ape-langchain 1.0.2

LangChain integration for APE (AI Programmatic Execution)

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.

Architecture: Decision Authority

AI components provide suggestions and structured input only.
All parsing, validation, and execution decisions are made exclusively by the APE runtime.
Invalid or hallucinated AI output is treated as untrusted input and rejected deterministically.

LangChain agents generate tool calls. APE validates and executes. Agents never bypass validation or directly execute logic.

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

# 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

Quick Start

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.

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.

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.

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.

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

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

tool = create_langchain_tool(
    "calc.ape",
    "add",
    description="Adds two numbers together with validation"
)

With Memory

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

# 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

# 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

# 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
• LangChain
• Documentation
• Issues