Analog AI
Operating system for AI agents
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Analog OS gives AI agents a smarter way to evolve. When a demanding challenge requires deep thought, it brings in a heavy-hitting model to crack it. It then anchors that breakthrough into local memory, allowing a smaller model to handle future instances effortlessly. By letting the heavy-hitter teach the smaller one, Analog OS slashes compute token burn while keeping every agent stateful and blazing fast.
40x
Cost Reduction
Frontier model level performance for everyday tasks, using mainly small models.
One-Time Frontier Cost: Expensive frontier models are called strictly once to reason through a novel task, resolve edge cases, and compile the logic into a reusable procedural skill.
Low-Cost Execution: Subsequent requests completely bypass the frontier model, routing instead to lightweight, small models that execute the pre-compiled memory trace.
One-Time Frontier Cost: Expensive frontier models are called strictly once to reason through a novel task, resolve edge cases, and compile the logic into a reusable procedural skill.
Low-Cost Execution: Subsequent requests completely bypass the frontier model, routing instead to lightweight, small models that execute the pre-compiled memory trace.
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Remembers FactsLong term knowledge update and symbolic reasoning -
Remembers ExperiencesRemembers what did user ask, what agent did, what was overall experience -
Remembers ProceduresRemembers how did agent use tools on multi-step workflows, uses smaller models on learned procedures
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.
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
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%BEAMOne of the biggest result among semantic memory products
- 70.7%
$0.12/taskMicrosoft State-Bench using frontier modelBigger than any result at leaderboard - 46.0%
$0.0175/taskMicrosoft State-Bench using small language model3x and even lower cost per task than any result at leaderboard - 91%HotPotQABiggest 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).
Partners & Supporters
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