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
पब्लिशर की अपनी कमांड। PlugMCP एंडपॉइंट नहीं।
Host a copy with our keys and logs.
npx -y [email protected]Claude Code में लोकल कमांड के लिए इंस्टॉल लिंक नहीं है। नीचे दी गई कमांड कॉपी करें और टर्मिनल में चलाएँ।
claude mcp add io.github.cipher208-ariel-memory -- npx -y mcp-ariel-memory@1.0.0टर्मिनल में चलाएँ।
{ "mcpServers": { "io.github.cipher208-ariel-memory": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
रिमोट URL या आपकी मशीन पर कमांड? दोनों इंस्टॉल में क्या अंतर है
PlugMCP पर चलाएँ
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.
लॉन्चAlso 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]" ] } }}टूल
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FAQ
इस पेज पर Cursor, Claude Desktop, Claude Code या VS Code के लिए stdio कॉन्फ़िग कॉपी करें। यह पब्लिशर का इंस्टॉल इस्तेमाल करता है, PlugMCP प्रॉक्सी नहीं।
इस रिकॉर्ड में कोई ज़रूरी एनवायरनमेंट वेरिएबल दर्ज नहीं है।
Ariel Memory stdio पर लोकल चलता है।
पिछली जाँच 2026-11-09 को हुई। Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth