Cheshire Cat AI is a framework for building, training, and deploying customizable AI agents. It allows users to train agents on their documents and connect them to external APIs and applications.
Developer & AI Platformcheshirecat.aiTracked since 2026-08-211 discovery source
925Backlinks
594Referring domains
20Domain authority
+11.5%Referring domains · 90 days
Authority & distribution
How strong is Cheshire Cat AI’s web footprint?
Backlinks measure accumulated distribution; referring domains show how broadly that authority is spread. Both are observable on the open web, so both are published for every product here.
Backlinks
925Current snapshot
Referring domains
594Current snapshot
Authority
20Domain authority · category median 22
90-day move
+11.5%Referring domains, last 90 days
At 20, Cheshire Cat AI sits below the 22 median for its category.
Trajectory · Pro
The counts above are the latest of 370 daily observations. The curve through them is on the full profile.
Traffic makes more sense when you can connect it to the actual product promise.
“Cheshire Cat AI”
Cheshire Cat AI is a framework for building, training, and deploying customizable AI agents. It allows users to train agents on their documents and connect them to external APIs and applications.
Developer & AI PlatformSeen 2026-08-21 → 2026-08-21Coverage tier C
What we have measured
4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.
Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.
Canonical website: https://cheshirecat.ai
Observed across 1 discovery source.
Research questions
What the evidence answers about Cheshire Cat AI.
Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.
What is the primary purpose of Cheshire Cat AI?
Cheshire Cat AI is a framework for building, training, and deploying customizable AI agents. It allows users to train agents on their documents and connect them to external APIs and applications. This purpose is stated in the product description.
What problem does Cheshire Cat AI solve?
It solves the problem of creating and managing AI agents by providing a framework with four core primitives (Agent, Tool, Directive, Hook) to handle chat, tools, and agent loop logic. This enables developers to build agents with specific behaviors and integrations.
What are the key features or components offered by Cheshire Cat AI?
It offers a framework built in Pure Python with four core primitives: Agent (a loop that handles chat and answers), Tool (a method the LLM can call), Directive (middleware for RAG, memory, and guardrails), and Hook (to react to lifecycle events). It also supports multi-agent conversations and native MCP integration.
Who is Cheshire Cat AI designed for?
The product is designed for developers and creatives. The homepage states it is for 'the curious & creative' and the meta description calls it 'The AI Agent Framework for learners and creatives'. The taxonomy also identifies its audience as developers building AI applications.
What is Cheshire Cat AI?
Cheshire Cat AI is an open-source framework for building, training, and deploying customizable AI agents, targeted at developers. It is categorized as a 'Developer & AI Platform' tool.
5 of 6 research questions are answered for this product. The rest need source evidence we have not collected yet, so they are left unanswered rather than guessed.
Behind the lock
Unlock full Cheshire Cat AI intelligence.
Backlink trajectoryThe figures above are the latest observation. The daily curve behind them holds 370.
AI referral detailHow much traffic assistants send.
Search structure and ranking depthOrganic against paid, brand against non-brand, and how many keywords rank in the top three against the long tail — which is what decides whether the demand is portable or tied to the name.
The rest of the marketPage through every leaderboard 100 rows at a time, put any five products side by side, and query the whole dataset over API / MCP.
Worldwide estimates · Data period 2026-05-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured