EpochDB
Agentic Memory Engine
11 followers
Agentic Memory Engine
11 followers
EpochDB is a high-performance, state-aware memory engine designed for lossless, tiered storage, atomic state management, and multi-hop relational reasoning. It is built specifically for AI agents that require perfect historical recall, long-term state persistence, and deterministic fact corrections.
EpochDB is a high-performance, state-aware memory engine designed for lossless, tiered storage, atomic state management, and multi-hop relational reasoning. It is built specifically for AI agents that require perfect historical recall, long-term state persistence, and deterministic fact corrections.
Why EpochDB?
Flat vector databases retrieve text based on semantic similarity but struggle to resolve conflicting facts (e.g. "where does the user work now?" vs "where did they work last year?"). EpochDB solves this through Atomic State Management:
Topic Lock & Entity Seeding: Ensures retrieval stays within the target topic by seeding candidates directly from the Knowledge Graph.
State-Aware Supersession: Automatically identifies and filters out stale facts when they are updated.
Tiered HNSW Hierarchy: Sub-millisecond recall across working memory (L1 RAM) and historical archives (L2 Disk).
Memory Forking & Lineage: Supports logical branches (db.fork) for multi-agent collaboration and hypothetical reasoning without copying data.
Rich Domain Objects: Returns structured Memory, Entity, and Graph abstractions rather than raw database tuples.
Architecture
EpochDB uses a tiered hierarchy modeled after CPU caches to balance low latency and massive scale:
Performance, Latency & Token Efficiency
EpochDB achieves a perfect 1.000 score across named benchmark suites designed to validate engine logic:
Benchmark
What it tests
Metric
Score
LoCoMo
Multi-hop relational reasoning
Multi-hop recall
1.000
ConvoMem
Fact correction & preference recall
recall@3
1.000
LongMemEval
Longitudinal recall (cross-epoch)
recall@3
1.000
NIAH
Needle in a Haystack (High-noise)
precision@3
1.000
Operational Latency
Precision metrics across Hot and Cold tiers:
Direct/Multi-Hop Relational Retrieval (Hot Tier): 0.2 ms – 0.4 ms
Historical HNSW Retrieval (Cold Tier): ~4.0 ms (30x speedup from ~125 ms via persistent indexing)
Cold Tier Full Scan (pyarrow.dataset): 45.0 ms (for cross-epoch scalar aggregations)
Scalar Range Query (B-tree): 0.8 ms
Series Interpolation (IntervalTree): 1.2 ms
Constraint Satisfaction (Z3 SAT Solver): 2.5 ms
WAL Crash Recovery Replay: 9.1 ms
LangGraph Token Savings
When used as a checkpointer, EpochDB keeps LangGraph states "thin" by storing historical turns as Unified Memory Atoms and querying them selectively. This achieves linear O(N) token scaling (saving 55% to 79% of input tokens compared to standard checkpointers' quadratic O(N2) accumulation).
Sync vs. Async Concurrency Benchmark
This benchmark evaluates E2E latency and input token consumption under concurrent multi-user load, comparing three execution configurations using the live Gemini API (gemini-embedding-2 and gemini-3-flash-preview):
Sync LangGraph + Sync EpochDB: Sequential graph invocation with blocking I/O.
Async LangGraph + Async EpochDB: Concurrent graph execution (ainvoke) using async checkpointers and DB facades.
Async Astraea + EpochBlackboard: Decoupled event-driven reactive coordination running in parallel.
The scenario simulates 3 concurrent users executing 3 conversation turns each (9 turns total) over the live API:
Metric
Sync LangGraph
Async LangGraph
Async Astraea
E2E Latency (seconds)
352.060s
113.027s
39.869s
Average Turn Latency
11735.3ms
3767.6ms
1329.0ms
Throughput Speedup
1.00x (Baseline)
3.11x
8.83x
Total Input Tokens
28,385
24,145
21,932
Technical Specifications & Constants
+20.0 Topic Lock Boost: Set mathematically larger than the maximum possible Reciprocal Rank Fusion (RRF) score sum (which caps at ≈0.05 across semantic and recency ranks, using K=60). This acts as a "hard lock," ensuring query-intent-matched facts always outrank adjacent semantic noise.
0.0001x Supersession Penalty: Multiplicatively demotes stale facts (e.g. older conflicting values for the same subject-predicate pair) to the bottom of the retrieval pool, resolving contradictions deterministically while preserving database history.
1e-7 Signal-to-Noise Demotion: Once a Topic-Locked fact is identified, all non-locked background noise is demoted by 10−7 to keep the LLM's context window clean and free from distractors.
Quantitative logic & Triggers: Native support for Scalars, Time-Series, and Constraints. IntervalTree enables precise O(logn+k) range queries with base-unit normalization via persistent schema_registry.json.
Reactive Cascade Graphs: CascadeManager automatically triggers downstream policy updates, while Coefficient of Variation (CV) reflections auto-generate constraint atoms from observed historical data trends.
Analytical Cold Tier: Leveraging pyarrow.dataset for high-performance cross-epoch scanning and numeric aggregation directly over compressed Parquet archives.
ACID Crash Recovery: Zero data loss for in-flight memories via the synchronous Write-Ahead Log.