agentpress

AI Agents API Server Starter; FastAPI, Supabase, Redis

Stars: 67

Visit

AgentPress is a collection of simple but powerful utilities that serve as building blocks for creating AI agents. It includes core components for managing threads, registering tools, processing responses, state management, and utilizing LLMs. The tool provides a modular architecture for handling messages, LLM API calls, response processing, tool execution, and results management. Users can easily set up the environment, create custom tools with OpenAPI or XML schema, and manage conversation threads with real-time interaction. AgentPress aims to be agnostic, simple, and flexible, allowing users to customize and extend functionalities as needed.

README:

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.

See How It Works for an explanation of this flow.

Core Components

Threads: Manage Messages[] as threads.
Tools: Register code as callable tools with definitions in both OpenAPI and XML
Response Processing: Support for native-LLM OpenAPI and XML-based tool calling
State Management: Thread-safe JSON key-value state management
LLM: +100 LLMs using the OpenAI I/O Format powered by LiteLLM

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 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",
        execute_tools=True,
        native_tool_calling=True, # Contrary to xml_tool_calling = True
        parallel_tool_execution=True # Will execute tools in parallel, contrary to sequential (one after another)
    )

asyncio.run(main())

View conversation threads in a web UI:

streamlit run agentpress/thread_viewer_ui.py

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

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

For Tasks:

Click tags to check more tools for each tasks

create ai agents manage conversation threads register tools process responses execute tools

For Jobs:

ai engineer data scientist machine learning engineer software developer research scientist

Alternative AI tools for agentpress

Similar Open Source Tools

agentpress

github

: 67

mysql_mcp_server

A Model Context Protocol (MCP) server that enables secure interaction with MySQL databases. This server allows AI assistants to list tables, read data, and execute SQL queries through a controlled interface, making database exploration and analysis safer and more structured. It provides features such as listing available MySQL tables as resources, reading table contents, executing SQL queries with proper error handling, secure database access through environment variables, and comprehensive logging. The tool ensures security best practices by never committing environment variables or credentials, using a database user with minimal required permissions, implementing query whitelisting for production use, and monitoring and logging all database operations.

github

: 187

mcp-omnisearch

mcp-omnisearch is a Model Context Protocol (MCP) server that acts as a unified gateway to multiple search providers and AI tools. It integrates Tavily, Perplexity, Kagi, Jina AI, Brave, Exa AI, and Firecrawl to offer a wide range of search, AI response, content processing, and enhancement features through a single interface. The server provides powerful search capabilities, AI response generation, content extraction, summarization, web scraping, structured data extraction, and more. It is designed to work flexibly with the API keys available, enabling users to activate only the providers they have keys for and easily add more as needed.

github

: 189

Gmail-MCP-Server

Gmail AutoAuth MCP Server is a Model Context Protocol (MCP) server designed for Gmail integration in Claude Desktop. It supports auto authentication and enables AI assistants to manage Gmail through natural language interactions. The server provides comprehensive features for sending emails, reading messages, managing labels, searching emails, and batch operations. It offers full support for international characters, email attachments, and Gmail API integration. Users can install and authenticate the server via Smithery or manually with Google Cloud Project credentials. The server supports both Desktop and Web application credentials, with global credential storage for convenience. It also includes Docker support and instructions for cloud server authentication.

github

: 126

langchainrb

Langchain.rb is a Ruby library that makes it easy to build LLM-powered applications. It provides a unified interface to a variety of LLMs, vector search databases, and other tools, making it easy to build and deploy RAG (Retrieval Augmented Generation) systems and assistants. Langchain.rb is open source and available under the MIT License.

github

: 1.7k

OpenAI

OpenAI is a Swift community-maintained implementation over OpenAI public API. It is a non-profit artificial intelligence research organization founded in San Francisco, California in 2015. OpenAI's mission is to ensure safe and responsible use of AI for civic good, economic growth, and other public benefits. The repository provides functionalities for text completions, chats, image generation, audio processing, edits, embeddings, models, moderations, utilities, and Combine extensions.

