Projects sharing features with Claude Agent Sdk Python

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 an extension framework and orchestration system for Claude Code that uses lifecycle hooks to intercept and augment the execution flow of agents. It serves as a management layer for automating session startup, handling project context loading, and performing cleanup routines during AI coding sessions. The framework provides an orchestration system to spawn and manage specialized sub-agents with distinct prompts and toolsets to decompose complex technical tasks. It functions as a prompt engineering middleware to validate and inject context into user requests and as a tool guardr

This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified framework for creating conversational agents that can use tools, maintain state, and coordinate across multiple language model providers including OpenAI, Anthropic, Google, Amazon Bedrock, and locally-hosted models. The SDK supports multi-agent orchestration through graphs, teams, and swarms, allowing several specialized agents to collaborate on complex tasks. Agents can be composed as callable tools that other agents invoke, and the framework includes policy handlers that inspe

This project is an educational curriculum and architectural framework for building autonomous AI agents and multi-agent systems. It provides a structured learning path focused on the development of independent software components capable of planning, executing tasks, and utilizing external tools to achieve high-level goals. The framework emphasizes multi-agent system orchestration through distributed architectures where specialized agents collaborate using standardized communication protocols. It details specific design patterns such as dual-memory systems for maintaining short-term plans and

This is an asynchronous Swift client library for calling OpenAI’s API across Apple platforms. It provides native access to chat completions, image generation and editing, speech synthesis and transcription, text embeddings, and content moderation through a single interface built on Swift’s async-await concurrency model. The client supports structured output generation by constraining model responses to a provided JSON schema, and enables real-time consumption of generated text through streaming responses delivered as an AsyncSequence. It includes a thread-based conversation model for managing

This is a Python SDK for building asynchronous AI agents. A single async session connects to an agent backend, sends prompts (text or multimodal content such as images and documents), and streams results back as steps, text tokens, model thoughts, and tool calls. The session handles backend startup, authentication, tool wiring, hook registration, and cleanup, so developers don't write the agentic loop by hand. Agents run through a locally running harness on the developer's own machine, and the package ships with a pre-compiled platform-specific binary. What sets it apart is how much of the ag

Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers

Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

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 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

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

OpenIntelligentUI is a generative UI chat application and forkable starter in which an AI agent answers questions with live, interactive interface elements instead of plain text. Each chat message starts an agent run that chooses between a plain text reply, a prebuilt native component, or a custom generated interface, and the result streams into the conversation as it is produced. What sets it apart is how it handles generated interfaces. A routing step reads each turn and picks a renderer, using tables for simple data and charts, diagrams, calculators, or maps for richer requests. Agent-writ

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

Open Multi-Agent is a TypeScript framework for multi-agent orchestration that decomposes natural language goals into a runtime-generated directed acyclic graph of tasks. It functions as a task orchestrator and workflow state manager, coordinating multiple AI models to execute parallel and sequential operations. The framework is distinguished by a proposer-judge consensus protocol used to validate agent outputs through a quorum of agreement. It employs provider-agnostic model routing to assign specific models to tasks based on roles or execution phases and utilizes state-based workflow checkpo

This project is an AI-powered IDE extension and LLM coding assistant that provides a conversational interface for generating, refactoring, and debugging code. It functions as an AI agent framework and a Model Context Protocol client, connecting AI models to external data sources and tools to automate complex development tasks. The system is distinguished by its use of autonomous AI agents capable of multi-step task execution, including the ability to read files, modify code, and run terminal commands iteratively. It supports recursive agent orchestration through subagent delegation and employ

This project provides a collection of example implementations for building AI agents and workflows using the Spring AI Alibaba framework. It focuses on demonstrating how to create intelligent agents that iteratively reason and act to solve problems, coordinate multiple agents across services, and integrate human oversight into automated processes. The examples showcase key differentiators such as graph-based workflow automation with conditional routing, nested graphs, and parallel execution, as well as real-time streaming of agent responses to clients. The project also illustrates how to mana

This repository is a study resource for the Claude Certified Architect Foundations exam. It brings together a written study guide, scenario-based practice questions, and Anki flashcard decks. The guide explains the exam format, the 100–1000 scoring scale with a passing score of 720, and the five weighted domains. It also covers realistic scenarios such as customer support agents, multi-agent research, code review pipelines, and structured data extraction. The curriculum covers designing tool-using agents. It explains how to build an agent loop driven by the model's stop reason, and how to coo

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

Omnigent is a policy-governed orchestrator for coding agents. It runs several agent harnesses, such as Claude Code and Codex, together in one session, and a single harness identifier decides which agent handles a session, so teams can swap agents without rewriting their definitions. Sub-agents can each use their own harness, model, prompt, and sandbox inheritance, which lets one agent review another's work or split a task with it. Every agent action passes through layered policies set at the server, agent, and session levels. Each policy function allows, denies, or abstains, and a denial stop

This repository is a collection of example projects and guides for building with Cursor's coding agents. The samples cover running agents from TypeScript code against local workspaces or cloud runtimes, streaming their output live, and launching them from a terminal. Other examples include a cloud agent kanban dashboard, a task graph orchestrator, an app-prototyping chat, and a realtime voice intake demo. Several samples go beyond a basic quickstart. The kanban dashboard lists cloud agents as cards grouped by status, repository, or branch, previews their artifacts, and launches new agents fro

Potpie is an LLM codebase analysis platform and multi-agent orchestration framework designed to act as an AI software engineer. It parses repositories into a structured code knowledge graph, enabling AI agents to perform multi-hop reasoning, dependency tracing, and grounded technical analysis across large codebases. The system distinguishes itself through a spec-driven development framework where agents generate detailed technical specifications and architecture plans before implementing multi-file code changes. It utilizes a durable execution engine to coordinate specialized AI personas for

This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-i

Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

This repository is a long-form technical book about how a production AI coding agent runtime, often called an agent harness, is built. It dissects a real-world coding agent's architecture chapter by chapter and turns it into transferable lessons, and it also works as a step-by-step guide to designing and building your own harness around a language model. Python scripts in the repository support the book itself rather than the agent. The book's core subject is the machinery around the model: a streaming conversation loop that repeats tool calls until the model returns plain text, a tool protoc

This repository is a static analysis of the Claude Code terminal coding assistant, based on a reading of its TypeScript source. It documents how the agent is built, from its startup path and a single shared execution core to the streaming tool-call loop, sub-agents, and terminal interface layers. It is a reference and study resource about the agent rather than the agent itself. The analysis centers on a few areas. One is the shared core that routes queries, sub-agents, tools, and permission checks through the same control flow. Another is memory: layered, file-based records that users can ins

Thunderbolt is an open-source, self-hostable AI chat client that runs on web, desktop (macOS, Linux, Windows) and mobile (iOS, Android) from one shared web codebase inside lightweight native shells. It is local-first: each device keeps a local database as the source of truth, and data syncs to a central backend with optional end-to-end encryption, so the server stores only ciphertext for protected content. New devices are approved from trusted ones by fingerprint, recovery phrase or passkey, and revoked devices lose access. What sets it apart is its approach to model and agent choice and to p

This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce