# Chapter 1 - CLI and Agent Loop
Now let's add BAML and create our first agent with a CLI interface.
First, we'll need to install [BAML](https://github.com/boundaryml/baml)
which is a tool for prompting and structured outputs.
npm install @boundaryml/baml
Initialize BAML
npx baml-cli init
Remove default resume.baml
rm baml_src/resume.baml
Add our starter agent, a single baml prompt that we'll build on
cp ./walkthrough/01-agent.baml baml_src/agent.baml
<details>
<summary>show file</summary>
```rust
// ./walkthrough/01-agent.baml
class DoneForNow {
intent "done_for_now"
message string
}
function DetermineNextStep(
thread: string
) -> DoneForNow {
client "openai/gpt-4o"
prompt #"
{{ _.role("system") }}
You are a helpful assistant that can help with tasks.
{{ _.role("user") }}
You are working on the following thread:
{{ thread }}
What should the next step be?
{{ ctx.output_format }}
"#
}
test HelloWorld {
functions [DetermineNextStep]
args {
thread #"
{
"type": "user_input",
"data": "hello!"
}
"#
}
}
```
</details>
Generate BAML client code
npx baml-cli generate
Enable BAML logging for this section
export BAML_LOG=debug
Add the CLI interface
cp ./walkthrough/01-cli.ts src/cli.ts
<details>
<summary>show file</summary>
```ts
// ./walkthrough/01-cli.ts
// cli.ts lets you invoke the agent loop from the command line
import { agentLoop, Thread, Event } from "./agent";
export async function cli() {
// Get command line arguments, skipping the first two (node and script name)
const args = process.argv.slice(2);
if (args.length === 0) {
console.error("Error: Please provide a message as a command line argument");
process.exit(1);
}
// Join all arguments into a single message
const message = args.join(" ");
// Create a new thread with the user's message as the initial event
const thread = new Thread([{ type: "user_input", data: message }]);
// Run the agent loop with the thread
const result = await agentLoop(thread);
console.log(result);
}
```
</details>
Update index.ts to use the CLI
```diff
src/index.ts
+import { cli } from "./cli"
+
async function hello(): Promise<void> {
console.log('hello, world!')
async function main() {
- await hello()
+ await cli()
}
```
<details>
<summary>skip this step</summary>
cp ./walkthrough/01-index.ts src/index.ts
</details>
Add the agent implementation
cp ./walkthrough/01-agent.ts src/agent.ts
<details>
<summary>show file</summary>
```ts
// ./walkthrough/01-agent.ts
import { b } from "../baml_client";
// tool call or a respond to human tool
type AgentResponse = Awaited<ReturnType<typeof b.DetermineNextStep>>;
export interface Event {
type: string
data: any;
}
export class Thread {
events: Event[] = [];
constructor(events: Event[]) {
this.events = events;
}
serializeForLLM() {
// can change this to whatever custom serialization you want to do, XML, etc
// e.g. https://github.com/got-agents/agents/blob/59ebbfa236fc376618f16ee08eb0f3bf7b698892/linear-assistant-ts/src/agent.ts#L66-L105
return JSON.stringify(this.events);
}
}
// right now this just runs one turn with the LLM, but
// we'll update this function to handle all the agent logic
export async function agentLoop(thread: Thread): Promise<AgentResponse> {
const nextStep = await b.DetermineNextStep(thread.serializeForLLM());
return nextStep;
}
```
</details>
The the BAML code is configured to use OPENAI_API_KEY by default
As you're testing, you can change the model / provider to something else
as you please
client "openai/gpt-4o"
[Docs on baml clients can be found here](https://docs.boundaryml.com/guide/baml-basics/switching-llms)
For example, you can configure [gemini](https://docs.boundaryml.com/ref/llm-client-providers/google-ai-gemini)
or [anthropic](https://docs.boundaryml.com/ref/llm-client-providers/anthropic) as your model provider.
If you want to run the example with no changes, you can set the OPENAI_API_KEY env var to any valid openai key.
export OPENAI_API_KEY=...
Try it out
npx tsx src/index.ts hello
you should see a familiar response from the model
{
intent: 'done_for_now',
message: 'Hello! How can I assist you today?'
}
# Chapter 1 - CLI and Agent Loop
Now let's add BAML and create our first agent with a CLI interface.
