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GitHub - agentvoy/agentvoy: The universal AI agent platform. Scaffold, configure, guard, and deploy AI agents across 7 frameworks — OpenAI, Anthropic, CrewAI, LangGraph, Google ADK, LlamaIndex, AutoGen. One command. Any model. Deploy anywhere.
cthecm · 2026-05-19 · via Hacker News - Newest: "AI"

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

GitHub Stars CI License npm version PyPI version npm downloads


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.

AgentVoy CLI — create a project in seconds

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 pipeline
src/
├── agents/
│   ├── researcher.py
│   ├── writer.py
│   └── reviewer.py
└── pipeline.py          # Sequential orchestration

Quick Start

AgentVoy — running an agent app

# 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 dev

Starts 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 .env and 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
Google 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 docker

Docker — 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 docker

This 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

  1. Create packages/core/src/adapters/my-framework.ts
  2. Implement FrameworkAdapterscaffold() and validateConfig()
  3. Register in packages/core/src/adapters/index.ts
  4. Submit a PR

Adding a new deployment target

  1. Create packages/core/src/deployers/my-target.ts
  2. Implement DeploymentAdaptergenerateFiles() and validate()
  3. Register in packages/core/src/deployers/index.ts
  4. 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