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

Dashboard KPI overview with active experiments, success rate, budget spend, and pending approvals.	Agent Template Gallery Browse 14 pre-built agent templates across 5 categories. Search, filter by category, and deploy with one click.
Agent LLM Configuration Per-agent provider and model selection with fallback chains. Supports Anthropic, OpenAI, Google, and local agents.	Agent Evolution AI-driven agent self-improvement. Analyze execution history, propose personality and config changes, and apply with one click.
Crew Execution Live progress tracking during multi-agent crew execution. Each task shows its assigned skill, provider, and elapsed time.	Task Output Expand any completed task to inspect the AI-generated output, including structured JSON responses.
Visual Workflow Builder DAG-based workflow editor with conditional branching, human tasks, switch nodes, and dynamic forks.	Tool Management Manage MCP servers, built-in tools, and external integrations with risk classification and per-agent assignment.
AI Assistant Sidebar Context-aware AI chat embedded in every page with 28 built-in tools for querying and managing the platform.	Experiment Detail Full experiment lifecycle view with timeline, tasks, transitions, artifacts, metrics, and outbound delivery.
Settings & Webhooks Global platform settings, AI provider keys (BYOK), outbound connectors, and webhook configuration.	Error Handling Failed tasks display detailed error information including provider, error type, and request IDs for debugging.

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 coordinator, QA, and worker roles; domain-specific evaluation rubrics; weighted QA scoring per task type
• Pre-Execution Scout Phase -- Optional cheap LLM pre-call before memory retrieval that identifies what knowledge the agent needs, enabling targeted semantic search instead of generic recall
• Step Budget Awareness -- Agents receive an execution budget section in their system prompt, targeting 80% of allowed steps for core work and reserving the rest for synthesis
• 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_compute skill type backed by RunPod, Replicate, Fal.ai, and Vast.ai; configure via compute_manage MCP 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, Vercel, Netlify, and generic webhooks/polling sources via unified driver interface with OAuth 2.0 support
• Autonomous Web Dev Pipeline -- Agents can open PRs, merge, dispatch CI workflows, create releases, and trigger Vercel/Netlify/SSH deploys through MCP tools and integration drivers
• Per-Call Working Directory -- Local and bridge agents can operate in a configured working directory per-agent, enabling isolated project contexts
• Playbooks -- Sequential or parallel multi-step workflows combining skills
• Workflows -- Visual DAG builder with 8 node types: agent, conditional, human task, switch, dynamic fork, do-while loops; pre-built Web Dev Cycle template
• Projects -- One-shot and continuous long-running agent projects with cron scheduling, budget caps, milestones, and overlap policies
• Human-in-the-Loop -- Approval queue and human task forms with SLA enforcement and escalation
• Multi-Channel Outbound -- Email (SMTP), Telegram, Slack, and webhook delivery with rate limiting
• Webhooks -- Inbound signal ingestion (HMAC-SHA256) and outbound webhook delivery with retry and event filtering
• Budget Controls -- Per-experiment and per-project credit ledger with pessimistic locking and auto-pause on overspend
• Marketplace -- Browse, publish, and install shared skills, agents, and workflows
• REST API -- 175+ endpoints under /api/v1/ with Sanctum auth, cursor pagination, and auto-generated OpenAPI 3.1 docs at /docs/api
• MCP Server -- 316+ Model Context Protocol tools across 38 domains for LLM/agent access (stdio + HTTP/SSE)
• Tool Management -- MCP servers (stdio/HTTP), built-in tools (bash/filesystem/browser), risk classification, per-agent assignment
• Credentials -- Encrypted credential vault for external services with rotation, expiry tracking, and per-project injection
• Testing -- Regression test suites for agent outputs with automated evaluation
• Local Agents -- Run Codex and Claude Code as local execution backends (auto-detected, zero cost)
• Audit Trail -- Full activity logging with searchable, filterable audit log
• AI Gateway -- Provider-agnostic LLM access via PrismPHP with circuit breakers and fallback chains
• BYOK -- Bring your own API keys for Anthropic, OpenAI, or Google
• Queue Management -- Laravel Horizon with 6 priority queues and auto-scaling

Quick Start (Docker)

git clone https://github.com/escapeboy/agent-fleet-o.git
cd agent-fleet
make install

This will:

1. Copy .env.example to .env
2. Build and start all Docker services
3. Run the interactive setup wizard (database, admin account, LLM provider)

Visit http://localhost:8080 when complete.

Quick Start (Manual — Web Setup)

Requirements: PHP 8.4+, PostgreSQL 17+, Redis 7+, Node.js 20+, Composer

git clone https://github.com/escapeboy/agent-fleet-o.git
cd agent-fleet
composer install
npm install && npm run build
cp .env.example .env
# Edit .env — set DB_HOST, DB_DATABASE, DB_USERNAME, DB_PASSWORD, REDIS_HOST
php artisan key:generate
php artisan migrate
php artisan horizon &
php artisan serve

Then open http://localhost:8000 in your browser. The setup page will guide you through creating your admin account.

Alternative: Run php artisan app:install for an interactive CLI setup wizard that also seeds default agents and skills.

Authentication

• No email verification — the self-hosted edition skips email verification entirely. Accounts are active immediately on registration.
• Single user — all registered users join the default workspace automatically.

No-Password Mode (local installs)

If you're running FleetQ locally on your own machine and don't want to enter a password on every visit, set APP_AUTH_BYPASS=true in .env:

APP_AUTH_BYPASS=true   # Auto-login as first user
APP_ENV=local          # Required — bypass is disabled in production

With bypass enabled, the app logs you in automatically on every request. A logout link is still shown but you'll be logged back in on the next page load — this is intentional.

Warning: Never set APP_AUTH_BYPASS=true on a server accessible from the internet.

Configuration

All configuration is in .env. Key variables:

# Database (PostgreSQL required)
DB_CONNECTION=pgsql
DB_HOST=postgres
DB_DATABASE=agent_fleet

# Redis (queues, cache, sessions, locks)
REDIS_HOST=redis
REDIS_DB=0          # Queues
REDIS_CACHE_DB=1    # Cache
REDIS_LOCK_DB=2     # Locks

# LLM Providers -- at least one required for AI features
ANTHROPIC_API_KEY=
OPENAI_API_KEY=
GOOGLE_AI_API_KEY=

# Auth bypass -- local no-password mode (never use in production)
APP_AUTH_BYPASS=false

Additional LLM keys can be configured in Settings > AI Provider Keys after login.

To use local models (Ollama, LM Studio, vLLM):

LOCAL_LLM_ENABLED=true
LOCAL_LLM_SSRF_PROTECTION=false  # set false if Ollama is on a LAN IP (192.168.x.x)
LOCAL_LLM_TIMEOUT=180

Then configure endpoints in Settings > Local LLM Endpoints.

SSH Host Access

Agents can execute commands on the host machine (or any remote server) via SSH using the built-in SSH tool type. This is useful for running local scripts, interacting with the filesystem, or orchestrating host-level processes from an agent.

How it works

1. The platform stores SSH private keys encrypted in the Credential vault.
2. An SSH Tool is configured with host, port, username, credential_id, and an optional allowed_commands whitelist.
3. On the first connection to a host, the server's public key fingerprint is stored via TOFU (Trust On First Use). Subsequent connections verify the fingerprint — a mismatch raises an error to prevent MITM attacks.
4. Manage trusted fingerprints via Settings > SSH Fingerprints or the tool_ssh_fingerprints MCP tool.

Setup (Docker — connecting container to host)

The containers reach the host machine via host.docker.internal, which is pre-configured in docker-compose.yml via extra_hosts: host.docker.internal:host-gateway.

Step 1 — Enable SSH on the host

OS	Command
macOS	System Settings → General → Sharing → Remote Login → On
Ubuntu/Debian	sudo apt install openssh-server && sudo systemctl enable --now ssh
Fedora/RHEL	sudo dnf install openssh-server && sudo systemctl enable --now sshd
Windows	Settings → System → Optional Features → OpenSSH Server, then Start-Service sshd

Step 2 — Generate an SSH key pair

ssh-keygen -t ed25519 -C "fleetq-agent@local" -f ~/.ssh/fleetq_agent_key -N ""

Step 3 — Authorize the key on the host

cat ~/.ssh/fleetq_agent_key.pub >> ~/.ssh/authorized_keys
chmod 600 ~/.ssh/authorized_keys

Step 4 — Create a Credential in FleetQ

Navigate to Credentials → New Credential:

• Type: SSH Key
• Paste the contents of ~/.ssh/fleetq_agent_key (private key)

