ape-anthropic 1.0.6
Anthropic Claude integration for APE (AI Programmatic Execution)
License: MIT
Requires Python: >=3.11

# ape-anthropic

Anthropic Claude integration for APE (AI Programmatic Execution).

## What is ape-anthropic?

**ape-anthropic** bridges APE's deterministic validation layer with Anthropic's Claude tool use API. It prevents hallucinations in Claude function parameters by enforcing strict type checking and constraints before execution.

## Why ape-anthropic?

Claude's tool use is powerful but unreliable:
- Function parameters can be incorrectly formatted
- Type mismatches cause runtime errors
- Missing required fields break execution
- No validation before calling your code

**ape-anthropic solves this** by adding APE as a validation layer:

```
Claude → JSON parameters → APE validation → Deterministic execution ✓
```

## Installation

```bash
# Core package (schema conversion + execution)
pip install ape-anthropic

# With Anthropic SDK (for code generation)
pip install ape-anthropic[anthropic]

# Development dependencies
pip install ape-anthropic[dev]
```

**Prerequisites:**
- Python >= 3.11
- ape-lang >= 0.2.0

## Test Coverage

✅ **All tests passing**

- **Total tests: 49**
- Last verified via pytest discovery

See [../ape/docs/APE_TESTING_GUARANTEES.md](../ape/docs/APE_TESTING_GUARANTEES.md) for details on what these tests guarantee.

The test suite covers:
- Schema conversion (APE → Claude)
- Executor (Claude → APE runtime)
- Utils (error formatting, validation)
- End-to-end integration
- Generator (NL → APE code)

To verify test counts:
```bash
pytest packages/ape-anthropic/tests --collect-only -q
```

## Quick Start

```python
from anthropic import Anthropic
from ape_anthropic import ApeAnthropicFunction

# 1. Create Ape task file
# calculator.ape:
# task add:
#     inputs: a: Integer, b: Integer
#     outputs: sum: Integer
#     constraints: a > 0, b > 0
#     steps: sum = a + b

# 2. Load as Claude tool
func = ApeAnthropicFunction.from_ape_file("calculator.ape", "add")

# 3. Get Claude tool schema
tool_schema = func.to_claude_tool()

# 4. Use with Claude
client = Anthropic(api_key="your-key")
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[tool_schema],
    messages=[{"role": "user", "content": "Add 5 and 3"}]
)

# 5. Execute with APE validation
if response.stop_reason == "tool_use":
    tool_use = response.content[-1]
    result = func.execute(tool_use.input)
    print(f"Result: {result}")  # 8
```

## API Reference

### Schema Conversion

**`ape_task_to_claude_schema(task: ApeTask) -> dict`**

Converts APE task to Claude tool schema.

```python
from ape_anthropic import ape_task_to_claude_schema, ApeTask

task = ApeTask(
    name="calculate_tax",
    inputs={"amount": "float", "rate": "float"},
    output="float",
    description="Calculate tax amount"
)

schema = ape_task_to_claude_schema(task)
# {
#     "name": "calculate_tax",
#     "description": "Calculate tax amount",
#     "input_schema": {
#         "type": "object",
#         "properties": {
#             "amount": {"type": "number"},
#             "rate": {"type": "number"}
#         },
#         "required": ["amount", "rate"]
#     }
# }
```

### Execution

**`execute_claude_call(module: ApeModule, function_name: str, input_dict: dict) -> Any`**

Executes Claude tool use with APE validation.

```python
from ape import compile
from ape_anthropic import execute_claude_call

module = compile("calculator.ape")
result = execute_claude_call(module, "add", {"a": 5, "b": 3})
# 8
```

**`class ApeAnthropicFunction`**

High-level wrapper for Ape → Claude integration.

