{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-composiohq--open-chatgpt-atlas","slug":"composiohq--open-chatgpt-atlas","name":"open-chatgpt-atlas","type":"repo","url":"https://github.com/ComposioHQ/open-chatgpt-atlas","page_url":"https://unfragile.ai/composiohq--open-chatgpt-atlas","categories":["chatbots-assistants"],"tags":["ai-automation","ai-browser","browser-extension","browser-use-agent","chatgpt","chatgpt-atlas","chatgpt-atlas-download","chromium","electron-js","electron-js-desktop","gpt","openai"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-composiohq--open-chatgpt-atlas__cap_0","uri":"capability://automation.workflow.vision.based.browser.automation.via.screenshot.to.action.mapping","name":"vision-based browser automation via screenshot-to-action mapping","description":"Captures full-page screenshots, sends them to Google's Gemini 2.5 Computer Use model for visual understanding, and receives normalized 1000x1000 coordinate grids for precise click, type, and scroll actions. This approach enables the AI to interact with any web UI without requiring DOM parsing or element selectors, making it resilient to dynamic content and obfuscated interfaces.","intents":["Automate complex multi-step web workflows that require visual understanding of page layout","Interact with web applications that use dynamic or shadow DOM elements","Execute browser tasks without maintaining brittle CSS selectors or XPath expressions","Handle visual CAPTCHA or image-based authentication flows"],"best_for":["Teams building browser automation agents without access to application APIs","Developers prototyping RPA solutions that must work across heterogeneous web UIs","Non-technical users who want to automate repetitive web tasks via natural language"],"limitations":["Latency overhead from screenshot capture + API round-trip to Gemini (typically 2-5 seconds per action)","Vision model may struggle with small text, complex tables, or heavily styled content","Coordinate normalization to 1000x1000 grid can lose sub-pixel precision on high-DPI displays","No built-in handling for multi-window or cross-origin iframe interactions"],"requires":["Google AI Studio API key with access to gemini-2.5-computer-use-preview model","Chrome browser with Manifest V3 support OR Electron runtime","Network connectivity to Google's API endpoints"],"input_types":["Natural language instructions (e.g., 'Click the login button and enter my credentials')","Current browser viewport state (captured as screenshot)"],"output_types":["Structured action coordinates (x, y, action_type)","Execution logs with screenshot annotations"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_1","uri":"capability://tool.use.integration.multi.provider.tool.routing.with.500.api.integrations","name":"multi-provider tool routing with 500+ api integrations","description":"Routes natural language requests through Composio's Tool Router to generate direct API calls against 500+ integrated services (Gmail, Slack, GitHub, Salesforce, etc.) instead of simulating UI clicks. The system maintains a schema registry of available tools, matches user intent to applicable APIs, and executes calls with proper authentication and error handling, bypassing visual automation entirely for supported platforms.","intents":["Send emails, create calendar events, or manage contacts via Gmail API without UI interaction","Post messages, create channels, or manage workflows in Slack programmatically","Create issues, manage repositories, or trigger CI/CD workflows in GitHub","Execute CRM operations in Salesforce or other enterprise SaaS platforms"],"best_for":["Enterprise teams integrating with proprietary SaaS platforms that expose stable APIs","Developers building AI agents that require sub-second response times for API-backed tasks","Organizations with strict audit requirements (API calls are more auditable than UI automation)"],"limitations":["Only works for services with Composio integration; unsupported platforms fall back to visual automation","Requires OAuth tokens or API keys for each integrated service, adding credential management complexity","API schema changes in upstream services can break tool definitions without warning","Rate limiting on upstream APIs is not abstracted; agents can hit quotas quickly"],"requires":["Composio API key with access to Tool Router","OAuth credentials or API keys for each target service (Gmail, Slack, GitHub, etc.)","Network connectivity to Composio's MCP server and upstream service APIs"],"input_types":["Natural language intent (e.g., 'Send a Slack message to #engineering with the deployment status')","Tool schema definitions (auto-fetched from Composio registry)"],"output_types":["API call results (JSON responses from upstream services)","Execution logs with API request/response pairs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_10","uri":"capability://planning.reasoning.multi.model.llm.routing.with.fallback.support","name":"multi-model llm routing with fallback support","description":"Routes requests to different LLM models based on task type: Gemini 2.5 Computer Use for visual browser automation, standard Gemini for text-based tool selection and reasoning, and Composio's Tool Router for API-based execution. Implements fallback logic to switch models if the primary choice fails or times out.","intents":["Use the most appropriate model for each task (vision for UI, text for reasoning, API for integrations)","Gracefully degrade to alternative models if the primary choice is unavailable","Optimize