feat: Add multi-provider support (GPT, Gemini, etc.)

.env.example

@@ -1,4 +1,21 @@
-# API Configuration
+# Provider Selection (defaults to anthropic for backward compatibility)
+AI_PROVIDER=anthropic  # Options: anthropic, openai, gemini, or any OpenAI-compatible service
+
+# Model Name (auto-defaults based on provider, but can be overridden)
+MODEL_NAME=kimi-k2-turbo-preview
+
+# Anthropic Configuration
 ANTHROPIC_API_KEY=sk-xxx
 ANTHROPIC_BASE_URL=https://api.moonshot.cn/anthropic
-MODEL_NAME=kimi-k2-turbo-preview
+
+# OpenAI Configuration
+OPENAI_API_KEY=sk-xxx
+OPENAI_BASE_URL=https://api.openai.com/v1
+
+# Google Gemini Configuration (via OpenAI-compatible endpoint)
+GEMINI_API_KEY=xxx
+GEMINI_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai/
+
+# Example: Custom OpenAI-compatible service
+# CUSTOM_API_KEY=xxx
+# CUSTOM_BASE_URL=https://api.custom-service.com/v1

README.md

@@ -37,11 +37,12 @@ A progressive tutorial that demystifies AI coding agents like Kode, Claude Code,
 ## Quick Start
 
 ```bash
-pip install anthropic python-dotenv
+# Install dependencies
+pip install -r requirements.txt
 
 # Configure your API
 cp .env.example .env
-# Edit .env with your API key
+# Edit .env with your API key (supports Anthropic, OpenAI, Gemini, etc.)
 
 # Run any version
 python v0_bash_agent.py  # Minimal
@@ -171,4 +172,4 @@ MIT
 
 **Model as Agent. That's the whole secret.**
 
-[@baicai003](https://x.com/baicai003)
+[@baicai003](https://x.com/baicai003)

provider_utils.py

@@ -0,0 +1,242 @@
+"""
+Provider utilities for multi-provider AI agent support.
+
+This module provides a unified interface for multiple AI providers (Anthropic, OpenAI, Gemini),
+allowing the existing agent code (v0-v4) to run unchanged.
+
+It uses the Adapter Pattern to make OpenAI-compatible clients look exactly like
+Anthropic clients to the consuming code.
+"""
+
+import os
+import json
+from typing import Any, Dict, List, Union, Optional
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# =============================================================================
+# Data Structures (Mimic Anthropic SDK)
+# =============================================================================
+
+class ResponseWrapper:
+    """Wrapper to make OpenAI responses look like Anthropic responses."""
+    def __init__(self, content, stop_reason):
+        self.content = content
+        self.stop_reason = stop_reason
+
+class ContentBlock:
+    """Wrapper to make content blocks look like Anthropic content blocks."""
+    def __init__(self, block_type, **kwargs):
+        self.type = block_type
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def __repr__(self):
+        attrs = ", ".join(f"{k}={v!r}" for k, v in self.__dict__.items())
+        return f"ContentBlock({attrs})"
+
+# =============================================================================
+# Adapters
+# =============================================================================
+
+class OpenAIAdapter:
+    """
+    Adapts the OpenAI client to look like an Anthropic client.
+
+    Key Magic:
+    self.messages = self 
+
+    This allows the agent code to call:
+    client.messages.create(...)
+
+    which resolves to:
+    adapter.create(...)
+    """
+    def __init__(self, openai_client):
+        self.client = openai_client
+        self.messages = self  # Duck typing: act as the 'messages' resource
+
+    def create(self, model: str, system: str, messages: List[Dict], tools: List[Dict], max_tokens: int = 8000):
+        """
+        The core translation layer. 
+        Converts Anthropic inputs -> OpenAI inputs -> OpenAI API -> Anthropic outputs.
+        """
+        # 1. Convert Messages (Anthropic -> OpenAI)
+        openai_messages = [{"role": "system", "content": system}]
+
+        for msg in messages:
+            role = msg["role"]
+            content = msg["content"]
+
+            if role == "user":
+                if isinstance(content, str):
+                    # Simple text message
+                    openai_messages.append({"role": "user", "content": content})
+                elif isinstance(content, list):
+                    # Tool results (User role in Anthropic, Tool role in OpenAI)
+                    for part in content:
+                        if part.get("type") == "tool_result":
+                            openai_messages.append({
+                                "role": "tool",
+                                "tool_call_id": part["tool_use_id"],
+                                "content": part["content"] or "(no output)"
+                            })
+                        # Note: Anthropic user messages can also contain text+image, 
+                        # but v0-v4 agents don't use that yet.