Or via API:

curl -X POST http://localhost:8080/api/v1/credentials \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Host SSH Key",
    "credential_type": "ssh_key",
    "secret_data": {"private_key": "<contents of fleetq_agent_key>"}
  }'

Step 5 — Create an SSH Tool

Navigate to Tools → New Tool → Built-in → SSH Remote, or via API:

curl -X POST http://localhost:8080/api/v1/tools \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Host SSH",
    "type": "built_in",
    "risk_level": "destructive",
    "transport_config": {
      "kind": "ssh",
      "host": "host.docker.internal",
      "port": 22,
      "username": "your-username",
      "credential_id": "<credential-id>",
      "allowed_commands": ["ls", "pwd", "whoami", "uname", "date", "df"]
    },
    "settings": {"timeout": 30}
  }'

Step 6 — Assign the tool to an agent

In the Agent detail page, go to Tools and assign the SSH tool. The agent will now have an ssh_execute function available during execution.

Command security policy

The platform enforces a multi-layer security hierarchy for bash and SSH commands:

1. Platform-level — always blocked: rm -rf /, mkfs, shutdown, reboot, pipe-to-shell patterns
2. Organization-level — configure in Settings → Security Policy or via the tool_bash_policy MCP tool
3. Tool-level — allowed_commands whitelist in the tool's transport config
4. Project-level — additional restrictions in project settings
5. Agent-level — per-agent overrides on the tool pivot

More restrictive layers always win. A command blocked at the platform level cannot be unblocked by any other layer.

SSH fingerprint management

Trusted host fingerprints are viewable and removable via:

• API: GET /api/v1/ssh-fingerprints / DELETE /api/v1/ssh-fingerprints/{id}
• MCP: tool_ssh_fingerprints with list or delete action

Remove a fingerprint when a host's SSH key is legitimately rotated — the next connection will re-verify via TOFU.

Architecture

Built with Laravel 12, Livewire 4, and Tailwind CSS. Domain-driven design with 16 bounded contexts:

Domain	Purpose
Agent	AI agent configs, execution, personality, evolution
Crew	Multi-agent teams with lead/member roles
Experiment	Pipeline, state machine, playbooks
Signal	Inbound data ingestion
Outbound	Multi-channel delivery
Approval	Human-in-the-loop reviews and human tasks
Budget	Credit ledger, cost enforcement
Metrics	Measurement, revenue attribution
Audit	Activity logging
Skill	Reusable AI skill definitions
Tool	MCP servers, built-in tools, risk classification
Credential	Encrypted external service credentials
Workflow	Visual DAG builder, graph executor
Project	Continuous/one-shot projects, scheduling
Assistant	Context-aware AI chat with 28 tools
Marketplace	Skill/agent/workflow sharing
Integration	External service connectors (GitHub, Slack, Notion, Airtable, Linear, Stripe, Generic)

Docker Services

Service	Purpose	Port
app	PHP 8.4-fpm	--
nginx	Web server	8080
postgres	PostgreSQL 17	5432
redis	Cache/Queue/Sessions	6379
horizon	Queue workers	--
scheduler	Cron jobs	--
vite	Frontend dev server	5173

Common Commands

make start          # Start services
make stop           # Stop services
make logs           # Tail logs
make update         # Pull latest + migrate
make test           # Run tests
make shell          # Open app container shell

Or with Docker Compose directly:

docker compose exec app php artisan tinker       # REPL
docker compose exec app php artisan test          # Run tests
docker compose exec app php artisan migrate       # Run migrations

Upgrading

make update

This pulls the latest code, rebuilds containers, runs migrations, and clears caches.

Tech Stack

• Framework: Laravel 12 (PHP 8.4)
• Database: PostgreSQL 17
• Cache/Queue: Redis 7
• Frontend: Livewire 4 + Tailwind CSS 4 + Alpine.js
• AI Gateway: PrismPHP
• Queue: Laravel Horizon
• Auth: Laravel Fortify (2FA) + Sanctum (API tokens)
• Audit: spatie/laravel-activitylog
• API Docs: dedoc/scramble (OpenAPI 3.1)
• MCP: laravel/mcp (Model Context Protocol)

Contributing

Contributions are welcome. Please open an issue first to discuss proposed changes.

1. Fork the repository
2. Create a feature branch (git checkout -b feat/my-feature)
3. Make your changes and add tests
4. Run php artisan test to verify
5. Submit a pull request

License

FleetQ Community Edition is open-source software licensed under the GNU Affero General Public License v3.0.