tensory 0.1.1a2

Embedded claim-native memory for AI agents. Collision detection built-in.

tensory

Context-aware memory for AI agents. One file. Built-in collision detection.

I evaluated every open-source memory library — Mem0, Graphiti/Zep, Cognee, Letta — and none fit what I needed: a single-file, claim-native memory with built-in contradiction detection, temporal reasoning, and zero infrastructure. So I built tensory.

pip install tensory

Install & Run

MCP Server (for Claude Code / Cursor)

Add to your MCP settings — one line, no install needed:

"tensory": {
  "command": "uvx",
  "args": ["--from", "tensory[mcp]", "tensory-mcp"],
  "env": {
    "TENSORY_DB": "~/.local/share/tensory/memory.db",
    "OPENAI_API_KEY": "sk-..."
  }
}

Dashboard (visualize your agent's memory)

# One command, no install
uvx --from "tensory[ui]" tensory-dashboard

# With your MCP database
uvx --from "tensory[ui]" tensory-dashboard --db ~/.local/share/tensory/memory.db

# Or via Docker (runs in background, always on)
docker run -d -p 7770:7770 --name tensory-dashboard \
  -v ~/.local/share/tensory:/data \
  --restart unless-stopped \
  ghcr.io/tensory/tensory

Open http://localhost:7770 — graph explorer, claims browser, memory stats.

As a library

pip install tensory                # core (SQLite + search)
pip install "tensory[mcp]"         # + MCP server
pip install "tensory[ui]"          # + dashboard
pip install "tensory[all]"         # everything

Why not existing solutions?

Library	What's great	What didn't work for me
Mem0	Simple API, good DX	Chunk-based, no collision detection, no temporal tracking. Lost facts silently.
Graphiti/Zep	Strong knowledge graph, MMR search	Requires Neo4j, heavy infra. Graph-first, not claim-first.
Cognee	Pipeline architecture	Complex setup, graph-oriented, no built-in contradiction resolution.
Letta/MemGPT	Context window management	Agent framework, not a memory library. Different abstraction level.
Hindsight	Best benchmark scores (92% LoCoMo)	Closed-source cloud service. Can't self-host or inspect.

What I wanted: embed a SQLite file, extract atomic claims, detect when facts contradict or supersede each other, and search across text + vectors + entity graph — all in one pip install.

Quick Start

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")

What is tensory?

An embedded, claim-native memory library for AI agents. Instead of storing raw text chunks, tensory extracts atomic claims — verifiable statements with entities, confidence, and temporal validity.

• One file — single SQLite database, no Docker, no Neo4j required
• Context-aware extraction — same text yields different claims depending on why you're reading it
• Built-in collision detection — automatically finds contradictions and superseding facts
• Cognitive mechanisms — salience decay, surprise scoring, priming, all without LLM calls
• MMR-diversified search — hybrid retrieval with entity crowding prevention
• Pluggable LLM & embeddings — OpenAI, Anthropic (with proxy), Ollama, or bring your own

Architecture: 4 Storage Layers

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.

Full API Example

from tensory import Tensory, Claim, Context
from tensory.embedder import OpenAIEmbedder

# Initialize with embedder + LLM
store = await Tensory.create(
    "memory.db",
    llm=my_llm_fn,                                    # any async (str) -> str
    embedder=OpenAIEmbedder(api_key="sk-..."),         # or dim=512 for cheaper
)

# Create a research context — the lens for extraction
ctx = await store.create_context(
    goal="Track DeFi team movements and protocol partnerships",
    domain="crypto",
)

# Mode 1: 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      — extracted claims
# result.relations   — entity relations
# result.collisions  — auto-detected conflicts

# Mode 2: Pre-extracted claims (no LLM needed)
await store.add_claims([
    Claim(text="EigenLayer team grew to 60", entities=["EigenLayer"])
])

# Search — hybrid (vector + FTS + graph), context-weighted
results = await store.search("EigenLayer", context=ctx)

# Timeline — how facts about an entity evolved
history = await store.timeline("EigenLayer")

# Re-evaluate — same text, different research goal → different claims
tech_ctx = await store.create_context(goal="Track Big Tech AI strategy")
new_claims = await store.reevaluate(episode_id=result.episode_id, context=tech_ctx)

# Maintenance
await store.cleanup(max_age_days=90)
stats = await store.stats()
source_profile = await store.source_stats("reddit:r/defi")
observations = await store.consolidate(days=7, min_cluster=3)

Embeddings

OpenAI text-embedding-3-small recommended — $0.02/1M tokens (~$0.20 per 100K claims):

from tensory.embedder import OpenAIEmbedder

# Standard (1536 dims)
embedder = OpenAIEmbedder(api_key="sk-...")

# Cost-optimized (512 dims — native Matryoshka reduction, 3x smaller storage)
embedder = OpenAIEmbedder(api_key="sk-...", dim=512)

# Via proxy (CLIProxyAPI, LiteLLM, etc.)
embedder = OpenAIEmbedder(base_url="http://localhost:8317", api_key="local-key")

# No embeddings (FTS + graph search still work)
store = await Tensory.create("memory.db")  # NullEmbedder by default

LLM Extraction

Any async function (str) -> str works as LLM. Ready-made adapters in examples/llm_adapters.py:

from examples.llm_adapters import anthropic_llm, openai_llm, ollama_llm

# OpenAI
store = await Tensory.create("memory.db", llm=openai_llm())

# Anthropic via proxy (as in openHunter)
store = await Tensory.create("memory.db", llm=anthropic_llm(
    base_url="http://localhost:8317", api_key="local-key",
))

# Ollama (free, local)
store = await Tensory.create("memory.db", llm=ollama_llm("llama3.1"))

# Or from env vars (ANTHROPIC_BASE_URL, ANTHROPIC_API_KEY)
from examples.llm_adapters import anthropic_from_env
store = await Tensory.create("memory.db", llm=anthropic_from_env())

Context as Lens

The core innovation: a Context defines why you're reading. One episode can yield completely different claims under different contexts via reevaluate().

