Analog AI is an agentic OS powered by state-of-the-art memory and reasoning engine. By anchoring neural models with symbolic reasoning, it delivers predictable and auditable agentic workflows, while drastically optimizing operational costs.
  • Procedural Memory
    Dynamic skill learning
  • Semantic Memory
    Dynamic knowledge update and symbolic reasoning
  • Explainability
    Auditable reasoning process
usecase
How to use Analog AI?
Integration with established harnessing platforms—including OpenClaw, Hermes, or custom solutions built via LangChain, CrewAI, etc. Functioning as an LLM with a persistent, dynamic memory, it continuously learns and refines its base of facts and operational skills.
  • 1x
    Computational resources needed for Analog AI + small open source models
  • ~3x
    Computational resources needed for frontier models
Analog AI maximizes efficiency with a smart routing engine that matches your data to the right model. 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.
01
User
Sends a message
Incoming
02
AI agent
Understands & asks clarifying questions
Thinking…
03
User
Reviews
Reviewing
04
Final Response
Sent back
Delivered
05
Knowledge
Knowledge improves
+1 insight
↻ loop continues — knowledge grows with every supervised conversation
0
Messages handled
–
Accuracy
–
Avg response
Long horizon think and act
Begins with foundational knowledge and continuously adapts to the noisy flow of everyday information.
Consists of two modules AnalogAI Deepthink and AnalogAI Deepact
Permission handling
Understanding, when something is or isn't allowed
Hypothetical reasoning
Handling multi-hop if-else logic
Deductions and contradiction handling
Inferring new statements based on the existing data and resolving contradictions
Causal reasoning
Making long causal chain, to understand, what leads to the certain outcome
Spatiotemporal reasoning
Understanding, that certain facts are not generally true, but true for the certain time and location
Skill learning
Automatically learns skills under your supervision
Benchmark Results
Testing symbolic reasoning capabilities, as well as procedural and semantic memory
  • 59.2%
    BEAM
    Tests multi-session long-term memory.
  • 70.7%
    Microsoft State-Bench
    Tests skill learning
  • 91%
    HotPotQA
    Tests multi-hop question answering.
Partners & Supporters
Made on
Tilda