different-ai/openwork
Openwork
Openwork is an LLM agent orchestration platform and cross-platform desktop application designed for building and running automated workflows. It serves as a local AI agent host and session manager, allowing users to connect local project folders to various large language models and remote cloud workers.
The project distinguishes itself through a local-first execution model that enables agents to process files directly on a host machine. It implements human-in-the-loop permissioning to intercept agent resource requests, requiring explicit user approval before accessing specific local system files or directories. Additionally, it uses a plugin-based skills interface to extend agent capabilities and supports template-based workflow persistence for saving and sharing repeatable prompt sequences.
The platform includes capabilities for agentic task monitoring through execution plan visualization and action auditing. It provides a hybrid processing model that links a local interface to remote cloud workers and utilizes server-sent events for real-time progress updates and permission requests.
The application supports multi-language interface localization for a global user base.
Features
- Autonomous Agent Orchestration - Provides a platform for running autonomous AI agents that process local files and execute complex workflows.
- Local Desktop Agents - Functions as a local AI agent host that executes automated workflows directly on the user's hardware.
- Agent Orchestration Platforms - Provides a framework for managing, monitoring, and coordinating the execution of autonomous agents and their workflows.
- Agent Session Management - Offers a workspace for organizing agent interaction history, monitoring execution plans, and managing session state.
- LLM Model Integrations - Provides a unified configuration system to integrate multiple large language models using various provider keys.
- Human-in-the-loop Controls - Requires explicit human approval before allowing AI agents to access specific local system files or directories.
- Local AI Execution Environments - Provides a local execution environment for running AI agents with integrated user-controlled resource permissioning.
- Prompt Templates - Provides a system for saving and managing reusable prompt structures to standardize repetitive agent tasks.
- Local File Processing Tools - Implements direct reading and processing of local system files to enable agents to work on host-machine data.
- Workflow Automation Templates - Standardizes repetitive AI tasks across teams by managing reusable prompt-sequence templates.
- Prompt Sequence Templates - Saves recurring prompt sequences as local configuration files to enable repeatable and shareable automated tasks.
- Resource-Level Access Controls - Provides a human-in-the-loop mechanism to review and approve agent requests for specific local system resource access.
- Resource Access Control Layers - Implements software layers that intercept and validate agent requests before allowing access to protected local system resources.
- Local-First Architectures - Prioritizes a local-first execution model where agent workflows run directly on the host machine's file system.
- Agent Interaction Monitors - Provides real-time monitoring of AI agents through execution timelines and detailed action audits.
- Agent Skill Extensions - Allows extending agent capabilities through a plugin-based skills interface that loads external functional modules.
- Action Auditing - Tracks the history of agent operations to provide visibility into the timing and reasoning of actions.
- Model Provider Adapters - Standardizes interactions across different LLM providers using a common interface for requests and responses.
- Hybrid AI Orchestrators - Implements a hybrid processing model that connects local interfaces to remote cloud workers for scalable AI task execution.
- Prompt Automation Tools - Includes tools for defining, saving, and executing reusable prompt templates to automate repetitive AI tasks.
- Hybrid Execution Bridges - Implements a hybrid model connecting a local desktop application to either local host stacks or remote cloud workers.
- Remote Worker Execution - Links the local desktop interface to remote cloud workers to scale processing power for complex tasks.
- Server-Sent Events - Uses server-sent events to push real-time progress updates and resource permission requests from the backend to the UI.
- Execution Timeline Visualizers - Visualizes the sequence of agent tasks as a timeline to monitor the progress of automated workflows.
- Cross-Platform Desktop Applications - Ships as a cross-platform desktop application that integrates local project folders with LLM providers.
- AI Agents and Automation - Open-source desktop alternative to Claude Cowork for running agents, skills, and MCP locally with team collaboration features.
- Computer Use - Open-source alternative for computer-use automation.
