About Analog AI

Analog AI functions as an external cognitive layer for AI agents, designed to retain facts, learn skills, and maintain a consistent logical framework. It targets AI agents and harnesses used in enterprise and research contexts, acting as an auditable memory and decision system that spans multiple sessions and runs.

The system operates through a continuous loop of orchestration, symbolic bridging, and dynamic learning. It can connect via API to existing agent frameworks and tools, enabling memory and reasoning to drive long-term utility across tasks and workflows.

Analog AI offers an API and Python SDK for integration, with references to OpenClaw, Hermes, VsCode, LangChain, and CrewAI.

Use cases

Enterprise Automation: Automate complex, high-volume tasks where accuracy and cost-efficiency are vital.
Multi-Agent Coordination: Use Analog OS as the brain for multi-agent teams, maintaining state and policies.
Academic Research: Integrate Analog AI into research platforms for persistent, reasoning-capable agents.
Prototyping: Developers add memory and reasoning to existing agents via the SDK.

Key features

Remembers Facts
Remembers Experiences
Remembers Procedures
Frontier model level performance for non-critical workflows
One-Time Frontier Cost
Low-Cost Execution

Reviews

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Frequently asked questions

What is a Second brain?

It is the orchestration layer that sits between your agents (LangChain, CrewAI, etc.) or agent harnesses (OpenClaw, Hermes, VsCode, etc) and the compute models, handling state, memory, and logical governance.

How does the Smart Router work?

It analyzes the complexity of an incoming prompt. Routine queries are routed to cost-effective small models; complex multi-hop reasoning triggers calls to frontier models.

Can I connect my own agents?

Analog AI integrates via API with existing agentic frameworks like OpenClaw, Hermes, or your own custom builds.

Is it self-learning?

Yes. The system updates its memory graph based on supervised interactions, allowing agents to refine their skills and "knowledge" in real-time.

How reliable is it?

We currently hit 60% on the BEAM benchmark for long-term memory, 70.7% on Microsoft State-Bench, and 91% on HotPotQA.

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