--- title: "core vs agentos" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cheshire-cat-ai-core-vs-framerslab-agentos" tools: ["cheshire-cat-ai-core", "framerslab-agentos"] --- # core vs agentos *GraphCanon updated Sep 20, 2026* ## Verdict Pick core if cheshire Cat AI, an AI agent microservice, is built in Python and supports function-calling and vector search, making it suitable for projects that require conversational AI capabilities with these features; pick agentos if agentOS supports eleven LLM providers with runtime tool forging capabilities for cognitive memory in TypeScript. [core](https://cheshirecat.ai) reports 3.1k GitHub stars, 409 forks, and 11 open issues, last pushed Jul 29, 2026. [agentos](https://docs.agentos.sh) has 670 stars, 97 forks, and 11 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [core's repository](https://github.com/cheshire-cat-ai/core) and [agentos's repository](https://github.com/framerslab/agentos). | | [core](/tools/cheshire-cat-ai-core.md) | [agentos](/tools/framerslab-agentos.md) | | --- | --- | --- | | Tagline | AI agent microservice | TypeScript AI agent framework providing cognitive memory and runtime tool forging with support for multi-agent orchestration | | Stars | 3,086 | 670 | | Forks | 409 | 97 | | Open issues | 11 | 11 | | Language | Python | TypeScript | | Adopt for | Cheshire Cat AI, an AI agent microservice, is built in Python and supports function-calling and vector search, making it suitable for projects that require conversational AI capabilities with these features. | AgentOS supports eleven LLM providers with runtime tool forging capabilities for cognitive memory in TypeScript. | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0, which is a copyleft license requiring that any derivative works be released under the same license. | Apache-2.0 | | Categories | AI Agents | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [core](/tools/cheshire-cat-ai-core.md) | [agentos](/tools/framerslab-agentos.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 1d | | Stars delta | +5 (30d) | +69 (30d) | | Open issues delta | +3 (30d) | +2 (30d) | | Full report | [trust report](/tools/cheshire-cat-ai-core/trust.md) | [trust report](/tools/framerslab-agentos/trust.md) | ## Decision facts: core - **Pricing:** freemium - The code is free and open-source under GPL-3.0, but any derivative works must also be open-source. - **Requirements:** Min 2 GB RAM; Requires Docker; Requires Docker for deployment. - **Adopt for:** Cheshire Cat AI, an AI agent microservice, is built in Python and supports function-calling and vector search, making it suitable for projects that require conversational AI capabilities with these features. - **License detail:** GPL-3.0, which is a copyleft license requiring that any derivative works be released under the same license. ## Decision facts: agentos - **Adopt for:** AgentOS supports eleven LLM providers with runtime tool forging capabilities for cognitive memory in TypeScript. ## Choose when ### Choose core if… - core is primarily Python; agentos is TypeScript. - License: core is GPL-3.0, agentos is Apache-2.0. - Pricing: The code is free and open-source under GPL-3.0, but any derivative works must also be open-source.. - Requirements: Min 2 GB RAM; Requires Docker; Requires Docker for deployment.. - Tags unique to core: ag-ui-protocol, agent, ai, assistant. - Use Cheshire Cat AI when you need a microservice that supports function-calling and vector search, as it is specifically designed with these features. ### Choose agentos if… - agentos is primarily TypeScript; core is Python. - License: agentos is Apache-2.0, core is GPL-3.0. - Tags unique to agentos: agent-framework, cognitive-memory, llm-orchestration, multi-agent. - Need for cognitive memory and real-time tool generation in AI agents ## When NOT to use core - Avoid using Cheshire Cat AI if your project requires proprietary licensing, as it is licensed under GPL-3.0, which may not be compatible with proprietary software. - Do not use it if your project does not require or benefit from function-calling or vector search capabilities, as these are core features of Cheshire Cat AI. ## When NOT to use agentos - Preferring frameworks without multi-agent orchestration support - Prioritizing environments with less than eleven LLM provider options ## Common questions ### What is the difference between core and agentos? core: AI agent microservice. agentos: TypeScript AI agent framework providing cognitive memory and runtime tool forging with support for multi-agent orchestration. See the comparison table for live GitHub stats and shared categories. ### When should I choose core over agentos? Choose core over agentos when core is primarily Python; agentos is TypeScript; License: core is GPL-3.0, agentos is Apache-2.0; Pricing: The code is free and open-source under GPL-3.0, but any derivative works must also be open-source.; Requirements: Min 2 GB RAM; Requires Docker; Requires Docker for deployment.; Tags unique to core: ag-ui-protocol, agent, ai, assistant; Use Cheshire Cat AI when you need a microservice that supports function-calling and vector search, as it is specifically designed with these features. ### When should I choose agentos over core? Choose agentos over core when agentos is primarily TypeScript; core is Python; License: agentos is Apache-2.0, core is GPL-3.0; Tags unique to agentos: agent-framework, cognitive-memory, llm-orchestration, multi-agent; Need for cognitive memory and real-time tool generation in AI agents. ### When should I avoid core? Avoid using Cheshire Cat AI if your project requires proprietary licensing, as it is licensed under GPL-3.0, which may not be compatible with proprietary software. Do not use it if your project does not require or benefit from function-calling or vector search capabilities, as these are core features of Cheshire Cat AI. ### When should I avoid agentos? Preferring frameworks without multi-agent orchestration support Prioritizing environments with less than eleven LLM provider options ### Is core or agentos more popular on GitHub? core has more GitHub stars (3,086 vs 670). Stars measure visibility, not whether either tool fits your constraints. ### Are core and agentos open source? Yes - both are open-source projects on GitHub (core: GPL-3.0, agentos: Apache-2.0). ### Where can I find alternatives to core or agentos? GraphCanon lists graph-backed alternatives at [core alternatives](/tools/cheshire-cat-ai-core/alternatives) and [agentos alternatives](/tools/framerslab-agentos/alternatives) ([core markdown twin](/tools/cheshire-cat-ai-core/alternatives.md), [agentos markdown twin](/tools/framerslab-agentos/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/cheshire-cat-ai-core-vs-framerslab-agentos.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, core or agentos? core: Steady. agentos: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for core and agentos? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [core trust report](/tools/cheshire-cat-ai-core/trust); [agentos trust report](/tools/framerslab-agentos/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cheshire-cat-ai-core`](/api/graphcanon/graph?tool=cheshire-cat-ai-core) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_