Introduction to Open Devon

The introduction highlights the Open Devon project, an open-source initiative aiming to create an autonomous AI software engineer.

Open Devon Project Details

• Open Devon aims to build an open-source version of a fully autonomous coding assistant, catering to the growing interest in agents capable of autonomously coding and managing software projects.

• The project is MIT licensed and community-driven, inspired by the potential demonstrated in the initial demo of De Devon. It serves as a platform for building and running software development agents interfacing with AI and LLMS.

• Open Devon focuses on providing tools for both agent builders and users, emphasizing transparency, safety, and community collaboration. The project involves volunteers from various backgrounds working towards enhancing the end-user experience in software development.

Community Collaboration and Vision

• The project embraces a diverse community comprising academic professionals, developers, and end-users united in improving software development experiences through autonomy. The goal is to make software development more creative and engaging by delegating repetitive tasks to autonomous agents.

• Emphasis is placed on open-source principles to drive innovation collaboratively within the community. The aim is to streamline software development processes by enabling developers to focus on creativity while leveraging autonomous tools for efficiency.

Progress Update & Achievements

This section provides insights into the progress made by the Open Devon project within its early stages.

Project Statistics & Achievements

• Despite being two months old, Open Devon has garnered significant traction with 116 unique contributors, over 700 merged PRS, and 24,000 stars on GitHub. This rapid growth showcases strong community engagement and commitment towards advancing open-source initiatives.

• Beyond numerical achievements, the quality of applications developed within the project has been remarkable. Academic contributions have enhanced agent capabilities significantly, surpassing expectations for an open-source endeavor at this stage.

Rationale Behind Open Devon Development

Exploring the motivation behind developing Open Devon amidst existing AI-driven coding tools like Chat GPT and GitHub Co-Pilot.

Differentiation & Inspiration

• While existing tools like Chat GPT can generate code snippets efficiently, Open Devon aims for deeper integration into developer workflows. Inspired by tools like GitHub Co-Pilot that enhance productivity through LLMS-driven code completion features seamlessly integrated into daily programming tasks.

New Section

In this section, the speaker discusses the limitations of tools like Chat GPT and Co-Pilot and highlights the advantages of using agents in the development process.

Advantages of Agents Over Existing Tools

• Chat GPT excels at writing Green Field code but struggles with large existing codebases.

• Agents can integrate seamlessly into developer workflows, unlike tools like Co-Pilot, reducing copy-paste tasks.

• Agents excel in the debug-fix loop by editing code, running tests, and identifying issues for self-correction.

• Agents can handle large unbounded tasks efficiently by breaking them down into manageable steps.

• Agents act as a hub between users, external data sources, language models, and runtime environments for effective task completion.

New Section

This part delves into how agents function as intermediaries between users, data sources, language models (LLM), and runtime environments to accomplish tasks effectively.

Functioning of Agents

• Users assign tasks to agents who can access external data sources like codebases or documentation.

• Language models serve as the core driving force behind agent operations by providing atomic instructions.

• The agent's loop involves generating prompts based on user input, taking actions in runtime environments, observing outcomes, and updating states iteratively.

• Context window management is crucial for agents as they leverage current inputs and learned knowledge to progress towards goals intelligently.

Designing Agents for Software Engineering Tasks

In this section, the speaker discusses the process of designing agents for software engineering tasks and introduces the concept of microagents to streamline task execution.

Designing Agents

• The focus in designing an agent is on determining what information to input into the context window at each turn of the loop to drive progress.

• Microagents are introduced as a framework to abstract away complexities in designing agents, enabling efficient handling of small tasks.

• A specific microagent within the OpenDevon system is highlighted for generating quality commit messages based on Git staging area content.

Agent Instructions and Actions

• Agents receive detailed instructions including their role, objectives, and how to interpret historical data before executing actions.

• Agents have three main actions available: running commands, rejecting tasks if conditions are not met, or completing tasks by providing messages in a specified JSON format.

Task Execution Loop

• The designed prompt runs in a loop against the language model (LLM) to guide agents towards creating commit messages effectively.

Measuring Agent Quality in Software Engineering

This segment delves into assessing agent quality in software engineering through benchmarks like sbench and evaluating agent performance based on real-world issues from GitHub repositories.

Benchmarking Agent Quality

• Academic efforts focus on measuring agent quality by evaluating their ability to solve software engineering-related tasks using benchmarks like sbench from Princeton University.

• Sbench assesses an agent's capability by testing its effectiveness in resolving real-world issues found on GitHub repositories through unit tests and code changes.

