by itayinbarr · Agent Tool · ★ 892
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little-coder A pi-based coding agent optimized for small local language models. little-coder started as a Claude Code-inspired Python CLI that adapts a cloud-style coding agent to 5–25 GB local models served via Ollama or llama.cpp. With v0.1.0 the agent has been ported onto pi () as a set of TypeScript extensions. Every mechanism the whitepaper cites as load-bearing — Write-vs-Edit invariant, per-turn skill injection, algorithm-cheat-sheet injection, thinking-budget cap, output-parser, quality monitor, per-model profiles, evidence-aware compaction — is preserved as a pi extension. Paper result (v0.0.2): (9.7B, 6.6 GB) + little-coder scored 45.56 % mean across two full runs of the Aider Polyglot benchmark — above gpt-4.5-preview (44.9 %) and gpt-oss-120b high (41.8 %) on the public leaderboard. Matched-model vanilla Aider baseline: 19.11 %. The whitepaper — Honey, I Shrunk the Coding Agent — is published on Substack: https://open.substack.com/pub/itayinbarr/p/honey-i-shrunk-the-coding-agent. The exact codebase that produced those numbers is preserved at tag (commit ) — check it out to reproduce.
| Stars | 892 |
| Forks | 55 |
| Language | TypeScript |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 47.042/100 |
| Last Updated | 2026-05-03 |
| Created | 2026-04-11 |
| Platforms | node |
| Est. Tokens | ~84k |
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little-coder is A coding agent optimized to smaller LLMs. It is categorized as a Agent Tool with 892 GitHub stars.
little-coder is primarily written in TypeScript. It covers topics such as ai-coding-assistant, aider-polygot, benchmark.
You can find installation instructions and usage details in the little-coder GitHub repository at github.com/itayinbarr/little-coder. The project has 892 stars and 55 forks, indicating an active community.
little-coder is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to little-coder on Agent Skills Hub include openyak, bigcodebench, claude-code-local. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.