Search Results for "execute" - Page 2
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Showing 37 open source projects for "execute"
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Nanocoder
A beautiful local-first coding agent running in your terminal
...The tool is designed as a privacy-focused alternative to proprietary AI coding assistants, allowing users to run local models or connect to external APIs while keeping full control over their data and development workflow. Built with TypeScript and distributed as a CLI application, nanocoder enables developers to interact with AI agents that can read files, modify code, execute commands, and assist with debugging tasks. The platform supports multiple AI providers through OpenAI-compatible APIs and can also integrate with local model runtimes such as Ollama or LM Studio. Its architecture emphasizes extensibility through custom commands and integration with Model Context Protocol servers that allow the AI agent to access additional tools. -
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Deepnote
Deepnote is a drop-in replacement for Jupyter
...Built on top of the Jupyter kernel ecosystem, it maintains compatibility with existing notebook workflows while introducing additional features focused on collaboration and automation. The system supports programming languages such as Python, R, and SQL and allows users to execute and analyze data directly within interactive notebooks. Deepnote emphasizes team-based data science by enabling real-time collaboration similar to shared document editors, allowing multiple users to work simultaneously on the same notebook environment. -
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BrowserNode
Make websites accessible for AI agents. Automate tasks online
...Built as an implementation compatible with the Browser-use ecosystem, Browsernode allows agents to perform actions such as navigating pages, extracting information, filling forms, or interacting with dynamic web interfaces. The system integrates with Playwright to control Chromium-based browsers and execute automation scripts in a reliable environment. Developers can configure the framework to connect to different language model providers so that AI agents can interpret instructions and decide which browser actions to perform. -
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Personal AI Infrastructure
Agentic AI Infrastructure for magnifying HUMAN capabilities
...Unlike once-stateless chatbots, this platform captures context, memory, goals, preferences, and feedback to enable an AI that understands you and improves over time, using a full agentic stack rather than simple question-answer loops. PAI blends tools like browsing, code editing, execution, and more into a continuous Observe → Think → Plan → Execute → Verify → Learn cycle, letting the system refine its behavior with each use. Its architecture supports long-term memory, verification of actions, and ongoing self-improvement, blurring the line between “assistant” and persistent, evolving collaborator. -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Preswald
Python tool for browser-based interactive data apps in one file
...It packages application logic, data processing, and user interface components into a single self-contained output, enabling easy sharing and deployment without requiring local dependencies. Preswald leverages a WebAssembly runtime along with technologies like Pyodide and DuckDB to execute Python code directly in the browser environment. This approach allows developers to create dashboards, reports, notebooks, and data tools that are portable, fast, and capable of running offline. Preswald emphasizes a code-first workflow where users define applications entirely in Python while using built-in UI components such as tables, charts, and forms. ... -
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Eko
Build Production-ready Agentic Workflow with Natural Language
...It provides a unified interface for managing agents, offering features such as web resource access and high task complexity handling. Eko is open-source and can be used to execute tasks like browser automation, system operations, and software testing. -
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Ralph AI Agent
AI agent loop that runs repeatedly until all PRD items are complete
Ralph is a Rust-based AI agent runtime that focuses on safe, modular, and programmable autonomous behavior. It provides a reactive loop where agents can repeatedly assess the current context, reason about the next best action using large language models, and execute actions across integrated tools and services. The runtime emphasizes safety boundaries by sandboxing operations, enforcing time and token limits, and isolating execution layers to prevent unpredictable side effects. Ralph also includes a built-in plugin system that lets developers attach custom tools, environment connectors, or monitoring hooks without modifying core logic. ... -
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LaVague
Framework for building AI agents that automate complex web tasks
...It implements the concept of a Large Action Model framework, allowing agents to interpret a user-provided objective and translate it into a sequence of actions performed in a browser. These agents can navigate web pages, retrieve information, fill out forms, and execute multi-step workflows automatically. LaVague is centered around a World Model that analyzes the current webpage state and determines the next set of instructions, combined with an Action Engine that converts those instructions into executable automation code. It can use browser automation tools such as Selenium or Playwright to interact with websites programmatically. ... -
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Adeus
An open source AI wearable device that captures what you say
Adeus is an experimental open-source framework focused on building autonomous AI agents capable of reasoning, planning, and executing complex tasks with minimal human intervention. The project explores the concept of agent-based AI systems that can break down objectives into smaller steps, execute them sequentially, and adapt based on intermediate results. It provides a structured environment for orchestrating workflows that involve multiple actions, such as retrieving data, generating content, or interacting with external tools. The framework is designed to be modular, allowing developers to customize agent behavior, integrate APIs, and extend capabilities depending on the use case. ... -
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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10
Maestro
Agent Orchestration Command Center
...Designed for power users who live on the keyboard and rarely touch the mouse. Collaborate with AI to create detailed specification documents, then let Auto Run execute them automatically, each task in a fresh session with clean context. Allowing for long-running unattended sessions, my current record is nearly 24 hours of continuous runtime. Run multiple agents in parallel with a Linear/Superhuman-level responsive interface. Currently supporting Claude Code, OpenAI Codex, and OpenCode with plans for additional agentic coding tools (Aider, Gemini CLI, Qwen3 Coder) based on user demand. -
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AutoGPT.js
Auto-GPT on the browser
...Unlike traditional AutoGPT implementations that require server infrastructure, AutoGPT.js is designed to run primarily in the browser, making it easier to deploy and experiment with autonomous agents. The platform uses web APIs and language model integrations to give the agent the ability to plan tasks, execute commands, and store short-term memory during operations. Developers can also configure the system to connect to different language model APIs and adjust parameters such as temperature or prompt configuration. The project demonstrates how autonomous AI agents can operate within modern web environments while maintaining user privacy and accessibility. -
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GPT-Code UI
An open source implementation of OpenAI's ChatGPT Code interpreter
An open source implementation of OpenAI's ChatGPT Code interpreter. Simply ask the OpenAI model to do something and it will generate & execute the code for you. You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services.
