--- title: "promptsource vs agents-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigscience-workshop-promptsource-vs-pguso-agents-from-scratch" tools: ["bigscience-workshop-promptsource", "pguso-agents-from-scratch"] --- # promptsource vs agents-from-scratch *GraphCanon updated Aug 15, 2026* ## Verdict Pick promptsource if promptSource aids in creating, sharing, and using natural language prompts for large language models; 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. [promptsource](https://github.com/bigscience-workshop/promptsource) reports 3.0k GitHub stars, 375 forks, and 43 open issues, last pushed Oct 23, 2023. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) has 954 stars, 240 forks, and 3 open issues, last pushed Jul 25, 2026. Figures are from public GitHub metadata via [promptsource's repository](https://github.com/bigscience-workshop/promptsource) and [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch). | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Tagline | Toolkit for creating, sharing and using natural language prompts | Build AI agents locally without relying on frameworks or cloud APIs. | | Stars | 3,029 | 954 | | Forks | 375 | 240 | | Open issues | 43 | 3 | | Language | Python | Python | | Adopt for | PromptSource aids in creating, sharing, and using natural language prompts for large language models. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | | Categories | Developer Tools, Model Training | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1027d | 18d | | Open issues (now) | 43 | 3 | | Stars delta | +2 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/bigscience-workshop-promptsource/trust.md) | [trust report](/tools/pguso-agents-from-scratch/trust.md) | ## Shared compatibility - **Python**: [promptsource](/tools/bigscience-workshop-promptsource.md) - Python runtime; [agents-from-scratch](/tools/pguso-agents-from-scratch.md) - Python runtime ## Decision facts: promptsource - **Adopt for:** PromptSource aids in creating, sharing, and using natural language prompts for large language models. ## Decision facts: agents-from-scratch - **Requirements:** Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs. - **Adopt for:** 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. - **License detail:** MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. ## Choose when ### Choose promptsource if… - License: promptsource is Apache-2.0, agents-from-scratch is MIT. - Tags unique to promptsource: few-shot, fine-tuning, language-models, machine-learning. - Also covers Model Training. - When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### Choose agents-from-scratch if… - License: agents-from-scratch is MIT, promptsource is Apache-2.0. - 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 AI Agents. - 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 promptsource - Avoid if you require complex prompt customization beyond what simple templating can offer, as PromptSource is not designed for intricate configurations. - Not suitable for users focused on real-time interaction with prompts, since it lacks dynamic features for immediate adjustments. ## 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. ## Common questions ### What is the difference between promptsource and agents-from-scratch? promptsource: Toolkit for creating, sharing and using natural language prompts. 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 promptsource over agents-from-scratch? Choose promptsource over agents-from-scratch when License: promptsource is Apache-2.0, agents-from-scratch is MIT; Tags unique to promptsource: few-shot, fine-tuning, language-models, machine-learning; Also covers Model Training; When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### When should I choose agents-from-scratch over promptsource? Choose agents-from-scratch over promptsource when License: agents-from-scratch is MIT, promptsource is Apache-2.0; 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 AI Agents; 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 promptsource? Avoid if you require complex prompt customization beyond what simple templating can offer, as PromptSource is not designed for intricate configurations. Not suitable for users focused on real-time interaction with prompts, since it lacks dynamic features for immediate adjustments. ### 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 promptsource or agents-from-scratch more popular on GitHub? promptsource has more GitHub stars (3,029 vs 954). Stars measure visibility, not whether either tool fits your constraints. ### Are promptsource and agents-from-scratch open source? Yes - both are open-source projects on GitHub (promptsource: Apache-2.0, agents-from-scratch: MIT). ### Where can I find alternatives to promptsource or agents-from-scratch? GraphCanon lists graph-backed alternatives at [promptsource alternatives](/tools/bigscience-workshop-promptsource/alternatives) and [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) ([promptsource markdown twin](/tools/bigscience-workshop-promptsource/alternatives.md), [agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/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/bigscience-workshop-promptsource-vs-pguso-agents-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, promptsource or agents-from-scratch? promptsource: Dormant. agents-from-scratch: 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 promptsource and agents-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [promptsource trust report](/tools/bigscience-workshop-promptsource/trust); [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigscience-workshop-promptsource`](/api/graphcanon/graph?tool=bigscience-workshop-promptsource) - 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/_