INFRASTRUCTURE FOR RELIABLE AI AGENTS

The runtime for AI agents.

Cheela is the runtime for production AI agents, sitting between your application and the model to make execution observable, predictable, and controllable.

See the runtime
Modular
Swap providers, tools, and capabilities without rewriting your application.
Precise
Small APIs, strict boundaries, and clear execution flow.
Controlled
Policy, routing, and execution stay inside the runtime.
Execution Pipeline
From application to action. Every step is visible.
Live
Execution Pipeline
1Application
2Agent
3Provider
4LLM
5Runtime
6Capability
7Action
Any Model
OpenAI • Anthropic • Gemini
One Runtime
One API. Every provider.

Problem

Agent systems fail when the execution is invisible.

Teams can prototype a demo quickly, but reliability collapses when policy, model selection, retries, and tool execution live in different places.

The failure mode is structural.

Business logic hides inside prompts, observability arrives too late, and every model change introduces new regressions.

Agent output is not the same as execution intent.
Model calls need a runtime contract, not ad hoc orchestration.
Policy belongs in the runtime, not scattered across the application.
Outcome
Cheela moves those responsibilities into a single, observable layer.

Reliability becomes measurable, reviewable, and repeatable across every deployment.

Solution

Minimal APIs. Maximum control.

Opinionated where reliability matters. Flexible everywhere else.

Provider Agnostic
Switch providers without rewriting your application.
Composable
Compose only the runtime capabilities your product needs.
Observable
Every action can be traced back to an execution path.
Predictable
Control flow stays predictable even as models change.
Developer ergonomics
A platform that stays out of the way until it matters.

Built to scale from prototypes to production without changing how your system works.

Architecture

A runtime that makes execution explicit.

The animation mirrors how every request moves through the runtime, from application to model.

Runtime path
Every step is visible, inspectable, and controllable.
1
Application
Entry point for the product workflow.
2
Agent
Decision-making layer with policy and context.
3
Provider
Execution, observability, and retry semantics live here.
4
LLM
Declared skill or operation available to the agent.
5
Runtime
Concrete side effect or external interaction.
6
Capability
Selected provider or integration target.
7
Action
Model inference that produces the next action.
Behavior
The animation reflects how the runtime actually executes every request.
Motion communicates execution, never decoration.
Designed to guide attention without distraction.
Respects accessibility preferences by default.

Features

The platform is shaped around reliability, not hype.

Each feature reinforces engineering clarity, execution control, and confidence in production.

Controlled execution

Keep execution predictable with built-in routing, retries, and policy.

Composable capabilities

Build agents from small, well-defined capabilities instead of one-off prompts and brittle orchestration glue.

Runtime observability

Every execution produces traces, metrics, and state transitions for debugging and review.

Runtime policy

Permissions, approvals, and guardrails travel with every execution.

Provider agnostic

Connect any provider without locking your runtime to a single model vendor.

Developer experience

Small APIs, readable examples, and predictable behavior make the runtime easy to adopt.

Developer Experience

Real TypeScript, not screenshots.

Documentation that reads like production code, not marketing.

createAgentRuntime.ts·TypeScript
01import { createRuntime } from "@cheela/runtime";
02 
03const runtime = createRuntime({
04 name: "support-agent",
05 provider: "anthropic",
06 capabilities: ["search", "summarize", "escalate"],
07 policy: {
08 approvals: ["payment", "deletion"],
09 maxSteps: 8,
10 },
11});
12 
13export async function handleRequest(input: string) {
14 const run = await runtime.execute({input});
15 
16 if (!run.allowed) {
17 return run.reason;
18 }
19 
20 return run.result;
21}
Copyable examples
Docs that teach the runtime, not just the API.

Copy real examples, understand the runtime, and ship faster.

What ships
Syntax-highlighted codeCopy buttonsStep-by-step guidesAPI referencesRuntime patterns

Developer Platform

Engineering should be visible.

Trust comes from transparent engineering, clear documentation, and real code.

Corenpm
@cheela/runtime

Execution engine for reliable AI agents with policies, routing, and observability.

View Package
Packagenpm
@cheela/provider

Provider adapters for OpenAI, Anthropic, Gemini, and more.

View Package
Packagenpm
@cheela/sdk

TypeScript SDK for building capabilities, actions, and agent runtimes.

View Package

Roadmap

Growing the runtime, not the complexity.

Every release strengthens reliability, observability, and developer experience.

Now
A stable runtime for production AI agents.

Focused on reliability first, with new capabilities added only when they strengthen the runtime.

01
Fine-grained capability policies
02
Multi-provider routing with observability
03
Human approval checkpoints
04
Reusable runtime templates for teams
05
Enterprise audit export and retention

FAQ

Questions every engineering team asks.

Direct answers about how Cheela works and why it exists.

Why does Cheela focus on runtime instead of prompting?

Prompts are useful, but they are not enough to make agent systems reliable. Cheela centers the runtime so policy, execution, and observability are explicit.

Can teams use their own model providers?

Yes. Cheela is provider-agnostic and designed so the model layer can change without rewriting the rest of the agent architecture.

Is Cheela opinionated about deployment?

It is opinionated about control and reliability, not about where you run it. The platform is built to fit modern TypeScript and cloud workflows.

Get started

Ship an agent platform your team can actually maintain.

Build AI systems your team can understand, inspect, and evolve over time.

—
People on the waitlist
Read the Docs