Attanix is the first AttentionDB, a new kind of database built for relevance over similarity.
It understands structure, relationships, and context to return what matters.
No guesswork. No black boxes. Just focused, explainable memory.
Similarity is not significance. •Retrieval is not memory. •Memory is not optional.
Give your LLM a salience-aware memory engine — fast.
$ attanix.query("Where is user authentication handled?")
➜ auth.py > check_credentials()
➜ routes/login.py > login_user()
(Ranked by structural relevance and call depth)
🧠 Context-aware.
🎯 Salience-first.
🚫 No hallucination.
Attanix builds salience graphs, not just vector matches. It understands structure, relationships, and context to return what matters, not just what looks similar.
See your codebase as a graph of logic, ownership, and dependencies. Query anything from auth flows to call chains and get precise, contextual results.
Blend semantic scoring with filters and relationships. Attanix ranks results by true relevance, not just vector proximity.
Every result is grounded in your real data. Auditable. Explainable. Predictable.
Attanix implements a sophisticated memory layer designed specifically for AI applications, ensuring that information flow is optimized for both precision and relevance.
Unstructured inputs: logs, documents, chats, records.
Each operation is backed by deterministic, auditable memory that preserves context and relationships — not just raw data.
Applies transformer-style attention to rank by salience.
Each operation is backed by deterministic, auditable memory that preserves context and relationships — not just raw data.
Clusters and links data by relevance, context, and structure.
Each operation is backed by deterministic, auditable memory that preserves context and relationships — not just raw data.
Queries return focused, explainable, reproducible answers.
Each operation is backed by deterministic, auditable memory that preserves context and relationships — not just raw data.
Attanix supports memory from raw ingestion to structured, salience-ranked recall. It's built for every layer of your AI system — agents, tools, and co-pilots.
Feed in codebases, logs, docs, or structured data. No chunking required.
Build salience graphs that map logical, contextual, and structural relationships.
Determine what matters. Salience scores based on attention, not cosine.
Navigate logic paths, dependencies, and flow chains inside real-world code or data.
Return results that are explainable, deterministic, and purpose-aligned.
Query via CLI, embed in agents, or use the API in custom LLM pipelines.
Today's memory layers are just orchestration wrappers: LangChain pipelines, embedding hacks, and chained vector stores. This isn't memory — it's glue. You ask a question and get files that "seem" related, not what matters.
Agents need more than just context - they need to understand relationships and dependencies. AttentionDB builds salience graphs that help agents navigate codebases, understand API relationships, and make informed decisions about code structure and dependencies.
Generate code that respects existing patterns and relationships. AttentionDB understands your codebase's structure, making it possible to generate code that maintains consistency with your existing architecture, patterns, and dependencies.
LLMs are going agent-native. AI is leaving the lab. Real systems need context that isn't just accurate — it's reliable, auditable, and structured. Memory that focuses is what will power them.
Experience how Attanix delivers deterministic, contextually-aware results. Type in a query or select from the examples to see how our system retrieves information with precision and salience-awareness.
Try one of these examples:
Explore our technical documentation, case studies, and blog to learn more about Attanix's capabilities.
Attanix's Attentional Salience Graph (ASG) enables precise, structured memory for AI systems. Deploy in 60 seconds.
Perfect for:
Anthropic's announcement of AI employees marks a new era of autonomous agents. Learn how Attanix's structured memory systems provide the foundation for trustworthy AI-native enterprises.
A few days ago, a support AI made headlines for all the wrong reasons. Discover how Attanix's structured memory engine prevents AI hallucinations and keeps agents grounded with deterministic, explainable behavior.
Learn how Attanix's salience-aware memory system helps LLMs maintain context and improve response accuracy.
Explore the importance of deterministic behavior in AI and how Attanix ensures consistent, reliable outputs.
Discover how Attanix's memory engine helps developers navigate and understand complex codebases more effectively.
Join the growing community of developers who are building more reliable, context-aware AI with Attanix.