github

: 2.4k

Agentarium

Agentarium is a powerful Python framework for managing and orchestrating AI agents with ease. It provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments. The framework offers advanced agent management, robust interaction management, a checkpoint system for saving and restoring agent states, data generation through agent interactions, performance optimization, flexible environment configuration, and an extensible architecture for customization.

github

: 854

LightRAG

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

github

: 20.6k

redisvl

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

github

: 158

clarifai-python

The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

github

: 392

pocketgroq

PocketGroq is a tool that provides advanced functionalities for text generation, web scraping, web search, and AI response evaluation. It includes features like an Autonomous Agent for answering questions, web crawling and scraping capabilities, enhanced web search functionality, and flexible integration with Ollama server. Users can customize the agent's behavior, evaluate responses using AI, and utilize various methods for text generation, conversation management, and Chain of Thought reasoning. The tool offers comprehensive methods for different tasks, such as initializing RAG, error handling, and tool management. PocketGroq is designed to enhance development processes and enable the creation of AI-powered applications with ease.

github

: 178

json-repair

JSON Repair is a toolkit designed to address JSON anomalies that can arise from Large Language Models (LLMs). It offers a comprehensive solution for repairing JSON strings, ensuring accuracy and reliability in your data processing. With its user-friendly interface and extensive capabilities, JSON Repair empowers developers to seamlessly integrate JSON repair into their workflows.

github

: 135

UnrealGenAISupport

The Unreal Engine Generative AI Support Plugin is a tool designed to integrate various cutting-edge LLM/GenAI models into Unreal Engine for game development. It aims to simplify the process of using AI models for game development tasks, such as controlling scene objects, generating blueprints, running Python scripts, and more. The plugin currently supports models from organizations like OpenAI, Anthropic, XAI, Google Gemini, Meta AI, Deepseek, and Baidu. It provides features like API support, model control, generative AI capabilities, UI generation, project file management, and more. The plugin is still under development but offers a promising solution for integrating AI models into game development workflows.

github

: 263

ai-gateway

LangDB AI Gateway is an open-source enterprise AI gateway built in Rust. It provides a unified interface to all LLMs using the OpenAI API format, focusing on high performance, enterprise readiness, and data control. The gateway offers features like comprehensive usage analytics, cost tracking, rate limiting, data ownership, and detailed logging. It supports various LLM providers and provides OpenAI-compatible endpoints for chat completions, model listing, embeddings generation, and image generation. Users can configure advanced settings, such as rate limiting, cost control, dynamic model routing, and observability with OpenTelemetry tracing. The gateway can be run with Docker Compose and integrated with MCP tools for server communication.

github

: 663

mcp

Semgrep MCP Server is a beta server under active development for using Semgrep to scan code for security vulnerabilities. It provides a Model Context Protocol (MCP) for various coding tools to get specialized help in tasks. Users can connect to Semgrep AppSec Platform, scan code for vulnerabilities, customize Semgrep rules, analyze and filter scan results, and compare results. The tool is published on PyPI as semgrep-mcp and can be installed using pip, pipx, uv, poetry, or other methods. It supports CLI and Docker environments for running the server. Integration with VS Code is also available for quick installation. The project welcomes contributions and is inspired by core technologies like Semgrep and MCP, as well as related community projects and tools.

github

: 58

wikipedia-mcp

The Wikipedia MCP Server is a Model Context Protocol (MCP) server that provides real-time access to Wikipedia information for Large Language Models (LLMs). It allows AI assistants to retrieve accurate and up-to-date information from Wikipedia to enhance their responses. The server offers features such as searching Wikipedia, retrieving article content, getting article summaries, extracting specific sections, discovering links within articles, finding related topics, supporting multiple languages and country codes, optional caching for improved performance, and compatibility with Google ADK agents and other AI frameworks. Users can install the server using pipx, Smithery, PyPI, virtual environment, or from source. The server can be run with various options for transport protocol, language, country/locale, caching, access token, and more. It also supports Docker and Kubernetes deployment. The server provides MCP tools for interacting with Wikipedia, such as searching articles, getting article content, summaries, sections, links, coordinates, related topics, and extracting key facts. It also supports country/locale codes and language variants for languages like Chinese, Serbian, Kurdish, and Norwegian. The server includes example prompts for querying Wikipedia and provides MCP resources for interacting with Wikipedia through MCP endpoints. The project structure includes main packages, API implementation, core functionality, utility functions, and a comprehensive test suite for reliability and functionality testing.

