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+# multillm-agentwrap
+
+Agent wrapper provider for multillm - wraps chat providers with agentic capabilities.
+
+## Overview
+
+The `agentwrap` provider allows you to use any chat provider (OpenAI, Google, Anthropic, etc.) with agentic capabilities including:
+
+- **Tool execution loop**: Automatically executes tools and sends results back
+- **Conversation history management**: Maintains context across tool calls
+- **Multi-turn interactions**: Continues until task is complete or max turns reached
+
+## Installation
+
+```bash
+pip install multillm-agentwrap
+```
+
+Or with uv in a workspace:
+
+```bash
+uv add multillm-agentwrap
+```
+
+## Usage
+
+### Basic Usage
+
+```python
+import asyncio
+import multillm
+
+async def main():
+    client = multillm.Client()
+
+    # Wrap any chat model with agentic capabilities
+    async for msg in client.run("agentwrap/openai/gpt-4", "Hello!"):
+        if msg.type == "text":
+            print(msg.content)
+
+asyncio.run(main())
+```
+
+### With Tools
+
+```python
+import asyncio
+import multillm
+
+# Define a custom tool
+calculate_tool = multillm.Tool(
+    name="calculate",
+    description="Perform a calculation",
+    parameters={
+        "type": "object",
+        "properties": {
+            "expression": {"type": "string", "description": "Math expression"}
+        },
+        "required": ["expression"]
+    },
+    handler=lambda args: {"result": eval(args["expression"])}
+)
+
+async def main():
+    client = multillm.Client()
+
+    # Use with tools
+    async for msg in client.run(
+        "agentwrap/google/gemini-pro",
+        "What's 25 * 4?",
+        tools=[calculate_tool]
+    ):
+        if msg.type == "text":
+            print(msg.content)
+        elif msg.type == "tool_use":
+            print(f"  → Using tool: {msg.tool_name}")
+        elif msg.type == "tool_result":
+            print(f"  ← Result: {msg.tool_result}")
+
+asyncio.run(main())
+```
+
+### With Options
+
+```python
+from multillm import AgentOptions
+
+async def main():
+    client = multillm.Client()
+
+    options = AgentOptions(
+        max_turns=5,
+        system_prompt="You are a helpful assistant.",
+        temperature=0.7
+    )
+
+    async for msg in client.run(
+        "agentwrap/anthropic/claude-3-5-sonnet-20241022",
+        "Explain quantum computing",
+        options=options
+    ):
+        if msg.type == "text":
+            print(msg.content)
+
+asyncio.run(main())
+```
+
+## Supported Chat Providers
+
+Any chat provider supported by multillm can be wrapped:
+
+- `agentwrap/openai/gpt-4` - OpenAI GPT-4
+- `agentwrap/openai/gpt-4-turbo` - OpenAI GPT-4 Turbo
+- `agentwrap/openai/gpt-3.5-turbo` - OpenAI GPT-3.5 Turbo
+- `agentwrap/google/gemini-pro` - Google Gemini Pro
+- `agentwrap/google/gemini-1.5-pro` - Google Gemini 1.5 Pro
+- `agentwrap/anthropic/claude-3-5-sonnet-20241022` - Anthropic Claude 3.5 Sonnet
+- `agentwrap/openrouter/...` - Any OpenRouter model
+
+## Model Format
+
+The model string follows the format:
+
+```
+agentwrap/<chat-provider>/<model-name>
+```
+
+Where:
+- `agentwrap` - The agent wrapper provider
+- `<chat-provider>` - The chat provider to wrap (openai, google, anthropic, openrouter)
+- `<model-name>` - The specific model from that provider
+
+## How It Works
+
+1. **Receives prompt**: User sends initial message
+2. **Calls chat API**: Uses the wrapped chat provider via `chat_complete()`
+3. **Returns response**: If no tool calls, returns text and stops
+4. **Executes tools**: If tool calls present, executes them with provided handlers
+5. **Continues loop**: Sends tool results back and gets next response
+6. **Repeats**: Steps 3-5 until no more tool calls or max turns reached
+
+## Configuration
+
+Configure the wrapped provider via multillm config:
+
+```python
+config = {
+    "openai": {"api_key": "sk-..."},
+    "google": {"api_key": "..."},
+    "agentwrap": {
+        "max_turns": 10  # Default max turns if not specified in options
+    }
+}
+
+client = multillm.Client(config)
+```
+
+## Agent Options
+
+All `AgentOptions` are supported:
+
+```python
+from multillm import AgentOptions
+
+options = AgentOptions(
+    system_prompt="Custom system prompt",
+    max_turns=15,           # Max tool execution iterations
+    temperature=0.8,        # Sampling temperature