cost by using cheaper models for simple tasks and expensive models only when needed","Support future model additions without refactoring the routing logic"],"best_for":["Teams building multi-model AI systems with task-specific model selection","Developers who want to optimize cost and latency by choosing models per task","Organizations with redundancy requirements for critical automation workflows"],"limitations":["Model-specific APIs have different response formats, requiring per-model parsing logic","Fallback logic adds complexity and potential for cascading failures","Different models have different rate limits and quotas, requiring per-model tracking","Model selection logic must be maintained as new models are released"],"requires":["API keys for multiple LLM providers (Google Gemini, Composio)","Model-specific request/response parsing logic"],"input_types":["Task type (visual automation, reasoning, API execution)","User prompt and context"],"output_types":["Model-specific responses (screenshots + actions, text, API calls)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_11","uri":"capability://image.visual.screenshot.capture.and.normalization.for.consistent.coordinate.grids","name":"screenshot capture and normalization for consistent coordinate grids","description":"Captures full-page screenshots from the browser viewport, normalizes them to a 1000x1000 coordinate grid regardless of actual screen resolution or DPI, and sends them to the vision model. This normalization ensures that coordinate predictions from the model are consistent across different devices and screen sizes, with a reverse-mapping step to translate normalized coordinates back to actual pixel positions.","intents":["Ensure consistent action coordinates across devices with different screen resolutions","Handle high-DPI displays without losing precision in coordinate mapping","Provide the vision model with a consistent input format regardless of device","Enable reliable click and type actions by accurately mapping model predictions to screen pixels"],"best_for":["Teams building cross-device browser automation that must work on mobile and desktop","Developers who need consistent coordinate systems across heterogeneous hardware","Applications requiring pixel-perfect accuracy in UI interaction"],"limitations":["Screenshot capture and encoding adds 500ms-2s latency per action","Normalization to 1000x1000 grid loses sub-pixel precision on high-DPI displays","Large screenshots consume significant bandwidth; compression is necessary for mobile networks","Coordinate mapping errors compound across multiple actions, requiring periodic screenshot refreshes"],"requires":["Browser API for screenshot capture (chrome.tabs.captureVisibleTab or Electron's BrowserView.webContents.capturePage)","Image encoding library (PNG or JPEG)"],"input_types":["Browser viewport"],"output_types":["Normalized screenshot (1000x1000 grid), coordinate mapping metadata"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_12","uri":"capability://automation.workflow.error.recovery.and.retry.logic.with.exponential.backoff","name":"error recovery and retry logic with exponential backoff","description":"Implements automatic retry logic for transient failures (API timeouts, rate limits, network errors) using exponential backoff with jitter. Failed actions are logged with full context (screenshot, prompt, error message) for debugging, and the agent can decide whether to retry the same action, try an alternative approach, or escalate to the user.","intents":["Automatically recover from transient API failures without user intervention","Avoid overwhelming rate-limited APIs by using exponential backoff","Provide detailed error logs for debugging automation failures","Allow agents to adapt strategies when actions fail"],"best_for":["Teams building resilient automation that must handle unreliable networks or APIs","Developers who need detailed error diagnostics for production debugging","Applications where manual intervention for transient failures is unacceptable"],"limitations":["Exponential backoff can delay recovery by minutes for heavily rate-limited APIs","Retry logic cannot distinguish between transient and permanent failures without heuristics","Logging full context (screenshots, prompts) can consume significant storage","Retries may cause duplicate side effects if the original action partially succeeded"],"requires":["Retry logic implementation with exponential backoff","Error classification logic to distinguish transient vs permanent failures"],"input_types":["Failed API calls or actions"],"output_types":["Retry attempts with backoff delays, error logs with context"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_2","uri":"capability://automation.workflow.dual.deployment.architecture.with.chrome.extension.and.electron.desktop.app","name":"dual-deployment architecture with chrome extension and electron desktop app","description":"Shares a unified core logic layer across two distinct deployment targets: a Manifest V3 Chrome Extension (using chrome.debugger and content script injection for tab automation) and a standalone Electron desktop app (using BrowserView and native IPC for full browser control). Both targets implement the same AI routing logic but use different automation primitives and persistence mechanisms (chrome.storage.local vs electron-store).","intents":["Deploy the same agent logic as a lightweight browser extension without requiring a separate application","Run a standalone desktop browser with