Evaluation Process

• Evaluating agents involves cloning repositories pre-issue resolution, adding unit tests from pull requests (PR), prompting agents with issue resolutions, applying changes made by agents, and verifying test outcomes for success or failure.

• Sbench light offers a cost-effective evaluation subset of issues that are more manageable for agents but still challenging enough to gauge performance accurately.

Agent Performance Insights

• State-of-the-art agents currently achieve around a 15% to 20% success rate on Sbench light evaluations, showcasing room for improvement while demonstrating significant progress in automating issue resolutions.

• Recent advancements have seen notable improvements with a score increase from academia's best-performing agent, indicating rapid development within this technology domain despite ongoing challenges and opportunities for growth.

New Section

In this section, the speaker discusses the user interface of Devon and how users can interact with agents through various functionalities.

User Interface and Agent Interaction

• The Devon user interface resembles the one shown in the demo, featuring a main chat window for interacting with the agent, providing feedback, and requesting further instructions.

• Users can engage with the agent by checking directions, asking questions about their progress, accessing a terminal to run commands, utilizing a code editor to view and modify files in its workspace, browsing the internet via a web browser, and working within a Jupiter notebook for data analysis tasks.

New Section

This part focuses on personalized preferences for agent interaction within development environments like Vim or VS Code plugins.

Personalized Agent Interaction

• Users express interest in agents seamlessly integrating into their development workflow without switching between different environments. They seek an interactive experience similar to tools like co-pilot that assist in editing code, running tests in the background, and suggesting improvements.

• The goal is to create an open platform where agents can operate not only through web interfaces but also via VS Code plugins, Vim plugins, GitHub interactions (such as fixing issues or addressing comments), command-line usage, or integration within CI/CD environments. Agents should function as remote teammates collaborating effectively with software engineers.

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This segment involves a demonstration showcasing tasks performed by an agent within different scenarios.

Demonstration of Agent Tasks

• A demo begins with simple tasks like creating a bash script that prints "hello," demonstrating iterative development where users can request edits to enhance functionality incrementally.

• The focus shifts towards attaching Open Devon to existing code bases for more complex tasks such as modifying code files directly within projects like Open Devon's own code base.

New Section

Here, the speaker showcases how agents navigate through codebases and adapt to unfamiliar environments.

Navigating Code Bases

• The demonstration involves instructing Open Devon to work within specific folders of a code base by pointing it towards designated directories at startup. This allows agents to analyze file contents systematically while adapting dynamically based on encountered challenges like mismatched arguments in code files.

Detailed Discussion on Command Line Arguments

The discussion revolves around changes in command line arguments and the need to scroll for diagnosis, emphasizing the importance of fixing issues promptly.

Command Line Argument Changes

• The transition from "DX" to "-n" for command line arguments prompts a need to scroll for diagnosis.

• There is a suggestion to interrupt processes like package installations by running "poetry install" instead of installing packages individually.

Ensuring Safety with Sandbox Environment

The conversation highlights the significance of safeguarding against risky commands like "rm -RF" through a secure Sandbox environment.

Preventing Risky Commands

• Emphasizes the use of guard rails within a Sandbox environment to prevent dangerous commands like "rm -RF."

• Discusses how running operations within a Sandbox ensures proper file permissions and user IDs alignment for safe execution.

Agent Architecture and Micro Agents Overview

Exploring the architecture of agents and micro agents, focusing on their functionalities and contributions to task delegation.

Agent Architecture Explanation

• Provides an overview of agent architecture, highlighting micro agents' role in simplifying tasks.

• Describes the agent abstraction layer's function, passing states and actions while executing specific tasks based on predefined actions.

Micro Agents Functionality

• Details the action space available for micro agents, including running commands, reading/writing files, browsing the web, and sending messages.

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In this section, the speaker discusses the structure of code agents and their delegation process for various tasks.

Code Structure and Delegation Process

• The code structure allows passing tasks to different agents for implementation, including those fixing typos in plain text documents like readmes.

• Agents verify changes made in the codebase by running commands to ensure their correctness, regardless of task size.

• Agents can delegate subtasks to other specialized agents within their workflow, such as math agents for complex calculations or Postgres agents for database-related tasks.

New Section

This part delves into how micro-agents are invoked from a higher-level agent reasoning loop and the role of a delegator agent in managing task delegation.

Invocation of Micro-Agents

• Micro-agents are invoked through a delegator agent that manages task delegation based on the last action performed, such as coding, verifying, or studying the repository.

• The delegator agent delegates tasks only to other agents and orchestrates them in a loop to progress towards completing the end goal efficiently.

