| 0 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Monitors changes made by AI agents (Cursor, Copilot, Claude Code, Codex, Continue, Codeium) in real-time and generates issue cards when operations fail, using terminal output analysis, VS Code Problems panel monitoring, and dependency tracking to identify divergence between expected and actual repository state before user commits.
Unique: Adds a supervision layer specifically for AI agents by monitoring terminal output, Problems panel, and file changes simultaneously to detect failures before commit — most code editors lack this multi-signal failure detection for agent-generated code.
vs alternatives: Unlike native Copilot or Claude Code error handling, Unfold AI provides cross-agent failure detection and pre-commit review gates, catching issues from any supported agent in a unified interface.
Captures automatic checkpoints around meaningful work during AI-assisted coding sessions and enables comparison between current state, previous checkpoints, and checkpoint-to-checkpoint diffs. On Pro/Ultra plans, generates AI-powered semantic titles for older checkpoints to make session history navigable without manual annotation.
Unique: Combines automatic checkpoint capture with AI-generated semantic titles (Pro/Ultra) to make session history navigable by meaning rather than timestamp — most editors only offer git history or manual save points, not AI-annotated session checkpoints.
vs alternatives: Provides finer-grained session history than git commits (captures intermediate agent work) and adds semantic understanding via AI titles, whereas VS Code's native undo/redo lacks agent-aware context and Cursor's built-in history lacks cross-session comparison.
Generates natural language commit messages for agent-assisted changes by analyzing the full session context (checkpoints, changes, failures, root causes, fixes applied). Commit summaries are grounded in actual session evidence rather than generic templates, providing meaningful context for future code review and history.
Unique: Generates commit messages grounded in full session evidence (failures, fixes, root causes) rather than just file diffs — most git tools generate messages from diffs alone without semantic context.
vs alternatives: Unlike conventional commit tools or AI-powered commit message generators, Unfold AI includes session-specific context (failures, recovery steps, root causes) in commit messages, making them more informative for future reviewers.
Analyzes all changes made during an AI-assisted session and generates pre-commit risk signals by tracking which agent made which changes, identifying high-risk patterns (dependency modifications, API changes, security-sensitive code), and attributing changes to specific agents or user actions. Provides structured change summaries grounded in actual session evidence.
Unique: Generates pre-commit risk signals by analyzing agent-specific change patterns and dependency modifications in real-time, with attribution tracking — most code editors lack agent-aware risk assessment and change attribution.
vs alternatives: Unlike generic pre-commit hooks or linters, Unfold AI understands which AI agent made which change and flags agent-specific risk patterns (e.g., incomplete refactors by Copilot), providing context-aware risk signals rather than syntax-only checks.
When an agent operation fails, analyzes session context (terminal output, file changes, Problems panel diagnostics, dependency state) and generates an AI-powered explanation of the likely root cause. Uses session timeline reconstruction to correlate failures with specific agent actions and provide actionable context for recovery.
Unique: Generates AI-powered root cause explanations by correlating terminal output, file changes, and session timeline — most debugging tools show raw errors; Unfold AI adds semantic analysis of why the agent's action failed.
vs alternatives: Unlike VS Code's native error messages or agent-specific error handling, Unfold AI provides cross-agent root cause analysis grounded in session context, making it faster to diagnose failures from any supported agent.
Generates a proposed fix plan for detected failures, claiming to identify the 'smallest safe fix' needed to recover from the failure. On Pro/Ultra plans, provides auto-apply capability to automatically apply the fix plan to the codebase; on Free plan, presents fix plan as a suggestion for manual review and application.
Unique: Generates agent-specific fix plans by analyzing failure context and proposes 'smallest safe fix' — most agents lack built-in failure recovery; Unfold AI adds automated fix proposal and optional auto-apply for Pro/Ultra users.
vs alternatives: Unlike Copilot or Claude Code's error handling (which requires manual user fixes), Unfold AI proposes specific fixes and can auto-apply them on Pro/Ultra plans, reducing manual debugging overhead.
Provides an interactive chat interface within VS Code that is pre-loaded with full session context (checkpoints, changes, failures, agent actions) so users can ask questions about what happened during their AI-assisted coding session. Chat responses are grounded in actual session evidence rather than general knowledge.
Unique: Provides a chat interface pre-loaded with full session context (checkpoints, changes, failures) so responses are grounded in actual session evidence — most chat interfaces lack session-specific context.
vs alternatives: Unlike generic ChatGPT or Copilot chat, Unfold AI's chat knows your full session history and can answer questions about what your agent did, making it more useful for session-specific debugging.
Monitors changes from multiple AI agents (Cursor, GitHub Copilot, Claude Code, Codex, Continue, Codeium) simultaneously and surfaces all failures, changes, and risk signals in a unified dashboard within VS Code. Tracks which agent made which change and correlates failures to specific agent actions across the session.
Unique: Provides unified monitoring and attribution for multiple AI agents (Cursor, Copilot, Claude Code, Codex, Continue, Codeium) in a single VS Code dashboard — most agents operate in isolation without cross-agent visibility.
vs alternatives: Unlike individual agent error handling, Unfold AI provides a unified view of all agent activity and failures, making it easier to manage multi-agent workflows and identify which agent caused issues.
+3 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 ChatGPT - Unfold AI at 42/100. ChatGPT - Unfold AI 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