Analog OS is an agentic operating system designed to make AI agent workflows deterministic, stateful, and cost-efficient. It pairs cloud-based neural models with local symbolic reasoning, allowing agents to learn continuously.
  • Remembers Facts
    Long term knowledge update and symbolic reasoning
  • Remembers Experiences
    Remembers what did user ask, what agent did, what was overall experience
  • Remembers Procedures
    Remembers how did agent use tools on multi-step workflows, uses smaller models on learned procedures


3x

Cost Reduction

Frontier model level performance for agent harnesses, using mainly small models, at 1/3 the spend

Analog AI maximizes efficiency with a procedure learning and smart LLM routing engines. By using lightweight models for everyday tasks and reserving high-powered frontier models only for complex reasoning, Analog AI delivers the same results with 3x lower costs across non-critical agentic workflows.
How to use
Integrations
Chat completion API, for easy integration with established harnessing platform OpenClaw, Hermes, etc. and python SDK, for custom solutions built via LangChain, CrewAI, etc.
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Long Horizon Think and Act
Begins with foundational knowledge and continuously adapts to the noisy flow of everyday information.
Consists of two modules
Deepthink, for remembering things and
Deepact, for skill learning and execution
Permission handling
Understanding, when something is or isn't allowed, making agentic reasoning safer
Hypothetical reasoning
Handling multi-hop if-else logic. Can predict possible outcomes.
Deductions and contradiction handling
Inferring new statements based on the existing data and resolving contradictions
Multiplayer support
Recognizing different users, with different authorities and treating them differently
Spatiotemporal reasoning
Understanding, that certain facts are not generally true, but true for the certain time and location
Dynamic skill learning
Automatically learns skills, analyses mistakes
Benchmark Results
Better than anyone at Microsoft state-bench and HotPotQA, one of the best at Beam
  • 59.2%
    BEAM
    One of the biggest result among semantic memory products
  • 70.7%

    $0.12/task
    Microsoft State-Bench using frontier model
    Bigger than any result at leaderboard
  • 46.0%

    $0.0175/task
    Microsoft State-Bench using small language model
    3x and even lower cost per task than any result at leaderboard
  • 91%
    HotPotQA
    Biggest result, than any semantic memory products
Research
Analog AI is on the forefront of the research at emotional intelligence. Our engine is already capable, to express broad range of emotions (surprise, confusion, excitement, happiness, disappointment, love, confidence, etc).

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