+
+            elif role == "assistant":
+                if isinstance(content, str):
+                    # Simple text message
+                    openai_messages.append({"role": "assistant", "content": content})
+                elif isinstance(content, list):
+                    # Tool calls (Assistant role)
+                    # Anthropic splits thought (text) and tool_use into blocks
+                    # OpenAI puts thought in 'content' and tools in 'tool_calls'
+                    text_parts = []
+                    tool_calls = []
+
+                    for part in content:
+                        # Handle both dicts and objects (ContentBlock)
+                        if isinstance(part, dict):
+                            part_type = part.get("type")
+                            part_text = part.get("text")
+                            part_id = part.get("id")
+                            part_name = part.get("name")
+                            part_input = part.get("input")
+                        else:
+                            part_type = getattr(part, "type", None)
+                            part_text = getattr(part, "text", None)
+                            part_id = getattr(part, "id", None)
+                            part_name = getattr(part, "name", None)
+                            part_input = getattr(part, "input", None)
+
+                        if part_type == "text":
+                            text_parts.append(part_text)
+                        elif part_type == "tool_use":
+                            tool_calls.append({
+                                "id": part_id,
+                                "type": "function",
+                                "function": {
+                                    "name": part_name,
+                                    "arguments": json.dumps(part_input)
+                                }
+                            })
+
+                    assistant_msg = {"role": "assistant"}
+                    if text_parts:
+                        assistant_msg["content"] = "\n".join(text_parts)
+                    if tool_calls:
+                        assistant_msg["tool_calls"] = tool_calls
+
+                    openai_messages.append(assistant_msg)
+
+        # 2. Convert Tools (Anthropic -> OpenAI)
+        openai_tools = []
+        for tool in tools:
+            openai_tools.append({
+                "type": "function",
+                "function": {
+                    "name": tool["name"],
+                    "description": tool["description"],
+                    "parameters": tool["input_schema"]
+                }
+            })
+
+        # 3. Call OpenAI API
+        # Note: Gemini/OpenAI handle max_tokens differently, but usually support the param
+        response = self.client.chat.completions.create(
+            model=model,
+            messages=openai_messages,
+            tools=openai_tools if openai_tools else None,
+            max_tokens=max_tokens
+        )
+
+        # 4. Convert Response (OpenAI -> Anthropic)
+        message = response.choices[0].message
+        content_blocks = []
+
+        # Extract text content
+        if message.content:
+            content_blocks.append(ContentBlock("text", text=message.content))
+
+        # Extract tool calls
+        if message.tool_calls:
+            for tool_call in message.tool_calls:
+                content_blocks.append(ContentBlock(
+                    "tool_use",
+                    id=tool_call.id,
+                    name=tool_call.function.name,
+                    input=json.loads(tool_call.function.arguments)
+                ))
+
+        # Map stop reasons: OpenAI "stop"/"tool_calls" -> Anthropic "end_turn"/"tool_use"
+        # OpenAI: stop, length, content_filter, tool_calls
+        finish_reason = response.choices[0].finish_reason
+        if finish_reason == "tool_calls":
+            stop_reason = "tool_use"
+        elif finish_reason == "stop":
+            stop_reason = "end_turn"
+        else:
+            stop_reason = finish_reason # Fallback
+
+        return ResponseWrapper(content_blocks, stop_reason)
+
+# =============================================================================
+# Factory Functions
+# =============================================================================
+
+def get_provider():
+    """Get the current AI provider from environment variable."""
+    return os.getenv("AI_PROVIDER", "anthropic").lower()
+
+def get_client():
+    """
+    Return a client that conforms to the Anthropic interface.
+
+    If AI_PROVIDER is 'anthropic', returns the native Anthropic client.
+    Otherwise, returns an OpenAIAdapter wrapping an OpenAI-compatible client.
+    """
+    provider = get_provider()
+
+    if provider == "anthropic":
+        from anthropic import Anthropic
+        base_url = os.getenv("ANTHROPIC_BASE_URL")
+        # Return native client - guarantees 100% behavior compatibility
+        return Anthropic(
+            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            base_url=base_url
+        )
+
+    else:
+        # For OpenAI/Gemini, we wrap the client to mimic Anthropic
+        try:
+            from openai import OpenAI
+        except ImportError:
+            raise ImportError("Please install openai: pip install openai")
+
+        if provider == "openai":
+            api_key = os.getenv("OPENAI_API_KEY")
+            base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
+        elif provider == "gemini":
+            api_key = os.getenv("GEMINI_API_KEY")
+            # Gemini OpenAI-compatible endpoint
+            base_url = os.getenv("GEMINI_BASE_URL", "https://generativelanguage.googleapis.com/v1beta/openai/")
+        else:
+            # Generic OpenAI-compatible provider
+            api_key = os.getenv(f"{provider.upper()}_API_KEY")
+            base_url = os.getenv(f"{provider.upper()}_BASE_URL")
+
+        if not api_key:
+            raise ValueError(f"API Key for {provider} is missing. Please check your .env file.")
