Open-source alternatives to Adk Samples

LangChain.js is a framework for building, executing, and monitoring stateful agentic applications. It provides an orchestration engine that models workflows as directed graphs, allowing developers to connect language models, data sources, and external tools into modular, multi-step processes. The platform distinguishes itself through its focus on stateful execution and human-in-the-loop control. It manages agent lifecycles by persisting execution state across threads, enabling fault tolerance and the ability to pause workflows at designated breakpoints for manual review or modification. This

This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov

PydanticAI is a Python framework designed for building production-grade autonomous agents. It provides a unified interface for interacting with diverse language models, enabling developers to construct agents that perform complex tasks through structured data validation, tool execution, and multi-turn conversation management. The library centers on type-safe schema enforcement, ensuring that model inputs and outputs remain consistent and reliable throughout the agent's lifecycle. The framework distinguishes itself through a robust architecture that emphasizes modularity and testability. It ut

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

This project is a Python library designed for building, testing, and deploying autonomous agents that execute complex workflows. It functions as a multi-agent orchestration framework, enabling the creation of systems where specialized agents communicate, delegate tasks, and integrate with external services to complete multi-step automated processes. The framework distinguishes itself by combining deterministic code execution with adaptive language model reasoning. It utilizes structured graph-based logic and state-machine execution to maintain persistent context across multi-turn interactions

This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin

This project serves as a comprehensive, curated directory of resources, tools, and platforms dedicated to the generative artificial intelligence ecosystem. It functions as a central hub for developers and researchers to discover the frameworks, models, and services necessary for building, deploying, and managing intelligent software applications. The directory distinguishes itself by providing a structured index of specialized tooling across several technical domains. It covers the full lifecycle of generative AI, including the development of autonomous agent systems, the implementation of re

PocketFlow is a graph-based framework for designing and executing large language model operations and reasoning patterns. It serves as an orchestrator for building goal-oriented autonomous agents, multi-agent systems, and retrieval-augmented generation pipelines. The system is distinguished by its ability to coordinate autonomous AI agents that use shared memory and tools to solve complex goals, supported by a structured output engine that enforces schema-consistent responses. It utilizes graph-based workflow orchestration to manage sequences of model operations and supports supervisor-based

DocsGPT is a retrieval-augmented generation platform and private knowledge base used to build AI agents that perform grounded search and analysis. It functions as a multi-model AI orchestrator and enterprise agent builder, allowing for the integration of various local and cloud language models to customize reasoning and text generation. The project provides a visual environment for developing automated assistants using conditional logic and third-party API connectivity. It enables the creation of private AI agents capable of performing enterprise search and detailed document analysis using pr

Eigent is a comprehensive platform for developing, configuring, and orchestrating autonomous AI agents. It functions as an agent development environment and workflow automation engine, enabling users to build modular agents equipped with custom toolsets, domain-specific skill packages, and external API connections to perform targeted operational tasks. The framework distinguishes itself through a robust multi-agent orchestration layer that coordinates teams of specialized agents to execute complex workflows. By utilizing hierarchical task decomposition, the system breaks high-level goals into

This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven

The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit

This project is a TypeScript SDK and application framework for integrating large language models into software. It provides a unified interface and multi-provider model wrapper to interact with various AI model providers through a single, consistent API. The toolkit includes a generative UI framework and an AI agent orchestrator. These tools enable the creation of autonomous agents capable of executing functions and the development of AI-driven user interfaces with specialized state management for streaming chatbot components. The framework covers broad capability areas including stream-base

This is a Python SDK for interacting with large language models via API. It serves as a client library to generate text, process messages, and manage conversational states, while providing a specialized interface for connecting to models hosted across different cloud infrastructure providers. The SDK includes a tool-calling framework that maps Python functions to JSON schemas, allowing models to execute external tools. It also features a built-in token counting utility to estimate input size before transmission and a server-sent events client for receiving model tokens in real time. The libr

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

This project is a comprehensive framework for building, evaluating, and connecting autonomous agent systems. It provides a library of standardized architectural patterns for implementing complex agent workflows, including multi-agent orchestration, iterative reasoning, and memory management. By offering a unified interface for model providers, the framework allows for consistent agent execution across different artificial intelligence services. The framework distinguishes itself through a focus on rigorous benchmarking and deterministic control. It includes a suite of tools for evaluating age

Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario

This project is a framework for managing generative AI services through a unified provider interface and adapter layer. It provides a standardized API for calling multiple cloud-based and locally hosted models, translating provider-specific parameters and responses into a uniform format. The system includes an agent orchestrator designed for long-running tasks, featuring state persistence for resuming runs and execution tracing to monitor decision-making processes. It integrates the Model Context Protocol to connect models to external servers and filesystems and employs a policy-based executi

SuperduperDB is an AI agent orchestrator and database-integrated machine learning platform. It serves as a framework for building stateful AI agents and retrieval-augmented generation applications by integrating large language models directly with database backends. The project enables the deployment of self-hosted AI infrastructure and the management of language models on private hardware using local checkpoints. It distinguishes itself by allowing users to attach AI components directly to data fields, triggering model execution and automated transformations based on database insertions and

This project is a comprehensive framework for building AI-powered applications, providing a unified toolkit for orchestrating language models, autonomous agents, and interactive user interfaces. It serves as a central library for managing the entire lifecycle of AI interactions, from initial prompt generation and model provider abstraction to complex, multi-step reasoning and tool execution. The framework distinguishes itself through its deep integration with frontend development, specifically by enabling generative user interfaces that render dynamic components directly from model outputs. I

The Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides designed for building applications with Google Gemini models. It serves as a central resource for developers to integrate multimodal generative artificial intelligence into their software, providing the necessary frameworks to manage model interactions, stateful workflows, and structured data extraction. The repository distinguishes itself by offering specialized toolkits for autonomous agent orchestration, enabling the construction of agents that can execute code, browse the web

Agency Swarm is a multi-agent orchestration framework and development kit designed to coordinate specialized AI agents through defined communication patterns and handoffs. It functions as a system for managing agent swarms, providing an API gateway to expose these coordinated collectives as production-ready HTTP endpoints. The project distinguishes itself through its Model Context Protocol integration layer, which connects agents to external data sources and capabilities. It implements specialized orchestration patterns, such as the orchestrator-worker model and role-based delegation, to tran

Agentic is a tool marketplace and management platform designed for the Model Context Protocol. It provides a gateway and proxy that enables the discovery, publishing, and distribution of vetted tools for agentic AI frameworks. The platform specializes in Model Context Protocol monetization, allowing developers to transform services into paid products through integrated authentication, usage-based billing, and subscription management. It also includes a converter that transforms OpenAPI specifications into compatible protocol servers for use in AI workflows. The system covers a broad range of

Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project

This project is a collection of generative AI implementations focused on the development of AI agents, retrieval-augmented generation pipelines, and vector search integration. It provides a framework for connecting managed cloud databases to language models to create context-aware applications. The project covers the orchestration of autonomous agents that use multi-step reasoning and external tools to complete tasks. It includes implementations for semantic retrieval using high-dimensional embeddings and the use of model-agnostic prompting to ensure consistent outputs across different large