First, we'll need to install [BAML](https://github.com/boundaryml/baml)
which is a tool for prompting and structured outputs.
npm install @boundaryml/baml
Initialize BAML
npx baml-cli init
Remove default resume.baml
rm baml_src/resume.baml
Add our starter agent, a single baml prompt that we'll build on
cp ./walkthrough/01-agent.baml baml_src/agent.baml
<details>
<summary>show file</summary>
```rust
// ./walkthrough/01-agent.baml
class DoneForNow {
intent "done_for_now"
message string
}
client<llm> Qwen3 {
provider "openai-generic"
options {
base_url env.BASETEN_BASE_URL
api_key env.BASETEN_API_KEY
}
}
function DetermineNextStep(
thread: string
) -> DoneForNow {
client Qwen3
// use /nothink for now because the thinking tokens (or streaming thereof) screw with baml (i think (no pun intended))
prompt #"
{{ _.role("system") }}
/nothink
You are a helpful assistant that can help with tasks.
{{ _.role("user") }}
You are working on the following thread:
{{ thread }}
What should the next step be?
{{ ctx.output_format }}
"#
}
test HelloWorld {
functions [DetermineNextStep]
args {
thread #"
{
"type": "user_input",
"data": "hello!"
}
"#
}
}
```
</details>
Generate BAML client code
npx baml-cli generate
Enable BAML logging for this section
export BAML_LOG=debug
Add the CLI interface
cp ./walkthrough/01-cli.ts src/cli.ts
<details>
<summary>show file</summary>
```ts
// ./walkthrough/01-cli.ts
// cli.ts lets you invoke the agent loop from the command line
import { agentLoop, Thread, Event } from "./agent";
export async function cli() {
// Get command line arguments, skipping the first two (node and script name)
const args = process.argv.slice(2);
if (args.length === 0) {
console.error("Error: Please provide a message as a command line argument");
process.exit(1);
}
// Join all arguments into a single message
const message = args.join(" ");
// Create a new thread with the user's message as the initial event
const thread = new Thread([{ type: "user_input", data: message }]);
// Run the agent loop with the thread
const result = await agentLoop(thread);
console.log(result);
}
```
</details>
Update index.ts to use the CLI
```diff
src/index.ts
+import { cli } from "./cli"
+
async function hello(): Promise<void> {
console.log('hello, world!')
async function main() {
- await hello()
+ await cli()
}
```
<details>
<summary>skip this step</summary>
cp ./walkthrough/01-index.ts src/index.ts
</details>
Add the agent implementation
cp ./walkthrough/01-agent.ts src/agent.ts
<details>
<summary>show file</summary>
```ts
// ./walkthrough/01-agent.ts
import { b } from "../baml_client";
// tool call or a respond to human tool
type AgentResponse = Awaited<ReturnType<typeof b.DetermineNextStep>>;
export interface Event {
type: string
data: any;
}
export class Thread {
events: Event[] = [];
constructor(events: Event[]) {
this.events = events;
}
serializeForLLM() {
// can change this to whatever custom serialization you want to do, XML, etc
// e.g. https://github.com/got-agents/agents/blob/59ebbfa236fc376618f16ee08eb0f3bf7b698892/linear-assistant-ts/src/agent.ts#L66-L105
return JSON.stringify(this.events);
}
}
// right now this just runs one turn with the LLM, but
// we'll update this function to handle all the agent logic
export async function agentLoop(thread: Thread): Promise<AgentResponse> {
const nextStep = await b.DetermineNextStep(thread.serializeForLLM());
return nextStep;
}
```
</details>
The the BAML code is configured to use BASETEN_API_KEY by default
To get a Baseten API key and URL, create an account at [baseten.co](https://baseten.co),
and then deploy [Qwen3 32B from the model library](https://www.baseten.co/library/qwen-3-32b/).
```rust
function DetermineNextStep(thread: string) -> DoneForNow {
client Qwen3
// ...
```
If you want to run the example with no changes, you can set the BASETEN_API_KEY env var to any valid baseten key.
If you want to try swapping out the model, you can change the `client` line.
[Docs on baml clients can be found here](https://docs.boundaryml.com/guide/baml-basics/switching-llms)
For example, you can configure [gemini](https://docs.boundaryml.com/ref/llm-client-providers/google-ai-gemini)
or [anthropic](https://docs.boundaryml.com/ref/llm-client-providers/anthropic) as your model provider.
For example, to use openai with an OPENAI_API_KEY, you can do:
client "openai/gpt-4o"
Set your env vars
export BASETEN_API_KEY=...
export BASETEN_BASE_URL=...
Try it out
npx tsx src/index.ts hello
you should see a familiar response from the model
{
intent: 'done_for_now',
message: 'Hello! How can I assist you today?'
}
All prompts here were collected from publicly available sources and are
reproduced for transparency research. Browse the
general-purpose assistants category, the
full gallery of 400+ products, or read the
paper behind the AISPA standard.