| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Creates and manages isolated tmux sessions for AI coding agents (Claude Code, OpenCode, Mistral Vibe, Gemini CLI, etc.) through a Rust-based wrapper that abstracts tmux complexity. Each session is assigned a unique 8-character UUID and human-readable title, with lifecycle management (attach/detach/kill) exposed via CLI and TUI. The system maintains session state in persistent storage keyed by profile, enabling recovery and resumption across terminal restarts.
Unique: Wraps tmux with domain-specific abstractions (Instance, GroupTree, Storage) designed explicitly for AI agent lifecycle management, rather than generic terminal multiplexing. Implements automatic status detection (Running/Waiting/Idle) by parsing agent-specific process output patterns, and provides hierarchical session grouping via a tree structure stored in profile-isolated persistent storage.
vs alternatives: Simpler than managing raw tmux for multi-agent workflows and more specialized than generic terminal multiplexers like Zellij or screen, with built-in awareness of AI agent state transitions.
Maintains multiple independent profiles (contexts) where each profile has its own session storage, worktree configuration, and Docker sandbox settings. Profiles are stored in a configuration directory and loaded on-demand, enabling developers to switch between completely isolated workspaces (e.g., 'project-a', 'project-b', 'experimentation') without session collision. The Storage system (src/session/storage.rs) provides profile-keyed persistence with automatic directory creation and cleanup.
Unique: Implements profile isolation at the storage layer (src/session/storage.rs) with automatic directory scoping, allowing complete session independence without manual path management. Profiles are composable with worktree and Docker sandbox configurations, enabling per-project agent behavior customization.
vs alternatives: More lightweight than containerized workspace solutions (Docker Compose) while providing stronger isolation than simple directory-based organization, with explicit profile switching semantics.
Supports multiple AI coding agent providers (Claude Code, OpenCode, Mistral Vibe, Codex CLI, Gemini CLI, Pi.dev, GitHub Copilot CLI, Factory Droid Coding) with agent-specific configuration and status detection patterns. Each agent type has a profile in AGENTS.md defining its CLI invocation, output patterns for status detection, and configuration requirements. The system abstracts agent differences, allowing users to create sessions for any supported agent without learning provider-specific details.
Unique: Implements agent abstraction via AGENTS.md configuration file defining CLI invocation, status detection patterns, and requirements for each supported provider. Allows users to create sessions for any agent without provider-specific code, with extensible status detection based on agent output patterns.
vs alternatives: More flexible than single-agent tools and more practical than requiring users to manage agent CLIs directly, with explicit support for multiple providers and automatic status detection.
Persists session metadata (title, agent type, working directory, group membership, parent-child relationships) to disk in profile-scoped storage, enabling sessions to survive terminal restarts, SSH disconnections, and system reboots. When aoe is restarted, it reads session metadata from storage and can reattach to existing tmux sessions or recreate them if they were lost. The system maintains a session index for fast lookup and supports session cleanup (removing orphaned metadata for deleted sessions).
Unique: Implements profile-scoped session persistence (src/session/storage.rs) with automatic metadata serialization and recovery on startup. Maintains a session index for fast lookup and supports orphaned session cleanup, enabling seamless session recovery across system restarts.
vs alternatives: More reliable than tmux's default session persistence (which is lost on server restart) and more lightweight than full database-backed session management, with explicit profile isolation.
Allows users to define session templates and default configurations in YAML files (profile configuration, worktree settings, Docker sandbox config, agent defaults). When creating a session, users can reference a template to inherit configuration, reducing repetitive setup. Configuration is hierarchical: global defaults, profile-level defaults, and session-level overrides. The system validates configuration on load and provides helpful error messages for invalid settings.
Unique: Implements hierarchical configuration (global, profile, session) with YAML-based templates and defaults, enabling teams to standardize session setup without code changes. Configuration is profile-scoped and supports overrides at multiple levels.
vs alternatives: More flexible than hardcoded defaults and more practical than manual configuration for each session, with explicit support for team-wide standardization.
Organizes sessions into a tree structure (GroupTree in src/session/group_tree.rs) where sessions can be nested under logical groups (e.g., 'frontend', 'backend', 'experiments'). Groups are displayed hierarchically in the TUI and can be collapsed/expanded for navigation. The system supports sub-sessions and parent-child relationships, enabling developers to logically cluster related agent sessions and manage them as units.
Unique: Implements a tree-based session organization model (GroupTree) that persists group membership in profile storage, enabling logical clustering without requiring separate configuration files. Supports sub-sessions and parent-child relationships, allowing developers to fork sessions and maintain lineage.
vs alternatives: More structured than flat session lists (like tmux's default) while simpler than full project management systems, with explicit parent-child semantics for session forking workflows.
Monitors tmux session processes to automatically detect and classify agent state as Running, Waiting, or Idle by parsing agent-specific output patterns and process introspection. The status detection implementation (src/session/instance.rs and src/tmux/) analyzes terminal output and process trees to infer whether an agent is actively executing code, waiting for user input, or idle. Status is cached and updated on-demand to avoid expensive polling.
Unique: Implements agent-specific status detection patterns (defined in AGENTS.md) that parse output from different AI coding agents (Claude Code, OpenCode, Mistral Vibe, Gemini CLI, etc.) rather than generic process state. Uses process tree introspection combined with terminal output analysis to infer semantic state (Running vs Waiting vs Idle).
vs alternatives: More intelligent than simple process state checks (running/stopped) and more practical than requiring explicit status reporting from agents, with built-in awareness of multiple agent types.
Creates and manages Git worktrees for each session, enabling parallel development branches without switching the main working directory. When a session is created with worktree support, the system automatically creates a new worktree at a path derived from a configurable template (e.g., ~/.agent-of-empires/worktrees/{profile}/{session-id}), checks out a specified branch, and cleans up the worktree when the session is destroyed. This allows multiple agents to work on different branches simultaneously without file system conflicts.
Unique: Integrates Git worktree management directly into the session lifecycle (src/git/), with automatic creation and cleanup tied to session creation/destruction. Uses configurable path templates to organize worktrees by profile and session ID, enabling scalable parallel development without manual git commands.
vs alternatives: More integrated than manual git worktree commands and more flexible than Docker-based isolation, with explicit support for multi-agent parallel development on the same repository.
+5 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 agent-of-empires at 42/100. agent-of-empires 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