integrated AI assistant for users who prefer not to install extensions","Maintain a single codebase for both deployment targets to reduce maintenance burden","Access native system APIs (file system, clipboard, notifications) via Electron when needed"],"best_for":["Teams building cross-platform AI assistants with minimal code duplication","Organizations with strict browser extension policies that require a desktop alternative","Developers who want to offer users choice between lightweight (extension) and integrated (desktop) experiences"],"limitations":["Chrome Extension is limited to automating the active tab; cannot coordinate across multiple tabs or windows","Electron app adds ~50MB binary size and requires separate distribution/update infrastructure","Manifest V3 restrictions limit background script execution time, requiring careful async/await patterns","IPC layer between Electron main and renderer processes adds ~50-100ms latency per message round-trip"],"requires":["Node.js 18+","npm or yarn package manager","Google Chrome or Microsoft Edge for extension deployment","Electron runtime (bundled in desktop app build)"],"input_types":["Natural language prompts from user","Browser state (DOM, screenshots, network logs)"],"output_types":["Browser actions (clicks, typing, navigation)","API responses from tool integrations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_3","uri":"capability://safety.moderation.local.first.privacy.model.with.direct.client.to.api.calls","name":"local-first privacy model with direct client-to-api calls","description":"All API requests to model providers (Google Gemini, Composio) are made directly from the client (extension or desktop app) without routing through an intermediary backend server. This eliminates the need for a centralized proxy, reduces latency, and ensures user prompts and browser state never touch a third-party server beyond the official API providers.","intents":["Ensure user privacy by avoiding data collection through a proprietary backend","Reduce latency by eliminating an extra network hop through a proxy server","Reduce operational costs by not running a centralized backend infrastructure","Comply with data residency requirements by keeping data flows direct to official API providers"],"best_for":["Privacy-conscious users and organizations that distrust intermediary services","Teams with strict data governance policies requiring direct-to-provider API calls","Developers building open-source tools where backend transparency is a core value"],"limitations":["Client must store API keys locally, increasing exposure to credential theft via malware or extension vulnerabilities","No centralized rate limiting or quota management; each client independently hits provider limits","Debugging and monitoring are decentralized; no single point to observe all agent executions","Cannot implement server-side caching or request deduplication across multiple users"],"requires":["API keys for Google AI Studio and Composio stored in browser/app local storage","Direct network access to Google and Composio API endpoints (no proxy support)"],"input_types":["User prompts and browser state"],"output_types":["Direct API responses from providers"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_4","uri":"capability://planning.reasoning.agentic.loop.with.streaming.response.handling","name":"agentic loop with streaming response handling","description":"Implements a multi-turn agentic loop where the LLM receives tool availability (both Computer Use and Tool Router), decides which tool to invoke, executes the action, observes the result (screenshot or API response), and iteratively refines its approach. The system handles streaming responses from the LLM, allowing real-time display of reasoning and action execution without waiting for full completion.","intents":["Execute multi-step workflows that require observing intermediate results before deciding next steps","Display real-time agent reasoning and action execution to users for transparency and debugging","Handle tool failures gracefully by allowing the agent to retry or switch strategies","Support long-running tasks that span multiple LLM invocations"],"best_for":["Developers building transparent AI agents where users need to see reasoning","Teams implementing complex workflows that require adaptive decision-making","Applications requiring real-time feedback on agent progress"],"limitations":["Streaming adds complexity to state management; must handle partial responses and interruptions","Each loop iteration incurs latency (screenshot + API call + processing), making rapid workflows slow","LLM may enter infinite loops or fail to converge on a solution without explicit iteration limits","Streaming responses cannot be easily cached or replayed for debugging"],"requires":["LLM with streaming support (Gemini 2.5 Computer Use)","Client-side streaming parser to handle partial JSON responses"],"input_types":["User intent (natural language)","Tool definitions (Computer Use + Tool Router schemas)","Observation data (screenshots, API responses)"],"output_types":["Streaming LLM responses (partial JSON with reasoning and tool calls)","Execution logs with intermediate states"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_5","uri":"capability://code.generation.editing.content.script.injection.for.dom.manipulation.and.event.handling","name":"content script injection for dom manipulation and event handling","description":"The Chrome Extension uses content scripts injected into the