Atlas

Atlas is a persistent AI memory infrastructure for intelligent agents and LLM applications. It is built to address the problem of stateless models that forget between sessions, forcing repeated context and making long-term retrieval harder in multi-session workflows.

The system combines episodic, semantic, and working memory. Episodic memory stores raw experience chunks as embeddings for verbatim and semantically searchable recall. Semantic memory represents knowledge as a graph, supporting entity relationships and multi-hop reasoning. Working memory handles per-session rolling context, entity tracking, topic vector blending, and a hot-fact cache. Atlas also supports hybrid retrieval across these memory layers, automatic chunking, embedding, and knowledge-graph extraction for incoming text, documents, or interactions, and memory maintenance that decays, reinforces, and compresses information using Ebbinghaus principles. The product description also highlights session continuity, shared memory namespaces across multiple agents, and retrieval latency stated as sub-300ms.

Atlas is intended for technical buyers building AI assistants and chatbots, AI agents and automation systems, adaptive learning systems, digital government and public services, healthcare information systems, and knowledge-sharing platforms. It is described as working with OpenAI, Anthropic, Gemini, and open-source models, and as being framework agnostic, with support for LangChain, CrewAI, LlamaIndex, and raw API calls. The page also says it connects directly to an agent flow with two lines of code.

Delivery is through a high-performance REST API, with docs and API key access available from the site. Pricing is usage-based and billed monthly in INR. The listed plans are Free at ₹0 per month, Starter at ₹999 per month, Pro at ₹4999 per month, and Scale at ₹19999 per month, with features such as Ingest and Retrieve APIs, episodic and semantic memory, namespaces, memory consolidation, graph QA, memory pruning, Prometheus metrics, RAGAS evaluation, and SLA support appearing on higher tiers. The service is in public beta, and the page also states a 90.2% accuracy result on the LongMemEval benchmark.