agents-from-scratch
Build AI agents locally without relying on frameworks or cloud APIs.
GraphCanon updated Aug 12, 2026 · GitHub synced Aug 12, 2026
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Decision brief
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.
Good fit when
- 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.
- Your project requires a local-only setup absent any internet connections for data processing, thus eliminating outside variables and ensuring privacy.
Avoid when
- 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.
- Requirements:
- Min 8 GB RAM; Local large language model availability is critical as the tool does not utilize any cloud APIs.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (18d since push)
- As of Aug 12, 2026
- Provenance
- Not a fork · Personal account
- As of Aug 12, 2026
- Security (OSV)
- No lockfile
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install agents-from-scratchHow it fits your stack(1)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A project that guides users in constructing artificial intelligence agents from the ground up using only local large language models and Python, emphasizing transparency and understanding of the underlying principles of agent architecture.
Capability facts
- Languages
- python
Source: github.language · Aug 12, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 12, 2026)
3. Run: `python complete_example.py`Source link
Tags
README
Quick Start For detailed setup instructions, see QUICKSTART.md In short: 1. Install dependencies: 2. Download a GGUF model to the folder 3. Run: Note: The file contains executable code examples demonstrating all 12 lessons. You can use it as a reference to see how all the concept...
For agents
This page has a .md twin and JSON over the API.