Agently vs ClawTeams: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agently and ClawTeams — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agently
Agently
Company brain across your entire stack that spawns specialized AI agents, orchestrated by Jarvis, to autonomously ship real work.
Key features
- Company Brain Across 100+ Tools: Live ingest of Slack, Notion, Linear, Stripe, HubSpot, GitHub, Gmail, Google Drive, Figma, PostHog, Asana, Jira and more via two-way OAuth MCP connectors.
- Jarvis Orchestrator: A meta-agent that spins up specialized agents, routes work between them, and runs a shared board so founders set direction while Jarvis ships.
- Specialized Agent Roster: Prebuilt Researcher, Revenue, Growth, Support, Ops, and Briefer agents that trigger on real signals from the stack.
- Signal-to-Action Loop: Detects at-risk renewals, failed charges, escalated tickets, and doc changes, then decides and executes the follow-up work with an audit trail.
- Shippable Pages Artifacts: Outputs land as real files — presentations, gated PDFs, sheets, HTML pages, and Notion-style docs — that teams can share, gate, or export.
- Live Command Center: A dashboard shows every task, agent, and shipped artifact in real time with per-tool activity history.
- 60-Second Onboarding: Connecting tools sends the brain live within a minute so agents can start acting on the stack right away.
Best for
- Weekly Briefs and Board Updates: Automatically draft the weekly status doc, launch tracker, and board deck from live signals across the stack.
- Revenue Ops on Autopilot: Catch failed Stripe charges, at-risk renewals, and pipeline changes, then draft recovery emails and update HubSpot deals.
- Customer Support Escalations: Watch Linear and Slack for escalated tickets and reply/update them with grounded context from Notion and Gmail.
- Growth and Distribution Audits: Assemble funnel diagnostics and distribution audits as gated PDFs, complete with metrics and recommendations.
- Executive One-Person Chief of Staff: Solo founders and small teams replace recurring meetings with agent-drafted briefs and shipped artifacts.
- Ops and Compliance Reporting: Continuously reconcile tool state and generate signed-off reports for leadership without a human in the loop.
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