AnyCoder

AI coding agent in your terminal. Works with any LLM.

中文文档 | Installation | Quick Start | Supported Models

DeepSeek, Qwen, GPT-5, Claude, Gemini, Kimi, GLM, Ollama local models - pick your favorite and start coding.

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$ anycoder -m deepseek

> read main.py and fix the broken import

  Reading main.py
  ╭──────────────────────────────────────╮
  │ [6 lines total]                      │
  │      1  from utils import halper     │
  │      ...                             │
  ╰──────────────────────────────────────╯

  Editing main.py
  ╭──────────────────────────────────────╮
  │ Edited main.py                       │
  │ --- a/main.py                        │
  │ +++ b/main.py                        │
  │ @@ -1 +1 @@                          │
  │ -from utils import halper             │
  │ +from utils import helper             │
  ╰──────────────────────────────────────╯

Fixed: halper → helper.

  deepseek-chat | tokens: 1,247 | cost: $0.0004

Why AnyCoder?

Claude Code is the best AI coding tool out there, but it only works with Anthropic's API. Want to use DeepSeek (cheap and fast)? Qwen (great for Chinese devs)? A local model via Ollama? You're out of luck.

AnyCoder gives you the same experience - file editing, shell commands, codebase search, context management - with whatever LLM you want.

What it does:

• 100+ LLM providers via litellm - one CLI, any model
• Agent loop with tool use - reads files, writes code, runs commands, searches codebases
• Streaming output - see responses as they generate, token by token
• Context compression - auto-compresses when conversations get long (snip tool outputs first, then summarize)
• Search & replace editing - precise modifications with uniqueness checking and diff output
• Dangerous command blocking - catches rm -rf /, fork bombs, curl | bash, etc.
• Parallel tool execution - runs multiple independent tool calls concurrently
• Session persistence - save and resume conversations with /save and --resume
• .env support - drop a .env in your project root and go
• ~1,450 lines of Python - small enough to read, hack, and extend

Installation

pip install anycoder

Quick Start

# Set your API key (pick one)
export DEEPSEEK_API_KEY=sk-...    # DeepSeek (default)
export OPENAI_API_KEY=sk-...      # OpenAI
export ANTHROPIC_API_KEY=sk-...   # Claude
export GEMINI_API_KEY=...         # Gemini

# Use DeepSeek (default, cheap and fast)
anycoder

# Use Kimi K2.5
anycoder -m kimi

# Use Claude Sonnet 4.6
anycoder -m claude

# Use GPT-5.4
anycoder -m gpt5

# Use Qwen
anycoder -m qwen

# Use local Ollama, data never leaves your machine
anycoder -m ollama/qwen3:32b

# One-shot mode
anycoder "add error handling to the login function in auth.py"
anycoder -p "find all TODO comments and list them"

# Resume a saved session
anycoder --resume session_1712345678

Or use a .env file in your project root:

# .env
DEEPSEEK_API_KEY=sk-...
ANYCODER_MODEL=deepseek

Supported Models

Use short aliases or full litellm model names:

Alias	Model	Provider
deepseek	DeepSeek Chat (V3)	DeepSeek
deepseek-r1	DeepSeek Reasoner (R1)	DeepSeek
gpt5 / gpt-5	GPT-5.4	OpenAI
gpt4o	GPT-4o	OpenAI
o4-mini	o4-mini	OpenAI
claude	Claude Sonnet 4.6	Anthropic
claude-opus	Claude Opus 4.6	Anthropic
claude-haiku	Claude Haiku 4.5	Anthropic
gemini	Gemini 2.5 Flash	Google
gemini-pro	Gemini 2.5 Pro	Google
qwen	Qwen Plus	Alibaba
qwen-max	Qwen Max	Alibaba
kimi	Kimi K2.5	Moonshot AI
glm	GLM-4 Plus	Zhipu AI

Local Models (Ollama)

ollama serve
anycoder -m ollama/llama3.1
anycoder -m ollama/codestral
anycoder -m ollama/qwen3:32b

Custom OpenAI-Compatible APIs

export ANYCODER_API_BASE=https://your-api.com/v1
export ANYCODER_API_KEY=your-key
anycoder -m your-model-name

Tools

AnyCoder has 6 built-in tools that the LLM calls automatically:

Tool	What it does
bash	Run shell commands with dangerous command blocking and cd tracking
read_file	Read files with line numbers, offset/limit for large files
write_file	Create new files or overwrite existing ones
edit_file	Search-and-replace edits with uniqueness checking and diff output
glob	Find files by pattern (**/*.py, src/**/*.ts)
grep	Search file contents with regex

You describe what you want in natural language. The agent decides which tools to use.

