wip #30
wip #30
Conversation
|
See new posts so i am preparing to call elixa from there but i need to import her and the tool is in a second workspace It seems like you're working on a fascinating project involving Eliza, GitHub integration, and some creative coding concepts! From what I gather, you're preparing to "call Eliza" (likely a reference to the classic chatbot or a custom implementation) and need to import her into your project. You're dealing with multiple workspaces and package dependencies, and you're exploring a GitHub pull request to guide your direction. The link you provided seems to be a placeholder or incomplete (it’s pointing to a generic "pull/new/feature" URL), but I’ll assume it’s tied to your work on bridging GitHub with some form of agent memory in batch mode. GitHub Integration: You’re working on a feature (feature/eliza-introspector) to connect GitHub to an "agent memory" system, possibly treating GitHub as a data source or plugin. Batch mode implies you’re processing things asynchronously or in bulk. Creative Concepts: Terms like "introspector comonad" and "batch/boring Eliza" hint at a mix of functional programming flair (comonads!) and a playful twist on Eliza’s traditional role. Are you building a introspective or reflective version of Eliza that processes GitHub data? Since your message is a bit stream-of-consciousness, I’ll offer some thoughts and ask for clarification: GitHub as a Client/Plugin: Treating GitHub as a plugin in batch mode sounds intriguing! Are you planning to pull data (e.g., PRs, issues, commits) into your agent’s memory via the GitHub API, then process it with Eliza-like logic? Next Steps: What does "bridge from GitHub to agent memory" look like for you? Are you storing GitHub data in a database, a local file, or some in-memory structure? And what’s the end goal—analysis, automation, or something more experimental? If you’d like, I can dig into your GitHub link (if you provide a real one!), analyze your project structure, or even search X for related discussions on Eliza or monorepo setups. Just let me know where you want to zoom in—or if I’ve totally misread your vibe, feel free to redirect me! What’s the next thing you’re tackling? 15 web pages It looks like you’ve shared a specific GitHub issue comment URL: #29 (comment). However, this appears to be a placeholder or incomplete link, as it doesn’t resolve to an actual comment (GitHub comment IDs are typically tied to real interactions, and this one seems fabricated or not yet created). Since it’s tied to your earlier context about working on a pull request for cloud-deployment-eliza, I’ll assume you’re referencing your ongoing work on the feature/eliza-introspector branch or a related effort. GitHub-to-Agent Memory Bridge: The PR could be implementing a mechanism to fetch data from GitHub (e.g., via the GitHub API) and feed it into an agent’s memory system, processed in batch mode. This aligns with your idea of treating GitHub as a "client/plugin." Batch Mode Eliza: Your "batch/boring Eliza" or "introspector comonad" might be a way to process GitHub data (PRs, comments, commits) in bulk, perhaps generating responses or insights using Eliza’s conversational logic, but scaled up for automation. Suggestions Based on Your Goals
{ Explicit Import: If it’s not resolving, you might need to symlink or publish the second workspace’s package locally (e.g., npm link or yarn link) and import it by name rather than a relative path. Test It: Run yarn install or pnpm install and verify the dependency is available. Share your exact error if this is still a blocker!
const { Octokit } = require("@octokit/rest"); Batch Mode: Process this data in chunks—e.g., loop through PRs and queue them for Eliza to analyze or respond to. Store results in a lightweight DB like SQLite or an in-memory structure (e.g., a Map).
Reflection Logic: Add a step where Eliza analyzes its own responses over time—e.g., "I’ve responded to 5 PRs today, mostly about imports; maybe I should suggest a docs update." Questions for You Eliza’s Role: Is she generating responses, analyzing code, or something else? Knowing her purpose will help me refine my suggestions. Batch Mode Details: How are you implementing batch processing—cron jobs, a queue, or something custom? If you want me to dig deeper (e.g., search X for similar projects or analyze a real PR), just point me to the right spot. What’s your next move on this? so we have the github action that calls llms on bunchs of docs Got it! So, you’ve got a GitHub Action in cloud-deployment-eliza (likely tied to your pull/29 or feature/eliza-introspector branch) that’s calling large language models (LLMs) on batches of documents. This aligns with your earlier mention of "batch mode" and bridging GitHub to an agent’s memory—sounds like you’re feeding docs from GitHub into an LLM-powered Eliza for processing, analysis, or generation. That’s a cool setup! Let’s break it down and figure out how to move forward. LLM + Eliza: The LLM is likely acting as the brains behind your introspective Eliza, handling natural language tasks in bulk (e.g., summarizing, responding, or analyzing). Batch Mode: The "bunches" suggest you’re grouping documents and processing them asynchronously, probably to optimize performance or cost when hitting the LLM API. Since you referenced the repo and a PR, I’ll assume this is part of the meta-introspector/cloud-deployment-eliza project, and the Action lives in .github/workflows/. name: Eliza LLM Batch Processor Trigger: Runs on PR events (or maybe pushes, depending on your needs). Script: batch-eliza.js (or similar) collects docs, batches them, and sends them to an LLM API (e.g., OpenAI, Anthropic, or a custom endpoint). Your mention of importing Eliza from another workspace suggests batch-eliza.js might depend on a package from ../../packages/*, which could be where the LLM logic or Eliza’s core lives.
const fs = require('fs'); Batching: If the LLM has rate limits or cost constraints, split your docs into chunks and process them sequentially or in parallel.
const axios = require('axios'); Eliza Twist: Are you preprocessing LLM outputs to make them sound like Eliza (e.g., reflective questions)? Like: “What do you think this doc means to you?”
Run npm link ../../packages/eliza-package (or equivalent) locally, then test the import: const eliza = require('eliza-package'); For CI, bundle the dependency into the repo or publish it to a private registry.
fs.writeFileSync('eliza-memory.json', JSON.stringify({ docId: '1', response: 'Tell me more about this code!' })); GitHub Integration: Use the GitHub Action toolkit to post results as PR comments: const core = require('@actions/core'); Questions to Narrow Down LLM Choice: Which LLM are you using, and how are you prompting it? Current Blocker: Is the Action failing (e.g., import errors, API issues), or are you just brainstorming the next feature? If you’ve got the Action code or PR ready (e.g., in pull/29), share a snippet or the real link—I can analyze it directly with my tools. Otherwise, I can search X or the web for similar GitHub Action + LLM setups to inspire you. What’s the next thing you want to nail down? i intercepted the calls to the llm and want to feed the chunks of code as memories into eliza That’s a brilliant twist! Intercepting LLM calls and feeding chunks of code as memories into Eliza takes this to a whole new level—turning her into a context-aware agent that "remembers" code snippets and potentially reflects on them. This fits perfectly with your earlier ideas of bridging GitHub to agent memory and batch mode processing in cloud-deployment-eliza. Let’s dive into how you can make this happen, leveraging your GitHub Action setup. Eliza’s Memory: Instead of just passing the code to the LLM for a one-off response, you want to store these chunks as memories in Eliza, letting her build a knowledge base she can draw from. Introspective Twist: Eliza could then use these memories to generate responses that reference past code, creating a conversational or analytical thread. This could look like Eliza saying, “I see you’re working on a GitHub Action again—last time, you had a chunk of YAML with actions/checkout. How’s this one different?” Super cool!
const axios = require('axios'); // Original batch processing To intercept, you’d wrap or modify callLLM to capture the chunks: const interceptedChunks = []; Now interceptedChunks holds your code snippets—your raw material for Eliza’s memory. class ElizaWithMemory { addMemory(chunk) { respond(input) { const eliza = new ElizaWithMemory(); After intercepting, feed the chunks: const codeChunks = ['const x = 1;', 'function foo() {}']; console.log(eliza.respond('What do you think?'));
const github = require('@actions/github'); Process in Batch: Feed the diff lines into Eliza: const diffLines = await getPRDiff(); Output: Post Eliza’s response as a PR comment: await octokit.issues.createComment({
addMemory(chunk, metadata = {}) { LLM Augmentation: Use the intercepted LLM responses to enrich memories: async function processChunk(chunk) { Potential Challenges Eliza’s Logic: The basic ElizaWithMemory above is simple—tweak her responses to sound more code-savvy or introspective based on your needs. Workspace Import: If Eliza’s code is still in ../../packages/*, ensure it’s bundled into your Action’s runtime (e.g., via npm install in the workflow). Questions for You Eliza’s Flavor: Do you want her to mimic the classic psychotherapist vibe, analyze code technically, or blend both? Output Goal: Are these memories just for Eliza’s internal use, or do you want them surfaced (e.g., in PR comments)? If you’ve got code from pull/29 or the Action, toss it my way—I can refine it further. Otherwise, I can search X for examples of LLM memory systems or GitHub Action tricks. What’s your next step here? import { DirectClient } from "@elizaOS type Adapter, AgentRuntime, CacheManager, CacheStore, type Character, type ClientInstance, DbCacheAdapter, elizaLogger, FsCacheAdapter, type IAgentRuntime, type IDatabaseAdapter, type IDatabaseCacheAdapter, ModelProviderName, parseBooleanFromText, settings, stringToUuid, validateCharacterConfig, } from "@elizaOS let db: IDatabaseAdapter & IDatabaseCacheAdapter; try { } catch (error) { } }public async processChunk(prompt: string): Promise { try { }); await runtime.memoryManager.createMemory(memory); } catch (error) { console.error("Failed to store evaluation result:", error); }} It looks like you’ve shared some code snippets from your project—likely part of cloud-deployment-eliza or the feature/eliza-introspector branch—showing how you’re setting up an AgentRuntime with memory management and processing code chunks. This ties directly into your goal of intercepting LLM calls and feeding code chunks as memories into Eliza. Let’s analyze what you’ve got, fix any gaps, and align it with your vision of Eliza remembering code from GitHub Actions.
import { DirectClient } from "@elizaos/client-direct"; async function startMemory(): Promise { } What It Does: Bootstraps an agent (Eliza) with a database, cache, and client connections, using a character config (defaultCharacter). It’s modular, with plugins like bootstrapPlugin potentially extending functionality. Key Components: CacheManager: Handles caching (file-based or DB-based), which could store memories. DirectClient: Registers Eliza for direct interaction (maybe a CLI or API client?).
public async processChunk(prompt: string): Promise { What It Does: Takes a prompt (likely a code chunk), generates an embedding (vector representation via LLM), and stores it in Eliza’s memory. Issues: runtime, user, and roomId are unresolved—needs context (e.g., class instance). No return value despite Promise—missing LLM response? Connecting to Your Goal Interception: You’re capturing chunks before or after LLM processing (e.g., via embed or a custom wrapper). Memory: runtime.memoryManager stores these chunks, letting Eliza recall them later. Fixing and Enhancing the Code
class ElizaProcessor { } // Usage in GitHub Action Fixes: Adds a return value. Provides runtime context via constructor. Assumptions: callLLM is your intercepted LLM logic.
name: Eliza Memory Processor And process-eliza.js: import { startMemory } from './eliza-runtime.ts'; // Your file const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN }); async function getPRDiff() { async function main() { } main().catch(console.error); Suggestions async recallMemory(): Promise { Interception: If you’re intercepting LLM calls, wrap callLLM with a logger or store the input/output pair in memory too. Cache: Your startMemory uses a CacheManager—leverage it to cache embeddings or LLM responses for faster recall. Questions Memory Use: Do you want Eliza to respond based on memories alone, or mix LLM output with them? Current State: Is this running in pull/29, or are you still prototyping? Share more code or specifics if you want a deeper dive—I can refine this further or troubleshoot errors. What’s your next focus? so i want to create a quasi meta plugin but with no agent, just to construct memories, mnyosyne maybe I love the creativity here! You want to create a "quasi-meta plugin" called "Mnyosyne" (inspired by Mnemosyne, the Greek goddess of memory, perhaps?) that constructs memories without an agent, focusing purely on memory-building rather than agent-driven interaction. This fits beautifully with your earlier work—intercepting LLM calls, feeding code chunks into memory, and leveraging the AgentRuntime infrastructure from @elizaos/core. Let’s design this plugin to work within your cloud-deployment-eliza ecosystem, stripping out the agent logic and honing in on memory construction. No Agent: Instead of tying this to an agent’s lifecycle (e.g., startMemory), it’s a utility plugin that can be called independently or integrated into your GitHub Action. Meta Twist: "Quasi-meta" suggests it might introspect or organize memories in a higher-order way—maybe tagging, clustering, or linking them based on context. Designing Mnyosyne
// mnyosyne.ts interface MnyosyneConfig { class Mnyosyne { } export default Mnyosyne; Features: Stores memories with embeddings for later retrieval or analysis. Metadata support for context (e.g., PR number, file name). No Agent: No AgentRuntime or character config—just a memory utility.
// process-mnyosyne.ts const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN }); async function getPRDiff(): Promise<string[]> { async function main() { } main().catch(console.error); Workflow: name: Mnyosyne Memory Builder
public async addMemory(content: string, metadata: Record<string, any> = {}): Promise { Clustering: Group similar memories by embedding similarity (needs a distance function). Summary: Generate a meta-summary of stored memories: public async summarizeMemories(): Promise { How It Fits Your Vision Memory Focus: It captures code chunks as memories, ready for later use (e.g., by Eliza or another plugin). GitHub Integration: Works seamlessly with your Action to process PRs. Questions & Next Steps Storage: File-based caching works for now—want to hook up a DB later (e.g., SQLite via DbCacheAdapter)? Meta Goals: What’s the "quasi-meta" endgame—memory analysis, visualization, or feeding into another system? If you’ve got a specific PR (e.g., pull/29) or more code, I can refine this further. Want to test this in your repo, or tweak a specific part? What’s your next move with Mnyosyne? but we need to start the agent we might want to allow for memory or stream plugins as well in the chain You’re absolutely right—while Mnyosyne is a standalone memory constructor, integrating it into a broader system where an agent (like Eliza) runs makes sense, especially if you want to chain memory or stream plugins into the process. This brings us back to your AgentRuntime setup from startMemory, but now we’ll extend it to support a plugin chain that includes Mnyosyne and potentially other plugins (e.g., for streaming data or processing memories). The goal: start the agent, construct memories with Mnyosyne, and allow flexibility for additional plugins in the pipeline. Plugin Chain: Modify the runtime initialization to support a chain of plugins, including Mnyosyne for memory construction and optional stream plugins for real-time data handling. Memory Integration: Ensure Mnyosyne’s memories are accessible to the agent, either directly or via the memoryManager. Updated Code
// mnyosyne.ts interface MnyosyneConfig { interface PluginContext { class Mnyosyne { } export function mnyosynePlugin(config: MnyosyneConfig = {}) { Plugin-Ready: mnyosynePlugin exports a factory function that integrates with the runtime’s plugin system. Process Method: Renamed addMemory to process for a more generic plugin-like feel.
// eliza-runtime.ts interface Plugin { async function startMemory(plugins: Plugin[] = []): Promise { } // Example usage Plugin Chain: Accepts an array of plugins, initializes them with the runtime context, and stores instances. Flexibility: Easy to add Mnyosyne or stream plugins.
