Lattice0 FAQs

Question 1

What makes Lattice0 different from other LLM memory systems?

Accepted Answer

Lattice0 stands out by being RAM-first, using a graph-primary data model for complex relationships, and featuring LLM-driven consolidation. Unlike typical vector-store wrappers, it actively makes agent memory smarter by merging duplicates, detecting contradictions, and generating summaries during idle times.

Question 2

Is Lattice0 suitable for privacy-sensitive environments?

Accepted Answer

Absolutely. Lattice0 operates locally as a single binary with zero runtime dependencies. It supports configurable LLM backends including Ollama, Claude Code, and any OpenAI-compatible endpoint, allowing you to run everything on your machine with local models, ensuring data privacy and control.

Question 3

What kind of performance and latency can I expect from Lattice0?

Accepted Answer

Lattice0 is RAM-resident, meaning the entire working set lives in memory. This delivers sub-5ms latency, allowing queries to hit L3/RAM speeds instead of being bottlenecked by SSDs or network-bound cloud databases, providing a seamless experience for developers and agents alike.

Question 4

How does Lattice0 improve LLM agent intelligence over time?

Accepted Answer

Through its unique LLM-driven consolidation process, Lattice0 gets smarter while you sleep. It actively merges duplicate memories, detects contradictions, promotes episodic details into semantic facts, writes summaries, and proposes new graph edges, ensuring your agent's knowledge evolves and improves.

Question 5

What are the primary use cases for Lattice0?

Accepted Answer

Lattice0 excels for long-running agents that accumulate context across sessions, developer tooling requiring sub-5ms recall, and privacy-sensitive applications. It's ideal for agents needing to track evolving knowledge and workloads where retrieval quality (via its hybrid pipeline) is paramount, rather than multi-user or massive document RAG.

Lattice0

Lattice0

主要功能

使用案例

主要功能

使用案例