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* refactor: carve out extraction panel * refactor: create spec versions for node types * refactor: create a GenericNode and remove custom nodes * feat: add python and typescript sdk * add dograh sdk * fix: fetch draft workflow definition over published one * fix: fix routes of SDKs to use code gen * chore: remove doclink dependency to reduce image size * chore: format files * chore: bump pipecat * feat: let mcp fetch archived workflows on demand * chore: fix tests * feat: add sdk documentation * chore: change banner and add badge
111 lines
3.6 KiB
Text
111 lines
3.6 KiB
Text
---
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title: "Build an agent"
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description: "Assemble a Dograh voice agent programmatically with the SDK and save it as a draft"
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---
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The SDK mirrors the node-and-edge model of the [Voice Agent Builder](/voice-agent/introduction). You create a `Workflow`, add nodes (`startCall`, `agentNode`, `endCall`, …) with `add()`, connect them with `edge()`, and persist the result via `save_workflow`.
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## Prerequisites
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- A Dograh [API key](/configurations/api-keys) exported as `DOGRAH_API_KEY`
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- An existing agent ID to save drafts against (create one in the Dograh UI or via [`POST /api/v1/workflow/create`](/api-reference/agents/create-from-template))
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## Build and save
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The example below builds a three-node loan-qualification agent and saves it as a **new draft version** on an existing agent. Your published agent keeps serving calls until you explicitly publish the draft.
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<CodeGroup>
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```python Python
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from dograh_sdk import DograhClient, Workflow
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with DograhClient(api_key="YOUR_API_KEY") as client:
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wf = Workflow(client=client, name="loan_qualification")
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greeting = wf.add(
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type="startCall",
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name="greeting",
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prompt="You are Sarah from Acme Loans. Greet the caller warmly.",
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)
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qualify = wf.add(
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type="agentNode",
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name="qualify",
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prompt="Ask about loan amount, purpose, and monthly income.",
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)
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done = wf.add(
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type="endCall",
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name="done",
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prompt="Thank them and end the call politely.",
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)
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wf.edge(greeting, qualify, label="interested", condition="Caller wants to continue.")
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wf.edge(qualify, done, label="done", condition="All qualification questions answered.")
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client.save_workflow(workflow_id=123, workflow=wf)
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```
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```typescript TypeScript
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import { DograhClient, Workflow } from "@dograh/sdk";
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const client = new DograhClient({ apiKey: "YOUR_API_KEY" });
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const wf = new Workflow({ client, name: "loan_qualification" });
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const greeting = await wf.add({
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type: "startCall",
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name: "greeting",
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prompt: "You are Sarah from Acme Loans. Greet the caller warmly.",
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});
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const qualify = await wf.add({
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type: "agentNode",
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name: "qualify",
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prompt: "Ask about loan amount, purpose, and monthly income.",
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});
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const done = await wf.add({
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type: "endCall",
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name: "done",
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prompt: "Thank them and end the call politely.",
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});
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wf.edge(greeting, qualify, { label: "interested", condition: "Caller wants to continue." });
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wf.edge(qualify, done, { label: "done", condition: "All qualification questions answered." });
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await client.saveWorkflow(123, wf);
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```
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</CodeGroup>
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## Edit an existing agent
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Load an agent into an editable `Workflow`, mutate it, then save:
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<CodeGroup>
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```python Python
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wf = client.load_workflow(workflow_id=123)
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wf.name = "loan_qualification_v2"
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client.save_workflow(workflow_id=123, workflow=wf)
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```
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```typescript TypeScript
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const wf = await client.loadWorkflow(123);
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wf.name = "loan_qualification_v2";
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await client.saveWorkflow(123, wf);
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```
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</CodeGroup>
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## Discover node types
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Each node's `type` string and required fields come from the backend's node-spec catalog. Fetch it at runtime to validate what you can build:
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<CodeGroup>
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```python Python
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types = client.list_node_types()
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for spec in types.node_types:
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print(spec.name, [p.name for p in spec.properties])
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```
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```typescript TypeScript
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const types = await client.listNodeTypes();
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for (const spec of types.node_types) {
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console.log(spec.name, spec.properties.map(p => p.name));
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}
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```
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</CodeGroup>
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<Note>
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For a full description of each node type and its fields, see the [Nodes](/voice-agent/start-call) section of the Voice Agent Builder docs.
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</Note>
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