The universal AI agent platform.
Scaffold, configure, guard, and deploy AI agents across any framework.
Quick Start | Two Paths | DevTools | Frameworks | Deploy | Guard Config | Commands | Contributing
AgentVoy is a CLI tool and SDK that lets you scaffold and deploy production-ready AI agent projects in seconds — with built-in guardrails, security defaults, and support for every major agent framework.
One command. Any framework. Any model. Deploy anywhere.
Two Paths
AgentVoy asks upfront what you want to build:
$ npx agentvoy create my-project
? What do you want to build?
> Agent — Local agent for development & experimentation
App — Deployable agentic app (API + UI + Docker + cloud)
Path A — Agent
Fast local development. Flat project structure, interactive REPL, zero infra.
npx agentvoy create my-project --yes
# Creates: my-project-agent/my-project-agent/
├── agent.py # Agent logic
├── tools.py # Custom tools
├── run.py # Interactive REPL
├── agent.guard.yml # Guardrails & permissions
├── requirements.txt
└── .env.example
Path B — App
Deployable agentic app with a FastAPI server, Streamlit chat UI, real-time DevTools, and cloud configs.
npx agentvoy create my-project --build-mode app --deploy-target docker --yes
# Creates: my-project-app/my-project-app/
├── src/
│ ├── agents/
│ │ └── agent.py # Agent logic
│ ├── tools/
│ │ └── tools.py # Custom tools
│ ├── trace/
│ │ └── tracer.py # Execution tracing (auto-generated)
│ └── config/
│ └── settings.py
├── server.py # FastAPI — /run, /health, /dev, /ws/trace
├── streamlit_app.py # Chat UI with model picker & glass theme
├── devtools.html # Real-time agent DevTools dashboard
├── Dockerfile
├── docker-compose.yml
├── agent.guard.yml
├── requirements.txt
└── .env.example
Multi-agent pipelines are supported — choose sequential pipeline and name your agents:
npx agentvoy create my-project --build-mode app --agent-mode multi --yes
# Creates: researcher → writer → reviewer pipelinesrc/
├── agents/
│ ├── researcher.py
│ ├── writer.py
│ └── reviewer.py
└── pipeline.py # Sequential orchestration
Quick Start
# Interactive — guided prompts for framework, model, and build mode npx agentvoy create my-project # Agent mode with defaults (OpenAI + GPT-4o) npx agentvoy create my-project --yes # App with Docker, fully non-interactive npx agentvoy create my-project \ --framework openai \ --provider anthropic \ --model claude-sonnet-4-20250514 \ --build-mode app \ --deploy-target docker \ --yes # Add guardrails to an existing project npx agentvoy init # Validate your config npx agentvoy validate
DevTools
App-mode projects include a built-in DevTools dashboard for real-time agent observability.
agentvoy dev — Live development server
cd my-project-app
agentvoy devStarts your agent server with hot-reload and opens the DevTools dashboard at http://localhost:8080/dev.
What you get
- Real-time trace streaming — WebSocket-powered event feed showing every agent action as it happens
- Event timeline — agent_start, llm_call, tool_call, guard_check, pipeline_stage, agent_complete
- Pipeline visualization — see multi-agent stages progress in real time
- Detail inspector — click any event to see full payload (model, tokens, latency, tool I/O)
- Dark-themed dashboard — single-page HTML, no extra dependencies
Endpoints
Every app-mode project exposes these DevTools endpoints:
| Endpoint | Description |
|---|---|
GET /dev |
DevTools dashboard UI |
WS /ws/trace |
Real-time trace event stream |
GET /dev/events |
All events as JSON |
GET /health |
Health check |
Trace instrumentation
All 7 framework adapters are instrumented out of the box. The tracer collects:
| Event | Data |
|---|---|
agent_start |
agent name, prompt, model |
llm_call |
model, latency, tokens in/out |
tool_call |
tool name, input, output, latency |
guard_check |
check type (input/output), pass/fail |
pipeline_stage |
stage name, index, status |
agent_complete |
agent name, result preview |
Streamlit Chat UI
App-mode projects include a production-ready chat interface:
- Glassmorphism dark theme — styled with backdrop blur and gradient accents
- Dynamic model switching — auto-detects API keys from
.envand shows available models (GPT-4o, Claude Sonnet, Gemini Flash, etc.) - Guard summary — sidebar displays guardrail results for each response
- Powered by AgentVoy footer on every generated app
Frameworks
| Framework | Language | Status |
|---|---|---|
| OpenAI Agents SDK | Python | Available |
| Google ADK | Python | Available |
| CrewAI | Python | Available |
| LangGraph | Python | Available |
| Anthropic SDK | Python | Available |
| LlamaIndex | Python | Available |
| AutoGen | Python | Available |
Model Providers
| Provider | Models | API Key Env |
|---|---|---|
| OpenAI | gpt-4o, o1, gpt-4-turbo | OPENAI_API_KEY |
| Anthropic | claude-opus-4, claude-sonnet-4 | ANTHROPIC_API_KEY |
| gemini-2.0-flash, gemini-2.5-pro | GOOGLE_API_KEY |
|
| Ollama | llama3, mistral, codellama | Local — no key needed |
| Groq | llama-3.3-70b-versatile | GROQ_API_KEY |
| Mistral | mistral-large-latest | MISTRAL_API_KEY |
Deploy
agentvoy deploy — One-command deployment
cd my-project-app
agentvoy deploy --target dockerDocker — builds the image and runs the container:
agentvoy deploy --target docker # Builds: docker build -t my-project . # Runs: docker run -p 8080:8080 --env-file .env my-project # Agent: http://localhost:8080 # DevTools: http://localhost:8080/dev
Fly.io — deploys to the cloud in one step:
agentvoy deploy --target fly-io # Checks flyctl auth, sets secrets from .env, deploys # Live URL: https://my-project.fly.dev # DevTools: https://my-project.fly.dev/dev
Dry run — generate deployment files without deploying:
agentvoy deploy --target docker --dry-run
Deploy during creation
Pick a deployment target when creating an app project:
? Deployment target:
> Docker
Fly.io
Railway
GCP Cloud Run
AWS Lambda
Deploy an existing agent project
cd my-project-agent
npx agentvoy deploy --target dockerThis generates server.py, streamlit_app.py, devtools.html, and all deployment files — without touching your existing agent code.
Deployment targets
| Target | Files generated | CLI required |
|---|---|---|
| Docker | Dockerfile, .dockerignore, docker-compose.yml |
docker |
| Fly.io | deploy/fly.toml |
flyctl |
| Railway | deploy/railway.json |
railway |
| GCP Cloud Run | deploy/cloud-run.yaml |
gcloud |
| AWS Lambda | deploy/template.yaml, deploy/lambda_handler.py |
aws, sam |
Guard-to-infrastructure mapping
agent.guard.yml settings flow directly into deployment configuration:
guardrails: behavior: timeout: 5m → Docker HEALTHCHECK interval, Cloud Run timeout cost_limit: $1.00 → Container memory limit (512Mi) permissions: execution: allow_shell: false → non-root Docker user
Agent Guard Config
Every AgentVoy project includes agent.guard.yml — a universal declarative config for security and behavior.
version: "1.0" identity: name: my-agent description: Research assistant agent version: 0.1.0 model: provider: anthropic model: claude-sonnet-4-20250514 api_key_env: ANTHROPIC_API_KEY permissions: network: mode: restricted allow: ["*.github.com", "*.stackoverflow.com"] filesystem: read: ["./**"] write: ["./output/**"] tools: require_approval: ["delete_*", "send_*", "deploy_*"] execution: allow_shell: false allow_subprocess: false guardrails: input: block_prompt_injection: true max_tokens: 4096 pii_detection: warn content_filter: moderate output: block_harmful_content: true max_output_tokens: 8192 behavior: max_iterations: 20 timeout: 5m cost_limit: "$1.00" observability: tracing: true log_level: info cost_tracking: true
Runtime enforcement — agentvoy-guard
pip install agentvoy-guard
from agentvoy_guard import Guard guard = Guard.from_config() # reads agent.guard.yml with guard.session() as session: session.check_input(user_prompt) result = my_agent.run(user_prompt) session.check_output(result)
Commands
agentvoy create [name] # Create a new agent or app project agentvoy dev # Start agent server with DevTools dashboard (app mode) agentvoy deploy # Deploy to Docker, Fly.io, or other targets agentvoy init # Add agent.guard.yml to an existing project agentvoy validate # Validate your agent.guard.yml config agentvoy list # List supported frameworks, models, and targets
Architecture
agentvoy/ # TypeScript monorepo
packages/
core/ # Types, config, adapters, deployers
src/
adapters/ # Framework adapters (openai, crewai, …)
deployers/ # Deployment adapters (docker, fly-io, …)
tracer.ts # Agent execution tracer generator
devtools-dashboard.ts # DevTools HTML dashboard generator
api-wrapper.ts # server.py generator (with /dev endpoints)
streamlit-app.ts # Streamlit chat UI generator
pipeline.ts # Multi-agent pipeline generator
types.ts # Universal type system
config.ts # agent.guard.yml parser
cli/ # agentvoy CLI (create, dev, deploy, init, validate, list)
create-agentvoy/ # npx create-agentvoy shorthand
agentvoy-guard/ # Python runtime enforcement package
Contributing
Adding a new framework adapter
- Create
packages/core/src/adapters/my-framework.ts - Implement
FrameworkAdapter—scaffold()andvalidateConfig() - Register in
packages/core/src/adapters/index.ts - Submit a PR
Adding a new deployment target
- Create
packages/core/src/deployers/my-target.ts - Implement
DeploymentAdapter—generateFiles()andvalidate() - Register in
packages/core/src/deployers/index.ts - Submit a PR
Development
git clone https://github.com/agentvoy/agentvoy.git cd agentvoy npm install npm run build # Smoke test agent mode node packages/cli/dist/index.js create test-project --yes # Smoke test app mode node packages/cli/dist/index.js create test-project --build-mode app --deploy-target docker --yes # Test DevTools cd test-project-app pip install -r requirements.txt agentvoy dev # opens http://localhost:8080/dev
License
Apache 2.0 — see LICENSE for details.
Built by Chinmay Murugkar

























