Search Results for "create" - Page 7
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Showing 612 open source projects for "create"
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1
Claude Code Skills & Plugins
232+ Claude Code skills & agent plugins for Claude Code, Codex
...It is designed to integrate seamlessly into AI workflows, providing a plug-and-play approach to extending functionality. The repository also includes examples and templates, making it easier for developers to create their own custom skills. It supports a wide range of use cases, from development to content generation. Overall, Claude Skills acts as a library of reusable expertise modules for AI systems. -
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ChatGPT Shortcut
Curated AI prompt manager with search, sharing, and browser access
...ChatGPT-Shortcut includes tagging and filtering features that help users locate relevant prompts efficiently without manually browsing long lists. In addition to built-in prompts, users can create, edit, and organize their own prompts for personal use. It also supports community participation where users can share prompts and vote on contributions, allowing useful prompts to surface through community feedback. Browser extension support further improves accessibility by enabling a sidebar interface. -
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Pipecat
Framework for building real-time voice and multimodal AI agents
...Pipecat allows applications to integrate multiple AI services and transports, enabling flexible deployment across different environments and communication channels. Developers can create a wide range of interactive systems including voice assistants, customer service agents, interactive storytelling applications, and multimodal interfaces that combine voice, video, images, and text. Its modular architecture allows components to be composed into pipelines that process audio, text, and video streams in real time. -
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Gitingest
Create prompt-friendly codebase digests from any Git repository URL
Gitingest is a developer utility that converts an entire Git repository into a structured, prompt-friendly text digest suitable for use with large language models. It analyzes a repository and produces a consolidated textual representation that includes the file structure and code content in an organized format. This makes it easier to provide meaningful code context when working with AI systems that require compact, readable inputs. Developers can generate these digests from either a local... -
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5
TypedAI
TypeScript AI platform with AI chat, Autonomous agents
...The platform also includes specialized software engineering agents that can assist with tasks such as code reviews or repository analysis. Developers can integrate multiple model providers and tools into the platform to create flexible agent pipelines. -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. ... -
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Claude Agent SDK
Programmatically build AI agents with Claude Code's capabilities
Claude Agent SDK (TypeScript) is an official TypeScript/JavaScript software development kit from Anthropic that makes it easier to build AI agents using the same agent architecture that powers Claude Code and other Claude-based autonomous systems. It provides abstractions and tools that let developers programmatically create, configure, and run intelligent agents that can read files, run commands, edit source code, manage sessions, and execute workflows without having to implement the agent loop manually — the SDK handles much of the complexity for you. Since it’s designed to tap into Claude Code’s capabilities, it integrates tightly with the underlying agent runtime, giving access to built-in tools like file system access, command execution, and code editing, and enabling features such as custom agents, hooks, and subagents. -
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Shire
Empower Your Dev Ecosystem with AI Agents
...Within AutoDev, we envisioned an AI-driven integrated development environment for developers, which included Shire’s predecessor, DevIns. DevIns was designed to empower users to create custom AI agents tailored to their own IDEs, thus forging a personalized AI-powered development realm. -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
...Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As shown below, it results in readable code that can always be visualized. Hamilton loads that definition and automatically builds the DAG for you. Hamilton brings modularity and structure to any Python application moving data: ETL pipelines, ML workflows, LLM applications, RAG systems, BI dashboards, and the Hamilton UI allows you to automatically visualize, catalog, and monitor execution. -
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10
Kubeflow
Machine Learning Toolkit for Kubernetes
Kubeflow is an open source Cloud Native machine learning platform based on Google’s internal machine learning pipelines. It seeks to make deployments of machine learning workflows on Kubernetes simple, portable and scalable. With Kubeflow you can deploy best-of-breed open-source systems for ML to diverse infrastructures. You can also take advantage of a number of great features, such as services for managing Jupyter notebooks and support for a TensorFlow Serving container. Wherever you... -
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Unity MCP
AI-powered bridge connecting LLMs and advanced AI agents
...By exposing Unity editor functionality through MCP tools, the plugin allows external AI systems to understand the structure of a game project and manipulate it programmatically. Developers can use natural language prompts to instruct AI assistants to create objects, modify scenes, or generate gameplay scripts automatically. The system supports both editor-level automation and runtime integration, meaning AI models can also be used inside compiled games for dynamic behavior such as interactive characters or debugging tools. -
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OpenAI API client for Kotlin
OpenAI API client for Kotlin with multiplatform capabilities
OpenAI API client for Kotlin with multiplatform and coroutines capabilities. -
