Introducing Knol: Context Engineering for AI Applications
Today we're open-sourcing Knol — a Rust-native context engineering platform that gives LLM applications persistent memory with sub-5ms latency, powered by a single PostgreSQL database.
The Context Engineering Revolution
For years, the AI community has used the term "memory" to describe how applications retain information about users and past interactions. But memory is the wrong mental model. What AI applications actually need is **context engineering** — the ability to assemble precisely the right information at the right moment to ground LLM responses.
Knol is built from the ground up for context engineering. Instead of a simplistic "memory store," it provides a multi-layered system of episodic, semantic, working, and procedural memories that work together to create rich, contextual understanding. Each layer serves a specific purpose, and they integrate seamlessly.
Why PostgreSQL + Rust Changes Everything
We made two critical architectural choices: Rust for performance and PostgreSQL for simplicity. While other memory systems spread data across multiple databases (Neo4j for graphs, Qdrant for vectors, Redis for cache), Knol runs on a single PostgreSQL instance with pgvector extension.
This means no operational complexity, no vendor lock-in, and no cross-database consistency headaches. Your data lives in one place. Your backups, permissions, and disaster recovery procedures already exist.
// Deploy Knol with a single Helm chart
// All data in PostgreSQL with native vector support
// Sub-5ms P95 latency on retrieval operationsThe Four Memory Layers
Knol's architecture mirrors human cognition, giving applications the same contextual depth that makes human conversations coherent:
- **Episodic Memory**: Raw conversation events with full context and metadata. The foundation of everything. - **Semantic Memory**: Distilled facts, preferences, and knowledge extracted from conversations via LLM analysis. - **Working Memory**: Short-lived session context that provides immediate conversational continuity. - **Procedural Memory**: Learned patterns and behavioral preferences that enable deep personalization.
Together, these layers create applications that truly understand their users.
What's Next
Knol is open-source and available today on GitHub. We've included Python and TypeScript SDKs, integrations with LangChain and CrewAI, and comprehensive documentation. Self-hosting is fully supported.
The future of AI isn't better models — it's smarter context. And context engineering is how you build it.