dograh Documentation

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Open source voice AI platform. Self-hosted alternative to Vapi and Retell. On Prem, BYOK across Speech to Speech or LLM/STT/TTS, with a visual workflow...

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Python 31 files · ~125,102 tokens #ai-calling #asterisk-ari #conversational-ai #inbound-calls #local-llm #no-code #on-prem-voice-agent-platform #open-source

RAW Doc

Docker & Self-Hosted Deployment Verified OCI

Deploy dograh on any server, homelab, or cloud container platform.

Raw YAML Docker Page → JSON API

1-Click Terminal Command

bash (docker run)

docker run -d --name dograh -p 80:80 --restart always traefik:v3.1

Configured Services & Ports

⚙️ traefik

image: traefik:v3.1

🔌 Ports: 80:80, 443:443

docker-compose.yml

yaml (docker-compose.yml)

# Standalone Traefik + Let's Encrypt — STANDS IN FOR Hostinger's managed Traefik.
# =================================================================
# On Hostinger's VPS Docker Manager you do NOT deploy this — their platform
# already runs Traefik. Use this file to reproduce that environment on a
# generic VPS (e.g. a plain EC2 box) so you can test docker-compose.yaml
# end to end: TLS issuance, HTTP->HTTPS redirect, WebSocket upgrade, routing.
#
# It also documents exactly what we need Hostinger's Traefik to provide:
#   - an HTTPS entrypoint (here: websecure / :443)
#   - a Let's Encrypt certresolver (here: letsencrypt)
#   - the Docker provider watching a shared network (here: traefik)
#   - a long idleTimeout so long-lived signaling WebSockets aren't cut
#
# Bring up BEFORE the app stack, on the same external network:
#   docker network create traefik-proxy
#   docker compose -f docker-compose.traefik.yaml --env-file .env up -d
#   docker compose --env-file .env up -d
# =================================================================

services:
  traefik:
    image: traefik:v3.1
    container_name: traefik
    restart: unless-stopped
    command:
      - --providers.docker=true
      - --providers.docker.exposedbydefault=false
      - --entrypoints.web.address=:80
      - --entrypoints.websecure.address=:443
      # Global HTTP->HTTPS redirect (the ACME HTTP-01 challenge is still served
      # on :80 — Traefik handles the challenge ahead of this redirect).
      - --entrypoints.web.http.redirections.entrypoint.to=websecure
      - --entrypoints.web.http.redirections.entrypoint.scheme=https
      # Keep long-lived WebSockets (signaling) from being cut while idle.
      - --entrypoints.websecure.transport.respondingTimeouts.idleTimeout=3600s
      # Let's Encrypt via HTTP-01. Must match TRAEFIK_CERTRESOLVER in the app .env.
      - --certificatesresolvers.letsencrypt.acme.httpchallenge=true
      - --certificatesresolvers.letsencrypt.acme.httpchallenge.entrypoint=web
      - --certificatesresolvers.letsencrypt.acme.email=${ACME_EMAIL:?set ACME_EMAIL in .env}
      - --certificatesresolvers.letsencrypt.acme.storage=/letsencrypt/acme.json
      # For repeated test runs, point at LE staging to avoid prod rate limits:
      # - --certificatesresolvers.letsencrypt.acme.caserver=https://acme-staging-v02.api.letsencrypt.org/directory
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock:ro
      - traefik-acme:/letsencrypt
    networks:
      - traefik

volumes:
  traefik-acme:

networks:
  traefik:
    external: true
    name: ${TRAEFIK_NETWORK:-traefik-proxy}

Technical Documentation: dograh-hq/dograh

ℹ️ Provenance: Multi-Tier Ingestion · CodeWiki Reference

Overview (`README.md`)

Dograh AI

The open-source, self-hostable alternative to Vapi & Retell — build production voice agents with a visual workflow builder, test them in minutes, and let AI coding assistants help design and edit them through MCP.

100% open source, self-hostable — no vendor lock-in, unlike Vapi or Retell
Full control & transparency — every line of code is open, with flexible LLM / TTS / STT integration
Maintained by YC alumni and exit founders, committed to keeping voice AI open

🎥 Featured

📺 Prefer a 2-minute product walkthrough? Click here.

