Comparison
core vs agents-from-scratch
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-from-scratch if agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without.
Markdown twin · core alternatives · agents-from-scratch alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | core | agents-from-scratch |
|---|---|---|
| Maintenance | Steady (50d since push) As of Sep 18, 2026 · github_public_v1 | Steady (56d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 20, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- core
- AI agent microservice
- agents-from-scratch
- Build AI agents locally without relying on frameworks or cloud APIs.
Stars
- core
- 3.1k
- agents-from-scratch
- 1.0k
Forks
- core
- 409
- agents-from-scratch
- 251
Open issues
- core
- 11
- agents-from-scratch
- 4
Language
- core
- Python
- agents-from-scratch
- Python
Adopt for
- core
- 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-from-scratch
- agents-from-scratch is for those who want absolute control over their AI agent development using only local resources and Python, focusing on deep learning without relying on external frameworks or cloud dependencies.
Persona
- core
- -
- agents-from-scratch
- -
Runtime
- core
- -
- agents-from-scratch
- -
License
- core
- GPL-3.0, which is a copyleft license requiring that any derivative works be released under the same license.
- agents-from-scratch
- MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes.
Last pushed
- core
- Jul 29, 2026
- agents-from-scratch
- Jul 25, 2026
Categories
- core
- AI Agents
- agents-from-scratch
- AI Agents, Developer Tools
Trust and health
Days since push
- core
- 50d
- agents-from-scratch
- 56d
Open issues (now)
- core
- 11
- agents-from-scratch
- 4
Stars delta
- core
- +5 (30d)
- agents-from-scratch
- +63 (30d)
Open issues delta
- core
- +3 (30d)
- agents-from-scratch
- +1 (30d)
Owner type
- core
- Organization
- agents-from-scratch
- User
Full report
- core
- Trust report
- agents-from-scratch
- Trust report
Choose core if…
- License: core is GPL-3.0, agents-from-scratch is MIT.
- 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 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.
Choose agents-from-scratch if…
- License: agents-from-scratch is MIT, core is GPL-3.0.
- Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs..
- Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm.
- Also covers Developer Tools.
- You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture.
When NOT to use agents-from-scratch
- You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks.
- If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (cheshire-cat-ai/core) · observed Sep 20, 2026
- GitHub forks (cheshire-cat-ai/core) · observed Sep 20, 2026
- Last push (cheshire-cat-ai/core) · observed Jul 29, 2026
- License file (GPL-3.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 20, 2026
- GitHub stars (pguso/agents-from-scratch) · observed Sep 20, 2026
- GitHub forks (pguso/agents-from-scratch) · observed Sep 20, 2026
- Last push (pguso/agents-from-scratch) · observed Jul 25, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: core 3.1k · agents-from-scratch 1.0k (synced Sep 20, 2026).
Common questions
- What is the difference between core and agents-from-scratch?
- core: AI agent microservice. agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. See the comparison table for live GitHub stats and shared categories.
- When should I choose core over agents-from-scratch?
- Choose core over agents-from-scratch when License: core is GPL-3.0, agents-from-scratch is MIT; 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-from-scratch over core?
- Choose agents-from-scratch over core when License: agents-from-scratch is MIT, core is GPL-3.0; Requirements: Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.; Tags unique to agents-from-scratch: agent-architecture, ai-agents, llm, local-llm; Also covers Developer Tools; You plan to teach yourself or others about the fundamentals of creating AI agents from ground zero with complete transparency into each layer of architecture.
- 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-from-scratch?
- You are working on an application that needs to be deployed quickly. The tool's approach from first principles can be time-consuming compared to using established frameworks. If you need scalability or cloud capabilities such as easy scaling with demand, this tool will not provide these features since it strictly operates on local infrastructure.
- Is core or agents-from-scratch more popular on GitHub?
- core has more GitHub stars (3,086 vs 1,017). Stars measure visibility, not whether either tool fits your constraints.
- Are core and agents-from-scratch open source?
- Yes - both are open-source projects on GitHub (core: GPL-3.0, agents-from-scratch: MIT).
- Where can I find alternatives to core or agents-from-scratch?
- GraphCanon lists graph-backed alternatives at core alternatives and agents-from-scratch alternatives (core markdown twin, agents-from-scratch markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, core or agents-from-scratch?
- core: Steady. agents-from-scratch: Steady. 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-from-scratch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: core trust report; agents-from-scratch trust report.