epochdb 0.2.0

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, leveraging int8 Scalar Quantization for a 4x reduction in disk footprint.

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

Architecture at a Glance

graph LR
    A[Agent] -->|Retrieve| B(EpochDB Engine)
    subgraph "Reasoning Core"
        B --> C{Semantic Search}
        B --> D{Relational Expansion}
        C & D --> R{Hybrid RRF Ranking}
    end
    C & D --> E[Working Memory - RAM]
    C & D --> F[Historical Archive - Parquet]

Installation

pip install epochdb

Quickstart: LangGraph + EpochDB

Integrate EpochDB into your LangGraph workflows to provide agents with perfect, multi-hop memory that persists across lifetimes.

from epochdb import EpochDB
from langgraph.graph import StateGraph, END

# 1. Initialize EpochDB (e.g., 3072D for Gemini 2.0)
db = EpochDB(storage_dir="./agent_memory", dim=3072)

# 2. Define a Retrieval Node with Relational Expansion (Multi-Hop)
def retrieve_memory(state):
    # query_emb: np.ndarray from your embedder
    # expand_hops=2: Bridges logical gaps (e.g., Jeff -> Project -> Tech)
    results = db.recall(query_emb, top_k=3, expand_hops=2)
    context = "\n".join([r.payload for r in results])
    return {"context": context}

# 3. Define a Storage Node (Atom + KG Triples)
def store_memory(state):
    db.add_memory(
        payload=f"User said: {state['input']}",
        embedding=input_vector,
        triples=[("user", "mentioned", "EpochDB")] # Builds the KG
    )
    return state

[!TIP] Tiered Persistence: Unlike standard vector stores, EpochDB automatically manages the lifecycle of these memories, flushing them to immutable Parquet files (Cold Tier) while keeping the most relevant atoms in RAM (Hot Tier).

[!TIP] Native LangGraph Checkpointer: EpochDB now includes a built-in checkpointer. You can persist your entire graph state (thread context) directly in the same EpochDB storage directory using EpochDBCheckpointer(db).

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 tiered architecture and ACID-compliant transactional layer.

Benchmarks & Examples

• benchmark.md: Full 5-way comparative analysis vs. Chroma, Lance, FAISS, and Qdrant.
• example_langgraph.py: A complete, multi-session agent implementation.
• demo.py: An interactive "Detective Story" proving Relational Expansion logic.