Autonomous Intelligence is an open source research project aimed at building collaborative multi-agent AI systems. The agentic framework provides a robust infrastructure for creating and managing multiple AI agents, and enables developers and organizations to build, deploy, and optimize AI agents that work well in dynamic, complex environments.
The framework provides a robust infrastructure for creating and managing multiple AI agents. It enables seamless collaboration between agents to tackle complex tasks, dynamically adapting to user inputs and changing conditions.
The registry offers a catalog of prebuilt agents optimized for specific domains, such as coding, data analysis, and natural language processing. This allows users to quickly integrate tailored solutions into their workflows without extensive setup.
Task execution is automated through dynamic selection of the best agents, tools, and workflows for the job. The system handles dependencies, monitors progress, and troubleshoots issues in real time.
Users can design and visualize workflows using an intuitive drag-and-drop interface or directly interact with agents through conversational natural language queries.
Developers can leverage tools for integrating AI agent functionality into their applications. The platform supports customization and modular development for scalability and efficiency.
Autonomous Intelligence's architecture is built around a decentralized network of specialized agents that work both independently and collaboratively. These agents communicate through defined pathways, managed by an orchestrator that ensures tasks are distributed and executed efficiently.
| Component | Description |
|---|---|
| Orchestrator | Central hub for task assignment, execution, and monitoring. Manages agent interactions and refines workflows dynamically. |
| Agent | An autonomous unit programmed to perform tasks, make decisions, and communicate with other agents. |
| Task | A specific assignment completed by an agent, providing all necessary details like description, tools, and responsibilities. |
| Crew | A collaborative group of agents working together to achieve a set of tasks. Crews define strategies for task execution and agent collaboration. |
| Process Implementations | Frameworks for agent collaboration. This includes sequential tasks that are executed in an orderly progression, or hierarchical tasks are managed via a structured chain of command |
| Large Language Models (LLMs) | Backbone of intelligent agents, enabling capabilities like natural language understanding and reasoning. Includes models like GPT, Claude, Mistral, Gemini, and Llama that are Optimized for complex workflows. |
| Tool | A skill or function agents use to perform actions, that includes capabilities like search, computer use, data extraction, file uploading and advanced interactions. |
| Agent Registry | A structured catalog organizing agents by domain, task type, and functionality, allowing users to deploy agents seamlessly. |
| Frontend Interface | Drag-and-drop visual workflow builder and real-time dashboards for monitoring, debugging, and optimizing agent performance. |
- Input Query: The user provides a task, e.g., “Reach out to a list of 10,000 New York-based heads of AI who work in mid-sized finance companies.”
- Data Collection: The orchestrator leverages an AI-powered data foundation and the web to source the most reliable leads. The agent processes the input criteria to generate a list, such as Job Title: Data Scientist, Industry: Technology, Company Size: >1,000, Location: United States
- Agent Workflow: The AI workflow processes the input by applying specific rules and guidelines to filter the data. Agents collaborate to refine the lead list and create tailored email drafts for each contact.
- Email and List Generation: The system outputs a curated list of leads, including contact information, along with tailored email content ready for automated delivery.
- Automation: Emails are automatically sent to the generated list of leads. The system tracks progress, showing the number of emails sent and responses received daily.
- Feedback Loop: User feedback is incorporated to improve the lead generation process, refine email drafts, or adjust selection criteria for future tasks.
| Feature | Description |
|---|---|
| Dynamic Workflow Creation | Build complex workflows with drag-and-drop tools. |
| Agent Collaboration | Enable sequential, parallel, or hierarchical execution of tasks. |
| Customizable Framework | Integrate third-party apps and tools, such as Slack, AWS, or Google Workspace. |
| Real-Time Debugging | Visualize and troubleshoot workflows using interactive flowcharts. |
| Model-Agnostic Design | Supports various models, including OpenAI, Claude, Llama, and Mistral. |
| Private Deployments | Operate securely in local environments with privacy-preserving configurations. |
Within the Agent Registry, we will be adding many domain specific agents. Here are a few example use cases, with corresponding demos:
| Use Case | Description | Link |
|---|---|---|
| AI Assisted Coding | Automate feature implementations and pull requests | Watch Video |
| AI Assisted RFPs | Draft, refine, and submit grant proposals efficiently | Watch Video |
| AI Assisted Outreach | Automate email campaigns, sequences, and follow-ups | Learn More |
| Job Applications | Automate resume customization and job application submissions | Learn More |
We provide the setup instructions in CODEBASE_SETUP.md
- Run on the Web: Access the product UI for direct interaction.
- Run On-Premise: Deploy locally for privacy-sensitive workflows.
- Call via API: Integrate agent capabilities into your own applications using the provided SDK.
- Slack: Join the community
- GitHub: Explore the code
- Documentation: See the docs