+
+        raw_client = OpenAI(api_key=api_key, base_url=base_url)
+        return OpenAIAdapter(raw_client)
+
+def get_model():
+    """Return model name from environment variable."""
+    model = os.getenv("MODEL_NAME")
+    if not model:
+        raise ValueError("MODEL_NAME environment variable is missing. Please set it in your .env file.")
+    return model

requirements.txt

@@ -1,2 +1,5 @@
+anthropic>=0.25.0
+openai>=1.0.0
+python-dotenv>=1.0.0
 pygame==2.5.2
 numpy==1.24.3

v0_bash_agent.py

@@ -47,21 +47,14 @@
     python v0_bash_agent.py "explore src/ and summarize"
 """
 
-from anthropic import Anthropic
-from dotenv import load_dotenv
+from provider_utils import get_client, get_model
 import subprocess
 import sys
 import os
 
-# Load environment variables from .env file
-load_dotenv()
-
-# Initialize API client with credentials from environment
-client = Anthropic(
-    api_key=os.getenv("ANTHROPIC_API_KEY"),
-    base_url=os.getenv("ANTHROPIC_BASE_URL")
-)
-MODEL = os.getenv("MODEL_NAME", "claude-sonnet-4-20250514")
+# Initialize API client and model using provider utilities
+client = get_client()
+MODEL = get_model()
 
 # The ONE tool that does everything
 # Notice how the description teaches the model common patterns AND how to spawn subagents

v0_bash_agent_mini.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 """v0_bash_agent_mini.py - Mini Claude Code (Compact)"""
-from anthropic import Anthropic; from dotenv import load_dotenv; import subprocess as sp, sys, os
-load_dotenv(); C = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"), base_url=os.getenv("ANTHROPIC_BASE_URL")); M = os.getenv("MODEL_NAME", "claude-sonnet-4-20250514")
+from provider_utils import get_client, get_model; import subprocess as sp, sys, os
+C = get_client(); M = get_model()
 T = [{"name":"bash","description":"Shell cmd. Read:cat/grep/find/rg/ls. Write:echo>/sed. Subagent(for complex subtask): python v0_bash_agent_mini.py 'task'","input_schema":{"type":"object","properties":{"command":{"type":"string"}},"required":["command"]}}]
 S = f"CLI agent at {os.getcwd()}. Use bash to solve problems. Spawn subagent for complex subtasks: python v0_bash_agent_mini.py 'task'. Subagent isolates context and returns summary. Be concise."
 

v1_basic_agent.py

@@ -56,23 +56,20 @@
 # Load configuration from .env file
 load_dotenv()
 
+# Import unified client provider
 try:
-    from anthropic import Anthropic
+    from provider_utils import get_client, get_model
 except ImportError:
-    sys.exit("Please install: pip install anthropic python-dotenv")
+    sys.exit("Error: provider_utils.py not found. Please ensure you are in the project root.")
 
 
 # =============================================================================
 # Configuration
 # =============================================================================
 
-API_KEY = os.getenv("ANTHROPIC_API_KEY")
-BASE_URL = os.getenv("ANTHROPIC_BASE_URL")
-MODEL = os.getenv("MODEL_NAME", "claude-sonnet-4-20250514")
 WORKDIR = Path.cwd()
-
-# Initialize client - handles both direct Anthropic and compatible APIs
-client = Anthropic(api_key=API_KEY, base_url=BASE_URL) if BASE_URL else Anthropic(api_key=API_KEY)
+MODEL = get_model()
+client = get_client()
 
 
 # =============================================================================

v2_todo_agent.py

@@ -66,21 +66,19 @@
 load_dotenv()
 
 try:
-    from anthropic import Anthropic
+    from provider_utils import get_client, get_model
 except ImportError:
-    sys.exit("Please install: pip install anthropic python-dotenv")
+    sys.exit("Error: provider_utils.py not found. Please ensure you are in the project root.")
 
 
 # =============================================================================
 # Configuration
 # =============================================================================
 
-API_KEY = os.getenv("ANTHROPIC_API_KEY")
-BASE_URL = os.getenv("ANTHROPIC_BASE_URL")
-MODEL = os.getenv("MODEL_NAME", "claude-sonnet-4-20250514")
 WORKDIR = Path.cwd()
 
-client = Anthropic(api_key=API_KEY, base_url=BASE_URL) if BASE_URL else Anthropic(api_key=API_KEY)
+client = get_client()
+MODEL = get_model()
 
 
 # =============================================================================