cycls 0.0.2.132

Single process, per-tenant, serverless, and stateful AI agent runtime.

Distribute Intelligence

Website | Docs | Tutorial

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Cycls

The deep-stack AI SDK for Python. Every layer of an AI agent — runtime, interface, intelligence, state — as a composable Python primitive, in one file, deployed with one command.

Agent extends App (chat product + managed LLM loop)
    └── App extends Function (blocking ASGI service)
        └── Function (Docker containerization)

Distribute Intelligence

Write an agent. Three primitives compose it. Deploy it with one command.

import cycls

image = cycls.Image().copy(".env")

web = (
    cycls.Web()
    .auth(cycls.Clerk())
    .title("My Agent")
)

llm = (
    cycls.LLM()
    .model("anthropic/claude-sonnet-4-6")
    .system("You are a helpful assistant.")
    .allowed_tools(["Bash", "Editor", "WebSearch"])
)


@cycls.agent(image=image, web=web)
async def my_agent(context):
    async for msg in llm.run(context=context):
        yield msg

cycls deploy my_agent.py   # live at https://my-agent.cycls.ai

Installation

pip install cycls

Requires Docker. See the full tutorial for a comprehensive guide.

The Primitives

Four composable builders, three decorators, one CLI.

Primitives (declare once, reuse anywhere):
  cycls.Image   — container build config (pip, apt, copy, run commands)
  cycls.Web     — UI, auth, branding, billing, analytics
  cycls.LLM     — model, system prompt, tools, runtime config
  cycls.Clerk   — Clerk JWT auth provider (or cycls.JWT for generic OIDC)

Decorators (compose primitives into deployable units):
  @cycls.function(image=)                    — non-blocking compute
  @cycls.app(image=)                         — blocking ASGI service
  @cycls.agent(image=, web=)                 — managed chat product

CLI:
  cycls run file.py        — local Docker with hot-reload
  cycls deploy file.py     — production deploy
  cycls ls                 — list deployments
  cycls logs <name> -f     — tail logs
  cycls rm <name>          — delete a deployment
  cycls init [name]        — scaffold a starter agent

Every primitive is a fluent immutable builder. Every decorator accepts exactly those primitives, never grab-bag kwargs.

Running

my_agent.local()             # local Docker + hot-reload (localhost:8080)
my_agent.local(watch=False)  # local Docker, no watch
my_agent.deploy()            # production: https://my-agent.cycls.ai

Or via the CLI (recommended):

cycls run my_agent.py       # local Docker + hot-reload
cycls deploy my_agent.py    # production

Get an API key at cycls.com.

Authentication

Auth providers are first-class objects. cycls.Clerk() uses Cycls's hosted Clerk by default; cycls.JWT(...) covers any OIDC provider (Auth0, WorkOS, Supabase, Okta, Firebase).

# Cycls's default Clerk (dev/prod dual mode, auto-switches)
web = cycls.Web().auth(cycls.Clerk())

# Custom Clerk tenant
web = cycls.Web().auth(cycls.Clerk(
    jwks_url="https://clerk.mycompany.com/.well-known/jwks.json",
))

# Generic OIDC (Auth0, WorkOS, etc)
web = cycls.Web().auth(cycls.JWT(
    jwks_url="https://my-prod.auth0.com/.well-known/jwks.json",
    dev_jwks_url="https://my-dev.auth0.com/.well-known/jwks.json",
))

@cycls.agent(web=web)
async def my_agent(context):
    user = context.user   # User(id, org_id, plan, features, ...)
    ...

Analytics & Billing

web = (
    cycls.Web()
    .auth(cycls.Clerk())
    .analytics(True)        # usage metrics on the Cycls dashboard
    .cms("cycls.ai")        # CMS entry → monetize via Cycls Pass subscriptions
    .title("My Agent")
)

Custom Tools

Tools are bare JSON schemas. Handlers are plain async functions registered via .on(name, handler). Handler return values flow to both the UI stream and the LLM's tool_result.

TOOLS = [
    {
        "name": "render_image",
        "description": "Display an image to the user.",
        "inputSchema": {
            "type": "object",
            "properties": {"src": {"type": "string"}},
            "required": ["src"],
        },
    }
]


async def render_image(args):
    return {"type": "text", "text": f"![image]({args['src']})"}


llm = (
    cycls.LLM()
    .model("anthropic/claude-sonnet-4-6")
    .tools(TOOLS)
    .on("render_image", render_image)
)

Multi-provider LLM

One adapter covers Anthropic natively and every OpenAI-compatible endpoint (OpenAI, Groq, vLLM, HUMAIN, self-hosted, ...) via provider/model strings:

cycls.LLM().model("anthropic/claude-sonnet-4-6")         # Anthropic native
cycls.LLM().model("openai/gpt-5.4")                      # OpenAI
cycls.LLM().model("groq/llama-3.3-70b").base_url(...)    # Groq or any OpenAI-compat
cycls.LLM().model("humain/jais").base_url(...)           # sovereign inference

Thinking/reasoning events, tool calls, and streaming are unified across providers.

