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การติดตั้งจากผู้เผยแพร่ ไม่ใช่เอนด์พอยต์ของ PlugMCP
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เปิดจากผู้เผยแพร่
Embedded, claim-native memory for AI agents. Single SQLite file. Built-in collision detection.
82.2% LoCoMo accuracy | 330+ tests | pyright strict | MIT
Tensory — Claim-native MCP Memory Server. LLM claim extraction → Graph + Vector + SQLite. Zero-setup persistent memory for Claude, Cursor & any MCP client.
Plugin | Benchmarks | API Guide | Architecture | Dashboard
Memory works automatically — no tool descriptions in context, no manual search calls:
claude plugin install --source github kryptogrib/tensory --path plugins/claude-code
On install, Claude Code asks for your API keys. That's it — memory activates on every session.
Full plugin docs: plugins/claude-code/README.md
pip install tensory
from tensory import Tensory, Claim
store = await Tensory.create("memory.db")
await store.add_claims([Claim(text="EigenLayer has 50 team members", entities=["EigenLayer"])])
results = await store.search("EigenLayer")
Extras: pip install "tensory[mcp]" | "tensory[ui]" | "tensory[all]"
{
"mcpServers": {
"tensory": {
"command": "uvx",
"args": ["--from", "tensory[mcp]", "tensory-mcp"],
"env": {
"TENSORY_DB": "~/.local/share/tensory/memory.db",
"OPENAI_API_KEY": "sk-..."
}
}
}
}
Provides 7 tools: tensory_add, tensory_remember, tensory_search, tensory_timeline, tensory_stats, tensory_health, tensory_reset.
The plugin approach is preferred — hooks work automatically, while MCP tools require the agent to call them and their descriptions consume context tokens.
tensory: single pip install, atomic claim extraction, contradiction detection, temporal reasoning — zero infrastructure.
Layer 0: RAW — episodes (raw text). Never deleted.
Layer 1: CLAIMS — atomic claims + embeddings + salience.
Layer 2: GRAPH — entities + relations + waypoints.
Layer 3: CONTEXT — research goals as extraction lenses.
Everything lives in a single SQLite file. No Docker, no Neo4j, no external services required.
Deep dive: docs/architecture.md
from tensory import Tensory, Context
from tensory.embedder import OpenAIEmbedder
store = await Tensory.create(
"memory.db",
llm=my_llm_fn, # any async (str) -> str
embedder=OpenAIEmbedder(api_key="sk-..."),
)
# Create a research context — the lens for extraction
ctx = await store.create_context(
goal="Track DeFi team movements and protocol partnerships",
domain="crypto",
)
# Raw text → auto-extract claims relative to context
result = await store.add(
"Google announced partnership with EigenLayer for cloud restaking...",
source="reddit:r/defi",
context=ctx,
)
# result.claims, result.relations, result.collisions
# Hybrid search — vector + FTS + graph, context-weighted
results = await store.search("EigenLayer", context=ctx)
# Timeline — how facts about an entity evolved
history = await store.timeline("EigenLayer")
Full API reference: docs/api-guide.md
uvx --from "tensory[ui]" tensory-dashboard --db ~/.local/share/tensory/memory.db
Open http://localhost:7770 — entity graph explorer, claims browser, memory stats.
docker run -d -p 7770:7770 --name tensory-dashboard \
-v ~/.local/share/tensory:/data \
--restart unless-stopped \
ghcr.io/kryptogrib/tensory
Tested on LoCoMo (Long-term Conversational Memory, ACL 2024):
Extraction cost: ~$0.08/conversation (Haiku + embeddings).
Full breakdown, per-category results, failure analysis: docs/benchmark-results.md
cp .env.example .env
Full configuration guide: docs/configuration.md
Alpha. 330+ tests, pyright strict, ruff clean, CI on every push.
What's next:
uv sync --all-extras # Install all deps
uv run pytest tests/ # Run all tests
uv run pyright tensory/ # Type check (strict mode)
uv run ruff check tensory/ tests/ # Lint
uv run ruff format tensory/ tests/ # Format
MIT
Deduplication logic adapted from Graphiti (Apache-2.0 License, Copyright 2024 Zep Software, Inc.)
Tensory — Claim-native MCP Memory Server. LLM claim extraction → Graph + Vector + SQLite. Zero-setup persistent memory for Claude, Cursor & any MCP client.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Tensory ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 Tensory — Claim-native MCP Memory Server. LLM claim extraction → Graph + Vector + SQLite. Zero-setup persistent memory for Claude, Cursor & any MCP client.
