For Multi-Agent SystemsAgent-Native Database
Model memory, state, events, artifacts, and relationships as first-class citizens. Returns structured evidence packages, not just top-k text snippets.
The Core Problem
Why Agent Systems Can't Rely on Vector Databases Alone
Traditional vector databases focus on text retrieval, but agent systems require event-driven state evolution, typed objects, provenance, version history, graph extensions, and structured evidence retrieval.
Core Architecture
Complete Pipeline from Events to Evidence
Event-driven ingestion, canonical object materialization, tiered retrieval optimization, graph relationship expansion, ultimately assembled into structured data packages with evidence chains.
Event Ingest
Write-first event pipeline
WAL
Persistent event log
Materialization
Convert to canonical objects
Tiered Retrieval
Hot / Warm / Cold tiered queries
Graph Expansion
Relation-aware reasoning
Evidence Assembly
Structured evidence package
Each stage is carefully designed to ensure data integrity, traceability, and high performance. From event writing to evidence return, the entire pipeline is optimized for multi-agent collaboration scenarios.
Core Features
Designed for Multi-Agent Reasoning
Plasmod provides a complete set of core capabilities carefully designed for Agent systems. From event ingestion to evidence retrieval, every环节 is deeply optimized for multi-agent collaboration scenarios.
Event-Native Ingest
Write-first event pipeline ensures all changes are persisted through WAL, supporting full event sourcing and replay capabilities.
Canonical Cognitive Objects
Memory, State, Artifact, ObjectVersion, Edge are first-class citizens, not just simple text chunks.
Tiered Retrieval Architecture
Intelligent tiered storage strategy: hot data for fast access, warm data for balanced performance, cold data for long-term archiving.
Structured Evidence Packages
Query results include complete provenance, proof trails, version history, and confidence scores, not just isolated text fragments.
Graph-Backed Reasoning
One-hop graph expansion based on relationships provides richer context and relation-aware retrieval capabilities.
Memory Lifecycle Governance
MemoryBank-level conflict resolution, version control, and lifecycle management ensure data consistency and reliability.
Technical Specifications
Production-Ready Infrastructure
From API design to performance metrics, Plasmod follows infrastructure-level engineering standards to provide reliable data layer support for your Agent system.
25+ HTTP API Routes
Complete RESTful API covering all core features
Go Server + Python SDK
High-performance Go runtime with easy-to-use Python client
10 Embedding Providers
Supports all major embedding model providers
One-hop Graph Expansion
Relation-based intelligent context expansion
Performance Benchmarks
Measured on prototype system, showing Plasmod performance under real-world workloads
Pricing
Start open source, scale to team and enterprise
Whether you are an individual researcher or a large enterprise, we have a plan for you. Open source version is completely free, enterprise edition provides full production-grade support.
Open Source
Perfect for individual developers and research projects
Team
Ideal for teams and startups
Enterprise
For large organizations and mission-critical workloads
All plans include core functionality. Team and Enterprise editions provide managed hosting, advanced features, and professional support. Enterprise pricing is customized based on your specific needs, including dedicated infrastructure and architecture consulting.
Built for Agent MemoryTrue Data Infrastructure
Go beyond simple text fragment retrieval. Build your agentic system on top of a database that understands events, objects, evidence, and relationships.