# Same news article...
crypto_ctx = await store.create_context(goal="Track DeFi protocols")
tech_ctx = await store.create_context(goal="Track Big Tech AI strategy")

result = await store.add("Google partners with EigenLayer...", context=crypto_ctx)
# → Claims about EigenLayer, restaking, DeFi partnerships

new_claims = await store.reevaluate(result.episode_id, context=tech_ctx)
# → Claims about Google's cloud strategy, AI infrastructure moves

Cognitive Mechanisms

All algorithmic, zero LLM calls:

Mechanism	What it does
Salience + decay	Claims fade over time (exponential, per-type rates)
Surprise score	Novel claims get salience boost
Priming	Recently-searched entities boost related results
Reinforce on access	Searched claims get stronger (+0.05 salience)
Waypoints	Auto-link similar claims (cosine >= 0.75)
Structural collision	Same entity + different value = auto-conflict
Consolidation	Cluster claims into OBSERVATION summaries
Source fingerprinting	Per-source reliability profiles
Sentiment tagging	Keyword-based sentiment + urgency detection

Collision Detection

Two-level, zero LLM:

1. Structural — same entities + overlapping temporal validity
2. Semantic — weighted composite: vector (40%) + entity overlap (25%) + temporal proximity (20%) + waypoint link (15%)

Salience updates on collision:

• contradiction → salience x 0.5
• supersedes → salience x 0.1
• confirms → salience + 0.2
• related → salience + 0.05

Hybrid Search

Three parallel channels merged via Reciprocal Rank Fusion (RRF), then diversified via MMR:

FTS5 + Vector + Graph → RRF merge (over-fetch 3x) → MMR reranking → top-k

Channel	Weight	Method
Vector	0.4	sqlite-vec cosine similarity
FTS	0.3	SQLite FTS5 full-text search
Graph	0.3	Entity traversal via recursive CTEs

MMR reranking (Maximal Marginal Relevance) prevents entity crowding — when a popular entity with many similar claims dominates results and pushes out specific facts. Falls back to entity-cap filtering when embeddings are unavailable.

Weights are configurable. Any channel that fails returns empty results (graceful degradation).

Graph Backends

Default: SQLiteGraphBackend — uses recursive CTEs, zero dependencies, sufficient for <100K claims.

# Default — everything in one SQLite file
store = await Tensory.create("memory.db")

# Enterprise — Neo4j (planned)
# store = await Tensory.create("memory.db", graph_backend=Neo4jBackend("bolt://localhost:7687"))

Configuration

Copy .env.example to .env:

cp .env.example .env

Variable	Purpose	Required
OPENAI_API_KEY	Embeddings + LLM extraction via OpenAI	Only if using OpenAI
ANTHROPIC_API_KEY	LLM extraction via Anthropic/proxy	Only if using Anthropic
ANTHROPIC_BASE_URL	Proxy URL (CLIProxyAPI etc.)	Optional
TENSORY_MODEL	Model for extraction	Default: claude-haiku-4-5-20251001

Benchmarks

Testing on LoCoMo (Long-term Conversational Memory, ACL 2024) via Open Memory Benchmark:

Memory System	Accuracy	Queries	Notes
Hindsight (cloud)	92.0%	1,540	Closed-source
Cognee	80.3%	152	Partial evaluation
Hybrid Search (Qdrant)	79.1%	1,540	Vector-only baseline
Tensory	testing	1,540	Full evaluation in progress

Tensory extraction cost per conversation: ~$0.12 (Haiku extraction + OpenAI embeddings). Full benchmark is running — results will be published here.

Status

Active development. 275+ tests, pyright strict, ruff clean, CI on every push.

The vision: become the best open-source memory for AI agents — a single-file SQLite database that gives your agent real long-term memory with contradiction detection, temporal reasoning, and cognitive mechanisms that work without LLM calls.

What's done

• Episodic + semantic + procedural memory (full cognitive stack)
• Hybrid search with MMR diversity (FTS5 + vector + graph → RRF → MMR)
• Two-level collision detection (structural + semantic)
• Context-aware extraction (same text, different lens → different claims)
• Topic segmentation for long texts (parallel extraction)
• MCP server for tool-use integration
• Smart context formatting (entity grouping, temporal annotations, memory-type routing)
• Web dashboard (graph explorer, claims browser, stats)
• LoCoMo benchmark integration (AMB provider with exact cost tracking)

What's next

• Full benchmark results on LoCoMo (1,540 questions, 10 conversations)
• Cross-encoder reranking for retrieval precision
• Session-aware diversification (SSD) for sequential agent queries
• Research agent built on tensory as a reference application
• LangChain / LlamaIndex integrations

Development

uv sync --all-extras                    # Install all deps
uv run pytest tests/                    # Run all tests (~275)
uv run pytest tests/test_store.py::test_name   # Single test
uv run pyright tensory/                 # Type check (strict mode)
uv run ruff check tensory/ tests/       # Lint
uv run ruff format tensory/ tests/      # Format
uv run python examples/demo.py          # Integration demo

License

MIT

Attribution

Deduplication logic adapted from Graphiti (Apache-2.0 License, Copyright 2024 Zep Software, Inc.)

---

Built because no existing memory library did what I needed. If you're building agents that need to remember, contradict, and evolve — give tensory a try.