⚖️ Dograh vs Vapi vs Retell

An honest comparison on the axes that matter most to teams evaluating voice AI platforms.

	Dograh	Vapi	Retell
License	BSD 2-Clause (open source)	Proprietary	Proprietary
Self-hostable	✅ Yes — one Docker command	❌ SaaS only	❌ SaaS only
Pricing	Free (self-host) · usage-based (cloud)	Per-minute SaaS	Per-minute SaaS
Bring your own LLM / STT / TTS	✅ Any provider, or use Dograh's stack	Configurable within their integrations	Configurable within their integrations
Source-level customization	✅ Every line is yours to modify	❌ Closed source	❌ Closed source
Data residency	Your infra, your rules	Their cloud	Their cloud
Vendor lock-in	None	Full	Full

🚀 Get Started

Download and setup Dograh on your Local Machine

Note
We collect anonymous usage data to improve the product. You can opt out by setting ENABLE_TELEMETRY=false before running the startup script.

Note
If you wish to run the platform on a remote server instead, checkout our Documentation

bash

curl -o docker-compose.yaml https://raw.githubusercontent.com/dograh-hq/dograh/main/docker-compose.yaml && curl -o start_docker.sh https://raw.githubusercontent.com/dograh-hq/dograh/main/scripts/start_docker.sh && chmod +x start_docker.sh && ./start_docker.sh

Note
By default the quickstart starts a public *.trycloudflare.com quick tunnel to the API (for inbound telephony webhooks), and signup is enabled. After creating your first account, you can close signup by setting ENABLE_SIGNUP=false in .env and re-running ./start_docker.sh (docker compose restart does not apply .env changes).

⚡ Prefer an AI agent to set it up for you?
If you use Claude Code or Codex, install the official Dograh setup skill and let your agent handle installation, configuration, and troubleshooting — it detects your OS, picks the right deploy path, runs Dograh's own setup scripts, and verifies the result.
text
# In Claude Code
/plugin marketplace add dograh-hq/dograh-plugins
/plugin install dograh@dograh
Then start a new session and ask it to "set up Dograh" (or run /dograh-setup). Codex is supported too — see the plugin repo.

Note
First startup may take 2-3 minutes to download all images. Once running, open http://localhost:3010 to create your first AI voice assistant!
For common issues and solutions, see 🔧 Troubleshooting.

🎙️ Your First Voice Bot

Open http://localhost:3010 in your browser.
Pick Inbound or Outbound, name your bot (e.g. Lead Qualification), and describe the use case in 5–10 words (e.g. Screen insurance form submissions for purchase intent).
Click Test Agent.
Use Test Audio to talk to your agent in the browser, or Test Chat to iterate faster in text. In Test Chat, you can edit or replay user turns and Dograh will regenerate the agent's replies and node transitions from that point.

🔑 No API keys needed. Dograh ships with auto-generated keys and its own LLM / TTS / STT stack. Connect your own keys for LLM, TTS, STT, or Telephony (e.g. Twilio, Vonage, Telnyx) anytime.

Featured On & Community Validation: Dograh was named #1 Product of the Day on Product Hunt.

Build Agents with MCP

Dograh ships with an MCP server, so coding agents can work directly inside your Dograh workspace.

Connect Codex, Claude Code, Cursor, or any MCP client to inspect existing agents, search Dograh docs, fetch node schemas, create new workflows, and save draft edits from natural language.

When asking your coding agent to build a voice agent, share a short script for
the use case instead of only a one-line prompt. Include the agent persona, call
flow, rules, objection handling, success criteria, and a sample conversation if
you have one.

See the MCP guide to connect your assistant.

Features

Voice Agent Builder

Visual workflow builder with start nodes, agent nodes, global instructions, tools, transitions, and end-call outcomes
Test Agent panel with Test Audio for browser voice testing and Test Chat for fast prompt iteration
QA node, knowledge bases, webhooks, embeds, and tool calling for production workflows

Voice & Telephony

Built-in telephony integrations including Twilio, Vonage, Telnyx, Plivo, Vobiz, Cloudonix, and Asterisk ARI
Human handoff with call transfer on supported telephony providers
Bring your own LLM, TTS, STT, and telephony providers; store artifacts in bundled MinIO or AWS/S3-compatible storage

Developer Experience

One-command Docker setup for self-hosting
Python backend and modular provider architecture for customization
Python and Node SDKs for programmatic agent creation and outbound calls

Deployment Options

Local Development

Refer Local Setup

Self-Hosted Deployment

For detailed deployment instructions including remote server setup with HTTPS, see our Docker Deployment Guide.

