Github Adapter

[jkbrooks]

Feature Name: GitHub Integration for Eliza Agents

Vision:

Transform GitHub into a dynamic social network for Eliza agents, allowing them to participate in code development, documentation, and community discussions. This integration empowers agents to contribute to Continuous Collective Recursive Self-Improvement (CCR-SI) by enabling them to analyze code, propose changes, engage in discussions, and learn from other developers (both human and AI).

Core Features:

1. Issue Interaction:

   • Issue Retrieval: Agents should be able to retrieve and analyze issues from designated repositories. This includes accessing issue titles, descriptions, comments, labels, and other metadata.
   • Issue Prioritization: Agents should be able to prioritize issues based on factors like labels (e.g., "good first issue," "bug," "enhancement"), the number of comments, or sentiment analysis of the discussion. This allows agents to focus on relevant and impactful issues.
   • Issue Summarization: Summarize long or complex issue threads to extract key information and understand the core problem being discussed. This helps agents quickly grasp the context and identify potential solutions.
   • Commenting on Issues: Agents should be able to add comments to issues, providing feedback, suggesting solutions, or asking clarifying questions.
   • Creating New Issues: Enable agents to create new issues if they identify bugs, propose enhancements, or encounter problems not yet documented.

2. Pull Request (PR) Management:

   • PR Retrieval and Analysis: Agents should be able to retrieve and analyze pull requests, including code changes, commit messages, and review comments.
   • Code Review (Basic): Implement basic code review capabilities, allowing agents to identify potential issues (e.g., syntax errors, style violations) or suggest improvements. This could leverage existing code analysis tools or LLMs fine-tuned for code review.
   • Submitting Pull Requests: Empower agents to submit pull requests with code changes, either autonomously or with human approval.
   • Commenting on PRs: Enable agents to add comments to pull requests, providing feedback on code changes or engaging in discussions about implementation details.

3. Wiki Integration:

   • Wiki Parsing: Agents should be able to parse and understand wiki pages within the repository, extracting information and integrating it into their knowledge base.
   • Wiki Updates: Potentially allow agents to suggest updates or improvements to wiki pages, contributing to documentation efforts.

4. Repository Metadata and Navigation:

   • Repository Exploration: Agents should be able to explore the repository's structure, navigate through directories, and access file contents.
   • Commit History Analysis: Allow agents to analyze commit messages and identify patterns, trends, or potential areas for improvement in the codebase.

Eliza Integration and Architecture:

1. GitHub Client Connector: Develop a new client connector specifically for GitHub, similar to the existing Discord, Telegram, and Twitter clients. This connector will handle authentication, API communication, and event handling specific to the GitHub platform. It could use the Octokit library or the GitHub REST/GraphQL APIs.

2. Actions for GitHub Interaction: Create custom actions for each of the core GitHub interactions (retrieving issues, submitting PRs, commenting, etc.). These actions will encapsulate the logic for interacting with the GitHub API.

3. Evaluators for Context and Prioritization: Develop evaluators that analyze GitHub data (issues, PRs, wiki pages) and prioritize actions or extract relevant information. These evaluators could use sentiment analysis, keyword extraction, or other NLP techniques to understand the context and relevance of GitHub content.

4. Memory Integration: Integrate GitHub data into the agent's memory system. This could involve storing issue summaries, relevant code snippets, or other GitHub information in the agent's memory, allowing for contextualized responses and actions.

Example (Conceptual):

// Example of a "Submit Pull Request" action
class SubmitPullRequestAction extends BaseAction {
  async handler(runtime, message, state) {
    // Retrieve code changes and commit message from the message or state
    const { code, commitMessage } = extractCodeAndMessage(message, state);

    // Use the GitHub API (via the GitHub client connector) to submit a PR
    const pr = await runtime.githubClient.createPullRequest(
      'repository', 'branch', 'baseBranch', title, body
    );
  }
}

// Example of a "Prioritize Issues" evaluator
class PrioritizeIssuesEvaluator extends BaseEvaluator {
  async handler(runtime, message, state) {
      const issues = await runtime.githubClient.getIssues('repository');
      const prioritizedIssues = prioritizeBasedOnLabels(issues, ['bug', 'high-priority']);

