--- title: "agents-from-scratch vs chipper" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pguso-agents-from-scratch-vs-tilmangriesel-chipper" tools: ["pguso-agents-from-scratch", "tilmangriesel-chipper"] --- # agents-from-scratch vs chipper *GraphCanon updated Sep 20, 2026* ## 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 chipper if chipper is an AI interface tool for tinkerers that supports agentic-AI functionalities and LLM inference using Hugging Face models, including Phi4, with Haystack. [agents-from-scratch](https://github.com/pguso/agents-from-scratch) reports 1.0k GitHub stars, 251 forks, and 4 open issues, last pushed Jul 25, 2026. [chipper](https://chipper.tilmangriesel.com/) has 484 stars, 48 forks, and 7 open issues, last pushed May 19, 2026. Figures are from public GitHub metadata via [agents-from-scratch's repository](https://github.com/pguso/agents-from-scratch) and [chipper's repository](https://github.com/TilmanGriesel/chipper). | | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | [chipper](/tools/tilmangriesel-chipper.md) | | --- | --- | --- | | Tagline | Build AI agents locally without relying on frameworks or cloud APIs. | AI interface for tinkerers using Ollama and Haystack RAG | | Stars | 1,017 | 484 | | Forks | 251 | 48 | | Open issues | 4 | 7 | | Language | Python | Python | | 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. | Chipper is an AI interface tool for tinkerers that supports agentic-AI functionalities and LLM inference using Hugging Face models, including Phi4, with Haystack RAG for retrieval-augmented generation. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive licensing allowing free use and distribution for both commercial and non-commercial purposes. | MIT | | Categories | AI Agents, Developer Tools | AI Agents, Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agents-from-scratch](/tools/pguso-agents-from-scratch.md) | [chipper](/tools/tilmangriesel-chipper.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 56d | 123d | | Open issues (now) | 4 | 7 | | Stars delta | +63 (30d) | 0 (30d) | | Full report | [trust report](/tools/pguso-agents-from-scratch/trust.md) | [trust report](/tools/tilmangriesel-chipper/trust.md) | ## 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. ## Decision facts: chipper - **Adopt for:** Chipper is an AI interface tool for tinkerers that supports agentic-AI functionalities and LLM inference using Hugging Face models, including Phi4, with Haystack RAG for retrieval-augmented generation. - **License detail:** MIT - **Runtime:** unknown ## Choose when ### 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. - 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. ### Choose chipper if… - Tags unique to chipper: agent, deepseek-chat, embedding, llm-inference. - Also covers Evaluation & Observability, Inference & Serving. - When you require an environment that integrates with Ollama API. ## 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. ## When NOT to use chipper - When the development requires real-time processing that is not aligned with Ollama's APIs or RAG capabilities. - If your project strictly adheres to proprietary AI interfaces, and external integrations like Haystack RAG are prohibited. - For teams preferring frameworks or tools that do not support agentic-AI features found in Chipper. ## Common questions ### What is the difference between agents-from-scratch and chipper? agents-from-scratch: Build AI agents locally without relying on frameworks or cloud APIs.. chipper: AI interface for tinkerers using Ollama and Haystack RAG. See the comparison table for live GitHub stats and shared categories. ### When should I choose agents-from-scratch over chipper? Choose agents-from-scratch over chipper 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; 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 choose chipper over agents-from-scratch? Choose chipper over agents-from-scratch when Tags unique to chipper: agent, deepseek-chat, embedding, llm-inference; Also covers Evaluation & Observability, Inference & Serving; When you require an environment that integrates with Ollama API. ### 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 chipper? When the development requires real-time processing that is not aligned with Ollama's APIs or RAG capabilities. If your project strictly adheres to proprietary AI interfaces, and external integrations like Haystack RAG are prohibited. For teams preferring frameworks or tools that do not support agentic-AI features found in Chipper. ### Is agents-from-scratch or chipper more popular on GitHub? agents-from-scratch has more GitHub stars (1,017 vs 484). Stars measure visibility, not whether either tool fits your constraints. ### Are agents-from-scratch and chipper open source? Yes - both are open-source projects on GitHub (agents-from-scratch: MIT, chipper: MIT). ### Where can I find alternatives to agents-from-scratch or chipper? GraphCanon lists graph-backed alternatives at [agents-from-scratch alternatives](/tools/pguso-agents-from-scratch/alternatives) and [chipper alternatives](/tools/tilmangriesel-chipper/alternatives) ([agents-from-scratch markdown twin](/tools/pguso-agents-from-scratch/alternatives.md), [chipper markdown twin](/tools/tilmangriesel-chipper/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/pguso-agents-from-scratch-vs-tilmangriesel-chipper.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agents-from-scratch or chipper? agents-from-scratch: Steady. chipper: Slowing. 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 chipper? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agents-from-scratch trust report](/tools/pguso-agents-from-scratch/trust); [chipper trust report](/tools/tilmangriesel-chipper/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pguso-agents-from-scratch`](/api/graphcanon/graph?tool=pguso-agents-from-scratch) - 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/_