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Host a copy with our keys and logs.
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BaşlatYayıncıdan
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.
Araçlar
Henüz araç listesi yok. Uzak sunucular ilk sağlık kontrolünden sonra bir liste alır.
SSS
Bu sayfadan Cursor, Claude Desktop, Claude Code veya VS Code için stdio yapılandırmasını kopyalayın. PlugMCP proxy'sini değil, yayıncının kurulumunu kullanır.
Bu kayıtta gerekli bir ortam değişkeni listelenmiyor.
Tensory yerel olarak stdio üzerinden çalışır.
Son kontrol: 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.