Methods:
- `from_ape_file(ape_file, function_name)` - Load from .ape file
- `to_claude_tool()` - Get Claude tool schema
- `execute(input_dict)` - Execute with validation

### Code Generation (Experimental)

**`generate_ape_from_nl(prompt: str, model: str = "claude-3-5-sonnet-20241022") -> str`**

Generate Ape code from natural language using Claude.

Requires: `pip install ape-anthropic[anthropic]`

```python
from ape_anthropic import generate_ape_from_nl

code = generate_ape_from_nl("Create a task that validates email addresses")
print(code)
```

## Features

### Type Safety
APE validates all parameters before execution:
- **Type checking**: str → string, int → integer, float → number
- **Required fields**: Missing parameters rejected
- **Constraint validation**: Business rules enforced
- **Deterministic execution**: No hallucinations

### Claude Tool Schema Format
```json
{
  "name": "function_name",
  "description": "Function description",
  "input_schema": {
    "type": "object",
    "properties": {
      "param": {"type": "number"}
    },
    "required": ["param"]
  }
}
```

### Error Handling
- **JSONDecodeError**: Invalid input format
- **TypeError**: Parameter type mismatch
- **ApeExecutionError**: Constraint violation
- **KeyError**: Unknown function

## Type Mapping

| APE Type | Claude JSON Schema |
|----------|-------------------|
| String   | string            |
| Integer  | integer           |
| Float    | number            |
| Boolean  | boolean           |
| List     | array             |
| Dict     | object            |

## Examples

### Calculator

```python
# calculator.ape
task multiply:
    inputs: x: Float, y: Float
    outputs: result: Float
    constraints: x >= 0, y >= 0
    steps: result = x * y

# main.py
from anthropic import Anthropic
from ape_anthropic import ApeAnthropicFunction

client = Anthropic(api_key="...")
func = ApeAnthropicFunction.from_ape_file("calculator.ape", "multiply")

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    tools=[func.to_claude_tool()],
    messages=[{"role": "user", "content": "Multiply 4 and 7"}]
)

if response.stop_reason == "tool_use":
    result = func.execute(response.content[-1].input)
    print(result)  # 28.0
```

### Multi-Tool Agent

```python
from ape_anthropic import ApeAnthropicFunction

# Load multiple tools
tools = [
    ApeAnthropicFunction.from_ape_file("math.ape", "add"),
    ApeAnthropicFunction.from_ape_file("math.ape", "subtract"),
    ApeAnthropicFunction.from_ape_file("string.ape", "reverse")
]

# Convert to Claude schemas
tool_schemas = [t.to_claude_tool() for t in tools]

# Execute based on Claude's choice
for tool_use in response.content:
    if hasattr(tool_use, 'name'):
        func = next(t for t in tools if t.function_name == tool_use.name)
        result = func.execute(tool_use.input)
```

## Architecture

```
┌─────────────────────────────────────────────────┐
│              Anthropic Claude API                │
└─────────────────┬───────────────────────────────┘
                  │ tool_use response
                  ▼
         ┌────────────────────┐
         │  ape-anthropic     │
         │  - Schema converter │
         │  - Input validator  │
         │  - Executor         │
         └────────┬───────────┘
                  │ validated params
                  ▼
         ┌────────────────────┐
         │   APE Runtime      │
         │  - Type checking   │
         │  - Constraints     │
         │  - Execution       │
         └────────────────────┘
```

## Comparison: Claude vs OpenAI

| Feature | Claude | OpenAI |
|---------|--------|--------|
| Tool format | `input_schema` | `parameters` |
| Tool ID | Required | Optional |
| Streaming | Yes | Yes |
| Max tools | No limit | No limit |
| ape-anthropic | ✅ | Use ape-openai |

## License

MIT

## Links

- **APE Core**: https://github.com/yourusername/ape
- **ape-langchain**: Integration with LangChain
- **ape-openai**: Integration with OpenAI
- **Documentation**: https://ape-lang.org

## Contributing

Contributions welcome! Please open issues or PRs.