To address your needs for structured and unstructured memory systems in an agentic software system with a small server footprint, scalability, and open-source solutions, here's a curated list of tools and frameworks:

1. Structured Data Storage (SQL/Relational)

For transactional, schema-driven data with ACID compliance and relational queries:

• PostgreSQL
  • Best For: Robust relational data, JSON support (via JSONB), and scalability with extensions like Citus (for horizontal scaling).
  • Footprint: Moderate, but optimized for performance.
  • Future-Proof: Scales vertically and horizontally (with Citus). Includes extensions like pgvector for AI/vector embeddings.
  • Alternatives:
    • SQLite: Ultra-lightweight (serverless), but limited to single-writer workloads. Ideal for small-scale prototyping.
    • DuckDB: Embedded analytical database for OLAP workloads (e.g., reporting).

2. Unstructured Data Storage

For documents, blobs, logs, or flexible schemas:

• MinIO
  • Best For: S3-compatible object storage (images, files, logs). Scales horizontally with a small footprint.
  • Future-Proof: Designed for multi-node, distributed storage.
• MongoDB
  • Best For: Document storage with dynamic schemas.
  • Footprint: Moderate; scales via sharding. AGPL license.
• Apache Cassandra
  • Best For: High-write unstructured workloads (e.g., logs, time-series).
  • Tradeoff: Higher operational complexity.

3. Hybrid/Vector Storage

For AI/ML use cases (e.g., semantic search, embeddings):

• PostgreSQL + pgvector
  • Best For: Storing vector embeddings alongside structured data.
  • Footprint: Leverages existing PostgreSQL infrastructure.
• Milvus or Weaviate
  • Best For: Dedicated vector databases. Scales for large-scale similarity search.
  • Tradeoff: Adds a new system to the stack.

4. Caching & Runtime Efficiency

To reduce latency and database load:

• Redis
  • Best For: In-memory caching, session storage, and real-time data (pub/sub).
  • Footprint: Lightweight, with optional persistence.
• Apache Kafka
  • Best For: Asynchronous event streaming (e.g., inter-service communication).
  • Tradeoff: Adds complexity for real-time pipelines.

5. Embedded Solutions (Minimal Footprint)

For edge or resource-constrained environments:

• SQLite: Single-file relational DB for small-scale structured data.
• DuckDB: In-process OLAP database for analytics.
• BadgerDB (Go) / RocksDB (C++): Embedded key-value stores for unstructured data.

6. Scalability Pathways

• PostgreSQL → Citus: Scale horizontally for distributed SQL.
• MinIO → Multi-Node Cluster: Expand object storage across servers.
• Redis Cluster: Distributed caching.

Recommended Stack

1. Structured: PostgreSQL (with pgvector for vectors).
2. Unstructured: MinIO (blobs) + MongoDB (documents).
3. Cache: Redis.
4. Analytics: DuckDB (embedded) or PostgreSQL + TimescaleDB (time-series).

This stack balances minimal initial footprint with clear scalability paths. All tools are open-source, battle-tested, and avoid vendor lock-in. For teams prioritizing simplicity, start with PostgreSQL (structured + JSONB) and MinIO (unstructured), then expand as needed.

Knowledge graphs are an excellent choice for modeling complex relationships, semantic context, and hierarchical data, which are critical for agentic systems requiring reasoning, inference, or network analysis. Below are open-source solutions tailored for small footprints, scalability, and integration with your existing structured/unstructured systems:

Graph Databases

1. Dedicated Knowledge Graph Databases

Neo4j (Community Edition)

• Best For: Property graph models with Cypher query language. Ideal for relationship-heavy data (e.g., social networks, dependency graphs).
• Footprint: Lightweight for small datasets; scales vertically.
• Scalability: Community Edition is single-node. For distributed scaling, consider the paid Enterprise Edition or alternative solutions.
• Integration: Export/import JSON/CSV, or use connectors (e.g., Neo4j APOC with PostgreSQL).

Apache Jena

• Best For: RDF-based knowledge graphs (W3C standards like SPARQL, OWL). Suited for semantic web/linked data use cases.
• Footprint: Modular (TDB2 for embedded storage). Minimal overhead for small datasets.
• Scalability: Use Fuseki server for horizontal scaling. Integrates with Hadoop/Spark for large-scale analytics.

JanusGraph

• Best For: Distributed, scalable property graphs. Built on Apache TinkerPop (Gremlin query language).
• Footprint: Depends on backend storage (supports Cassandra, ScyllaDB, BerkeleyDB).
• Scalability: Horizontally scalable with Cassandra/ScyllaDB. Ideal for large-scale industrial knowledge graphs.

Cayley

• Best For: Lightweight graph database with a simple Go API. Supports RDF-like triples and GraphQL.
• Footprint: Minimal (embeddable, single binary).
• Scalability: Limited to vertical scaling; use with PostgreSQL/BoltDB for small-to-medium workloads.

Ontotext GraphDB (Free Edition)

• Best For: RDF triplestore with OWL reasoning and SPARQL. Designed for semantic AI use cases.
• Footprint: Moderate; scales vertically.
• Scalability: Free Edition is single-node; paid versions support clustering.

2. Graph Extensions for Existing Databases

PostgreSQL + Apache AGE

• Best For: Graph queries (Cypher support) alongside relational data.
• Footprint: Leverages PostgreSQL’s existing infrastructure.
• Scalability: Inherits PostgreSQL’s scaling (Citus for distributed graphs).

ArangoDB

• Best For: Multi-model (key-value, document, graph) in one engine. Uses AQL query language.
• Footprint: Moderate; scales horizontally.
• Scalability: Distributed graphs via sharding.

3. Lightweight Embedded Options

• Dgraph (Go-based): Open-source, native GraphQL API. Scales horizontally but requires more resources.
• RDFLib (Python): In-memory RDF toolkit for small-scale graphs.

Use Cases for Knowledge Graphs in Agentic Systems

1. User/Team Relationship Modeling: Track interactions, permissions, or dependencies.
2. Semantic Search: Link unstructured data (e.g., documents in MinIO) with structured metadata.
3. Inference Engines: Derive implicit facts (e.g., "If User A reports to Team B, and Team B uses Tool C, User A has access to Tool C").

Recommended Stack

• Start Small: Use Neo4j Community or PostgreSQL + Apache AGE for minimal footprint.
• Scale Later: Migrate to JanusGraph (with Cassandra/ScyllaDB) or Dgraph for distributed graphs.
• Semantic AI: Pair Ontotext GraphDB with NLP pipelines (e.g., entity extraction from unstructured text).

Key Tradeoffs

• Embedded vs. Distributed: Lightweight tools (Cayley, RDFLib) sacrifice scalability for simplicity.
• Standards Compliance: RDF/SPARQL (Apache Jena, GraphDB) vs. property graphs (Neo4j, JanusGraph).

Knowledge graphs add relationship-aware intelligence to your system. If your agents need to reason over interconnected data (e.g., user roles, workflows, or dependencies), these tools are worth embedding into your architecture.