Comparison
agents-from-scratch vs agents
Verdict
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 relying on external frameworks or cloud dependencies; pick agents if the agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude.
Markdown twin · agents-from-scratch alternatives · agents alternatives
GraphCanon updated Aug 19, 2026
6views this month
vs
Trust & integrity
| Signal | agents-from-scratch | agents |
|---|---|---|
| Maintenance | Active (18d since push) As of Aug 12, 2026 · github_public_v1 | Very active (1d since push) As of Aug 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Aug 12, 2026 · github_public_v1 | Not a fork · Personal account As of Aug 19, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- agents-from-scratch
- Build AI agents locally without relying on frameworks or cloud APIs.
- agents
- Multi-harness agentic plugin marketplace for various AI agents
Stars
- agents-from-scratch
- 954
- agents
- 39k
Forks
- agents-from-scratch
- 240
- agents
- 4.1k
Open issues
- agents-from-scratch
- 3
- agents
- 5
Language
- agents-from-scratch
- Python
- agents
- Python
Adopt for
- 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.
- agents
- The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot
Persona
- agents-from-scratch
- -
- agents
- -
Runtime
- agents-from-scratch
- -
- agents
- -
License
- agents-from-scratch
- MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes.
- agents
- MIT
Last pushed
- agents-from-scratch
- Jul 25, 2026
- agents
- Aug 18, 2026
Categories
- agents-from-scratch
- AI Agents, Developer Tools
- agents
- AI Agents, Developer Tools
Trust and health
Maintenance
- agents-from-scratch
- Active (82%)
- agents
- Very active (96%)
Days since push
- agents-from-scratch
- 18d
- agents
- 1d
Open issues (now)
- agents-from-scratch
- 3
- agents
- 5
Stars delta
- agents-from-scratch
- Unknown
- agents
- +860 (30d)
Open issues delta
- agents-from-scratch
- Unknown
- agents
- +2 (30d)
Full report
- agents-from-scratch
- Trust report
- agents
- Trust report
Choose agents-from-scratch if…
- 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.
- 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.
Choose agents if…
- Tags unique to agents: agent-skills, agentic-ai, automation, workflows.
- You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments
- More GitHub stars (39k vs 954) - visibility, not fit.
When NOT to use agents
- You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support
- Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (pguso/agents-from-scratch) · observed Aug 12, 2026
- GitHub forks (pguso/agents-from-scratch) · observed Aug 12, 2026
- Last push (pguso/agents-from-scratch) · observed Jul 25, 2026
- License file (MIT) · observed Aug 12, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (wshobson/agents) · observed Aug 19, 2026
- GitHub forks (wshobson/agents) · observed Aug 19, 2026
- Last push (wshobson/agents) · observed Aug 18, 2026
- License file (MIT) · observed Aug 19, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agents-from-scratch 954 · agents 39k (synced Aug 12, 2026).
Common questions
- What is the difference between agents-from-scratch and agents?
- agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. agents: Multi-harness agentic plugin marketplace for various AI agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose agents-from-scratch over agents?
- Choose agents-from-scratch over agents when 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; 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 choose agents over agents-from-scratch?
- Choose agents over agents-from-scratch when Tags unique to agents: agent-skills, agentic-ai, automation, workflows; You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments; More GitHub stars (39k vs 954) - visibility, not fit.
- 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.
- When should I avoid agents?
- You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem
- Is agents-from-scratch or agents more popular on GitHub?
- agents has more GitHub stars (38,928 vs 954). Stars measure visibility, not whether either tool fits your constraints.
- Are agents-from-scratch and agents open source?
- Yes - both are open-source projects on GitHub (agents-from-scratch: MIT, agents: MIT).
- Where can I find alternatives to agents-from-scratch or agents?
- GraphCanon lists graph-backed alternatives at agents-from-scratch alternatives and agents alternatives (agents-from-scratch markdown twin, agents 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, agents-from-scratch or agents?
- agents-from-scratch: Active. agents: 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 agents-from-scratch and agents?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agents-from-scratch trust report; agents trust report.