| 0 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Enables users to create and edit prompt flow definitions using a directed acyclic graph (DAG) model persisted as flow.dag.yaml files. The extension provides both a visual editor (triggered via Ctrl+K,V or code lens) and YAML text editing with inline code lens actions, allowing developers to define multi-step LLM workflows by composing nodes (prompts, tools, Python functions) and connecting them via data dependencies. The visual editor abstracts YAML complexity while maintaining full fidelity with the underlying DAG structure.
Unique: Dual-mode editing (visual + YAML) with code lens integration allows developers to switch between abstraction levels without losing fidelity; the DAG model enforces structural correctness at definition time rather than runtime, catching dependency errors early in the authoring process.
vs alternatives: Tighter VS Code integration and YAML-first approach provides better version control and diff visibility than GUI-only flow builders like Langflow or LlamaIndex, while remaining more accessible than pure code-based frameworks.
Provides a debugging interface (triggered via F5 keybinding) that executes a prompt flow step-by-step with breakpoint support, allowing developers to inspect intermediate outputs, variable states, and node execution results. The debugger integrates with VS Code's standard debug protocol, displaying execution traces and enabling pause/resume/step-through workflows. This capability surfaces runtime behavior of LLM calls and tool invocations, helping developers identify logic errors, unexpected model outputs, or data transformation issues within their flows.
Unique: Integrates with VS Code's native debug protocol rather than implementing a custom debugger, enabling familiar debugging UX (breakpoints, watch expressions, call stack) for LLM workflows; node-level granularity provides abstraction appropriate for prompt flows while remaining more detailed than black-box API testing.
vs alternatives: More integrated debugging experience than LangChain's print-based debugging or LlamaIndex's logging, while avoiding the overhead of full Python debugger context switching for LLM-specific workflows.
Collects usage data about extension interactions (flow creation, debugging, testing, connection management) and sends telemetry to Microsoft for product improvement and analytics. The telemetry system tracks feature adoption, error rates, and user workflows to inform development priorities. While telemetry is enabled by default, users can disable it via VS Code settings, providing opt-out capability. This capability enables Microsoft to understand how developers use prompt flows and identify areas for improvement.
Unique: Integrated telemetry collection via VS Code's telemetry framework rather than custom implementation; provides opt-out capability through VS Code settings, respecting user privacy preferences.
vs alternatives: Standard approach for VS Code extensions; less invasive than extensions implementing custom telemetry, though users have limited visibility into what data is collected compared to transparent telemetry systems.
Restricts flow execution to local development machines, with explicit non-support for remote execution environments (SSH, containers, WSL, web-based VS Code). Flows execute within the selected local Python interpreter, limiting deployment to development and testing scenarios. This design choice prioritizes simplicity and local debugging experience over production deployment capabilities, positioning the extension as a development tool rather than a production orchestration platform.
Unique: Explicitly non-supporting remote execution (SSH, containers, WSL, web VS Code) reflects design choice to prioritize local development experience; this constraint simplifies architecture but limits deployment scenarios.
vs alternatives: Simpler local debugging experience than cloud-based flow platforms, but requires separate deployment pipeline for production; better for development-focused teams, worse for integrated dev-to-prod workflows.
Enables running test suites against prompt flows via Shift+F5 keybinding, executing flows against predefined test datasets and comparing outputs against expected results. The testing framework supports batch execution of flows with multiple input variations, collecting metrics (latency, token usage, success/failure rates) and surfacing test results in VS Code's test explorer. This capability allows developers to validate flow behavior across diverse inputs and detect regressions when modifying prompts or node logic.
Unique: Integrates testing directly into VS Code's test explorer UI, allowing developers to run and review flow tests alongside unit tests for Python code; batch execution model enables rapid iteration on prompts with quantitative feedback without manual test harness coding.
vs alternatives: More integrated testing experience than standalone evaluation frameworks like RAGAS or Promptfoo, though less feature-rich for advanced evaluation metrics like semantic similarity or LLM-as-judge scoring.
Provides a connection management system (accessible via sidebar 'Connections' section) that abstracts credentials and API endpoints for external services (e.g., Azure OpenAI, custom APIs). Developers define connections via YAML templates (create/update_{ConnectionType}_connection.yaml) with code lens guidance, and flows reference connections by name rather than embedding credentials. The extension handles credential storage and injection at runtime, supporting multiple connection types through a generic connection framework. This decouples flow definitions from environment-specific secrets and enables reuse across development, staging, and production environments.
Unique: Implements connection abstraction at the flow definition level, allowing flows to reference services by logical name rather than hardcoded endpoints; YAML-based connection templates enable version control of connection schemas while keeping actual credentials separate from flow definitions.
vs alternatives: More lightweight than full secret management systems (Vault, AWS Secrets Manager) while providing better credential isolation than embedding secrets in code; less feature-rich than enterprise secret stores but sufficient for local development and small-team collaboration.
Allows developers to add nodes to flows (via Ctrl+Cmd+N keybinding or visual editor) and define data dependencies between them, creating a directed acyclic graph of operations. Nodes represent discrete units of work: LLM prompts, tool invocations, Python functions, or data transformations. The extension manages node inputs/outputs, type checking, and data flow routing, ensuring outputs from upstream nodes correctly feed into downstream node inputs. This capability abstracts the complexity of orchestrating multiple LLM calls and tool invocations into a declarative dependency graph.
Unique: Declarative dependency model (vs imperative code) makes flow structure explicit and enables visual representation; DAG enforcement catches circular dependency errors at definition time rather than runtime, improving debuggability.
vs alternatives: More structured than LangChain's imperative chains while remaining more flexible than rigid workflow engines; visual representation provides better understanding of flow topology than code-only approaches.
Integrates with VS Code's Python extension to detect and manage the active Python interpreter, enabling flows to execute within a specific Python environment with isolated dependencies. The extension provides an 'Install dependencies' action in the sidebar that installs promptflow and promptflow-tools packages into the selected environment. This capability ensures flows run with correct package versions and allows developers to manage environment-specific dependencies (e.g., custom tool packages) without affecting system Python or other projects.
Unique: Leverages vscode-python's environment detection to provide seamless integration with existing Python workflows; sidebar 'Install dependencies' action reduces friction for setting up flow environments compared to manual pip install commands.
vs alternatives: More integrated than standalone dependency management tools, but less feature-rich than full environment management systems like Poetry or Conda; relies on vscode-python rather than implementing independent environment detection.
+4 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 Prompt flow for VS Code at 38/100. Prompt flow for VS Code 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