Cloud Version

Visit https://www.dograh.com for our managed cloud offering.

📚Documentation

You can go to https://docs.dograh.com for our documentation.

📦 SDKs

Python SDK — pypi.org/project/dograh-sdk
Node SDK — npmjs.com/package/@dograh/sdk

🤝Community & Support

👋 Coming from the Better Stack video? Drop your use case in our pinned GitHub Discussion — we read every reply and the founders personally onboard early adopters.

Slack — the cornerstone of Dograh AI contributions. Connect with maintainers, discuss features before coding, get help with setup, and stay current on contribution sprints.
GitHub Discussions — share use cases, ask questions, swap workflow recipes.
GitHub Issues — report bugs or request features.

👉 Join us → Dograh Community Slack

🙌 Contributing

We love contributions! Dograh AI is 100% open source and we intend to keep it that way.

Getting Started

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

⭐ Star History

📄 License

Dograh AI is licensed under the BSD 2-Clause License- the same license as projects that were used in building Dograh AI, ensuring compatibility and freedom to use, modify, and distribute.

🏢 About

Built with ❤️ by Dograh (Zansat Technologies Private Limited)
Founded by YC alumni and exit founders committed to keeping voice AI open and accessible to everyone.

Overview (`docs/README.md`)

Dograh AI Documentation

Local Setup

Install mintlify npm i mint
Clone the repository
cd dograh/docs and run mint dev

Overview (`examples/README.md`)

Dograh SDK Examples

Runnable examples of the Dograh SDK in Python and TypeScript.

Shared environment variables

Copy .env.example to .env in each example directory and fill in your values, then source .env (or use your preferred dotenv loader).

Variable	Description
`DOGRAH_API_ENDPOINT`	Dograh API base URL (e.g. `http://localhost:8000`)
`DOGRAH_API_TOKEN`	API token — sent as `X-API-Key`

The workflow ID and destination phone number are set as constants at the top of each example script — edit them there.

Python

bash

pip install dograh-sdk

export DOGRAH_API_ENDPOINT=http://localhost:8000
export DOGRAH_API_TOKEN=sk-...

# Fetch a workflow by ID and place a test phone call.
python python/fetch_workflow_and_call.py

# Create a new workflow from a definition.
python python/create_workflow.py

# Build a 3-node agent with the Workflow SDK and save it as a draft.
# Edit WORKFLOW_ID at the top of the file first.
python python/build_workflow_with_sdk.py

# Load an existing workflow, edit the startCall prompt, and save as a draft.
# Edit WORKFLOW_ID at the top of the file first.
python python/load_and_edit_workflow.py

TypeScript

Uses tsx to run directly.

bash

cd typescript
npm install

export DOGRAH_API_ENDPOINT=http://localhost:8000
export DOGRAH_API_TOKEN=sk-...

npm run call    # fetch_workflow_and_call.ts
npm run create  # create_workflow.ts
npm run build   # build_workflow_with_sdk.ts  (edit WORKFLOW_ID in the file first)
npm run edit    # load_and_edit_workflow.ts  (edit WORKFLOW_ID in the file first)

Overview (`ui/README.md`)

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

bash

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

The redirection happens server side using ui/src/stack.tsx after the user has logged in.

Sentry and PostHog

Initialized in ui/src/instrumentation-client.ts

Overview (`deploy/hostinger/README.md`)

Hostinger (managed-Traefik) deployment

Deploy Dograh where a shared, managed Traefik with Let's Encrypt already
terminates TLS and routes ingress — e.g. Hostinger's VPS Docker Manager.
The same files work on any host that fronts containers with Traefik.

