--- title: "core vs agents-towards-production" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cheshire-cat-ai-core-vs-nirdiamant-agents-towards-production" tools: ["cheshire-cat-ai-core", "nirdiamant-agents-towards-production"] --- # core vs agents-towards-production *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 agents-towards-production if agents-towards-production is an open-source project focused on providing comprehensive, step-by-step tutorials for developing AI agents from the prototype stage to enterprise-ready deployment. This guide. [core](https://cheshirecat.ai) reports 3.1k GitHub stars, 409 forks, and 11 open issues, last pushed Jul 29, 2026. [agents-towards-production](https://diamant-ai.com) has 21k stars, 2.8k forks, and 15 open issues, last pushed Aug 15, 2026. Figures are from public GitHub metadata via [core's repository](https://github.com/cheshire-cat-ai/core) and [agents-towards-production's repository](https://github.com/NirDiamant/agents-towards-production). | | [core](/tools/cheshire-cat-ai-core.md) | [agents-towards-production](/tools/nirdiamant-agents-towards-production.md) | | --- | --- | --- | | Tagline | AI agent microservice | End-to-end, code-first tutorials for building production-grade GenAI agents | | Stars | 3,086 | 21,298 | | Forks | 409 | 2,824 | | Open issues | 11 | 15 | | Language | Python | Jupyter Notebook | | 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. | agents-towards-production is an open-source project focused on providing comprehensive, step-by-step tutorials for developing AI agents from the prototype stage to enterprise-ready deployment. This guide includes best-pr | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0, which is a copyleft license requiring that any derivative works be released under the same license. | Other | | Categories | AI Agents | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [core](/tools/cheshire-cat-ai-core.md) | [agents-towards-production](/tools/nirdiamant-agents-towards-production.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 3d | | Open issues (now) | 11 | 15 | | Stars delta | +5 (30d) | +191 (30d) | | Open issues delta | +3 (30d) | +4 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/cheshire-cat-ai-core/trust.md) | [trust report](/tools/nirdiamant-agents-towards-production/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: agents-towards-production - **Adopt for:** agents-towards-production is an open-source project focused on providing comprehensive, step-by-step tutorials for developing AI agents from the prototype stage to enterprise-ready deployment. This guide includes best-pr ## Choose when ### Choose core if… - core is primarily Python; agents-towards-production is Jupyter Notebook. - License: core is GPL-3.0, agents-towards-production is Other. - 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 agents-towards-production if… - agents-towards-production is primarily Jupyter Notebook; core is Python. - License: agents-towards-production is Other, core is GPL-3.0. - Tags unique to agents-towards-production: agent-framework, agentic-ai, deployment, genai. - * When you aim to deploy AI agents using cloud services such as AWS Bedrock AgentCore Runtime, where automatic infrastructure management and standardized communication patterns are key. ## 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 agents-towards-production - * If your enterprise strictly forbids using cloud services; this tool emphasizes both cloud and on-prem deployment strategies but may not fit entirely on-prem infrastructures. - * When you are looking for a fully managed service without code-first or tutorial-guided approaches, as 'agents-towards-production' focuses heavily on hands-on tutorials and end-to-end guide creation. - * If your specific AI agent workload does not align with the foundational deployment patterns covered (containerization, AWS Bedrock, Ollama on-prem solutions, Runpod GPU deployment), other tools may, - other_remarks_and_conditions_of_use_or_nonuse_examples_with_links_or_code_snippets_e.g_github_issues__pull_requests__branch_names_etc_that_affect_anyoftheabove_can_be_cited_if_pertinent. ## Common questions ### What is the difference between core and agents-towards-production? core: AI agent microservice. agents-towards-production: End-to-end, code-first tutorials for building production-grade GenAI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose core over agents-towards-production? Choose core over agents-towards-production when core is primarily Python; agents-towards-production is Jupyter Notebook; License: core is GPL-3.0, agents-towards-production is Other; 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 agents-towards-production over core? Choose agents-towards-production over core when agents-towards-production is primarily Jupyter Notebook; core is Python; License: agents-towards-production is Other, core is GPL-3.0; Tags unique to agents-towards-production: agent-framework, agentic-ai, deployment, genai; * When you aim to deploy AI agents using cloud services such as AWS Bedrock AgentCore Runtime, where automatic infrastructure management and standardized communication patterns are key. ### 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 agents-towards-production? * If your enterprise strictly forbids using cloud services; this tool emphasizes both cloud and on-prem deployment strategies but may not fit entirely on-prem infrastructures. * When you are looking for a fully managed service without code-first or tutorial-guided approaches, as 'agents-towards-production' focuses heavily on hands-on tutorials and end-to-end guide creation. * If your specific AI agent workload does not align with the foundational deployment patterns covered (containerization, AWS Bedrock, Ollama on-prem solutions, Runpod GPU deployment), other tools may, other_remarks_and_conditions_of_use_or_nonuse_examples_with_links_or_code_snippets_e.g_github_issues__pull_requests__branch_names_etc_that_affect_anyoftheabove_can_be_cited_if_pertinent. ### Is core or agents-towards-production more popular on GitHub? agents-towards-production has more GitHub stars (21,298 vs 3,086). Stars measure visibility, not whether either tool fits your constraints. ### Are core and agents-towards-production open source? Yes - both are open-source projects on GitHub (core: GPL-3.0, agents-towards-production: Other). ### Where can I find alternatives to core or agents-towards-production? GraphCanon lists graph-backed alternatives at [core alternatives](/tools/cheshire-cat-ai-core/alternatives) and [agents-towards-production alternatives](/tools/nirdiamant-agents-towards-production/alternatives) ([core markdown twin](/tools/cheshire-cat-ai-core/alternatives.md), [agents-towards-production markdown twin](/tools/nirdiamant-agents-towards-production/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-nirdiamant-agents-towards-production.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, core or agents-towards-production? core: Steady. agents-towards-production: 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 agents-towards-production? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [core trust report](/tools/cheshire-cat-ai-core/trust); [agents-towards-production trust report](/tools/nirdiamant-agents-towards-production/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/_