The structured memory engine for agents and code generators.
Index real code. Track logic. Recall what matters—no hallucinations, no noise.
Attixa returns what matters, not just what matches. Built for developers, intelligent agents, and tools that need reliable, context-aware recall.
Similarity is not significance. •Retrieval is not memory. •Memory is not optional.
Read our full Memory Manifesto →Give your LLM a salience-aware memory engine — fast.
$ attixa.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.
Index your codebase and query anything: auth paths, call chains, config logic. Attixa ranks the right functions — not just files that happen to match a keyword.
Fuse structured filters with semantic scoring, attention boosts, and causal graphs.
Every result is auditable, explainable, and grounded in your actual data.
Attixa maps functions, files, and logic to return answers that reflect structure, not just keywords.
$ attixa.query("How does retry logic work?")
➜ retry.py > handle_retry()
➜ client.py > send_request()
(Salience weighted by execution path and usage frequency)Attixa 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.
Built on AttentionDB, Attixa ranks and retrieves information using attention-weighted relationships— not keyword matches or vector proximity.
Modern AI needs focus, not just storage.•Similarity ≠ significance.•Retrieval ≠ memory.
Attixa 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.
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 Attixa 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 Attixa's capabilities.
Attixa's Attentional Salience Graph (ASG) enables precise, structured memory for AI systems. Deploy in 60 seconds.
Perfect for:
Explore our technical insights, case studies, and deep dives into AttentionDB and AI memory systems.
Explore how AttentionDB revolutionizes memory systems by focusing on relevance and context, not just similarity. Learn about its unique approach to information retrieval.
Discover how to leverage AttentionDB to create intelligent agents that remember and recall information based on context and relevance.
A deep dive into how AttentionDB moves beyond traditional keyword-based search to provide more meaningful and context-aware results.
Join the growing community of developers who are building more reliable, context-aware AI with Attixa.