Atlas

Atlas is a persistent AI memory infrastructure designed for intelligent agents and large language model (LLM) applications that require long-term contextual understanding and continuity across sessions. It addresses the challenge of stateless LLMs, which typically cannot maintain memory between interactions, forcing users to repeatedly provide context and leading to inefficiencies in retrieval-augmented generation (RAG) pipelines. Atlas is positioned as a hybrid cognitive memory system, combining episodic, semantic, and working memory layers accessible via a high-performance REST API.

The tool's architecture incorporates three distinct memory types: episodic memory, which stores raw experience chunks as embeddings for verbatim and semantically searchable recall; semantic memory, which structures knowledge as a graph to support entity relationships and multi-hop reasoning; and working memory, which manages per-session rolling context, entity tracking, topic blending, and a cache for recent facts. This enables AI agents to retrieve contextual memories, reason across relationships, and maintain persistent knowledge over time. Atlas supports hybrid retrieval by combining vector similarity and graph traversal, and it features memory lifecycle management, automatically decaying, reinforcing, and compressing stored information according to Ebbinghaus principles.

Atlas is suitable for a range of use cases, including AI assistants and chatbots with persistent user context, autonomous agents with task history retention, adaptive learning systems in EdTech, digital government and public services, healthcare information management, and knowledge sharing platforms where multiple agents or users benefit from shared organizational memory. The platform allows for memory namespaces to be shared across agents, enabling global knowledge sharing so that new facts learned by one agent become accessible to all.

Integration is streamlined, with Atlas working seamlessly with various LLM providers such as OpenAI, Anthropic, Gemini, and open-source models, and it is compatible with frameworks like LangChain, CrewAI, and LlamaIndex. The service is delivered as a cloud-based API, requiring minimal setup and no infrastructure management. Pricing is usage-based, with a free tier offering 10,000 operations per month and paid plans scaling to higher usage, additional namespaces, advanced features like graph QA and memory pruning, and enhanced support options. All plans are billed monthly in INR, with the flexibility to upgrade or cancel at any time.