Search Results for "execute" - Page 5
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Showing 157 open source projects for "execute"
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured outputs. The project is positioned primarily as a proof of concept for deep research agents rather than a production-ready system. ... -
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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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Promptr
Promptr is a CLI tool that lets you use plain English to instruct GPT3
Promptr is a CLI tool that lets you use plain English to instruct OpenAI LLM models to make changes to your codebase. Changes are applied directly to the files that you reference from your prompt. -
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Agent S
Agent S: an open agentic framework that uses computers like a human
Agent S is an open-source agentic framework designed to enable autonomous computer use through an Agent-Computer Interface (ACI). Built to operate graphical user interfaces like a human, it allows AI agents to perceive screens, reason about tasks, and execute actions across macOS, Windows, and Linux systems. The latest version, Agent S3, surpasses human-level performance on the OSWorld benchmark, demonstrating state-of-the-art results in complex multi-step computer tasks. Agent S combines powerful foundation models (such as GPT-5) with grounding models like UI-TARS to translate visual inputs into precise executable actions. ... -
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Coinbase Agentic Wallet Skills
npx skills add coinbase/agentic-wallet-skills
...It provides a set of pre-built “skills” that abstract complex blockchain interactions into simple, callable capabilities, allowing agents to authenticate, manage funds, and execute transactions without requiring developers to implement low-level logic. These skills are designed to integrate seamlessly with the awal CLI and agent frameworks, enabling rapid deployment of wallet-enabled AI systems with minimal setup. The architecture is centered on composability, where each skill represents a discrete capability such as sending stablecoins, trading tokens, or interacting with paid APIs. ... -
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InternGPT
Open source demo platform where you can easily showcase your AI models
...The framework connects multiple specialized AI models that perform tasks such as object detection, segmentation, captioning, and visual editing while coordinating them through a central conversational interface. This architecture enables the system to plan actions, execute visual operations, and return results in a coherent dialogue with the user. -
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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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Amazon Q Developer CLI
Chat experience in your terminal
Amazon Q Developer CLI brings an agentic, chat-driven coding assistant to your terminal so you can ask for help, generate code, and perform routine dev tasks with natural language. It blends knowledge of your local workspace with command-line context to suggest commands, explain flags, and scaffold files or workflows. The tool aims to shorten the gap between intent and action by letting you request operations like creating a test, refactoring a function, or drafting a Dockerfile without... -
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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... -
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BotKube
An app that helps you monitor your Kubernetes cluster
...You can customize the objects and level of events you want to get from the Kubernetes cluster. You can turn on/off notifications simply by sending a message to @BotKube. BotKube can execute kubectl commands on Kubernetes cluster without giving access to Kubeconfig or underlying infrastructure. With BotKube you can debug your deployment, services or anything about your cluster right from your messaging window. -
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deep-q-learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
...It implements the core logic needed to train an agent using Q-learning with neural networks (i.e. approximating Q-values via deep nets), setting up environment interaction loops, experience replay, network updates, and policy behavior. For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. Because it’s self-contained and Python-based, it's well-suited for experimentation, modifications, or extension — for instance adapting to custom Gym environments, tweaking network architecture, or combining with other RL techniques. -
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code-act
Official Repo for ICML 2024 paper
...The system proposes a unified action representation where language models produce Python code that can be executed directly, allowing the model to interact with external tools and environments in a structured way. By integrating a Python interpreter with the agent architecture, the system enables the agent to execute code, observe the results, and iteratively refine its actions through multiple reasoning steps. This approach helps unify reasoning and action planning within large language model agents by using code as the primary interface between the model and the external world. The framework also includes training data, models, and evaluation tools designed to study how language models can become more capable autonomous agents. -
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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. ... -
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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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AgentPilot
A versatile workflow automation platform to create AI workflows
AgentPilot is a versatile workflow automation platform designed to help users create, organize, and execute AI-driven workflows. It supports everything from simple tasks using a single large language model (LLM) to complex multi-step processes. The platform features a user-friendly interface that allows for real-time interaction with workflows, and it supports flexible configurations, including branching workflows and customizable user interfaces. -
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superplan-plugin
Superplan is a CLI-first execution system.
...It turns planning into actual execution inside your repository. Instead of vague plans, chat history, or TODO lists, Superplan forces work into clear, step-by-step tasks that agents can execute, track, and resume at any time. The CLI is designed for agents to follow, not for humans to run manually. You define the work; your agent executes it through Superplan's structured runtime. -
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PqFun for CLIPS
DB-Library functions which provides a PostgreSQL interface for CLIPS
We have developed some DB-Library functions which provides a PostgreSQL interface via libpq for CLIPS Rule Based Programming Language. See https://sourceforge.net/p/pqfun-for-clips/code/HEAD/tree/63x-PqFun/Readme.md for more details. The latest snapshot is downloadable from https://sourceforge.net/p/pqfun-for-clips/code/HEAD/tree/63x-PqFun/. -
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MyFun for CLIPS
DB-Library functions which provides a MySQL interface for CLIPS
We have developed some DB-Library functions which provides a MySQL interface for CLIPS Rule Based Programming Language. See https://sourceforge.net/p/myfun-for-clips/code/HEAD/tree/63x-MyFun/Readme.md for more details. The latest snapshot is downloadable from https://sourceforge.net/p/myfun-for-clips/code/HEAD/tree/63x-MyFun/. -
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ToRA
Tool-integrated Reasoning LLM Agents
...Instead of relying solely on text generation, the system dynamically invokes tools such as symbolic solvers or programming libraries when deeper computation is required. This approach allows the model to reason step by step in natural language and then execute precise calculations or code through tool calls, creating a hybrid reasoning workflow. The framework was designed to address known weaknesses of large language models in mathematical problem solving and formal reasoning tasks. Training data includes tool-use trajectories that teach the model when to reason verbally and when to delegate tasks to specialized tools. -
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Ailice
AIlice is a fully autonomous, general-purpose AI agent
AIlice is an open-source autonomous AI agent framework built to function as a general-purpose assistant that can plan, decompose, and execute complex tasks through a structured multi-agent architecture. The project presents itself as a standalone assistant powered by open-source language models, with an internal design that treats user requests almost like executable programs rather than simple chat prompts. Its core IACT architecture allows the system to break large goals into smaller sub-tasks, assign them to dynamically created agents, and combine the results with a focus on resilience and fault tolerance. ... -
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Functionary
Chat language model that can use tools and interpret the results
...Function definitions are typically provided in JSON schema format, allowing the model to generate structured function calls compatible with modern tool-calling interfaces used in AI applications. Functionary can decide whether to execute tools sequentially or in parallel and can analyze the outputs of those tools to produce context-aware responses. This capability allows AI systems to interact with external services, APIs, or computation engines rather than relying solely on knowledge embedded in the model. -
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XAgent
An Autonomous LLM Agent for Complex Task Solving
XAgent is an AI-driven autonomous agent framework capable of handling multi-step tasks across different domains. It enables AI agents to perform decision-making, task planning, and self-learning based on user-defined objectives, making it ideal for automation and research applications. -
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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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gptee
LLMs done the UNIX-y way
...Now supporting GPT3.5 chat completions! gptee was designed for use within shell scripts and other programs and also works in interactive shells. You can compose commands and execute them in a script. Proceed with caution before running arbitrary shell scripts. Using a chat completion model (like gpt-3.5-turbo), you can then inject a system message with -s or --system messages. For davinci and other non-chat models, the output is prefixed to the prompt. Compose shell commands like you would in a script. Try with a custom model. ...