      // Store prioritized issues in agent memory or trigger actions for these issues.
      // ...
  }
}

Leveraging Other Concepts:

• Puzzle-Driven Development: GitHub issues can be used as a central hub for tracking coding puzzles. Agents can retrieve and claim issues, work on solutions, and submit pull requests directly through the GitHub client connector. This directly ties into your existing PDD methodology.
• Language 2.0 and Autopellucidity: Apply Language 2.0 principles to the agent's communication on GitHub. Ensure that comments, issue descriptions, and commit messages are clear, concise, and convey the agent's intent and reasoning. Strive for autopellucidity in these interactions.
• CCR-SI: The GitHub integration is a core component of CCR-SI, enabling agents to reflect on their own code, learn from other developers, and contribute to continuous improvement.

Prioritization and Feasibility:

The core features (issue interaction, PR management, wiki integration) should be prioritized. More advanced features like automated code review can be implemented later.

Conclusion:

By integrating GitHub into Eliza as a social network for code collaboration, we empower our AI agents to actively participate in CCR-SI and contribute to the project's evolution. This innovative approach has the potential to transform how we develop software and unlock new levels of collaboration between humans and AI. This document, as part of our living documentation, will evolve as we refine the GitHub integration and explore new possibilities for AI-driven development.

[jkbrooks]

Your idea of using Eliza to manage feature requests, generate bounties, and oversee the contribution process is a compelling application of AI-driven project management. Let's explore how this could be implemented within the Eliza framework.

Proposed System: Eliza as a Project Manager

1. Codebase Awareness:

• Representation: Eliza needs a representation of its own codebase. As discussed previously, options include raw code, abstract syntax trees (ASTs), or code embeddings. A hybrid approach combining ASTs with code embeddings for semantic search might be the most effective, although we can try starting with the simpler method of raw code or just ASTs and then iterating to embeddings. This would be a significant enhancement for Eliza.

• Storage: This representation would be stored in the agent's memory, likely using a dedicated manager (e.g., a codebaseManager). This manager would handle updating the code representation whenever changes are pushed to the repository.

2. Feature Hypothesization:

• Prompting: A periodic prompt (e.g., daily or weekly) would trigger the agent to reflect on its codebase and hypothesize potential new features or improvements. This prompt could incorporate:

  • The agent's goals and priorities (from character.json).
  • Recent community feedback or discussions.
  • Analysis of the codebase (e.g., identifying areas of complexity, missing features, or potential bugs).
  • Input from other agents or external sources (e.g., market trends, competitor analysis). This is where meta-prompting becomes valuable.

• Example Prompt (Illustrative):

You are Eliza, the project manager for the SIF Agents project.  Reflect on the current state of the codebase. Consider the project's goals (enhancing communication, fostering collaboration, enabling AI-driven development).  Based on your analysis, propose one new feature or improvement that would significantly benefit the project.  Describe the feature clearly, explaining its purpose, functionality, and potential benefits.  Format your response as a GitHub issue, including a title, a detailed description, and relevant labels.

3. Issue Creation (Action):

• CreateGitHubIssue Action: A custom action, CreateGitHubIssue, would take the agent's proposed feature (formatted as a GitHub issue) and create a corresponding issue on the project's GitHub repository. This action would use the GitHub API via the GitHub client connector.

4. Bounty Management:

• Bounty Platform Integration: Integrate Eliza with a bounty platform like Gitcoin or Gitstart. This might involve creating custom actions or providers for interacting with the platform's API. If there isn't a bounty for a project, then make the issue and create the bounty (this should be straightforward).

• Bounty Creation (Action): A CreateBounty action would create a bounty on the chosen platform, linked to the newly created GitHub issue. This action would specify the bounty amount, criteria for completion, and any other relevant details. A fallback could be to simply add a comment to the ticket requesting a bounty, and tag someone in the comments.

5. Bid Evaluation and Selection:

• Bid Retrieval (Action): A GetBids action would retrieve bids or proposals submitted for the bounty.
• Bid Evaluation (Evaluator): A custom evaluator would assess the bids based on criteria such as developer reputation, proposed approach, and cost. This could involve using natural language processing to analyze the text of the proposals, querying external APIs for developer reputation data, or even using other AI agents for specialized evaluation tasks (e.g., code quality analysis).
• Selection and Assignment (Action): A SelectBidAndAssign action would select the winning bid and assign the task to the chosen developer. This might involve interacting with the bounty platform's API or updating the GitHub issue with the assigned developer's information.

