| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes 600+ pre-built tool integrations through a single Model Context Protocol (MCP) server that directly connects to agentic IDEs like Cursor and Windsurf without requiring custom configuration per tool. The MCP server dynamically discovers available functions from functions.json metadata files and handles OAuth2 token management transparently, allowing agents to call external APIs with authenticated credentials automatically managed by the SecurityCredentialsManager and OAuth2Manager components.
Unique: Centralizes 600+ tool integrations behind a single MCP server with transparent OAuth2 credential management via SecurityCredentialsManager, eliminating per-tool configuration in IDEs. Uses hierarchical organization/project/agent structure to enforce fine-grained permissions through natural language custom instructions rather than role-based access control.
vs alternatives: Faster IDE integration than building custom MCP servers for each tool because it leverages pre-built connectors and handles authentication server-side, reducing IDE-side complexity to zero.
Manages per-user OAuth2 flows and API key storage across 600+ integrated services through the OAuth2Manager and SecurityCredentialsManager components, which handle token acquisition, refresh, and rotation automatically. The LinkedAccount model stores encrypted credentials in the database with automatic token refresh triggered before expiration, eliminating manual credential management for developers and ensuring agents always have valid authentication without interrupting execution.
Unique: Implements automatic token refresh via OAuth2Manager that proactively refreshes tokens before expiration based on service-specific refresh windows, preventing runtime auth failures. Uses LinkedAccount model to support multiple accounts per user per service, enabling agents to switch between different user contexts (e.g., multiple Gmail accounts) without re-authentication.
vs alternatives: More reliable than agent-side token management because refresh happens server-side with guaranteed uptime, and more flexible than static API key storage because it supports OAuth2 services that require periodic token rotation.
Implements a robust function execution pipeline (backend/app/services/) that validates incoming function calls against JSON schemas defined in functions.json, performs type checking and parameter coercion, evaluates project-level permissions, manages credential lookup and OAuth2 token refresh, and routes calls to the appropriate connector implementation. The pipeline includes comprehensive error handling with structured error responses, automatic retry logic for transient failures, and execution logging for audit trails.
Unique: Implements a comprehensive execution pipeline that combines schema validation, permission checking, credential management, and error handling in a single flow, ensuring that function calls are safe, authenticated, and logged. Pipeline is service-agnostic, applying the same validation and error handling logic to all 600+ connectors.
vs alternatives: More robust than agent-side error handling because validation and retries happen at the platform level, and more auditable than direct API calls because all executions are logged with full context.
Enables users to link multiple accounts for the same service (e.g., multiple Gmail accounts, multiple Slack workspaces) through the LinkedAccount model and OAuth2Manager, allowing agents to switch between different user contexts without re-authentication. The system stores encrypted credentials per linked account, tracks which account is active for each agent or project, and automatically selects the correct credentials when executing functions.
Unique: Supports multiple linked accounts per user per service through the LinkedAccount model, enabling agents to operate across multiple user contexts (e.g., multiple Gmail accounts) without re-authentication. Account selection is explicit and can be controlled by agents or configured at the project level.
vs alternatives: More flexible than single-account-per-service systems because it supports multiple contexts, and more secure than sharing credentials across users because each linked account is encrypted and isolated.
Enables agents to discover available tool capabilities at runtime by parsing functions.json metadata files that define function signatures, parameters, descriptions, and authentication requirements without hardcoding. The function execution pipeline in backend/app/services/ validates incoming function calls against these schemas, performs type checking, and routes calls to the appropriate connector implementation, supporting both direct function calling and MCP-based invocation with automatic parameter validation.
Unique: Uses declarative functions.json files as the source of truth for tool capabilities, enabling agents to discover functions without hardcoding and allowing new tools to be added by simply adding a new connector directory with a functions.json file. Schema-based validation in the function execution pipeline ensures type safety before calling external APIs.
vs alternatives: More maintainable than hardcoded tool lists because schema changes only require updating functions.json, and more flexible than static tool registries because new tools can be discovered at runtime without agent redeployment.
Enforces fine-grained access control through project-level custom instructions that define what agents can and cannot do using natural language constraints rather than role-based access control. These instructions are evaluated during function execution to determine if a requested operation is permitted, allowing developers to write policies like 'agents can only read emails, not send them' or 'agents cannot delete resources' without implementing custom authorization logic.
Unique: Uses natural language custom instructions as the policy mechanism rather than role-based access control, allowing non-technical stakeholders to define agent permissions without code. Policies are evaluated at the project level, applying uniformly to all agents in that project while supporting per-agent overrides through agent-specific instructions.
vs alternatives: More flexible than role-based access control because policies can express complex business logic (e.g., 'only allow deployments on Fridays'), and more maintainable than code-based authorization because policies are readable and auditable without requiring code review.
Provides native SDKs for Python and TypeScript that enable direct function calling without MCP, allowing developers to integrate ACI.dev into any LLM framework (LangChain, CrewAI, custom implementations) by instantiating an ACI client and calling functions directly. The SDKs handle credential lookup, OAuth2 token management, and function routing transparently, exposing a simple API like `aci.call('service.function', params)` that abstracts away authentication and service discovery complexity.
Unique: Provides language-native SDKs (Python and TypeScript) that abstract away MCP protocol complexity, allowing developers to use ACI.dev as a simple function-calling library within any framework. SDKs handle credential lookup from LinkedAccount storage and OAuth2 token refresh automatically, requiring only a single API key or OAuth2 credential per user.
vs alternatives: Simpler to integrate than MCP for framework-based agents because it requires no protocol implementation, and more flexible than REST APIs because SDKs provide type-safe function calling with automatic parameter validation.
Organizes users, tools, and permissions through a three-level hierarchy (Organization → Project → Agent) with quota enforcement via the QuotaManager component that tracks and limits function calls, API usage, and resource consumption per organization or project. The hierarchical structure enables multi-tenant isolation, allowing organizations to manage multiple projects with different agents while enforcing shared quotas and billing across the entire organization.
Unique: Implements a three-level hierarchy (Organization → Project → Agent) with quota enforcement at each level, enabling organizations to manage multiple projects with different agents while enforcing shared quotas. QuotaManager component provides real-time quota tracking and enforcement, preventing function calls that would exceed limits.
vs alternatives: More granular than simple per-user quotas because it supports per-project and per-organization limits, and more flexible than static quota allocation because quotas can be adjusted dynamically without redeploying agents.
+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 aci at 39/100. aci 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