| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 11 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Enforces a disciplined 5-mode engineering cycle (Understand → Explore → Choose → Execute → Verify) by requiring AI agents to frame problems before solving them, generate genuinely different solution variants, and compare options under parity constraints. Implements this via MCP tools (haft_problem, haft_solution, haft_decision) that validate reasoning artifacts against a formal specification before allowing progression to implementation.
Unique: Implements a formal specification-driven reasoning cycle with maturity (Unassessed → Shipped) and freshness (Healthy → Stale → At Risk) tracking, enforcing parity in comparisons via a knowledge graph that links decisions to codebase artifacts — unlike generic prompt engineering, this creates falsifiable contracts with evidence decay mechanics
vs alternatives: Differs from Cursor/Claude Code's native reasoning by adding governance layer that prevents decision drift and enforces structured comparison, whereas standard agents optimize for speed-to-code
Exposes Haft's reasoning capabilities as a Model Context Protocol (MCP) server via JSON-RPC transport, providing six specialized tools (haft_problem, haft_solution, haft_decision, haft_evidence, haft_check, haft_search) that AI agents can invoke natively within their execution environment. The server runs as a subprocess managed by the agent's MCP client, maintaining a persistent SQLite state store and knowledge graph indexed to the codebase.
Unique: Implements MCP as the primary delivery surface (not a secondary plugin), with six domain-specific tools designed for the FPF cycle rather than generic function calling — includes codebase-aware search and evidence decay scoring built into the protocol layer
vs alternatives: More specialized than generic MCP servers (e.g., Anthropic's file-system MCP) because tools are designed for reasoning governance, not file I/O; tighter integration with decision lifecycle than REST APIs
Enforces equal rigor in comparing competing solutions by requiring that all variants be evaluated against the same criteria, preventing bias toward preferred solutions. Implements parity checks via the haft_solution and haft_decision tools that validate solution descriptions follow the same structure and depth. Tracks comparison fairness metrics to ensure decisions are based on equivalent evidence.
Unique: Implements structural parity checks that validate all solutions follow the same evaluation template and depth — unlike generic decision frameworks, this prevents strawman alternatives and ensures fair comparison
vs alternatives: More rigorous than informal decision-making because it enforces structural equivalence; differs from decision matrices by focusing on comparison process rather than scoring
Monitors the health of engineering decisions across two axes: maturity (progress from Unassessed to Shipped) and freshness (Healthy → Stale → At Risk based on evidence age and drift detection). Implements R_eff (effective reasoning score) that decays over time as supporting evidence ages, triggering alerts when decisions drift from their original context. Uses SQLite schema with timestamp-based queries to identify stale decisions and prompt re-evaluation.
Unique: Implements a two-axis decision lifecycle model (maturity + freshness) with time-decay scoring (R_eff) that automatically degrades decision confidence — unlike static decision logs, this creates a living system where old decisions are flagged for re-evaluation without manual intervention
vs alternatives: More sophisticated than ADR (Architecture Decision Records) because it tracks decision health over time and flags staleness; differs from code review tools by focusing on decision validity rather than code quality
Builds a knowledge graph that links engineering decisions to codebase artifacts (modules, functions, files) using FPF Spec Search & Indexer. Enables semantic search over past decisions filtered by codebase context, allowing agents to query 'decisions affecting this module' or 'solutions tried for this problem pattern'. Stores graph in SQLite with projections that map decisions to code locations and vice versa.
Unique: Implements a bidirectional knowledge graph (decisions ↔ code artifacts) with FPF Spec Search that understands decision semantics and codebase structure simultaneously — unlike generic code search, this links reasoning to implementation and enables decision-centric queries
vs alternatives: More targeted than full-text search because it understands decision structure and codebase topology; differs from RAG systems by maintaining explicit decision-to-code mappings rather than embedding-based retrieval
Provides a terminal-based autonomous agent (haft agent command) that executes the engineering cycle without human intervention, using a ReAct-style coordinator to move through Understand → Explore → Choose → Execute → Verify phases. The coordinator maintains state in SQLite and can pause at checkpoints for human review. Implements a lemniscate cycle pattern that allows looping back to earlier phases if verification fails.
Unique: Implements a lemniscate cycle (figure-8 loop) that allows backtracking from Verify to earlier phases if verification fails, rather than linear progression — enables iterative refinement without restarting the entire cycle
vs alternatives: More structured than generic ReAct agents because it enforces FPF phases; differs from Devin/Claude Code by running autonomously in terminal without IDE, making it suitable for headless environments
Abstracts LLM provider differences (OpenAI Codex, Anthropic Claude, Google Gemini) behind a unified interface, allowing the same FPF reasoning cycle to work across different models. Routes tool calls and reasoning prompts to the configured provider via a provider adapter pattern, with fallback support for multiple models. Stores provider configuration in project policy files.
Unique: Implements provider abstraction at the reasoning level (not just API calls), allowing the same FPF cycle to work across Claude, Codex, and Gemini with different tool-calling conventions — uses adapter pattern to normalize provider differences
vs alternatives: More flexible than single-provider agents (Claude Code, Cursor) because it supports provider switching; differs from LangChain by focusing on reasoning governance rather than generic LLM chaining
Enforces project-level governance policies via .haft/ directory containing formal specifications (FPF Spec), provider configurations, and decision templates. Policies are versioned and can be checked via haft check command to ensure decisions comply with project standards. Implements a policy-as-code approach where governance rules are stored alongside the project and enforced by the Haft runtime.
Unique: Implements governance as versioned policy files in .haft/ directory (similar to .github/ workflows), making policies auditable and version-controlled alongside code — unlike external governance systems, policies live in the repository
vs alternatives: More integrated than external compliance tools because policies are co-located with code; differs from linters by enforcing reasoning discipline rather than code style
+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 haft at 40/100. haft 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