memor-db 0.1.2

A local-first, embedded bitemporal memory engine for LLM agents.

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Memor is an embedded, local-first bitemporal memory and retrieval engine for LLM agents.

Standard vector databases store static embeddings of chat chunks. Over time, as users change their location, job, or preferences, chunk-based retrieval suffers from temporal drift—returning stale or contradictory facts. Memor solves this by extracting structured Subject-Predicate-Object facts and storing them in a bitemporal SQLite engine that tracks exactly when every fact became true, when it was invalidated, and when it was recorded.

Key Capabilities

• Zero-Infrastructure Embedded Storage: Runs entirely on a local SQLite file (memory.db) with Write-Ahead Logging (WAL). No vector database clusters or cloud services required.
• Bitemporal Fact Audit Log: Tracks both world time (valid_from to valid_to) and system transaction time (recorded_at). Previous states are never silently overwritten.
• Dual-Path Contradiction Resolution: Fast-path heuristics instantly update single-valued predicates (e.g., lives_in, works_at). Ambiguous updates route through an LLM judge to determine whether new facts replace or append to existing knowledge.
• Hybrid Retrieval Pipeline: Combines BM25 lexical search, dense semantic embeddings (sentence-transformers), and entity overlap scoring.
• Recursive Multi-Hop Graph Traversal: Automatically traverses relationship chains (e.g., user -> brother -> Alex -> owns_pet -> dog) using SQLite WITH RECURSIVE common table expressions (CTEs) without requiring an external graph database.
• Parallel LLM Reranking: Reranks top candidates concurrently across thread pools for sub-second relevance refinement.
• Bounded Garbage Collection: Includes explicit, batch-based pruning (engine.prune(older_than_days=N)) that safely archives expired records to an immutable facts_archive table before deletion.
• Strict Multi-Tenancy & Thread Safety: All operations are strictly isolated by user_id and protected by thread locks and backpressure queues.

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Architecture Flow

graph TD
    A[User Turn / Dialogue] --> B[Async Extractor<br/>Structured Pydantic Schema]
    B --> C{Conflict Resolver}
    
    C -->|Single-Valued Predicate<br/>e.g. lives_in| D[Fast Path: Invalidate Old Row]
    C -->|Ambiguous Predicate| E[Slow Path: LLM Judge]
    
    E -->|Contradiction Detected| D
    E -->|Additive Fact| F[Insert New Fact<br/>valid_to = NULL]
    D --> F
    
    F --> G[(Bitemporal SQLite Store<br/>memory.db WAL)]
    
    subgraph Retrieval Pipeline
        H[Query Text] --> I[BM25 Lexical Score]
        H --> J[Dense Vector Similarity]
        H --> K[Entity Overlap Score]
        G --> L[WITH RECURSIVE<br/>Multi-Hop Graph CTE]
        L --> M[Candidate Fusion Engine]
        I --> M
        J --> M
        K --> M
        M --> N[Parallel ThreadPool Reranker]
        N --> O[Top-K High-Precision Context]
    end

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Installation

Install directly from the repository root or via pip:

git clone https://github.com/Dilraj07/Memor.git
cd Memor
pip install -e .

Set your API provider credentials in .env or as an environment variable:

export GROQ_API_KEY="gsk_your_api_key_here"

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Quickstart

import time
from memor.engine import MemoryEngine

# Initialize the embedded memory engine
engine = MemoryEngine()
user_id = "tenant_8842"

# 1. Add dialogue asynchronously (zero latency overhead on chat loops)
engine.add_memory(user_id, "I live in San Francisco and work as a kernel developer.")
engine.add_memory(user_id, "My brother Alex recently adopted a golden retriever.")

# 2. Add an update that supersedes a previous state
engine.add_memory(user_id, "I just relocated to Seattle last weekend.")

# 3. Flush pending background extraction queues before synchronous queries
engine.flush(user_id)

# 4. Retrieve precise context (automatically combines active facts & multi-hop chains)
context = engine.query(user_id, "Where does the user live and what pet does their family own?", k=3)
print(context)

Output:

- user lives in Seattle (Relevance: 9.50)
- user -> brother -> Alex -> adopted -> golden retriever (Relevance: 9.10)
- user works as kernel developer (Relevance: 7.20)

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How Bitemporal State Tracking Works

When a user's state changes, overwriting the record destroys temporal context. Memor instead updates the previous fact's valid_to timestamp and inserts the new state:

id	user_id	subject	predicate	object	valid_from	valid_to	status
101	tenant_8842	user	lives_in	San Francisco	2026-01-10T10:00:00Z	2026-07-11T14:30:00Z	Superceded
102	tenant_8842	user	lives_in	Seattle	2026-07-11T14:30:00Z	NULL	Active

Retrieval queries filter by WHERE valid_to IS NULL by default, ensuring immediate access to current truths while preserving the full historical timeline for auditing or time-travel queries.

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API Reference

MemoryEngine

add_memory(user_id: str, text: str, sync: bool = False) -> None

Submits natural language text for background fact extraction and bitemporal resolution.

• sync=False: Non-blocking submission to the internal worker pool. Applies backpressure if queue depth exceeds 100 turns.
• sync=True: Synchronous extraction and persistence.

flush(user_id: str) -> None

Blocks execution until all pending asynchronous extraction futures for user_id complete. Recommended before reading immediate read-after-write state.

query(user_id: str, query_text: str, k: int = 5) -> str

Runs multi-signal hybrid retrieval (lexical + dense embedding + graph traversal) and returns the top k reranked factual statements formatted for LLM prompt injection.

prune(older_than_days: int) -> int

Garbage collection utility. Moves invalidated records older than older_than_days into facts_archive and hard-deletes them from the active index in bounded transactional batches of 500.

shutdown() -> None

Gracefully terminates background thread pools and executor queues.

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Testing & Verification

Run the automated test suite across extraction, bitemporal pruning, and retrieval benchmarks:

pip install -e ".[dev]"
pytest tests/ -v