Memory infrastructure for AI agents
Persistent memory for agents that need to remember what happened, what they learned, and what they should do next across hours, days, or months of continuous execution.
Observe · Remember · Retrieve · Act · Learn
The continuous cognitive loop powering long-running autonomous agents.
Observe / Perceive
Reads user prompts, chat history, code diffs, tool returns, and environment logs.
Remember
Extracts entities, rules, user preferences, and failure modes into persistent vector & graph state.
Retrieve
Sub-0.4ms semantic queries fetch relevant context directly into the LLM reasoning window.
Act
Executes terminal commands, edits codebases, calls APIs, or triggers external webhooks.
Learn
Synthesizes contradiction-free procedural guidelines for all future sessions.
Agents forget. Autonomous agents remember.
Watch memory compound across time rather than degrading with every session reset.
Initial Onboarding
Agent observes codebase, learns user preferences, establishes baseline project conventions.
Zero Re-Explanation
Agent recalls previous test failures, architectural decisions, and preferred library patterns instantly.
Domain Mastery
Synthesizes auto-distilled guidelines; resolves contradictory instructions automatically.
Autonomous Expertise
Deep institutional memory spanning thousands of commits, redlines, research briefs, and multi-agent handoffs.
Five Agent Memory Primitives
A complete cognitive architecture built specifically for autonomous software systems.
Episodic Memory
Full chronological recall of tool invocations, user decisions, debug outcomes, and prompt trajectories across sessions.
Semantic Memory
Dense domain embeddings, factual guidelines, documentation graphs, and user preferences stored with sub-0.4ms retrieval.
Procedural Memory
Synthesized workflows, operational heuristics, and learned conventions distilled automatically from past runs.
Working Memory
Active context stack, current branch modifications, and short-term reasoning buffers kept outside token limits.
Relational Memory
Entity knowledge graph resolving duplicate terms, dependency trees, and temporal relationship changes over time.
Cognitive State Branching
Fork memory to explore speculative reasoning trees, test controversial code refactors, or run parallel prompts without corrupting trunk history.
Built for Agents That Run for Hours, Days, or Months
From single-developer IDE assistants to production enterprise multi-agent swarms.
Coding Agents
Remembers repository conventions, naming patterns, "we tried that already" bug logs, and architectural choices across daily development.
Research Agents
Maintains running claim registries, source confidence scores, and cross-document citations without losing its train of thought over multi-day sweeps.
Trading & Financial Agents
Tracks macro indicators, portfolio decisions, historical fill anomalies, and market regime transitions across 24/7 autonomous monitoring.
Customer & Support Agents
Instantly surfaces past ticket context, subscription tiers, enterprise SLAs, and user satisfaction history the moment a conversation begins.
Multi-Agent Systems
Shared memory substrate where planners, coders, reviewers, and QA agents read and write the exact same workspace graph without losing sync.
Frequently Asked Questions
How does Axonio keep agents from overflowing the LLM context window?
Axonio indexes memory using hybrid HNSW vector quantization and an entity graph. Instead of dumping raw transcripts into the prompt, the agent performs sub-0.4ms semantic queries (clawdb_search) to fetch only the precise facts and conventions needed for the immediate turn.
What happens when two memories contradict each other?
Axonio includes automatic reflection and auto-distillation. When conflicting facts are detected, the system consolidates them with temporal timestamps, preserving the historical trail while surfacing the current truth to the agent.
Can multiple agents in a swarm share memory?
Yes. An Axonio workspace acts as the unified memory substrate. A lead research agent, a code generator, and a QA verification agent can all read and write to the same memory space in real time.
How do I connect my agent to Axonio?
You can use the open-source SDK (@clawdb/sdk) in TypeScript, Python, Go, or Rust, or point any Model Context Protocol (MCP) client at your hosted gateway URL.