su-memory SDK: Building a Local-First AI Memory System with Causal Reasoning Capabilities

su-memory SDK is a local-first AI memory framework that fills the gaps of traditional vector databases in causal reasoning, temporal awareness, and multi-hop association capabilities. Using VectorGraphRAG, spacetime indexing, and causal graph technologies, it achieves an 87.8% multi-hop reasoning recall rate and a 96% latency reduction, providing LLM applications with true multi-hop causal reasoning capabilities.

Most current AI applications' memory solutions are based on vector similarity nearest neighbor searches, which can only handle 'find similar' tasks and are ineffective at reasoning questions like 'why' or 'what will happen'. Traditional systems lack the core human memory capabilities of causal reasoning, temporal awareness, and multi-hop association—this is the core problem that su-memory SDK aims to solve.

su-memory SDK adopts a 'four-in-one' architecture:

1. VectorGraphRAG: Integrates vector retrieval and graph traversal, enabling efficient multi-hop reasoning using HNSW index (m=32, efConstruction=64, efSearch=64) and vector quantization (FP32/FP16/INT8/Binary);
2. SpacetimeIndex: Combines spatial location and temporal encoding, supporting spacetime multi-hop queries;
3. MemoryGraph: Explicitly defines four causal relationships (cause/condition/result/sequence) to enhance interpretability;
4. TemporalSystem: Implements temporal awareness, simulating the time decay characteristic of human memory.

The project's released performance benchmark data:

• Query latency: P50=19ms (96% reduction compared to pre-optimization), P95=76ms;
• Throughput: 94 inserts per second, with ~10.66ms processing time per item;
• Memory usage: 1.53MB for 1000 memories;
• Multi-hop recall rate: 87.8% (46% improvement over baseline).

su-memory offers two versions: Lite and LitePro:

• Lite: TF-IDF/N-gram retrieval, memory <5MB, suitable for prototype validation;
• LitePro: Integrates Ollama bge-m3, supports full VectorGraphRAG, spacetime indexing, etc., memory <50MB, suitable for production environments. Application scenarios include long-term dialogue systems, knowledge management tools, predictive applications, and multimodal AI, and it is compatible with LangChain and VMC architectures.

Licensing model: Free for individuals (limited to 1000 items), commercial paid (99 yuan/month up to 9999 yuan for private deployment). Limitations:

• Scale limit: Enterprise version has an upper limit of 100,000 items, not suitable for large-scale document retrieval;
• Ecosystem maturity: Community and toolchain are still under construction;
• Dependency: LitePro requires Ollama to run local models, increasing deployment complexity.

su-memory represents the evolution of AI memory systems from storage retrieval to cognitive architecture. Its local-first approach, interpretability, and multimodal capabilities give it an advantage in privacy-sensitive and deep reasoning scenarios. It is recommended for AI application developers who need 'understanding/reasoning' rather than just 'matching/retrieval' to evaluate it. In the future, such local memory systems may become a standard component of next-generation AI applications.