active tab to interact with the DOM, capture user interactions, and relay information back to the background service worker. This enables the extension to read page structure, inject JavaScript, and monitor network activity without requiring full debugger protocol access for every interaction.","intents":["Extract page structure and content for analysis without full screenshot overhead","Inject JavaScript to modify page behavior or extract data programmatically","Monitor user interactions (clicks, form submissions) to understand page state","Capture network requests and responses for context-aware automation"],"best_for":["Chrome Extension developers who need lightweight DOM access without debugger overhead","Teams building content extraction or page analysis features","Developers implementing user interaction monitoring for analytics"],"limitations":["Content scripts cannot access cross-origin iframes or shadow DOM without special handling","Injected JavaScript runs in the page context, not the extension context, limiting access to extension APIs","Content script injection has a small performance overhead (~10-50ms per injection)","Cannot interact with browser UI elements outside the page (address bar, tabs, etc.)"],"requires":["Chrome Extension Manifest V3 with content_scripts permission","Target page must not have CSP restrictions blocking script injection"],"input_types":["Page DOM and JavaScript context"],"output_types":["Page structure (HTML/CSS), extracted data, event notifications"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_6","uri":"capability://automation.workflow.background.service.worker.orchestration.with.message.passing","name":"background service worker orchestration with message passing","description":"The Chrome Extension's background service worker acts as the orchestration hub, receiving messages from content scripts and the side panel UI, routing them to the appropriate handler (Computer Use or Tool Router), and managing the agentic loop lifecycle. Uses Manifest V3's message passing API to coordinate between extension components while respecting the 5-minute execution timeout.","intents":["Coordinate between multiple extension components (content script, side panel, background worker)","Manage the agentic loop lifecycle within Manifest V3's execution constraints","Handle API calls to Gemini and Composio with proper error recovery","Persist state across extension reloads using chrome.storage.local"],"best_for":["Chrome Extension developers building multi-component agents","Teams implementing message-passing architectures for browser extensions","Developers who need to work within Manifest V3's execution time limits"],"limitations":["Manifest V3 background service workers are terminated after 5 minutes of inactivity, requiring careful state persistence","Message passing between components adds latency (~5-20ms per message round-trip)","No built-in request deduplication; duplicate messages must be handled by the orchestrator","chrome.storage.local has a 10MB quota per extension, limiting persistent state size"],"requires":["Chrome Extension Manifest V3","Message passing handlers in background worker, content scripts, and UI components"],"input_types":["Messages from content scripts and side panel UI"],"output_types":["Routed messages to handlers, state updates to chrome.storage.local"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_7","uri":"capability://automation.workflow.side.panel.ui.with.real.time.agent.execution.visualization","name":"side panel ui with real-time agent execution visualization","description":"The Chrome Extension's side panel provides a chat-like interface where users input natural language prompts and observe real-time agent execution. The UI displays streaming LLM responses, screenshots with action annotations, and tool execution results, allowing users to monitor and interrupt the agent mid-execution.","intents":["Provide users with a familiar chat interface for interacting with the browser agent","Display real-time execution progress with screenshots and action annotations","Allow users to interrupt or modify agent behavior during execution","Show tool selection reasoning and API call results for transparency"],"best_for":["End users who want to see what the agent is doing in real-time","Developers debugging agent behavior and decision-making","Teams building transparent AI products where user trust is critical"],"limitations":["Side panel UI is limited to the active tab context; cannot show multi-tab workflows","Real-time screenshot updates can consume significant bandwidth and CPU","Streaming response rendering requires careful React/Vue state management to avoid jank","Interrupting mid-execution requires careful cleanup of in-flight API calls"],"requires":["Chrome Extension with side panel support (Chrome 114+)","React or similar UI framework for responsive rendering"],"input_types":["User text input (natural language prompts)"],"output_types":["Rendered chat messages, screenshots with annotations, tool results"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_8","uri":"capability://automation.workflow.electron.ipc.layer.with.main.renderer.process.isolation","name":"electron ipc layer with main-renderer process isolation","description":"The Electron desktop app uses Inter-Process Communication (IPC) to separate the main process (which controls BrowserView and system APIs) from the renderer process (which hosts the UI). The main process handles browser automation and API calls, while the renderer