Files

File	Role	Deploy on Hostinger?
`docker-compose.yaml`	The Dograh app stack. Single self-contained file — named volumes only, no host bind-mounts, no init/sidecar that reads files outside the compose.	✅ Yes
`.env.example`	Required + optional environment variables, with guidance. Copy to `.env` and fill in.	✅ Yes (as the env template)
`docker-compose.traefik.yaml`	A standalone Traefik + Let's Encrypt that stands in for the managed Traefik, so you can reproduce the environment on a plain VPS for testing. Also documents what the platform's Traefik must provide.	❌ No — reference only

What the app stack needs from Traefik

Routing is declared with Traefik labels on ui, api, and minio:
/api/v1 → api (includes the signaling WebSocket), /voice-audio → minio,
everything else → ui. For that to work the platform's Traefik must offer:

an HTTPS entrypoint — set TRAEFIK_ENTRYPOINT (e.g. websecure)
a Let's Encrypt certresolver — set TRAEFIK_CERTRESOLVER
the Docker provider watching a shared network — set TRAEFIK_NETWORK
a long idleTimeout so long-lived signaling WebSockets aren't cut
(recommended) a global HTTP→HTTPS redirect

Traefik upgrades WebSockets automatically — no special label is required.

WebRTC media (coturn) is NOT proxied by Traefik

Voice audio is UDP (ICE/DTLS-SRTP), relayed by the bundled coturn. A reverse
proxy cannot carry it. coturn publishes host ports that must be open in the
VPS firewall: UDP+TCP 3478 and 5349, and UDP 49152-49200. TURN_HOST
must be the public IP (or a domain resolving to it). Without this, calls
connect (signaling succeeds) but have no audio.

Deploy on Hostinger

The platform provides Traefik, so you only deploy the app stack:

Copy .env.example → .env and fill in PUBLIC_HOST, TURN_HOST, the
secrets, and the three TRAEFIK_* values (matched to Hostinger's Traefik).
Import / deploy docker-compose.yaml.
Ensure the coturn UDP/TCP ports above are open in the firewall.

Test on a generic VPS (self-managed stand-in Traefik)

On a box that does not already run Traefik:

bash

cp .env.example .env     # fill in PUBLIC_HOST, TURN_HOST, secrets, ACME_EMAIL
docker network create traefik-proxy
docker compose -f docker-compose.traefik.yaml --env-file .env up -d   # stand-in Traefik
docker compose --env-file .env up -d                                  # app stack

A no-cost trick for a real cert without owning a domain: set
PUBLIC_HOST=.sslip.io (sslip.io resolves any embedded IP), which
Let's Encrypt will happily issue for.

Overview (`evals/stt/README.md`)

STT Evaluation Benchmark

Benchmark for comparing Speech-to-Text providers using WebSocket streaming with focus on:

Speaker diarization - identifying who said what
Keyterm boosting - improving recognition of specific terms (Deepgram)

Providers

Provider	Diarization	Keyterm Boost	Streaming
Deepgram	Yes	Yes	WebSocket (v1/v2)
Speechmatics	Yes	Additional vocab	WebSocket RT

Setup

bash

# Install dependencies
pip install websockets

# Set API keys
export DEEPGRAM_API_KEY="your-key"
export SPEECHMATICS_API_KEY="your-key"

Note: Requires ffmpeg installed for audio conversion to PCM16.

Usage

Run from the project root directory:

bash

# Test both providers with diarization
python -m evals.stt.benchmark audio/multi_speaker.m4a --diarize

# Test only Deepgram
python -m evals.stt.benchmark audio/multi_speaker.m4a --diarize --providers deepgram

# Test with keyterm boosting (Deepgram)
python -m evals.stt.benchmark audio/multi_speaker.m4a --diarize --keyterms "Dograh" "Pipecat"

# Use different sample rate (default: 8000 Hz)
python -m evals.stt.benchmark audio/multi_speaker.m4a --diarize --sample-rate 16000

# Show word-level timings
python -m evals.stt.benchmark audio/multi_speaker.m4a --diarize --show-words

# Save results to JSON
python -m evals.stt.benchmark audio/multi_speaker.m4a --diarize --save

CLI Options

Option	Description
`audio_file`	Path to audio file (relative to evals/stt/ or absolute)
`--providers`	Providers to test: `deepgram`, `speechmatics` (default: both)
`--diarize`	Enable speaker diarization
`--keyterms`	Keywords to boost (Deepgram) / additional vocab (Speechmatics)
`--language`	Language code (default: en)
`--sample-rate`	Audio sample rate for streaming (default: 8000)
`--show-words`	Show individual word timings
`--save`	Save results to JSON in `results/`

