Add it to Claude Code
claude mcp add -e "FLEETQ_API_KEY=${FLEETQ_API_KEY}" fleetq -- npx @escapeboy/agent-fleet-oFLEETQ_API_KEYMake your agent remember this setup
fleetq's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.
npx conare@latestFree · one command · indexes the sessions already on disk. Set up in the browser instead →
What it does
- Visual DAG-based workflow builder with conditional branching and human-in-the-loop tasks.
- Multi-agent crew orchestration with shared context and defined roles.
- AI-driven agent self-improvement through execution history analysis.
- Pluggable compute provider support including RunPod, Replicate, and local Ollama instances.
- Comprehensive experiment lifecycle management with audit trails and KPI tracking.
Tools 1
compute_manageManage compute providers and GPU resources for agent skills.Environment Variables
FLEETQ_API_KEYrequiredAPI key for authenticating with the FleetQ platform.Try it
Original README from escapeboy/agent-fleet-o
FleetQ - Community Edition
Self-hosted AI Agent Mission Control platform. Build, orchestrate, and monitor AI agent experiments with a visual pipeline, human-in-the-loop approvals, and full audit trail.
Cloud Version
Prefer not to self-host? FleetQ Cloud is the fully managed version — no setup, no infrastructure, free to try.
Screenshots
<table> <tr> <td width="50%">Dashboard KPI overview with active experiments, success rate, budget spend, and pending approvals.
</td> <td width="50%">Agent Template Gallery Browse 14 pre-built agent templates across 5 categories. Search, filter by category, and deploy with one click.
</td> </tr> <tr> <td>Agent LLM Configuration Per-agent provider and model selection with fallback chains. Supports Anthropic, OpenAI, Google, and local agents.
</td> <td>Agent Evolution AI-driven agent self-improvement. Analyze execution history, propose personality and config changes, and apply with one click.
</td> </tr> <tr> <td>Crew Execution Live progress tracking during multi-agent crew execution. Each task shows its assigned skill, provider, and elapsed time.
</td> <td>Task Output Expand any completed task to inspect the AI-generated output, including structured JSON responses.
</td> </tr> <tr> <td>Visual Workflow Builder DAG-based workflow editor with conditional branching, human tasks, switch nodes, and dynamic forks.
</td> <td>Tool Management Manage MCP servers, built-in tools, and external integrations with risk classification and per-agent assignment.
</td> </tr> <tr> <td>AI Assistant Sidebar Context-aware AI chat embedded in every page with 28 built-in tools for querying and managing the platform.
</td> <td>Experiment Detail Full experiment lifecycle view with timeline, tasks, transitions, artifacts, metrics, and outbound delivery.
</td> </tr> <tr> <td>Settings & Webhooks Global platform settings, AI provider keys (BYOK), outbound connectors, and webhook configuration.
</td> <td>Error Handling Failed tasks display detailed error information including provider, error type, and request IDs for debugging.
</td> </tr> </table>Features
- Experiment Pipeline -- 20-state machine with automatic stage progression (scoring, planning, building, approval, execution, metrics collection)
- AI Agents -- Configure agents with roles, goals, backstories, personality traits, and skill assignments
- Agent Templates -- 14 pre-built templates across 5 categories (engineering, content, business, design, research)
- Agent Evolution -- AI-driven self-improvement: analyze execution history, propose config changes, and apply improvements
- Agent Crews -- Multi-agent teams with lead/member roles and shared context
- Skills -- Reusable AI skill definitions (LLM, connector, rule, hybrid, browser, RunPod, GPU compute) with versioning and cost tracking
- RunPod GPU Integration -- Invoke RunPod serverless endpoints or manage full GPU pod lifecycles as skills; BYOK API key; spot pricing; cost tracking
- Pluggable Compute Providers --
gpu_computeskill type backed by RunPod, Replicate, Fal.ai, and Vast.ai; configure viacompute_manageMCP tool; zero platform credits - Local LLM Support -- Run Ollama or any OpenAI-compatible server (LM Studio, vLLM, llama.cpp) as a provider; 17 preset Ollama models; zero cost; SSRF protection
- Integrations -- Connect GitHub, Slack, Notion, Airtable, Linear, Stripe, and generic webhooks/polling sources via unified driver interface with OAuth 2.0 support
- Playbooks -- Sequential o