory server sends your agent's data through a cloud API or requires a separate vector database.
a-memory stores everything in SQLite files on your machine.
cp, sync with rsync.Three problems a-memory solves:
① Agent self-evolution — your AI stops repeating mistakes between sessions. It remembers decisions, errors, and corrections in a dedicated agent layer, and an hourly consolidation sweep promotes what matters into long-term facts.
② User persona persistence — your agent knows who it's talking to even after weeks of silence. Preferences, history, emotional context live in the user layer, isolated from agent identity.
③ Project continuity — project tracks per-project context: decisions with rationale and outcomes, artifact maps, a graphify-powered code index — so a fresh session picks up where the last one left off.
pip install a-memory
a-memory # MCP server on stdio — connect from any MCP client
The server runs without these, but the privacy gate (hooks ingest) and the graph
miners need two spaCy NER models. Neither is on PyPI — en-core-web-sm 404s there and
ru-core-news-sm is an unrelated placeholder — so they can only be installed by wheel URL,
and a wheel URL cannot be a dependency: PyPI rejects any distribution whose metadata
carries a direct URL. One command installs them from the URLs the package already knows:
a-memory-ner # install (console script, ships in the wheel)
a-memory-ner --check # report what is missing, install nothing
From a source checkout, python scripts/install_ner_models.py is the same command.
Skipping them is safe: the gate falls back and reports degraded instead of failing.
Point your MCP client at it:
{
"mcpServers": {
"a-memory": {
"command": "a-memory"
}
}
}
HTTP transport with dashboard:
a-memory --transport http --port 8000 --dashboard
Or run from source:
git clone https://github.com/Cipher208/a-memory.git
cd a-memory
uv sync
uv run ariel-memory
Agents see exactly seven tools — one verb per intent, plus the event bridge (6 verbs + memory_hook), no tool-choice paralysis:
Quick demo — Python MCP client:
# think — routed to the right store automatically
await session.call_tool("think", {"text": "User prefers dark mode", "layer": "user"})
# dream — finds it across every store, a week later
res = await session.call_tool("dream", {"query": "dark mode preference"})
print(res["summary"])
66 fine-grained operations exist in total, grouped into coherent opt-in tiers: the 7 primitives are exposed by default; add context (recall protocol, /new session recap, smart context budget, steering hints, tool-output compression), insight (Memory Query DSL, provenance fact-blame, quality loop, reflections, stats), write (typed memory schemas, declarative rules engine, scratchpad, counterfactuals, episodes), plus wiki and review (staged mutations, memory_report_card, daily_brief). The usual live-agent surface is one preset — ARIEL_EXPOSE=agent (59 tools), equivalent to primitives,context,insight,write,wiki,review. The remaining 7 are admin-tier: ARIEL_EXPOSE=operator (66) or all.
Tier history, because older guides still name a
brieftier: it was dissolved on 2026-09-07 —daily_briefnow ridesreview. If a doc tells you to passbrief, it is pre-1.10 and the token is simply ignored.
⚠️ Env sanitization gotcha (stdio): MCP clients pass a sanitized environment to stdio servers — setting
ARIEL_EXPOSEin your shell profile does nothing. Define the tier set in your MCP client config (theenvblock of the server entry — see configuration guide). The server logs its resolved surface at startup (tool exposure: N/M tools) — if your agent reports seeing only the primitives, check that line first, then restart the client session (tool lists are cached per session).
graph TD
A[LLM Agent] -->|MCP Protocol| B[mcp_server]
B --> C{Importance Scoring}
C --> D[L1: ReflexBuffer]
D --> E[L2: SessionStore]
E --> F{EmotionTrigger?}
F -->|high emotion| G[L3: EpisodicMemory]
F -->|normal| H[L4: CoreMemory]
B --> I[RAG Engine]
I --> J[FTS5 Search]
I --> K[MIB Binary Search]
I --> L[Hybrid RRF Ranking]
B --> M[Wiki System]
M --> N[.md Files]
M --> O[SQLite Index]
B --> P[Knowledge Graphs]
P --> Q[Epistemic Graph]
P --> R[Temporal Graph]
B --> S[Project Store]
S --> T[Decisions / Artifacts / Code Index]
U[Hourly Sweep] -->|consolidate| G
U -->|promote| H
U -->|auto-VACUUM| V[(SQLite)]
Notes (Sep 2026): mem0 now ships a self-hosted Docker image and a managed cloud with hybrid BM25+entity search; chroma is 29k★ and added hybrid+FTS5 to its Cloud tier (OSS server remains vector-only). What still differentiates a-memory: zero-infra SQLite (no Docker), NaCl-encrypted auth/saga secrets, layer isolation, hourly self-maintenance, and the temporal graph timeline.
Cipher208/a-memory; PyPI package live (pip install a-memory)wiki_list → wiki_search → wiki_read, 4KB lint cap, promotion pipeline, shared SSOT sync, usage-driven evolution — skills guide)ARIEL_EXPOSE tiers: context / insight / write)memory_diagnose/memory_heal (DB/migrations/L1-files/breaker checks + remigrate/reset-breakers/purge), integrity score in the report card, <cache:break> markers + stable-first inject ordering for provider prompt caches; retrieval — faceted tag queries (dimension:value, same-dim OR / cross-dim AND), memory-kind weights in ACT-R scoring, disclosure triggers («when X, surface Y» recall-side rules); wiring — real context_threshold/memory_pressure emitters (Hermes plugin + autohooks daemon), on_turn_end event, wiki_write staged mutations with revert, transition-level consolidation revert, causal-link producer on memory_graph_add; validation — DREAM markers anchored to message start (document-fragment false positives eliminated), post-compaction semantic audit (episode coverage by the L4 set)changed_since delta mode, per-query ACT-R min-max, per-block max_chars, semantic dedup gate, channel-granular sources, 3-option conflict contract (supersede/retain/annotate), orphan-anchor GC, nightly gap-registry, per-agent harness breaker, provenance edge filter, wiki↔fact metadata backlinks, wiki_read related-facts hydration, RU lemma keys (pymorphy3), 2-class textcat pilot (flag-gated), S17 supplements 8-11 (cold-archive drill-down, confirming layer, junk-vector detector, HDBSCAN clusters), B6 anti-hub degree capdistill_and_route read a kind where it needed the speaker) plus a provenance-based purge of the 65 rows it had already mislabelled; a kind classifier that no longer hands a permanent label to harness boilerplate or to compound words (pre-commit, mergeCommit); the gap registry getting the reader S18 planned but never built; the L4 expectation measured against the L4 bar instead of the L3 one (71% of the diff_gap backlog was that one wrong number); and a quality gate that could not fail now failing correctly — an unpinned scanner had silently moved its JSON schema out from under itPRs welcome! See CONTRIBUTING.md.
MIT © Cipher208
⭐ If this project helps you, star it on GitHub.
Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth
{ "mcpServers": { "io.github.cipher208-ariel-memory": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}เครื่องมือ
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FAQ
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Ariel Memory ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-09 Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth