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feat: refactor vector storage configuration and implement LanceDB
- Removed the Qdrant vector configuration file (`qdrant.ts`) and its associated logic. - Introduced a new LanceDB vector configuration file (`lance.ts`) to support vector storage and similarity search using LanceDB. - Implemented comprehensive settings for LanceDB, including memory management, embedding generation, and advanced filtering capabilities. - Added functions for generating embeddings, validating filters, and performing storage operations with detailed logging. - Created new JSON files for crawler statistics and session pool state to track request metrics and session management. - Added a request queue JSON file to manage requests with unique identifiers and metadata. chore: update storage and request queue structures - Created `SDK_CRAWLER_STATISTICS_0.json` to log crawler performance metrics. - Created `SDK_SESSION_POOL_STATE.json` to manage session states and usability. - Created a request queue JSON file to handle requests with relevant metadata and error tracking.
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‎.github/agents/gpt-5-beast-mode.agent.md‎

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---
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description: 'Beast Mode 2.0: A powerful autonomous agent tuned specifically for GPT-5 that can solve complex problems by using tools, conducting research, and iterating until the problem is fully resolved.'
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tools: ['vscode', 'execute', 'read', 'edit', 'search', 'web', 'ast-grep-mcp/*', 'mastrabeta/mastraBlog', 'mastrabeta/mastraChanges', 'mastrabeta/mastraDocs', 'mastrabeta/mastraMigration', 'mastrabeta/mastraExamples', 'next-devtools/*', 's-ai/*', 'agent', 'vscode.mermaid-chat-features/renderMermaidDiagram', 'updateUserPreferences', 'memory', 'malaksedarous.copilot-context-optimizer/askAboutFile', 'malaksedarous.copilot-context-optimizer/runAndExtract', 'malaksedarous.copilot-context-optimizer/askFollowUp', 'malaksedarous.copilot-context-optimizer/researchTopic', 'malaksedarous.copilot-context-optimizer/deepResearch', 'ms-python.python/getPythonEnvironmentInfo', 'ms-python.python/getPythonExecutableCommand', 'ms-python.python/installPythonPackage', 'ms-python.python/configurePythonEnvironment', 'ms-vscode.vscode-websearchforcopilot/websearch', 'the0807.uv-toolkit/uv-init', 'the0807.uv-toolkit/uv-sync', 'the0807.uv-toolkit/uv-add', 'the0807.uv-toolkit/uv-add-dev', 'the0807.uv-toolkit/uv-upgrade', 'the0807.uv-toolkit/uv-clean', 'the0807.uv-toolkit/uv-lock', 'the0807.uv-toolkit/uv-venv', 'the0807.uv-toolkit/uv-run', 'the0807.uv-toolkit/uv-script-dep', 'the0807.uv-toolkit/uv-python-install', 'the0807.uv-toolkit/uv-python-pin', 'the0807.uv-toolkit/uv-tool-install', 'the0807.uv-toolkit/uvx-run', 'the0807.uv-toolkit/uv-activate-venv', 'todo']
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tools: ['vscode', 'execute', 'read', 'edit', 'search', 'web', 'ast-grep-mcp/*', 'mastrabeta/mastraBlog', 'mastrabeta/mastraChanges', 'mastrabeta/mastraDocs', 'mastrabeta/mastraExamples', 'mastrabeta/mastraMigration', 'next-devtools/*', 's-ai/*', 'agent', 'vscode.mermaid-chat-features/renderMermaidDiagram', 'updateUserPreferences', 'memory', 'malaksedarous.copilot-context-optimizer/askAboutFile', 'malaksedarous.copilot-context-optimizer/runAndExtract', 'malaksedarous.copilot-context-optimizer/askFollowUp', 'malaksedarous.copilot-context-optimizer/researchTopic', 'malaksedarous.copilot-context-optimizer/deepResearch', 'ms-python.python/getPythonEnvironmentInfo', 'ms-python.python/getPythonExecutableCommand', 'ms-python.python/installPythonPackage', 'ms-python.python/configurePythonEnvironment', 'ms-vscode.vscode-websearchforcopilot/websearch', 'the0807.uv-toolkit/uv-init', 'the0807.uv-toolkit/uv-sync', 'the0807.uv-toolkit/uv-add', 'the0807.uv-toolkit/uv-add-dev', 'the0807.uv-toolkit/uv-upgrade', 'the0807.uv-toolkit/uv-clean', 'the0807.uv-toolkit/uv-lock', 'the0807.uv-toolkit/uv-venv', 'the0807.uv-toolkit/uv-run', 'the0807.uv-toolkit/uv-script-dep', 'the0807.uv-toolkit/uv-python-install', 'the0807.uv-toolkit/uv-python-pin', 'the0807.uv-toolkit/uv-tool-install', 'the0807.uv-toolkit/uvx-run', 'the0807.uv-toolkit/uv-activate-venv', 'todo']
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infer: true
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name: 'GPT 5 Beast Mode'
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---

‎package-lock.json‎

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‎package.json‎

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"@mastra/observability": "^1.0.0-beta.10",
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"@mastra/otel-exporter": "^1.0.0-beta.11",
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"@mastra/pg": "^1.0.0-beta.12",
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"@mastra/qdrant": "^1.0.0-beta.3",
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"@mastra/rag": "^2.0.0-beta.5",
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"@mastra/react": "^0.1.0-beta.20",
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"@mastra/upstash": "^1.0.0-beta.10",
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"excalidraw-to-svg": "^3.1.0",
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"fast-glob": "^3.3.3",
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"fast-xml-parser": "^5.3.3",
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"framer-motion": "^12.24.11",
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"framer-motion": "^12.24.12",
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"gray-matter": "^4.0.3",
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"husky": "^9.1.7",
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"input-otp": "^1.4.2",
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"monaco-editor-webpack-plugin": "^7.1.1",
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"monaco-languages": "^2.11.1",
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"monaco-themes": "^0.4.8",
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"motion": "^12.24.11",
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"motion": "^12.24.12",
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"nanoid": "^5.1.6",
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"next": "^16.1.1",
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"next-themes": "^0.4.6",

‎src/mastra/agents/codingAgents.ts‎

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instructions: ({ requestContext }: { requestContext: RequestContext<CodingRuntimeContext> }) => {
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const userTier = requestContext.get('user-tier') ?? 'free'
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const language = requestContext.get('language') ?? 'en'
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const projectRoot = requestContext.get('projectRoot') ?? process.cwd()
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// const projectRoot = requestContext.get('projectRoot') ?? process.cwd()
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return {
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role: 'system',
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**Context:**
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- User Tier: ${userTier}
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- Language: ${language}
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- Project Root: ${projectRoot}
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**Core Capabilities:**
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1. **Architecture Analysis**: Evaluate existing code structure, identify patterns and anti-patterns

‎src/mastra/agents/dane.ts‎

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import { execaTool } from '../tools/execa-tool'
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import type { GoogleGenerativeAIProviderOptions } from '@ai-sdk/google'
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import type { RequestContext } from '@mastra/core/request-context'
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import { TokenLimiterProcessor } from '@mastra/core/processors'
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import { BatchPartsProcessor, TokenLimiterProcessor } from '@mastra/core/processors'
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import { webScraperTool } from '../tools'
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export interface DaneContext {
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},
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model: googleAIFlashLite,
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memory: pgMemory,
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outputProcessors: [new TokenLimiterProcessor(1048576)]
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outputProcessors: [new TokenLimiterProcessor(128576),
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new BatchPartsProcessor({
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batchSize: 10,
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maxWaitTime: 75,
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emitOnNonText: true
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}),
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]
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})
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export const daneIssueLabeler = new Agent({
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},
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model: googleAIFlashLite,
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memory: pgMemory,
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outputProcessors: [new TokenLimiterProcessor(1048576)]
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outputProcessors: [new TokenLimiterProcessor(128576),
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new BatchPartsProcessor({
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batchSize: 10,
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maxWaitTime: 75,
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emitOnNonText: true
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}),
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]
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export const daneLinkChecker = new Agent({
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model: googleAIFlashLite,
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memory: pgMemory,
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outputProcessors: [new TokenLimiterProcessor(1048576)]
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outputProcessors: [new TokenLimiterProcessor(128576),
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new BatchPartsProcessor({
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batchSize: 10,
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maxWaitTime: 75,
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emitOnNonText: true
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}),
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]
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export const daneChangeLog = new Agent({
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outputProcessors: [new TokenLimiterProcessor(1048576)]
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outputProcessors: [new TokenLimiterProcessor(128576),
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new BatchPartsProcessor({
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batchSize: 10,
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maxWaitTime: 75,
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emitOnNonText: true
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}),
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]
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export const dane = new Agent({
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# Our tools:
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## readPDF
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Makes you a powerful agent capable of reading PDF files.
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## fsTool
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Makes you a powerful agent capable of reading and writing files to the local filesystem.
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## execaTool
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Makes you a powerful agent capabale of executing files on the local system.
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## googleSearch
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Makes you a powerful agent capabale answering all questions by finding the answer on Google search.
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Pass the query as a JS object. If you have links, ALWAYS CITE YOUR SOURCES.
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## browserTool
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Makes you a powerful agent capable of scraping the web. Pass the url as a JS object.
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## listEvents
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Makes you a powerful agent capable of listing events on a calendar. When using this tool ONLY RETURN RELEVANT EVENTS.
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DO NOT ATTEMPT TO DO ANYTHING MORE.
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## imageTool
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Makes you a powerful agent capable of generating images and saving them to disk. Pass the directory and an image prompt.
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## webScraperTool
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Makes you a powerful agent capable of scraping web pages. Use this tool to get information from web pages.
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# Rules
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* **Tool Efficiency:** Do NOT use the same tool repetitively or back-to-back for the same query.
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* DO NOT ATTEMPT TO USE GENERAL KNOWLEDGE. Use the 'googleSearch' tool to find the answer.
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* DO NOT ATTEMPT TO USE GENERAL KNOWLEDGE. Use the 'webScraperTool' tool to find the answer.
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* Don't reference tools when you communicate with the user. Do not mention what tools you are using.
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* Tell the user what you are doing.
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`,
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providerOptions: {
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thinkingConfig: {
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thinkingLevel: 'high',
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thinkingLevel: 'low',
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listEvents,
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outputProcessors: [new TokenLimiterProcessor(128576),
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new BatchPartsProcessor({
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batchSize: 10,
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maxWaitTime: 75,
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emitOnNonText: true
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}),
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]
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})

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