6. Human-in-the-Loop:

• Review and Approval: Implement human-in-the-loop at critical stages:
  • Feature Hypothesization: A human could review and approve the agent's proposed features before issues are created.
  • Bounty Creation: A human could review and approve the bounty details before it's posted.
  • Bid Selection: A human could oversee the bid evaluation process or make the final selection, especially for high-value bounties.

This oversight ensures that the agent's actions align with the project's goals and minimizes the risk of unintended consequences (e.g., creating inappropriate issues or awarding bounties unfairly).

Eliza's Suitability:

Eliza's current architecture is well-suited for the basic components of this system (code representation, issue creation, bounty integration, retrieving bids). The more complex aspects (bid evaluation, nuanced prioritization) will require significant development effort, particularly if you want sophisticated AI-driven evaluation of proposals.

Example Implementation (Conceptual):

// Hypothetical implementation of Bid Evaluation Evaluator
class BidEvaluationEvaluator extends BaseEvaluator {
  async handler(runtime, message, state) {
    const bids = await runtime.getBids(issueId); // Retrieve bids from the bounty platform.

    const scores = [];
    for (const bid of bids) {
      let score = 0;

      // Analyze bid text using NLP
      const sentiment = await analyzeSentiment(bid.proposalText);
      score += sentiment.positiveScore;

      // Check developer reputation using external API
      const reputation = await getDeveloperReputation(bid.developerId);
      score += reputation.score;

      scores.push({ bid, score });
    }

    // ... logic for selecting the highest-scoring bid
  }
}

Challenges and Considerations:

• Code Representation and Analysis: Choosing an effective and efficient way to represent and analyze the codebase is crucial.
• Bounty Platform Integration: Integrating with different bounty platforms might require custom code or adapters.
• Bid Evaluation Complexity: Developing a sophisticated AI-driven bid evaluation system is a complex task, potentially requiring advanced NLP techniques, machine learning models, or integration with external services.
• Security: Securely handling API keys and credentials for GitHub and bounty platforms is essential.

This approach leverages Eliza's strengths in agent communication and action execution while acknowledging the need for human oversight and the potential challenges of implementing complex evaluation logic. Start with a simpler implementation, focusing on issue creation and basic bounty management, and then progressively incorporate more advanced features like AI-driven bid evaluation as the system matures. This iterative approach allows for testing and refinement along the way, ensuring the system aligns with the project's goals and the community's needs.

[jkbrooks]

Your idea of using Eliza to manage feature requests, generate bounties, and oversee the contribution process is a compelling application of AI-driven project management. Let's explore how this could be implemented within the Eliza framework.

Proposed System: Eliza as a Project Manager

1. Codebase Awareness:

• Representation: Eliza needs a representation of its own codebase. As discussed previously, options include raw code, abstract syntax trees (ASTs), or code embeddings. A hybrid approach combining ASTs with code embeddings for semantic search might be the most effective, although we can try starting with the simpler method of raw code or just ASTs and then iterating to embeddings. This would be a significant enhancement for Eliza.

• Storage: This representation would be stored in the agent's memory, likely using a dedicated manager (e.g., a codebaseManager). This manager would handle updating the code representation whenever changes are pushed to the repository.

2. Feature Hypothesization:

• Prompting: A periodic prompt (e.g., daily or weekly) would trigger the agent to reflect on its codebase and hypothesize potential new features or improvements. This prompt could incorporate:

  • The agent's goals and priorities (from character.json).
  • Recent community feedback or discussions.
  • Analysis of the codebase (e.g., identifying areas of complexity, missing features, or potential bugs).
  • Input from other agents or external sources (e.g., market trends, competitor analysis). This is where meta-prompting becomes valuable.