Commands

Command	Description
/model	Show current model
/model <name>	Switch model mid-conversation
/models	List all model aliases
/tokens	Token usage and estimated cost
/diff	Files modified this session
/compact	Manually compress context
/save [name]	Save session to disk
/sessions	List saved sessions
/clear	Clear conversation history
/help	Show all commands
/quit	Exit

Input: Enter to send, Esc+Enter for newline (multiline input), Ctrl+C to cancel, Ctrl+D to exit.

Architecture

~1,450 lines total. Here's how it's organized:

anycoder/
├── cli.py            REPL + slash commands          258 lines
├── llm.py            litellm streaming wrapper      184 lines
├── agent.py          Agent loop + parallel tools    179 lines
├── context.py        Two-phase compression           92 lines
├── config.py         Env + .env + model aliases      86 lines
├── session.py        Save/resume sessions            60 lines
├── prompts/system.py System prompt generation        50 lines
└── tools/
    ├── bash.py       Shell + safety + cd tracking   114 lines
    ├── edit_file.py  Search-replace + diff output    98 lines
    ├── grep_tool.py  Regex search + skip binary     111 lines
    ├── read_file.py  File reading + binary detect    70 lines
    ├── glob_tool.py  File pattern search             48 lines
    └── write_file.py File writing + tracking         39 lines

How the agent loop works:

1. User message gets added to conversation history
2. History + tool schemas are sent to the LLM (streaming)
3. If the LLM returns text, it's printed to the terminal
4. If the LLM returns tool calls, each tool is executed and results are appended
5. Go to step 2 until the LLM responds with text only (no more tool calls)
6. Context manager auto-compresses when approaching the token limit

Two-phase compression (inspired by Claude Code):

• Phase 1: Snip long tool outputs (keeps conversation structure intact)
• Phase 2: Summarize old conversation turns if still over threshold

Configuration

Environment variables or .env file:

Variable	Description	Default
ANYCODER_MODEL	Default model	deepseek/deepseek-chat
ANYCODER_API_BASE	Custom API base URL	-
ANYCODER_API_KEY	API key	-
DEEPSEEK_API_KEY	DeepSeek API key	-
OPENAI_API_KEY	OpenAI API key	-
ANTHROPIC_API_KEY	Anthropic API key	-
GEMINI_API_KEY	Google AI API key	-

Use as a Library

from anycoder import Agent, Config

config = Config(model="deepseek/deepseek-chat", api_key="sk-...")
agent = Agent(config)
agent.run("find all TODO comments in this project")

Comparison

Feature	Claude Code	Cline	Aider	AnyCoder
LLM support	Claude only	Multi	Multi	100+ via litellm
Language	TypeScript (closed)	TypeScript	Python	Python (MIT)
Install	npm	VS Code ext	pip	pip
File editing	Search & replace	Diff	Diff	Search & replace
Context compression	Yes	No	Yes	Yes (two-phase)
Streaming	Yes	Yes	Yes	Yes
Session persistence	Yes	No	Yes	Yes
Code size	512K lines	100K+	50K+	~1,450 lines
Best for	Using it	Using it	Using it	Using it AND reading the source

Development

git clone https://github.com/he-yufeng/AnyCoder.git
cd AnyCoder
pip install -e ".[dev]"
pytest tests/ -v

Related Projects

• CoreCoder - my other project: Claude Code's 512K-line source distilled into ~1,400 lines of Python, with 7 architecture deep-dive articles. AnyCoder builds on the same ideas but focuses on being a practical tool (litellm, session persistence, .env support) rather than a teaching codebase.

License

MIT. Use it, fork it, build something better.

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