Analog AI

Analog AI positions itself as a "second brain" for AI agents, built to solve two chronic problems: runaway token consumption and agent amnesia. When an agent meets a genuinely novel challenge, Analog AI calls a frontier model once, lets it reason through the problem, and then compiles that reasoning into a reusable procedural skill anchored in local memory. Subsequent similar requests bypass the frontier model entirely and are handled by lightweight small models replaying the stored memory trace. The site reports up to a 40x reduction in token consumption, and roughly 99% on repetitive tasks.

The system is organised around two modules. DeepThink handles reasoning: it evaluates multiple hypotheses, checks for logical errors, and refines its internal thinking before answering, rather than emitting the first plausible output. DeepAct turns that reasoning into autonomous action — running code, calling APIs, and adjusting steps based on real-time feedback across multi-step tasks. Memory is split into facts, experiences, preferences and procedures, with outdated information actively deprecated.

Analog AI also reports memory and reasoning benchmarks, including results on Microsoft State-Bench, HotPotQA and BEAM, and ships integrations through a chat-completion API plus a Python SDK, with support for platforms such as VS Code, Cursor and LangChain-style agent setups. Access is currently request-based. The company also publishes research on emotional intelligence, and its earlier "Analog Assistant" framing of emotionally aware digital humans remains part of its work.