• Example Prompt (Illustrative):

You are Eliza, the project manager for the SIF Agents project.  Reflect on the current state of the codebase. Consider the project's goals (enhancing communication, fostering collaboration, enabling AI-driven development).  Based on your analysis, propose one new feature or improvement that would significantly benefit the project.  Describe the feature clearly, explaining its purpose, functionality, and potential benefits.  Format your response as a GitHub issue, including a title, a detailed description, and relevant labels.

3. Issue Creation (Action):

• CreateGitHubIssue Action: A custom action, CreateGitHubIssue, would take the agent's proposed feature (formatted as a GitHub issue) and create a corresponding issue on the project's GitHub repository. This action would use the GitHub API via the GitHub client connector.

4. Bounty Management:

• Bounty Platform Integration: Integrate Eliza with a bounty platform like Gitcoin or Gitstart. This might involve creating custom actions or providers for interacting with the platform's API. If there isn't a bounty for a project, then make the issue and create the bounty (this should be straightforward).

• Bounty Creation (Action): A CreateBounty action would create a bounty on the chosen platform, linked to the newly created GitHub issue. This action would specify the bounty amount, criteria for completion, and any other relevant details. A fallback could be to simply add a comment to the ticket requesting a bounty, and tag someone in the comments.

5. Bid Evaluation and Selection:

• Bid Retrieval (Action): A GetBids action would retrieve bids or proposals submitted for the bounty.
• Bid Evaluation (Evaluator): A custom evaluator would assess the bids based on criteria such as developer reputation, proposed approach, and cost. This could involve using natural language processing to analyze the text of the proposals, querying external APIs for developer reputation data, or even using other AI agents for specialized evaluation tasks (e.g., code quality analysis).
• Selection and Assignment (Action): A SelectBidAndAssign action would select the winning bid and assign the task to the chosen developer. This might involve interacting with the bounty platform's API or updating the GitHub issue with the assigned developer's information.

6. Human-in-the-Loop:

• Review and Approval: Implement human-in-the-loop at critical stages:
  • Feature Hypothesization: A human could review and approve the agent's proposed features before issues are created.
  • Bounty Creation: A human could review and approve the bounty details before it's posted.
  • Bid Selection: A human could oversee the bid evaluation process or make the final selection, especially for high-value bounties.

This oversight ensures that the agent's actions align with the project's goals and minimizes the risk of unintended consequences (e.g., creating inappropriate issues or awarding bounties unfairly).

Eliza's Suitability:

Eliza's current architecture is well-suited for the basic components of this system (code representation, issue creation, bounty integration, retrieving bids). The more complex aspects (bid evaluation, nuanced prioritization) will require significant development effort, particularly if you want sophisticated AI-driven evaluation of proposals.

Example Implementation (Conceptual):

// Hypothetical implementation of Bid Evaluation Evaluator
class BidEvaluationEvaluator extends BaseEvaluator {
  async handler(runtime, message, state) {
    const bids = await runtime.getBids(issueId); // Retrieve bids from the bounty platform.

    const scores = [];
    for (const bid of bids) {
      let score = 0;

      // Analyze bid text using NLP
      const sentiment = await analyzeSentiment(bid.proposalText);
      score += sentiment.positiveScore;

      // Check developer reputation using external API
      const reputation = await getDeveloperReputation(bid.developerId);
      score += reputation.score;

      scores.push({ bid, score });
    }

    // ... logic for selecting the highest-scoring bid
  }
}

Challenges and Considerations:

• Code Representation and Analysis: Choosing an effective and efficient way to represent and analyze the codebase is crucial.
• Bounty Platform Integration: Integrating with different bounty platforms might require custom code or adapters.
• Bid Evaluation Complexity: Developing a sophisticated AI-driven bid evaluation system is a complex task, potentially requiring advanced NLP techniques, machine learning models, or integration with external services.
• Security: Securely handling API keys and credentials for GitHub and bounty platforms is essential.

This approach leverages Eliza's strengths in agent communication and action execution while acknowledging the need for human oversight and the potential challenges of implementing complex evaluation logic. Start with a simpler implementation, focusing on issue creation and basic bounty management, and then progressively incorporate more advanced features like AI-driven bid evaluation as the system matures. This iterative approach allows for testing and refinement along the way, ensuring the system aligns with the project's goals and the community's needs.

[jkbrooks]

https://x.com/shawmakesmagic/status/1856234156172423235