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agentify.fyi
Describe it. Deploy it. Own it.

Deploy AI agents into infrastructure you own.

Describe the agent, connect an account you already own with any provider in our list, and push. Agentify writes the infrastructure, provisions the runtime, and shows you what each agent costs.

Start buildingSee how it works1-Click Discord bot deploy

CONVERSATIONAL DEPLOY

Describe the agent. Ship the agent.

Tell Agentify what you want the agent to do. It proposes a stack — framework, model, and trigger — you approve it, and it deploys into the account you connected. No console to click through, no template to hand-write.

  1. 1

    Describe the characteristics

    What it should do, how it should behave, and what it is allowed to call.

  2. 2

    Choose the proposed stack

    Agentify picks a framework and model to fit — swap either before you ship.

  3. 3

    Deploy to your account

    It generates the template and applies it in the cloud account you own.

HOW IT WORKS

Connect once. Deploy on every push.

Agentify runs no agent workloads — it generates the infrastructure and applies it in the account you control.

  1. 1

    Connect your cloud or VPS

    Pick a provider from our list, then either apply one generated template that creates a role Agentify assumes for at most an hour at a time, or paste an API token and choose the server. Either way, the account stays yours.

  2. 2

    Define the agent

    Choose a framework, bind a model, write the prompts. Everything lands in an agentify.yaml you can review in a pull request.

  3. 3

    Deploy and watch the cost

    Agentify generates the template, streams stack events while the stack runs, and tags every resource so the agent's cost is attributable from the first invocation.

AFTER DEPLOY

See what every agent does — and what it costs.

The moment an agent goes live, Agentify turns its tagged resources and its own log group into a per-agent view of spend, usage, and health — no dashboards to wire up, no cost allocation to reverse-engineer.

Illustrative preview of the per-agent cost and observability dashboard.

START HERE

Three ways in.

Start from a framework, from a repository you already have, or from a template someone else already proved.

  • Scaffold from scratch
    Pick a framework and get an entry point, a container definition, an agentify.yaml, and a README — generated, not templated by hand.
    version: "1"
name: support-triage
framework: openclaw
model:
  provider: bedrock-default
    Scaffold an agent
  • Import existing repository
    Point Agentify at a GitHub or GitLab repository that already has its own infrastructure template. Pushes to your tracked branch deploy.
    Import a repository
  • Use community template
    Instantiate a published agent definition into a new project and fill in the fields it declares as required.
    Browse templates

Want one running this afternoon?

A Discord bot on a VPS you own: paste a bot token and a model key, and Agentify deploys it into your server.

1-click deploy a Discord OpenClaw bot

WORKFLOW

Push. Deploy. Repeat.

Git is the trigger and the manifest is the source of truth.

  • Push to your tracked branch and Agentify deploys that commit.
  • Stack events stream into the console while the deployment runs.
  • Roll back to any earlier deployment's template.
  • Prompt versions are immutable, diffable, and restorable.

OWNERSHIP

Your cloud account. Your infrastructure.

Agentify holds the definition; your account holds the agent.

  • Agents deploy into the account you connect, under access you granted yourself.
  • Access is a short-lived role where your provider offers one, and an encrypted token where it does not.
  • Disconnect Agentify and the resources it provisioned stay in your account.
  • Provider API keys reach the runtime by reference or as injected environment values, never written into the generated source.

VISIBILITY

Know what each agent costs.

Every provisioned resource is tagged with its agent id, so cost is attributed rather than guessed.

  • Cost per agent, broken down by provider service and by model usage.
  • Invocation count, input and output tokens, and cost per invocation.
  • Logs, traces, and error rate for each agent, read from your own log group.
  • Model cost estimates are labelled as estimates, not as provider invoices.

SUPPORTED

Frameworks and providers, out of the box.

Every framework below ships as a descriptor the scaffolder, the template generator, and this page all read, so the list is the platform rather than a claim about it.

Frameworks

  • OpenClaw
    Scaffolding stack

    A tool-calling agent gateway, packaged from its official container image, serving a single HTTP endpoint for agents that need to act rather than only answer.

    Scaffold includes

    • server.py
    • openclaw.json
    • requirements.txt
    • Dockerfile
    • README.md
    • Tools
    • Session memory
    • Tracing

    Model providers

    • Amazon Bedrock
  • Strands
    Scaffolding stack

    A model-driven agent loop with tools, AgentCore session memory, and OpenTelemetry traces, for agents whose next step should be the model’s decision.

    Scaffold includes

    • agent/main.py
    • Dockerfile
    • README.md
    • Tools
    • Session memory
    • Tracing

    Model providers

    • Amazon Bedrock
    • OpenAI
  • LangChain
    Scaffolding stack

    A composed prompt-template-and-executor agent with tool calling across every supported model provider, for work that fits a chain rather than a long-running conversation.

    Scaffold includes

    • app/chain.py
    • Dockerfile
    • README.md
    • Tools
    • Tracing

    Model providers

    • Amazon Bedrock
    • Anthropic
    • OpenAI
  • L
    LangGraph
    Scaffolding stack

    An explicit state graph with a tool node and a checkpointed session thread, for agents whose control flow you want written down and whose conversation should survive between turns.

    Scaffold includes

    • app/graph.py
    • Dockerfile
    • README.md
    • Tools
    • Session memory
    • Tracing

    Model providers

    • Amazon Bedrock
    • Anthropic
    • OpenAI
  • C
    Claude Agent SDK
    Scaffolding stack

    Anthropic's agent harness: Claude works inside the container with file and shell tools and MCP servers, for tasks that need doing rather than answering.

    Scaffold includes

    • app/agent.py
    • Dockerfile
    • README.md
    • Tools
    • Tracing

    Model providers

    • Amazon Bedrock
    • Anthropic

Model provider types

  • Amazon Bedrock
  • OpenAI
  • Anthropic

Deploy your first agent today.

Connect an account you own, describe the agent, and watch it go live — with its cost visible from the first invocation.

Start buildingBrowse templates