epochdb 0.1.1

An agentic memory engine designed for lossless, tiered verbatim storage and multi-hop retrieval.

EpochDB

EpochDB is an ACID-compliant agentic memory engine designed for lossless, tiered verbatim storage and multi-hop retrieval.

Why

I had this idea while playing with LMDB. I wanted to create a memory system that could store conversations in a hybrid way, using in-memory for the most recent conversations and on-disk for older conversations. So, in order to have immutable data, I decided to use Parquet files for the on-disk storage.

Overview

Traditional AI memory systems compress conversations through destructive summarization. EpochDB bypasses this constraint by storing "Unified Memory Atoms"—the raw text intrinsically paired with dense embeddings.

EpochDB uses a tiered architecture reminiscent of CPU caching:

1. L1: Working Memory: Sub-millisecond HNSW vector index in RAM.
2. L2: Historical Archive: Cold storage in immutable, time-partitioned .parquet files via PyArrow.

It uniquely handles multi-hop retrieval over time-partitioned data using a Global Entity Index.

Performance & Comparison

EpochDB is engineered specifically for Agentic workflows where logical continuity across long horizons is critical. In side-by-side benchmarks against industry standards, EpochDB remains the only local engine capable of complex multi-hop reasoning.

Benchmark	Store	Metrics	Note
LoCoMo	EpochDB	recall: 1.000	100% Multi-hop Accuracy
	ChromaDB	recall: 0.000	Failed to connect related events
	Qdrant	recall: 0.000	Failed to connect related events
ConvoMem	EpochDB	recall@3: 1.000	Perfect Semantic retrieval
	FAISS	recall@3: 1.000	Perfect Semantic retrieval

[!IMPORTANT] Relational Expansion: While tools like FAISS and ChromaDB are excellent for single-turn semantic search, they act as "flat" stores. EpochDB leverages its integrated Knowledge Graph to bridge logical gaps, successfully navigating multi-hop queries where competitors fail completely.

How It Works

See how_it_works.md for a detailed technical dive into the architecture.

Benchmarks & Examples

See the full comparison in benchmark.md. Check out example_langgraph.py for a real-world agent integration example.