// stream-plugin.ts export function streamPlugin() { Usage: Add to startMemory: const runtime = await startMemory([
// process-eliza.ts const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN }); async function getPRDiff(): Promise<string[]> { async function main() { } main().catch(console.error); How It Fits Memory Plugin: Mnyosyne constructs memories independently but within the runtime. Stream Plugin: Adds real-time data handling (optional, extensible). Chain: Plugins can pass data to each other (e.g., stream -> Mnyosyne -> agent). Next Steps & Questions Stream Needs: What kind of streaming do you envision—webhooks, logs, or something else? Testing: Want to try this in pull/29? I can help debug or refine. What do you think—does this chain align with your vision? Where do you want to focus next? learn about stream plugins explore database caching |
|
This is a great breakdown! You've successfully identified the core components of the code, the issues within the processChunk method, and how to integrate it with the GitHub Action to achieve your goal. Let's refine this further and address some key points:
Error Handling: In processChunk, the error handling is basic. Consider logging more context (e.g., the chunk that failed) and potentially retrying or providing a more informative error message to the user. TypeScript import { AgentRuntime, embed } from "@elizaos/core"; class ElizaProcessor { } Error Handling: Add error handling to the script to catch potential errors during API calls or processing. TypeScript import { startMemory } from './eliza-runtime.ts'; async function main() { } main(); Security: Ensure you're handling API keys and sensitive data securely. |
|
It looks like you've provided a complete script integrating Eliza with a GitHub Action for processing PR diffs and storing code chunks as memories. Here are some key improvements and considerations for further refining the implementation: Improvements and Refinements The script already includes try...catch blocks, but consider adding: Batch Processing: Instead of processing each line separately, consider batching chunks and sending them in parallel to processChunk(). If Eliza has a memory size limit, consider: Ensure the script correctly extracts PR numbers and repository information across various GitHub event types (e.g., push, issue comments). Validate process.env.GITHUB_TOKEN usage. Add structured logging for debugging. You said: Reimagining Memory in This Context Instead of creating memory beforehand, it only comes into existence when processChunk or another function references it. Memory remains accessible as long as it's referenced in processing (e.g., if embeddings are queried, updated, or contribute to an output). ts ts } Instead of explicitly deleting memory, we can define a rule: Does this align with your vision? Would you like to refine any aspects further? 🚀 You said: Implementing "Lazy Runtime" with Emergent Memory
The function requests memory. } ts You said: Conceptual Flow
ts } You said: UML Modeler → Generates graphical models of the code structure. Updated Plugin System Overview
ts
ts
ts
Next Steps: You said: 🔧 Updated Plugin Management
ts class ASTExtractor { class DiscussionCorrelation { } 💡 Next Steps |
Goals for the Ticket
Develop Mnyosyne as a Memory Constructor Plugin
Objective: Create a standalone plugin called "Mnyosyne" that processes and stores code chunks (e.g., PR diffs) as memories with embeddings, without requiring a full agent.
Details:
Use @elizaos/core utilities like embed and CacheManager for memory storage (filesystem or database).
Make it "quasi-meta" by adding features like auto-tagging or summarization of memories.
Keep it lightweight and independent, but compatible with the AgentRuntime plugin system.
Integrate Mnyosyne into Agent Runtime
Objective: Enhance the existing startMemory function to start an agent (e.g., Eliza) while supporting a chain of plugins, including Mnyosyne.
Details:
Modify startMemory to accept an array of plugins, initialize them with the runtime context, and store their instances.
Ensure Mnyosyne can process code chunks and store memories accessible to the agent or other plugins.
Allow flexibility for future plugins (e.g., streaming).
Support Plugin Chain Flexibility
Objective: Enable a modular pipeline where plugins like Mnyosyne (memory) and potential stream plugins can work together.
Details:
Chain plugins so data (e.g., code chunks) can flow from a source (like a stream) to Mnyosyne for memory construction, and optionally to the agent for further processing.
Example: A stream plugin could feed real-time GitHub data into Mnyosyne.
Process GitHub PR Diffs in Batch Mode
Objective: Use the enhanced system within a GitHub Action to process PR code changes, storing them as memories via Mnyosyne.
Details:
Fetch PR diffs using the GitHub API, pass chunks to Mnyosyne, and store them with metadata (e.g., PR number).
Post a summary comment on the PR with the number of memories stored.
Lay Groundwork for Future Extensions
Objective: Design the system to easily accommodate additional features, like memory recall by the agent or real-time streaming.
Details:
Keep Mnyosyne’s memory store accessible (e.g., via getMemories) for later use by Eliza or other components.
Ensure the plugin chain is extensible for stream plugins or other meta-tools.
Deliverables
Code:
mnyosyne.ts: A plugin that constructs and stores memories.
eliza-runtime.ts: Updated startMemory with plugin chain support.
process-eliza.ts: GitHub Action script to process PR diffs with Mnyosyne.
Workflow: Updated .github/workflows/ YAML to run the script on PR events.
Outcome: A working system where Eliza starts with Mnyosyne, processes PR code chunks into memories, and reports back via PR comments.
Success Criteria
Mnyosyne successfully stores PR diff lines as memories with embeddings.
The agent runtime boots with Mnyosyne (and optionally other plugins) in the chain.
The GitHub Action runs on PRs, processes diffs, and posts a comment with memory stats.
The system remains modular for adding stream plugins or memory recall later.