ClawTeams vs Orca: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ClawTeams and Orca — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ClawTeams
ClawTeam (HKUDS / community)
CLI-native swarm orchestration that spawns, coordinates, and monitors teams of AI agents to split work and deliver results back into chat.
Key features
- Leader-Worker Orchestration: A Leader agent automatically spawns and manages Worker agents, injects collaboration prompts, and supervises progress to coordinate complex tasks without manual intervention.
- Workspace Isolation: Each agent runs in an isolated git worktree and tmux window to allow parallel development and prevent conflicts; includes commands for checkpoints, merging, and cleanup.
- Task Dependency Tracking: Built-in task lifecycle and dependency management (pending → in_progress → completed/blocked) with --blocked-by chains and a task-wait primitive to block until dependencies finish.
- Inter-Agent Communication & File Transfer: Point-to-point inboxes, broadcasts, file transfers, and optional ZeroMQ P2P transport with offline fallback for robust agent messaging and artifact exchange.
- One-Command Team Templates: TOML-based team templates and a single-command launch (clawteam launch) to instantiate pre-configured swarms for research, hedge-fund analysis, content studios, or engineering teams.
- Monitoring & Dashboards: Terminal kanban board (board show, board live, board attach) and a web UI (board serve) for real-time team performance, progress tracking, and bottleneck identification.
- Compatibility & Extensibility: Works with multiple CLI agents and backends (OpenClaw, Claude Code, Codex, nanobot, Cursor, etc.), and supports custom agent CLIs in PATH for flexible integration.
- Local-First State Management: All state stored as atomic JSON files under ~/.clawteam (no central server required), enabling crash-safe, local orchestration and easy portability.
- Agent spawning and leader/worker orchestration: leader agent creates and manages multiple specialized worker agents
- Task decomposition and dependency management: create tasks, set --blocked-by dependencies, automatic unblocking and task wait until completion
- Workspace isolation: per-agent Git worktrees (separate branches) to avoid parallel conflicts and support checkpoints/merges/cleanup
- Inter-agent communication: point-to-point inbox, broadcasts, file transfer by default and optional ZeroMQ P2P transport with offline fallback
- CLI command surface: binary 'clawteam' (installed via pip) with commands for team lifecycle (spawn-team, discover, status, cleanup), task CRUD (create, list, update, get, stats, wait) and board controls
- Monitoring & UIs: terminal kanban board (board show, board live, board attach), tmux tiled views, and board serve for a Web UI real-time dashboard
- Team templates: TOML-defined team templates (roles, tasks, prompt words) and one-command launch (clawteam launch) for pre-built swarms (e.g., hedge-fund, research, dev teams)
- Compatibility: wide compatibility with CLI agents (OpenClaw, Claude Code, Codex, nanobot, Cursor, any CLI agent available in PATH)
- Transport & data handling: filesystem-based messaging default; optional ZeroMQ for P2P transfers; file transfer primitives included
- Multi-user and scaling features: config management, multi-user workflows, P2P transport, and support for large-scale ML experiment orchestration
Best for
- Large-Scale ML AutoResearch: Orchestrate multi-GPU experiments where a Leader spawns specialized training and evaluation agents, dynamically reallocates GPU resources, and converges model architectures and hyperparameters.
- Agentic Full-Stack Engineering: Parallelize software development by splitting tasks into API, backend, frontend, and tests; each agent works on an isolated git worktree and results are automatically merged and validated.
- Automated Investment Committees: Launch a pre-built hedge-fund template with multiple analyst agents (value, growth, technical, fundamentals, sentiment) plus a risk manager that aggregates signals and suggests portfolio actions.
- Content Production Studios: Run teams of writers, editors, and formatters as agents to draft, edit, and publish articles or social posts in parallel, with an overseer agent ensuring quality and consistency.
- Customer Support & Ops Automation: Deploy packs that manage ticket triage, draft responses, summarize feedback, and escalate issues across agent roles while tracking task state on the kanban board.
- Rapid Prototyping & Research Sprints: Use one-command templates to spin up cross-functional teams that research, prototype, and produce deliverables (design docs, experiments, reports) with minimal human orchestration.
- Automated Code Review & Refactoring: Spawn reviewer agents to analyze repositories, propose refactors, run tests, and create pull-ready branches in separate worktrees for safe parallel improvements.
- Automated ML research: spawn multi-agent experimental workflows across GPUs, automatic experiment design and dynamic resource reallocation
- Agentic engineering: parallel full-stack development with agents splitting API/backend/frontend/testing tasks and merging results
- Quantitative research / automated investing: multi-analyst agent teams for market research, portfolio optimization and execution
- Content production studios: parallelized research, drafting, editing and publishing pipelines
- Customer support and operations: agent teams for ticket triage, replies, summarization and escalation
O
Orca
Stably AI
Desktop AI orchestrator that runs Codex, Claude Code, OpenCode, and Pi side by side in parallel git worktrees, all tracked in one place.
Key features
- Parallel Agent Worktrees: Fan one prompt across up to five agents, each in its own isolated git worktree — compare results and merge the winner.
- Multi-Agent Support: Run Codex, Claude Code, OpenCode, and Pi side by side, all tracked in one unified interface.
- Mobile Companion App: Monitor and steer your agents from iOS or Android — get notified when an agent finishes and send follow-ups from anywhere.
- Ghostty-Class Terminals: WebGL-rendered terminal splits with infinite panes and scrollback that survives restarts.
- Design Mode Browser: Click any UI element in the embedded Chromium window to send its HTML, CSS, and a cropped screenshot straight into your agent's prompt.
- Cross-Platform Desktop: Native builds for macOS, Windows, and Linux so the orchestrator runs alongside your existing dev environment.
- Unified Prompt & History Tracking: Every prompt, tool call, and terminal action across all agents is captured in one place for easy review.
- Steer from Anywhere: Follow-up prompts from the mobile companion keep long-running agent runs moving even when you step away from the desk.
Best for
- Prompt Bake-Off: Send the same feature request to Codex, Claude Code, and OpenCode simultaneously and merge whichever branch wins.
- Long-Running Refactors: Kick off multi-hour agent runs in isolated worktrees and check in from your phone as they progress.
- Design-to-Code Handoff: Click a live UI element in Design Mode and hand its markup and a screenshot to the agent for pixel-accurate implementation.
- Parallel Bug Reproduction: Try multiple diagnostic approaches at once — each agent operates on its own worktree without collision.
- Terminal-Heavy Workflows: Use Ghostty-class terminal splits to keep build watchers, servers, and agent output visible side by side.
- Team Handoffs: Track every agent action in one place so the next engineer can pick up context without replaying a chat log.