displays results. This isolation provides security (renderer cannot directly access system APIs) and stability (renderer crashes don't crash the main process).","intents":["Isolate browser automation logic from UI rendering for stability and security","Access native system APIs (file system, clipboard, notifications) from the main process","Implement secure credential storage using OS-level keychains","Handle multi-window management and inter-window communication"],"best_for":["Teams building desktop applications with strict security requirements","Developers who need access to native system APIs alongside web automation","Organizations requiring OS-level credential storage and isolation"],"limitations":["IPC message passing adds ~50-100ms latency per round-trip, making rapid interactions slower","Serialization of complex objects (screenshots, large JSON) across process boundaries is expensive","Debugging IPC issues requires understanding both main and renderer process logs","Electron binary size is ~150MB, making distribution and updates slower than browser extensions"],"requires":["Electron runtime (bundled in app)","IPC message handlers in both main and renderer processes"],"input_types":["IPC messages from renderer process"],"output_types":["IPC responses with automation results or system API responses"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-composiohq--open-chatgpt-atlas__cap_9","uri":"capability://memory.knowledge.settings.persistence.with.environment.specific.configuration","name":"settings persistence with environment-specific configuration","description":"Manages user settings (API keys, model preferences, automation mode selection) with different persistence backends for Chrome Extension (chrome.storage.local with 10MB quota) and Electron (electron-store with filesystem-based JSON). Settings are loaded at startup and can be modified via a dedicated settings page, with validation and encryption for sensitive credentials.","intents":["Store user API keys and credentials securely without exposing them in code","Allow users to switch between Computer Use and Tool Router modes","Persist user preferences across extension/app restarts","Support multi-user scenarios where different users have different credentials"],"best_for":["Teams building multi-user applications with per-user configuration","Developers who need to support multiple API providers with user-selectable preferences","Organizations with security requirements around credential storage"],"limitations":["chrome.storage.local has a 10MB quota, limiting the amount of persistent data","Electron-store stores data as plaintext JSON on disk, requiring additional encryption for sensitive data","No built-in encryption for API keys in chrome.storage.local; requires manual encryption","Settings changes require page reload or explicit refresh to take effect"],"requires":["Chrome Extension or Electron runtime","Settings page UI (HTML/React component)"],"input_types":["User input from settings form (API keys, model selection, preferences)"],"output_types":["Persisted settings in chrome.storage.local or electron-store"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Google AI Studio API key with access to gemini-2.5-computer-use-preview model","Chrome browser with Manifest V3 support OR Electron runtime","Network connectivity to Google's API endpoints","Composio API key with access to Tool Router","OAuth credentials or API keys for each target service (Gmail, Slack, GitHub, etc.)","Network connectivity to Composio's MCP server and upstream service APIs","API keys for multiple LLM providers (Google Gemini, Composio)","Model-specific request/response parsing logic","Browser API for screenshot capture (chrome.tabs.captureVisibleTab or Electron's BrowserView.webContents.capturePage)","Image encoding library (PNG or JPEG)"],"failure_modes":["Latency overhead from screenshot capture + API round-trip to Gemini (typically 2-5 seconds per action)","Vision model may struggle with small text, complex tables, or heavily styled content","Coordinate normalization to 1000x1000 grid can lose sub-pixel precision on high-DPI displays","No built-in handling for multi-window or cross-origin iframe interactions","Only works for services with Composio integration; unsupported platforms fall back to visual automation","Requires OAuth tokens or API keys for each integrated service, adding credential management complexity","API schema changes in upstream services can break tool definitions without warning","Rate limiting on upstream APIs is not abstracted; agents can hit quotas quickly","Model-specific APIs have different response formats, requiring per-model parsing logic","Fallback logic adds complexity and potential for cascading failures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3579425233787328,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2026-02-20T15:19:26Z"},"community":{"stars":436,"forks":82,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=composiohq--open-chatgpt-atlas","compare_url":"https://unfragile.ai/compare?artifact=composiohq--open-chatgpt-atlas"}},"signature":"Yj/wsMx4ZlIfCejXfw+Pm77SGJ00MdQhvyOGWVKyZIfszcP+Db1DVxkZnEIv8bEcqPptzxOfmB9DfzYNfnmKBw==","signedAt":"2026-06-20T16:17:43.431Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/composiohq--open-chatgpt-atlas","artifact":"https://unfragile.ai/composiohq--open-chatgpt-atlas","verify":"https://unfragile.ai/api/v1/verify?slug=composiohq--open-chatgpt-atlas","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}