AISquare is the system of record for AI decisions. It captures the reasoning behind every AI agent decision as a persistent graph, enforces decision-level policies, lets humans correct behavior in place without redeploys, and keeps an auditable record of what happened.

What can I do with AISquare?

Four things: Understand decisions with a Reasoning Graph, Prevent bad actions with Policy Enforcement, Fix behavior with a Correction Loop, and Remember context with Enterprise Memory that travels across models and frameworks.

Which AI agent frameworks does AISquare work with?

AISquare works with the agent stacks teams already build on, including Agno, LangChain, LlamaIndex, and CrewAI, and model providers such as OpenAI, Claude, Gemini, and Mistral, via the aisquare-explainability Python SDK and the AISquare API.

Is AISquare free to start?

Yes. AISquare has a free tier with no credit card required, plus usage-based paid plans (Starter, Pro, Teams, and Enterprise).

Engineers who debug agent decisions, compliance teams who enforce controls, and leaders who need defensible, auditable AI.

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Creating an AI knowledge layer on top of your project repository

What if your codebase could explain itself? Here's how to build an AI-powered knowledge layer that turns your repository into a living, queryable resource for your entire team.

March 4, 2026·3 min read

Zahwah Jameel

DevRel Associate

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Developer Experience

Feb 18, 2026 · 3 min read

Learn new developer tools faster by turning official docs into an AI mentor

Official documentation is comprehensive but overwhelming. Here's how to transform any tool's docs into a personalized AI mentor that teaches you at your own pace.

Zahwah Jameel

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Research

Feb 3, 2026 · 4 min read

How explainable AI reduces hallucinations in domain-specific systems

Hallucinations aren't just a model problem — they're a transparency problem. Here's how explainability techniques can dramatically reduce false outputs in specialized AI systems.

Zahwah Jameel

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