Streaming Components

Yield structured objects from an agent body for rich streaming responses:

@cycls.agent(web=cycls.Web().auth(cycls.Clerk()))
async def demo(context):
    yield {"type": "thinking", "thinking": "Analyzing the request..."}
    yield "Here's what I found:\n\n"

    yield {"type": "table", "headers": ["Name", "Status"]}
    yield {"type": "table", "row": ["Server 1", "Online"]}
    yield {"type": "table", "row": ["Server 2", "Offline"]}

    yield {"type": "code", "code": "result = analyze(data)", "language": "python"}
    yield {"type": "callout", "callout": "Analysis complete!", "style": "success"}

Component	Streaming
{"type": "thinking", "thinking": "..."}	Yes
{"type": "code", "code": "...", "language": "..."}	Yes
{"type": "table", "headers": [...]} / {"type": "table", "row": [...]}	Yes
{"type": "status", "status": "..."}	Yes
{"type": "callout", "callout": "...", "style": "..."}	Yes
{"type": "image", "src": "..."}	Yes

Thinking Bubbles

The {"type": "thinking", ...} component renders as a collapsible thinking bubble. Consecutive thinking yields append to the same bubble until a different component type is yielded. Cycls automatically maps provider reasoning deltas (Claude extended thinking, OpenAI delta.reasoning) to this channel, so you get thinking bubbles without doing anything special.

Context Object

@cycls.agent(web=cycls.Web().auth(cycls.Clerk()))
async def chat(context):
    context.messages      # [{"role": "user", "content": "..."}]
    context.messages.raw  # Full data including UI component parts
    context.user          # User(id, org_id, plan, features, ...) when auth is set
    context.prod          # True via .deploy(), False via .local() — gate billing/analytics
    with context.workspace():   # Per-user persistent scope — enables cycls.Dict(...)
        usage = cycls.Dict("usage")

API Endpoints

Endpoint	Format
POST /chat/cycls	Cycls streaming protocol
POST /chat/completions	OpenAI-compatible

HTTP Extension

Agents expose the underlying FastAPI surface via .server for webhooks, health checks, OAuth callbacks, and any custom routes:

@cycls.agent(web=cycls.Web().auth(cycls.Clerk()))
async def my_agent(context):
    async for msg in llm.run(context=context):
        yield msg


@my_agent.server.api_route("/webhook", methods=["POST"])
async def stripe_webhook(request):
    payload = await request.json()
    ...
    return {"ok": True}


@my_agent.server.api_route("/profile", methods=["GET"])
async def profile(user = Depends(my_agent.auth)):
    return {"user_id": user.id}

Declarative Infrastructure

The cycls.Image primitive holds container build config. Every field is chainable; the resulting Image is passed to any decorator via image=.

image = (
    cycls.Image()
    .pip("openai", "pandas", "numpy", "transformers")
    .apt("ffmpeg", "imagemagick", "libpq-dev")
    .copy("./utils.py")
    .copy("./models/", "app/models/")
    .copy("/absolute/path/to/config.json")
    .run("echo 'hello from build' > /app/build_marker.txt")
)

@cycls.function(image=image)
def my_func(x):
    from utils import helper_function   # bundled via .copy()
    ...

.pip(*packages) — Python packages

Install any packages from PyPI during container build.

cycls.Image().pip("openai", "pandas", "numpy", "transformers")

.apt(*packages) — System packages

Install apt-get dependencies. Need ffmpeg? ImageMagick? Declare it.

cycls.Image().apt("ffmpeg", "imagemagick", "libpq-dev")

.copy(src, dst=None) — Bundle files

Include local files and directories. Works with relative or absolute paths, single files or whole trees. dst defaults to src; pass both to relocate.

(
    cycls.Image()
    .copy("./utils.py")                       # same path
    .copy("./models/", "app/models/")          # src → dst
    .copy("/home/user/configs/app.json")       # absolute
)

Import bundled modules in your function body:

@cycls.function(image=cycls.Image().copy("./utils.py"))
def my_func(x):
    from utils import helper_function
    ...

.run(command) — Build-time shell commands

cycls.Image().run("pip install --upgrade pip").run("apt-get clean")

.rebuild() — Force Docker cache bust

image = cycls.Image().pip("numpy").rebuild()   # skip Docker cache

Public static files via cycls.Web

Static files served from /public (images, downloads, assets) live on the Web primitive:

web = cycls.Web().copy_public("./assets/logo.png", "./downloads/")

Access them at https://your-app.cycls.ai/public/logo.png.

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What You Get

• One file — Primitives, code, and infrastructure together
• Three decorators — @function, @app, @agent, each one strict and composable
• Multi-LLM — Anthropic native + every OpenAI-compatible endpoint
• Managed loop — retries, compaction, sandbox, tool handlers, history, sessions
• CLI + SDK — cycls run, cycls deploy, or programmatic .local() / .deploy()
• No drift — what you see is what runs

No YAML. No Dockerfiles. No infrastructure repo. The code is the deployment.

Learn More

• Tutorial — comprehensive guide from basics to advanced
• Sandbox security — how the Bash tool is isolated
• Examples — working code samples

License

MIT