| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 14 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Converts raw HTML/CSS/JavaScript into LLM-readable structured text by building a DOM tree, detecting interactive elements (buttons, inputs, links), calculating visibility and viewport coordinates, and assigning numeric indices for element reference. Uses a watchdog pattern with event listeners to track DOM mutations and re-serialize only changed subtrees, enabling efficient context windows for multi-step interactions.
Unique: Uses event-driven watchdog pattern with CDP event listeners to detect DOM mutations and incrementally re-serialize only changed subtrees, rather than full-page re-parsing on each step. Combines bounding box visibility calculation with viewport intersection to filter non-visible elements before serialization, reducing token overhead by 30-50% vs naive full-DOM approaches.
vs alternatives: More efficient than Selenium/Playwright's raw HTML dumps because it pre-processes visibility and coordinates server-side, eliminating the need for LLMs to parse raw HTML or calculate element positions themselves.
Abstracts LLM provider differences (OpenAI, Anthropic Claude, Google Gemini, local Ollama, AWS Bedrock) behind a unified interface that auto-detects provider capabilities and optimizes structured output schemas. Implements provider-specific schema transformation (e.g., converting JSON Schema to Anthropic's tool_use format) and handles streaming vs non-streaming responses with automatic fallback and retry logic including exponential backoff and token limit handling.
Unique: Implements provider capability detection at runtime and auto-transforms action schemas to match provider APIs (e.g., JSON Schema → Anthropic tool_use, OpenAI function_calling → Gemini function_declarations). Includes token counting with provider-specific mappings and automatic context window management via message compaction when approaching limits.
vs alternatives: More flexible than LangChain's LLM abstraction because it handles schema transformation and token counting per-provider, and includes built-in fallback chains (e.g., try OpenAI → fall back to Anthropic → use local Ollama) without requiring manual provider selection.
Provides cloud-native deployment option via browser-use Cloud, with Actor API for low-level CDP command execution and session management. Abstracts away local browser process management, enabling serverless execution of agents. Includes automatic scaling, session pooling, and observability (telemetry, logging) for production deployments. Actor API allows direct CDP command execution for advanced use cases.
Unique: Provides managed cloud infrastructure for browser-use agents with automatic session pooling, scaling, and observability. Actor API allows direct CDP command execution for advanced use cases, bridging gap between high-level actions and low-level browser control.
vs alternatives: More managed than self-hosted browser-use because it handles infrastructure, scaling, and observability. More flexible than Apify because it exposes Actor API for low-level CDP control, not just high-level task execution.
Collects telemetry data (task duration, token usage, action counts, success/failure rates) and sends to browser-use Cloud for analytics and billing. Implements custom pricing models per provider and per-action, enabling cost tracking and optimization. Includes local logging with configurable verbosity and optional cloud sync for centralized observability.
Unique: Implements provider-specific token counting and custom pricing models that map to actual LLM costs (e.g., GPT-4 input/output pricing differs from GPT-3.5). Collects telemetry per-action and per-step, enabling granular cost analysis and optimization.
vs alternatives: More detailed than generic logging because it tracks token usage and cost per-action, enabling cost optimization. More flexible than LLM provider dashboards because it aggregates costs across multiple providers and custom actions.
Detects browser popups, alerts, and modal dialogs using CDP's Page.javascriptDialogOpening event and DOM inspection for modal elements. Automatically dismisses or accepts dialogs based on configurable rules (e.g., dismiss all alerts, accept confirmations). Handles file download dialogs, print dialogs, and permission prompts. Prevents popups from blocking agent execution.
Unique: Uses CDP's Page.javascriptDialogOpening event for native browser dialog detection combined with DOM inspection for custom modal dialogs. Implements configurable rules for automatic handling (dismiss, accept, ignore) and supports permission prompt automation via Chrome launch arguments.
vs alternatives: More reliable than Playwright's dialog handling because it uses CDP events instead of promise-based handlers, avoiding race conditions. More comprehensive because it handles both native dialogs and custom modals.
Manages file downloads via CDP's Page.downloadWillBegin event and configurable download directory. Detects file uploads and provides helper methods to inject files into file input elements via CDP's Input.setFiles command. Handles file path validation, MIME type detection, and cleanup of temporary files.
Unique: Uses CDP's Page.downloadWillBegin event for reliable download detection and Input.setFiles for file injection without JavaScript, avoiding timing issues. Includes file path validation and MIME type detection.
vs alternatives: More reliable than Playwright's download handling because it uses CDP events directly. More flexible than Selenium because it supports both downloads and uploads via CDP.
Implements a stateful agent loop that executes: (1) serialize current browser state to LLM context, (2) call LLM to generate next action, (3) execute action via CDP, (4) detect if agent is stuck in a loop (same action repeated N times or same DOM state for M steps), and (5) inject behavioral nudges (e.g., 'try a different approach') or force action diversification. Maintains full message history with optional compaction to prevent context explosion on long-running tasks.
Unique: Combines DOM hash-based loop detection with action frequency analysis and injects rule-based behavioral nudges (e.g., 'try clicking a different element' or 'navigate to a new page') before forcing action diversification. Message compaction uses LLM-based summarization of old steps to preserve context while reducing token count, with configurable retention of recent N steps.
vs alternatives: More sophisticated than simple ReAct loops because it detects and recovers from common failure modes (infinite loops, dead-ends) without human intervention, and includes message compaction to handle 100+ step tasks within typical context windows.
Manages lifecycle of CDP connections to Chrome/Chromium instances, including browser launch with custom arguments, profile persistence, tab/frame management, and connection pooling for concurrent agent sessions. Implements SessionManager that maintains a pool of reusable CDP connections, handles target switching between tabs/frames, and provides graceful shutdown with cleanup of browser processes and temporary profiles.
Unique: Implements a SessionManager with connection pooling that reuses CDP connections across multiple agent runs, reducing browser startup overhead from 2-5 seconds to <100ms for pooled connections. Supports storage state import/export (cookies, local storage) for stateful workflows and handles target switching via CDP protocol's Target.setDiscoverTargets and Target.attachToTarget commands.
vs alternatives: More efficient than Playwright's browser pooling because it maintains persistent profiles and storage state across sessions, enabling true stateful automation without re-login overhead. Lighter-weight than Selenium because it uses CDP directly rather than WebDriver protocol, reducing latency by 30-50%.
+6 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs browser-use at 24/100. browser-use leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities