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AgentPress: Building Blocks for AI Agents

AgentPress is a collection of simple, but powerful utilities that serve as building blocks for creating AI agents. Plug, play, and customize.

How It Works

Each AI agent iteration follows a clear, modular flow:

Message & LLM Handling
- Messages are managed in threads via ThreadManager
- LLM API calls are made through a unified interface (llm.py)
- Supports streaming responses for real-time interaction
Response Processing
- LLM returns both content and tool calls
- Content is streamed in real-time
- Tool calls are parsed using either:
  - Standard OpenAPI function calling
  - XML-based tool definitions
  - Custom parsers (extend ToolParserBase)
Tool Execution
- Tools are executed either:
  - In real-time during streaming (execute_tools_on_stream)
  - After complete response
  - In parallel or sequential order
- Supports both standard and XML tool formats
- Extensible through ToolExecutorBase
Results Management
- Results from both content and tool executions are handled
- Supports different result formats (standard/XML)
- Customizable through ResultsAdderBase

This modular architecture allows you to:

Use standard OpenAPI function calling
Switch to XML-based tool definitions
Create custom processors by extending base classes
Mix and match different approaches
Threads: Simple message thread handling utilities with streaming support
Tools: Flexible tool definition with both OpenAPI and XML formats
State Management: Thread-safe JSON key-value state management
LLM Integration: Provider-agnostic LLM calls via LiteLLM
Response Processing: Support for both standard and XML-based tool calling

Installation & Setup

Install the package:

pip install agentpress

Initialize AgentPress in your project:

agentpress init

Creates a agentpress directory with all the core utilities. Check out File Overview for explanations of the generated files.

If you selected the example agent during initialization:
- Creates an agent.py file with a web development agent example
- Creates a tools directory with example tools:
  - files_tool.py: File operations (create/update files, read directory and load into state)
  - terminal_tool.py: Terminal command execution
- Creates a workspace directory for the agent to work in

Quick Start

Set up your environment variables in a .env file:

OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here
GROQ_API_KEY=your_key_here

Create a calculator tool with OpenAPI schema:

from agentpress.tool import Tool, ToolResult, openapi_schema

class CalculatorTool(Tool):
    @openapi_schema({
        "type": "function",
        "function": {
            "name": "add",
            "description": "Add two numbers",
            "parameters": {
                "type": "object",
                "properties": {
                    "a": {"type": "number"},
                    "b": {"type": "number"}
                },
                "required": ["a", "b"]
            }
        }
    })
    async def add(self, a: float, b: float) -> ToolResult:
        try:
            result = a + b
            return self.success_response(f"The sum is {result}")
        except Exception as e:
            return self.fail_response(f"Failed to add numbers: {str(e)}")

Or create a tool with XML schema:

from agentpress.tool import Tool, ToolResult, xml_schema

class FilesTool(Tool):
    @xml_schema(
        tag_name="create-file",
        mappings=[
            {"param_name": "file_path", "node_type": "attribute", "path": "."},
            {"param_name": "file_contents", "node_type": "content", "path": "."}
        ],
        example='''
        <create-file file_path="path/to/file">
        File contents go here
        </create-file>
        '''
    )
    async def create_file(self, file_path: str, file_contents: str) -> ToolResult:
        # Implementation here
        pass

Use the Thread Manager with streaming and tool execution:

import asyncio
from agentpress.thread_manager import ThreadManager
from calculator_tool import CalculatorTool

async def main():
    # Initialize thread manager and add tools
    manager = ThreadManager()
    manager.add_tool(CalculatorTool)

    # Create a new thread
    thread_id = await manager.create_thread()
    
    # Add your message
    await manager.add_message(thread_id, {
        "role": "user", 
        "content": "What's 2 + 2?"
    })
    
    # Run with streaming and tool execution
    response = await manager.run_thread(
        thread_id=thread_id,
        system_message={
            "role": "system", 
            "content": "You are a helpful assistant with calculation abilities."
        },
        model_name="anthropic/claude-3-5-sonnet-latest",
        stream=True,
        native_tool_calling=True,
        execute_tools=True,
        execute_tools_on_stream=True
    )

    # Handle streaming response
    if isinstance(response, AsyncGenerator):
        async for chunk in response:
            if hasattr(chunk.choices[0], 'delta'):
                delta = chunk.choices[0].delta
                if hasattr(delta, 'content') and delta.content:
                    print(delta.content, end='', flush=True)

asyncio.run(main())

View conversation threads in a web UI:

streamlit run agentpress/thread_viewer_ui.py

File Overview

Core Components

agentpress/llm.py

LLM API interface using LiteLLM. Supports 100+ LLMs with OpenAI-compatible format. Includes streaming, retry logic, and error handling.

agentpress/thread_manager.py

Manages conversation threads with support for:

Message history management
Tool registration and execution
Streaming responses
Both OpenAPI and XML tool calling patterns

agentpress/tool.py

Base infrastructure for tools with:

OpenAPI schema decorator for standard function calling
XML schema decorator for XML-based tool calls
Standardized ToolResult responses

agentpress/tool_registry.py

Central registry for tool management:

Registers both OpenAPI and XML tools
Maintains tool schemas and implementations
Provides tool lookup and validation

agentpress/state_manager.py

Thread-safe state persistence:

JSON-based key-value storage
Atomic operations with locking
Automatic file handling

Response Processing

agentpress/llm_response_processor.py

Handles LLM response processing with support for:

Streaming and complete responses
Tool call extraction and execution
Result formatting and message management

Standard Processing

standard_tool_parser.py: Parses OpenAPI function calls
standard_tool_executor.py: Executes standard tool calls
standard_results_adder.py: Manages standard results

XML Processing

xml_tool_parser.py: Parses XML-formatted tool calls
xml_tool_executor.py: Executes XML tool calls
xml_results_adder.py: Manages XML results

Philosophy

Plug & Play: Start with our defaults, then customize to your needs.
Agnostic: Built on LiteLLM, supporting any LLM provider. Minimal opinions, maximum flexibility.
Simplicity: Clean, readable code that's easy to understand and modify.
No Lock-in: Take full ownership of the code. Copy what you need directly into your codebase.

Contributing

We welcome contributions! Feel free to:

Submit issues for bugs or suggestions
Fork the repository and send pull requests
Share how you've used AgentPress in your projects

Development

Clone:

git clone https://github.com/kortix-ai/agentpress
cd agentpress

Install dependencies:

pip install poetry
poetry install

For quick testing:

pip install -e .

License

MIT License

Built with ❤️ by Kortix AI Corp

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