dograh/sdk/typescript
Abhishek Kumar 6d93be3ef6 fix: number pool initialization in multi telephony setup
If there are multiple telephony configurations, the form number should be initialized from the campaigns given telephonic configuration rather than the organization default telephonic configuration.
2026-05-08 14:48:53 +05:30
..
scripts feat: refactor node spec and add mcp tools (#244) 2026-04-21 07:56:16 +05:30
src fix: number pool initialization in multi telephony setup 2026-05-08 14:48:53 +05:30
tests feat: refactor node spec and add mcp tools (#244) 2026-04-21 07:56:16 +05:30
.gitignore feat: refactor node spec and add mcp tools (#244) 2026-04-21 07:56:16 +05:30
LICENSE feat: refactor node spec and add mcp tools (#244) 2026-04-21 07:56:16 +05:30
package-lock.json feat: add examples to create workflow and use sdk 2026-04-24 14:09:24 +05:30
package.json chore: bump sdk version 2026-05-02 17:04:12 +05:30
README.md feat: refactor node spec and add mcp tools (#244) 2026-04-21 07:56:16 +05:30
tsconfig.json feat: refactor node spec and add mcp tools (#244) 2026-04-21 07:56:16 +05:30

@dograh/sdk

Typed builder for Dograh voice-AI workflows. Fetches the node-spec catalog from the Dograh backend at session start, validates every call against it at the call site, and produces wire-format JSON that round-trips through the Python ReactFlowDTO.

Install

npm install @dograh/sdk
# or
pnpm add @dograh/sdk

For local development against a checked-out monorepo, add a tsconfig paths entry:

{
  "paths": {
    "@dograh/sdk": ["../sdk/typescript/src/index.ts"]
  }
}

Usage

import { DograhClient, Workflow } from "@dograh/sdk";

const client = new DograhClient({
  baseUrl: "http://localhost:8000",
  apiKey: process.env.DOGRAH_API_KEY,
});

const wf = new Workflow({ client, name: "loan_qualification" });

const start = await wf.add({
  type: "startCall",
  name: "greeting",
  prompt: "You are Sarah from Acme Loans. Greet the caller warmly.",
  greeting_type: "text",
  greeting: "Hi {{first_name}}, this is Sarah.",
});

const qualify = await wf.add({
  type: "agentNode",
  name: "qualify",
  prompt: "Ask about loan amount and timeline.",
});

const done = await wf.add({ type: "endCall", name: "done", prompt: "Thank them." });

wf.edge(start, qualify, { label: "interested", condition: "Caller expressed interest." });
wf.edge(qualify, done, { label: "done", condition: "Qualification complete." });

await client.saveWorkflow(123, wf);

Client-side validation

Each add() call validates kwargs against the fetched spec. ValidationError is thrown immediately when:

  • an unknown field is passed (catches typos)
  • a required field is missing or empty
  • a scalar type is wrong (e.g., string for a boolean)
  • an options value isn't in the allowed list

When a spec carries an llm_hint, the hint is appended to the error so an LLM agent can self-correct on retry:

tool_uuids: expected tool_refs, got string
  Hint: List of tool UUIDs from `list_tools`.

Server-side Pydantic validators run on save and surface anything the client lets through.

Environment

DOGRAH_API_URL=http://localhost:8000   # default
DOGRAH_API_KEY=sk-...                  # sent as X-API-Key

License

BSD 2-Clause — see LICENSE.