+    max_tokens=2000,        # Max tokens to generate
+)
+```
+
+## Message Types
+
+The agent yields different message types during execution:
+
+### System Message
+```python
+AgentMessage(
+    type="system",
+    content="Agentic session started",
+)
+```
+
+### Text Message
+```python
+AgentMessage(
+    type="text",
+    content="The answer is 42",
+    raw=<original response object>
+)
+```
+
+### Tool Use Message
+```python
+AgentMessage(
+    type="tool_use",
+    tool_name="calculate",
+    tool_input={"expression": "6*7"},
+    raw=<tool call object>
+)
+```
+
+### Tool Result Message
+```python
+AgentMessage(
+    type="tool_result",
+    tool_name="calculate",
+    tool_result="42",
+    raw=<result dict>
+)
+```
+
+### Result Message
+```python
+AgentMessage(
+    type="result",
+    content="Final answer",
+)
+```
+
+## Comparison with Native Agent Providers
+
+### AgentWrap (This Provider)
+- ✅ Works with any chat provider
+- ✅ Simple tool execution loop
+- ✅ Full control over chat API settings
+- ❌ No built-in tools (must provide custom tools)
+- ❌ No file system access
+- ❌ More basic agentic capabilities
+
+### Native Agent Providers (e.g., Claude)
+- ✅ Advanced agentic capabilities
+- ✅ Built-in tools (Bash, Read, Write, etc.)
+- ✅ File system access
+- ✅ Plan mode, interactive sessions
+- ❌ Limited to specific providers
+
+## Use Cases
+
+### When to Use AgentWrap
+
+- **Different models**: Want agentic behavior with OpenAI, Google, or other chat models
+- **Custom tools**: Need specific tool implementations
+- **Simple workflows**: Basic tool calling without file system access
+- **Cost optimization**: Use cheaper chat models with agentic capabilities
+
+### When to Use Native Agents
+
+- **File operations**: Need to read/write files, run commands
+- **Complex workflows**: Multi-step tasks requiring planning
+- **Built-in tools**: Want Bash, Read, Write, Grep, etc.
+- **Claude-specific**: Need Claude's advanced agentic features
+
+## Limitations
+
+1. **No built-in tools**: Must provide all tools yourself (unlike Claude agent which has Bash, Read, Write, etc.)
+2. **No file system access**: Can't read/write files unless you implement those tools
+3. **No interactive mode**: Single-shot sessions only (no `run_interactive`)
+4. **Tool handlers required**: Tools must have Python handler functions
+
+## Examples
+
+### Calculator Agent
+
+```python
+import asyncio
+import multillm
+
+calculate = multillm.Tool(
+    name="calculate",
+    description="Evaluate a mathematical expression",
+    parameters={
+        "type": "object",
+        "properties": {
+            "expression": {"type": "string"}
+        },
+        "required": ["expression"]
+    },
+    handler=lambda args: {"result": eval(args["expression"])}
+)
+
+async def main():
+    client = multillm.Client()
+
+    async for msg in client.run(
+        "agentwrap/openai/gpt-4",
+        "What's (125 + 75) * 3?",
+        tools=[calculate]
+    ):
+        if msg.type == "text":
+            print(msg.content)
+
+asyncio.run(main())
+```
+
+### Multi-Tool Agent
+
+```python
+import asyncio
+import multillm
+from datetime import datetime
+
+get_time = multillm.Tool(
+    name="get_current_time",
+    description="Get the current time",
+    parameters={"type": "object", "properties": {}},
+    handler=lambda args: {"time": datetime.now().isoformat()}
+)
+
+get_weather = multillm.Tool(
+    name="get_weather",
+    description="Get weather for a location",
+    parameters={
+        "type": "object",
+        "properties": {
+            "location": {"type": "string"}
+        },
+        "required": ["location"]
+    },
+    handler=lambda args: {"temp": 72, "condition": "sunny"}
+)
+
+async def main():
+    client = multillm.Client()
+
+    async for msg in client.run(
+        "agentwrap/google/gemini-pro",
+        "What time is it and what's the weather in Tokyo?",
+        tools=[get_time, get_weather]
+    ):
+        if msg.type == "text":
+            print(msg.content)
+
+asyncio.run(main())
+```
+
+## License
+
+MIT
+
+## Contributing
+
+Contributions welcome! Please see the main multillm repository for guidelines.
+
+## See Also
+
+- [multillm](https://github.com/yourusername/multillm) - Main library
+- [multillm-claude](https://github.com/yourusername/multillm-claude) - Claude agent provider