corecoder 0.2.0

Minimal AI coding agent (~1300 LoC) inspired by Claude Code. Works with any LLM. (formerly NanoCoder)

CoreCoder

Formerly NanoCoder — renamed to avoid confusion with Nano-Collective/nanocoder. All links from the old repo redirect here automatically.

中文 | English | Claude Code Architecture Deep Dive (7 articles)

512,000 lines of TypeScript → 950 lines of Python.

I spent two days reverse-engineering the leaked Claude Code source — all half a million lines. Then I stripped it down to the load-bearing walls and rebuilt them in Python. The result: every key architectural pattern from Claude Code, in a codebase you can read in one sitting.

CoreCoder is not another AI coding tool. It's a blueprint — the nanoGPT of coding agents. Read it, fork it, build your own.

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$ corecoder -m kimi-k2.5

You > read main.py and fix the broken import

  > read_file(file_path='main.py')
  > edit_file(file_path='main.py', ...)

--- a/main.py
+++ b/main.py
@@ -1 +1 @@
-from utils import halper
+from utils import helper

Fixed: halper → helper.

What You Get

Claude Code's 512K lines distilled to 7 patterns that actually matter:

Pattern	Claude Code	CoreCoder
Search-and-replace editing (unique match + diff)	FileEditTool	tools/edit.py — 70 lines
Parallel tool execution	StreamingToolExecutor (530 lines)	agent.py — ThreadPool
3-layer context compression	HISTORY_SNIP → Microcompact → CONTEXT_COLLAPSE	context.py — 145 lines
Sub-agent with isolated context	AgentTool (1,397 lines)	tools/agent.py — 50 lines
Dangerous command blocking	BashTool (1,143 lines)	tools/bash.py — 95 lines
Session persistence	QueryEngine (1,295 lines)	session.py — 65 lines
Dynamic system prompt	prompts.ts (914 lines)	prompt.py — 35 lines

Every pattern is a real, runnable implementation — not a diagram or a blog post.

Install

pip install corecoder

Pick your model — any OpenAI-compatible API works. You can export env vars or drop a .env file in your project root:

# Kimi K2.5
export OPENAI_API_KEY=your-key OPENAI_BASE_URL=https://api.moonshot.ai/v1
corecoder -m kimi-k2.5

# Claude Opus 4.6 (via OpenRouter)
export OPENAI_API_KEY=your-key OPENAI_BASE_URL=https://openrouter.ai/api/v1
corecoder -m anthropic/claude-opus-4-6

# OpenAI GPT-5
export OPENAI_API_KEY=sk-...
corecoder -m gpt-5

# DeepSeek V3
export OPENAI_API_KEY=sk-... OPENAI_BASE_URL=https://api.deepseek.com
corecoder -m deepseek-chat

# Qwen 3.5
export OPENAI_API_KEY=sk-... OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
corecoder -m qwen-max

# Ollama (local)
export OPENAI_API_KEY=ollama OPENAI_BASE_URL=http://localhost:11434/v1
corecoder -m qwen3:32b

# One-shot mode
corecoder -p "add error handling to parse_config()"

Architecture

The whole thing fits in your head:

corecoder/
├── cli.py            REPL + commands               160 lines
├── agent.py          Agent loop + parallel tools    120 lines
├── llm.py            Streaming client + retry       150 lines
├── context.py        3-layer compression            145 lines
├── session.py        Save/resume                     65 lines
├── prompt.py         System prompt                   35 lines
├── config.py         Env config                      30 lines
└── tools/
    ├── bash.py       Shell + safety + cd tracking    95 lines
    ├── edit.py       Search-replace + diff            70 lines
    ├── read.py       File reading                     40 lines
    ├── write.py      File writing                     30 lines
    ├── glob_tool.py  File search                      35 lines
    ├── grep.py       Content search                   65 lines
    └── agent.py      Sub-agent spawning               50 lines

Use as a Library

from corecoder import Agent, LLM

llm = LLM(model="kimi-k2.5", api_key="your-key", base_url="https://api.moonshot.ai/v1")
agent = Agent(llm=llm)
response = agent.chat("find all TODO comments in this project and list them")

Add Your Own Tools (~20 lines)

from corecoder.tools.base import Tool

class HttpTool(Tool):
    name = "http"
    description = "Fetch a URL."
    parameters = {"type": "object", "properties": {"url": {"type": "string"}}, "required": ["url"]}

    def execute(self, url: str) -> str:
        import urllib.request
        return urllib.request.urlopen(url).read().decode()[:5000]

Commands

/model <name>    Switch model mid-conversation
/compact         Compress context (like Claude Code's /compact)
/tokens          Token usage
/save            Save session to disk
/sessions        List saved sessions
/reset           Clear history
quit             Exit

How It Compares

	Claude Code	Claw-Code	Aider	CoreCoder
Code	512K lines (closed)	100K+ lines	50K+ lines	1,300 lines
Models	Anthropic only	Multi	Multi	Any OpenAI-compatible
Readable?	No	Hard	Medium	One afternoon
Purpose	Use it	Use it	Use it	Understand it, build yours

The Deep Dive

I wrote 7 articles breaking down Claude Code's architecture — the agent loop, tool system, context compression, streaming executor, multi-agent, and 44 hidden feature flags. If you want to understand why CoreCoder is designed this way, start there.

License

MIT. Fork it, learn from it, ship something better. A mention of this project is appreciated.

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Built by Yufeng He · Agentic AI Researcher @ Moonshot AI (Kimi)

Claude Code Source Analysis — 170K+ reads, 6000 bookmarks on Zhihu