90.2% accuracy on LongMemEval benchmark
Atlas provides hybrid cognitive memory for AI agents using episodic, semantic and working memory layers through a high-performance REST API for LLM applications.
Stateless LLMs cannot maintain continuity. Users have to repeat context every single time.
Stuffing context windows with irrelevant chunks drastically increases costs and latency.
Standard vector databases fail to connect multi-hop concepts or understand complex relationships over time.
Atlas is a persistent AI memory infrastructure for intelligent agents and LLM applications. It combines episodic, semantic, and working memory to deliver long-term contextual understanding across multi-session interactions.
Unlike traditional RAG systems or standalone vector databases, Atlas enables AI agents to retrieve contextual memories, reason across relationships, and maintain persistent knowledge over time.
Raw experience chunks stored as embeddings. Your agents remember what happened — verbatim text, semantically searchable across all past interactions.
Structured knowledge as a graph. Entities, relations, and multi-hop reasoning. Ask 'how is X connected to Y?' — Atlas traverses the graph to find out.
Per-session rolling context. Entity tracking, topic vector blending, hot-fact cache. Your agent knows what was said five messages ago — every time.
Don't silo your AI's learning. Atlas allows memory namespaces to be shared across multiple agents. When one agent learns a new fact or solves a complex problem, your entire fleet instantly gains that knowledge.
Atlas stores, retrieves, and maintains long-term memory for AI agents using episodic, semantic, and working memory systems.
Pass any text, doc, or interaction to the API. Atlas automatically chunks, embeds, and extracts a knowledge graph.
Query Atlas with natural language. We perform hybrid retrieval across episodic, semantic, and working memory.
Memories automatically decay, reinforce, and compress over time using Ebbinghaus principles. Never bloat your context window again.
Ask "what did the user say about their project last month?" and get a real answer.
Memory that decays, reinforces, and compresses itself — no manual cleanup.
Vector similarity + graph traversal together, not either/or.
Multiple agents or personas share a common memory space.
Users pick up exactly where they left off, every time.
Works with LangChain, CrewAI, LlamaIndex, raw API calls.
Production-grade latency for real-time agent inference.
Performance stays consistent across multi-session conversations, where standard RAG degrades by up to 30%.
Long-term retrieval performance
From simple chatbots to complex multi-agent swarms, Atlas provides the cognitive layer required for true autonomy.
Persistent user context across sessions
Agents that remember task history and accumulated knowledge
Adaptive learning systems that remember each learner's journey
Reliable, contextual citizen-facing AI
Persistent patient context without re-asking the same questions
Teams where multiple agents or users build on shared organizational memory
Complexity is hidden by design, which is the point. Atlas connects directly to your agent flow with just two lines of code.
All plans billed monthly in INR. No hidden fees. Upgrade or cancel anytime.
10,000 ops/month
50,000 ops/month
"We replaced a custom Redis + Pinecone setup with Atlas in one afternoon. The multi-hop graph QA alone is worth the price — our support bot now answers questions that require reading three different documents."
Arjun Mehta
CTO, Synthflow AI
"The Ebbinghaus decay and automatic consolidation means our agents don't get confused by stale information. Memory management used to be our biggest headache. Now it's invisible."
Priya Nair
Lead Engineer, Rephrase.ai
"Per-key namespacing is a lifesaver for B2B SaaS. Each of our enterprise customers gets fully isolated memory without any extra infrastructure. The SDK abstracts all of it perfectly."
Varun Shah
Founder, AgentForge
We work with AI teams building production agents. Book a 30-minute call and we'll review your architecture, identify memory bottlenecks, and scope a pilot.
Video call with a founder. No sales pitch. Just architecture.
Free · No commitment · Usually within 48 hours
Common questions about persistent AI memory, cognitive memory systems, LLM memory infrastructure, and intelligent AI agents.