ape-langchain 1.0.6
LangChain integration for APE (AI Programmatic Execution)
License: MIT
Requires Python: >=3.11

# ape-langchain

LangChain integration for APE (AI Programmatic Execution).

## What is ape-langchain?

**ape-langchain** provides seamless integration between APE's deterministic functions and LangChain's agent framework. Convert APE tasks into LangChain tools with automatic validation and type safety.

## Why ape-langchain?

LangChain agents are powerful but need reliable tools:
- Tool parameters must be validated
- Type safety prevents runtime errors
- Deterministic execution ensures consistency
- Clear contracts between agent and tools

**ape-langchain solves this** by wrapping APE functions as LangChain tools:

```
LangChain Agent → APE Tool → Validation → Deterministic execution ✓
```

## Installation

```bash
# Core package
pip install ape-langchain

# With LangChain
pip install ape-langchain[langchain]

# Development dependencies
pip install ape-langchain[dev]
```

**Prerequisites:**
- Python >= 3.11
- ape-lang >= 0.2.0

## Test Coverage

✅ **Tests: 17 passing, 3 skipped**

- **Total tests: 20** (17 passing + 3 documented skips)
- 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 → LangChain)
- Utils (result formatting, input validation)

Skipped tests (documented with reasons):
- End-to-end integration (API difference: file-based vs task-based)
- Executor (API difference documented)
- Generator (needs implementation verification)

To verify test counts:
```bash
pytest packages/ape-langchain/tests --collect-only -q
```

## Quick Start

```python
from langchain.llms import OpenAI
from ape_langchain import create_langchain_tool

# 1. Create Ape task file
# calculator.ape:
# task add:
#     inputs: a: Integer, b: Integer
#     outputs: sum: Integer
#     constraints: deterministic
#     steps: sum = a + b

# 2. Create LangChain tool
tool = create_langchain_tool("calculator.ape", "add")

# 3. Use with LangChain
from langchain.agents import initialize_agent, AgentType

llm = OpenAI(temperature=0)
agent = initialize_agent(
    tools=[tool],
    llm=llm,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# 4. Run agent
result = agent.run("Add 5 and 3")
print(result)  # "The sum of 5 and 3 is 8"
```

## API Reference

### Schema Conversion

**`ape_task_to_langchain_schema(task: ApeTask) -> dict`**

Converts APE task to LangChain tool schema.

```python
from ape_langchain import ape_task_to_langchain_schema, ApeTask

task = ApeTask(
    name="calculate_tax",
    inputs={"amount": "float", "rate": "float"},
    output="float",
    description="Calculate tax amount"
)

schema = ape_task_to_langchain_schema(task)
```

### Tool Creation

**`create_langchain_tool(ape_file, function_name) -> StructuredTool`**

Create a LangChain StructuredTool from an Ape file.

```python
from ape_langchain import create_langchain_tool

tool = create_langchain_tool("math_ops.ape", "multiply")

# Use in agent
result = tool.run({"a": 4, "b": 7})
```

### Tool Wrapper

**`ApeLangChainTool`**

Low-level wrapper for more control.

```python
from ape_langchain import ApeLangChainTool

ape_tool = ApeLangChainTool.from_ape_file("calc.ape", "divide")
langchain_tool = ape_tool.as_structured_tool()

# Execute directly
result = ape_tool.execute(a=10, b=2)
```

### Chain Creation

**`create_ape_chain(ape_file, llm) -> AgentExecutor`**

Create a complete LangChain agent with all functions from an Ape file.

```python
from ape_langchain import create_ape_chain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
chain = create_ape_chain("calculator.ape", llm=llm)

result = chain.run("Calculate 15 * 8 and then add 42")
```

## Features

- ✅ **StructuredTool integration**: Full LangChain tool support
- ✅ **Automatic validation**: Type and constraint checking
- ✅ **Pydantic schemas**: Generated from APE types
- ✅ **Multi-tool agents**: Load entire APE modules as tools
- ✅ **Error handling**: Clear error propagation
- ✅ **Type mapping**: APE types → Python types → Pydantic

## Type Mapping

| Ape Type | Python Type | LangChain Schema |
|----------|-------------|------------------|
| String   | str         | string           |
| Integer  | int         | integer          |
| Float    | float       | number           |
| Boolean  | bool        | boolean          |
| List     | list        | array            |
| Dict     | dict        | object           |

## Advanced Usage

### Multiple Tools

```python
from ape_langchain import ApeLangChainTool
from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI

# Load multiple functions
add_tool = create_langchain_tool("calc.ape", "add")
multiply_tool = create_langchain_tool("calc.ape", "multiply")
divide_tool = create_langchain_tool("calc.ape", "divide")

# Create agent
llm = OpenAI(temperature=0)
agent = initialize_agent(
    tools=[add_tool, multiply_tool, divide_tool],
    llm=llm,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION
)

result = agent.run("Calculate (5 + 3) * 2 / 4")
```

### Custom Descriptions

```python
tool = create_langchain_tool(
    "calc.ape",
    "add",
    description="Adds two numbers together with validation"
)
```

### With Memory

```python
from langchain.memory import ConversationBufferMemory
from langchain.agents import initialize_agent

memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
    tools=[tool],
    llm=llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory
)
```

## Examples

### Basic Calculator Agent

```python
# calculator.ape
task add:
    inputs:
        a: Integer
        b: Integer
    outputs:
        result: Integer
    constraints:
        - deterministic
    steps:
        - result = a + b

task multiply:
    inputs:
        a: Integer
        b: Integer
    outputs:
        result: Integer
    constraints:
        - deterministic
    steps:
        - result = a * b
```

```python
# agent.py
from ape_langchain import create_ape_chain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
agent = create_ape_chain("calculator.ape", llm=llm)

agent.run("What is 7 times 8, then add 15?")
# Agent uses multiply(7, 8) → 56, then add(56, 15) → 71
```

## Development

```bash
# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .

# Type checking
mypy src/
```

## License

MIT License - see LICENSE file for details.

## Links

- [APE Language](https://github.com/Quynah/ape-lang)
- [LangChain](https://python.langchain.com/)
- [Documentation](https://github.com/Quynah/ape-lang/tree/main/packages/ape-langchain)
- [Issues](https://github.com/Quynah/ape-lang/issues)