Awesome AI Agents › Stateful Serverless Frameworks
LiteLLM is a Python SDK and proxy server that provides a unified OpenAI-compatible interface to call over 100 large language model APIs from multiple providers, featuring consistent output, retry logic, budget controls, and extensive observability integrations.
Flowise is an open-source drag-and-drop platform that enables users to easily build and deploy customized Large Language Model (LLM) application workflows with a user-friendly interface and flexible deployment options.
Parlant is a Conversation Modeling engine that enables precise, consistent, and reliable control over GenAI-driven conversational agents by enforcing structured behavioral guidelines and adapting dynamically to user interactions.
ToolBench is an open platform for training, serving, and evaluating large language models with advanced tool-use capabilities using a large-scale, richly annotated dataset of real-world APIs.
RivetKit is a stateful serverless framework that enables building scalable, real-time, and collaborative applications deployable across multiple platforms including Rivet, Cloudflare Workers, Bun, and Node.js.
LlamaDeploy is an async-first framework that enables seamless deployment, scaling, and productionization of agentic multi-service workflows built with llama_index, facilitating easy transition from development to cloud-based production environments.
Ragbits is a modular framework providing building blocks for rapid development, deployment, and monitoring of scalable and reliable Generative AI applications with flexible LLM integration and advanced document processing capabilities.
Mirascope is a flexible and user-friendly library that provides a unified interface to work with multiple large language model providers, simplifying AI-driven text generation and information extraction tasks.
Strands Agents Tools is a versatile toolkit that equips AI agents with powerful capabilities for file operations, system interaction, API communication, multimedia processing, memory management, and advanced reasoning to build intelligent applications efficiently.
any-agent is a Python library that provides a unified interface to multiple agent frameworks, enabling easy switching and evaluation of AI agents through a single API.
BeeAI is an open platform that enables discovery, execution, and composition of AI agents from any framework, supporting multi-agent workflows and seamless integration across languages.
llama-cpp-agent is a versatile framework designed to simplify interaction with Large Language Models by enabling chat, structured function calls, and retrieval augmented generation across multiple LLM providers.
do-agent is a system monitoring tool that collects and sends metrics from DigitalOcean Droplets to enable resource usage visualization and alerting.
Chipper is a modular and lightweight AI interface that enables users to build and customize Retrieval-Augmented Generation (RAG) pipelines with local and cloud models, featuring document processing, web scraping, and secure API proxy capabilities in a fully containerized environment.
django-ai-assistant integrates AI Assistants with Django to build intelligent applications using large language models and advanced AI features like AI Tool Calling and Retrieval-Augmented Generation.
Embodied-agents is a modular and extensible toolkit that integrates state-of-the-art multi-modal transformer models into robotics stacks, enabling seamless local and remote AI inference for language, motion, and sensory tasks in robotic systems.
FireAct is a comprehensive framework for fine-tuning language agents, providing tools, data, and models to facilitate advanced training and evaluation of language models like Llama and GPT.
StackImpact Go Profiler is a production-grade performance profiling tool for Go applications that provides continuous CPU, memory, latency, and error monitoring with detailed insights and minimal overhead.
Graphsignal is an AI optimization platform with a Python tracer that helps developers monitor, analyze, and optimize the performance and resource usage of machine learning models and applications.