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PyGPT
Open source personal AI Assistant for Linux, Windows and Mac
...Moreover, the application has implemented context memory support, context storage, history of contexts, which can be restored at any time and e.g. continue the conversation from point in history, and also has a convenient and intuitive system of presets that allows you to quickly and pleasantly create and manage your prompts. Plugins support is also available. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
15
terminalGPT
Get GPT like ChatGPT on your terminal
Get GPT like ChatGPT on your terminal Note: This doesn't use OpenAI ChatGPT, it uses text-davinci-003 model (by default) You'll need to have your own OpenAi apikey to operate this package. 1. Go to https://beta.openai.com 2. Select you profile menu and go to View API Keys 3. Select + Create new secret key 4. Copy generated key Get started: Using tgpt: npm -g install terminalgpt or yarn global add terminalgpt Run tgpt chat ps.: If it is your first time running it, it will ask for open AI key , paste generated key from pre-requisite steps -
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LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
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Xianyu Intelligent Monitor Bot
AI tool for real-time monitoring and analysis of Goofish listings
...It uses Playwright to simulate real user interactions with the marketplace, allowing the system to retrieve product data and track updates in near real time. ai-goofish-monitor can run multiple monitoring tasks simultaneously, each configured with specific keywords, price ranges, and filtering conditions. A built-in web management interface allows users to create tasks, review results, and manage monitoring rules without relying solely on command line tools. AI models analyze product descriptions, images, and seller information to determine whether a listing meets defined requirements and should be recommended to the user. -
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AgentScope
Build and run agents you can see, understand and trust
...It provides essential abstractions that evolve with advancing LLM capabilities, emphasizing reasoning, tool use, and flexible orchestration rather than rigid prompt constraints. With built-in support for ReAct agents, memory, planning, human-in-the-loop control, and real-time voice interaction, developers can create powerful agents in minutes. AgentScope integrates seamlessly with tools, long-term memory systems, MCP, A2A (Agent-to-Agent) protocols, and observability frameworks. It also supports reinforcement learning workflows for tuning agents and improving performance across complex tasks. Deployable locally, serverless in the cloud, or on Kubernetes with OpenTelemetry support, AgentScope is built for both experimentation and production environments. -
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Bolna
Conversational voice AI agents
Bolna is an end-to-end open-source platform for building conversational voice AI agents, enabling developers to create voice-first conversational assistants efficiently. -
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Atomic Agents
Building AI agents, atomically
The Atomic Agents framework is designed around the concept of atomicity to be an extremely lightweight and modular framework for building Agentic AI pipelines and applications without sacrificing developer experience and maintainability. The framework provides a set of tools and agents that can be combined to create powerful applications. It is built on top of Instructor and leverages the power of Pydantic for data and schema validation and serialization. All logic and control flows are written in Python, enabling developers to apply familiar best practices and workflows from traditional software development without compromising flexibility or clarity. -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid... -
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Rivet
Visual AI IDE for building agents with prompt chains and graphs
...Rivet also includes a TypeScript library that enables these visual graphs to be executed and integrated directly into applications, bridging the gap between prototyping and production use. Rivet supports multiple large language model providers and integrates with services such as embeddings and transcription systems, allowing developers to create richer AI-powered features. Its architecture emphasizes composability, where different components like prompts, APIs, and data processing steps can be combined into reusable pipelines. -
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Talk to Figma MCP
AI bridge enabling Cursor agents to read and modify Figma designs
cursor-talk-to-figma-mcp is an open source integration that connects AI coding agents with Figma through the Model Context Protocol (MCP). It allows AI tools such as Cursor or other compatible agents to directly communicate with Figma documents and interact with design elements programmatically. Through this integration, an AI assistant can read the structure of a design, retrieve information about nodes or selections, and perform modifications to the layout or content.... -
24
uzu
A high-performance inference engine for AI models
...By utilizing Apple’s unified memory architecture, uzu reduces memory copying overhead and improves inference throughput for local AI workloads. The system includes a simple high-level API that enables developers to run models, create inference sessions, and generate outputs with minimal configuration. -
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CowAgent
AI assistant based on large models that can actively think and plan
CowAgent, based on the chatgpt-on-wechat project, is an open-source AI agent framework that integrates large language models into the WeChat ecosystem to create intelligent conversational assistants. It enables automated message handling by connecting WeChat accounts with AI models that can generate contextual replies, process voice messages, and produce images directly inside chats. The platform has evolved beyond a simple chatbot into a more autonomous agent capable of planning complex tasks, maintaining long-term memory, and invoking external tools to complete workflows. ...