github

: 99

For similar tasks

superagent-py

Superagent is an open-source framework that enables developers to integrate production-ready AI assistants into any application quickly and easily. It provides a Python SDK for interacting with the Superagent API, allowing developers to create, manage, and invoke AI agents. The SDK simplifies the process of building AI-powered applications, making it accessible to developers of all skill levels.

github

: 99

restai

RestAI is an AIaaS (AI as a Service) platform that allows users to create and consume AI agents (projects) using a simple REST API. It supports various types of agents, including RAG (Retrieval-Augmented Generation), RAGSQL (RAG for SQL), inference, vision, and router. RestAI features automatic VRAM management, support for any public LLM supported by LlamaIndex or any local LLM supported by Ollama, a user-friendly API with Swagger documentation, and a frontend for easy access. It also provides evaluation capabilities for RAG agents using deepeval.

github

: 416

rivet

Rivet is a desktop application for creating complex AI agents and prompt chaining, and embedding it in your application. Rivet currently has LLM support for OpenAI GPT-3.5 and GPT-4, Anthropic Claude Instant and Claude 2, [Anthropic Claude 3 Haiku, Sonnet, and Opus](https://www.anthropic.com/news/claude-3-family), and AssemblyAI LeMUR framework for voice data. Rivet has embedding/vector database support for OpenAI Embeddings and Pinecone. Rivet also supports these additional integrations: Audio Transcription from AssemblyAI. Rivet core is a TypeScript library for running graphs created in Rivet. It is used by the Rivet application, but can also be used in your own applications, so that Rivet can call into your own application's code, and your application can call into Rivet graphs.

github

: 2.7k

ai2apps

AI2Apps is a visual IDE for building LLM-based AI agent applications, enabling developers to efficiently create AI agents through drag-and-drop, with features like design-to-development for rapid prototyping, direct packaging of agents into apps, powerful debugging capabilities, enhanced user interaction, efficient team collaboration, flexible deployment, multilingual support, simplified product maintenance, and extensibility through plugins.

github

: 278

foundationallm

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with large language models, centrally manage AI agents and assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, load balances across multiple endpoints, and is extensible to new data sources and orchestrators. FoundationaLLM addresses the need for customized copilots or AI agents that are secure, licensed, flexible, and suitable for enterprise-scale production.

github

: 88

learn-applied-generative-ai-fundamentals

This repository is part of the Certified Cloud Native Applied Generative AI Engineer program, focusing on Applied Generative AI Fundamentals. It covers prompt engineering, developing custom GPTs, and Multi AI Agent Systems. The course helps in building a strong understanding of generative AI, applying Large Language Models (LLMs) and diffusion models practically. It introduces principles of prompt engineering to work efficiently with AI, creating custom AI models and GPTs using OpenAI, Azure, and Google technologies. It also utilizes open source libraries like LangChain, CrewAI, and LangGraph to automate tasks and business processes.

github

: 165

shire

The Shire is an AI Coding Agent Language that facilitates communication between an LLM and control IDE for automated programming. It offers a straightforward approach to creating AI agents tailored to individual IDEs, enabling users to build customized AI-driven development environments. The concept of Shire originated from AutoDev, a subproject of UnitMesh, with DevIns as its precursor. The tool provides documentation and resources for implementing AI in software engineering projects.

github

: 212

ai-agents-masterclass

AI Agents Masterclass is a repository dedicated to teaching developers how to use AI agents to transform businesses and create powerful software. It provides weekly videos with accompanying code folders, guiding users on setting up Python environments, using environment variables, and installing necessary packages to run the code. The focus is on Large Language Models that can interact with the outside world to perform tasks like drafting emails, booking appointments, and managing tasks, enabling users to create innovative applications with minimal coding effort.

github

: 157

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 502

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 669

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 953

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 1.3k

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k