New Section

Here, the discussion centers around how micro-agents can be delegated by the delegator agent and managed by a manager agent within an open development system.

Delegating Micro-Agents

• The delegator agent can potentially delegate tasks to any agent within the system but currently is hardcoded with three specific agents for demonstration purposes.

Detailed Discussion on OpenDevon Challenges

In this section, the speaker discusses common issues and challenges faced in OpenDevon related to performance and error modes.

Common Issues in OpenDevon

• The evaluation results are still being analyzed to identify areas for improvement, aiming for a 5-10% performance boost.

• Challenges arise with edits requiring deep codebase knowledge, such as adding new features involving database migrations and frontend changes.

• Complex tasks like adding new features encompassing backend, frontend, and database work pose significant challenges for current agents.

Minimum Compute Requirements for Running OpenDevon Locally

This part delves into the minimum computational resources needed to run OpenDevon locally.

Minimum Compute Recommendations

• OpenDevon itself requires minimal compute as it primarily executes commands in a Docker file; heavy computation occurs in the LM and API.

• GP4 and Claude are recommended for optimal performance; smaller local LLMs may struggle with loops or inadequate changes due to underlying LLM power.

Context Window Management in OpenDevon

The discussion focuses on managing context windows effectively within the agent architecture of OpenDevon.

Context Window Management Insights

• Managing interaction history is crucial for guiding the agent's next steps efficiently; summarizing historical interactions can optimize context window usage.

New Section

In this section, the discussion revolves around the capabilities and future plans of a coding agent tool, focusing on expanding its functionalities to solve a broader range of problems.

Exploring Tool Capabilities

• The tool serves as a powerful baseline for various tasks.

• Initial tools include a code editor, terminal, and web search functionality.

• Plans are underway to enhance capabilities to address more general problem sets beyond basic coding tasks.

New Section

This part delves into ongoing efforts at CMU to improve browsing functionality and expand the tool's capacity to browse the web and access API documentation.

Enhancing Browsing Functionality

• CMU researchers are actively working on improving browsing capabilities.

• Separate browser agents are being developed alongside coding agents.

• Integration of browser gym is planned to enhance browsing features within the tool.

New Section

The conversation shifts towards future steps in advancing the coding agent tool to tackle a wider range of problems beyond basic coding challenges.

Advancing Coding Agent Capabilities

• Next steps involve integrating browser functionality into the coding agent tool.

• Aim is to progress from solving basic problems to addressing more complex issues.

• Evaluation against established benchmarks like Sweet Bench is planned for performance comparison with other academic agents.

New Section

Future goals include enabling automated agents within Open Devon platform to handle diverse software engineering challenges efficiently over time.

Long-Term Vision for Automated Agents

• Objective is for automated agents in Open Devon platform to resolve any GitHub issue or software engineering challenge effectively.

• Anticipated timeline involves years of development effort with quick progress initially followed by tackling more challenging issues over an extended period.

New Section

Discussion centers on creating a platform where AI engineers can leverage various components seamlessly for building autonomous software engineers efficiently.

Platform Vision for AI Engineers

• Open Devon aims to provide a platform where developers can integrate different agents effortlessly.

• Developers can import specific agents or clone entire setups for modification based on project requirements.

Illinois and Koda Agent Development

In this section, the discussion revolves around the development of the Koda agent by Illinois and plans to enhance its core abilities further.

Illinois's Role in Koda Agent Development

• Illinois is leading the development of the Koda agent.

• Published a paper on the Koda agent.

• Focused on advancing the core abilities of the Koda agent.

Advancements in Language Models

The conversation shifts towards advancements in language models like GPT-5 and their impact on technological progress.

Impact of GPT-5 and Technological Advancements

• Anticipation for GPT-5 improving technology significantly.

• Ability to seamlessly integrate new language models for enhanced performance.

Scalable Architecture Development

Focuses on building a scalable architecture to interact with agents beyond web interfaces.

Building Scalable Architecture

• Concentration on creating a more scalable and generic architecture.

• Aim to interact with agents through various platforms like GitHub, command line, and code editors.

Contributing to Open Deon

Discusses contributions to Open Deon, emphasizing decentralized collaboration and competition among contributors.

Decentralized Contribution Process

• Decentralized nature of contribution process.

• Introduction of evaluation pipeline for contributions.

Becoming a Contributor

Outlines how individuals can become contributors to Open Deon by engaging in friendly competition and contributing innovative ideas.

Pathway to Contribution

• Encouragement for friendly competition among contributors.