Directory Structure

text

evals/stt/
├── audio/              # Audio test files
│   └── multi_speaker.m4a
├── results/            # Saved benchmark results (JSON)
├── providers/          # STT provider implementations
│   ├── base.py         # Base classes
│   ├── deepgram_provider.py    # WebSocket streaming
│   └── speechmatics_provider.py # WebSocket streaming
├── audio_streamer.py   # PCM16 audio file streamer
├── benchmark.py        # Main runner script
└── README.md

How It Works

Audio Conversion: The AudioStreamer converts any audio file to raw PCM16 using ffmpeg
WebSocket Connection: Providers connect to their respective WebSocket APIs
Streaming: Audio is sent in chunks (configurable sample rate, default 8kHz)
Result Collection: Transcripts and speaker info are collected from WebSocket responses
Comparison: Results are parsed into a common format for comparison

Output Example

text

Audio file: /path/to/audio/multi_speaker.m4a
Providers: ['deepgram', 'speechmatics']
Diarization: True
Sample rate: 8000 Hz

============================================================
Provider: DEEPGRAM
============================================================

Duration: 45.32s
Speakers detected: 2 - ['0', '1']

Transcript:
Hello, welcome to the demo...

--- Speaker Segments ---
[0.0s] Speaker 0: Hello, welcome to the demo.
[2.5s] Speaker 1: Thanks for having me.
...

============================================================
COMPARISON SUMMARY
============================================================

Provider        Duration   Speakers   Words
---------------------------------------------
deepgram        45.32      2          312
speechmatics    45.32      2          308

Adding New Providers

Create a new file in providers/ (e.g., whisper_provider.py)
Implement the STTProvider abstract class with WebSocket streaming
Use AudioStreamer for PCM16 conversion
Add to providers/__init__.py
Add to benchmark.py provider choices

API Documentation

Deepgram Streaming: https://developers.deepgram.com/docs/live-streaming-audio
Deepgram Diarization: https://developers.deepgram.com/docs/diarization
Deepgram Keyterms: https://developers.deepgram.com/docs/keyterm
Speechmatics RT API: https://docs.speechmatics.com/rt-api-ref
Speechmatics Diarization: https://docs.speechmatics.com/features/diarization

Overview (`evals/visualizer/README.md`)

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

bash

npm install
npm run dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.

Learn More

To learn more about Next.js, take a look at the following resources:

Next.js Documentation - learn about Next.js features and API.
Learn Next.js - an interactive Next.js tutorial.

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.

Overview (`sdk/python/README.md`)

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 ReactFlowDTO-compatible JSON.

Install

bash

pip install dograh-sdk

For local development against a checked-out monorepo:

bash

pip install -e sdk/python/

Usage

python

from dograh_sdk import DograhClient, Workflow

with DograhClient(base_url="http://localhost:8000", api_key="...") as client:
    wf = Workflow(client=client, name="loan_qualification")

    start = 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.",
    )
    qualify = wf.add(
        type="agentNode",
        name="qualify",
        prompt="Ask about loan amount and timeline.",
    )
    done = wf.add(type="endCall", name="done", prompt="Thank the caller.")

    wf.edge(start, qualify, label="interested", condition="Caller expressed interest.")
    wf.edge(qualify, done, label="done", condition="Qualification complete.")

    client.save_workflow(workflow_id=123, workflow=wf)

What gets validated at the call site

The SDK fetches the spec for each node type via get_node_type and raises
ValidationError 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 message so
an LLM agent can self-correct on retry:

text

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

Server-side Pydantic validators run on save and surface anything the SDK lets
through (compound invariants, cross-field rules).

Environment

bash

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

License

BSD 2-Clause — see LICENSE.

Overview (`sdk/typescript/README.md`)

@dograh/sdk

Install

bash

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

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

json

{
  "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:

text

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

bash

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

License

BSD 2-Clause — see LICENSE.

Overview (`api/services/campaign/readme.md`)

campaign_orchestrator.py (CampaignOrchestrator)

Listens to retry events, batch completed event, sync completed events from redis pubsub, and schedules batches
Monitors stale campaigns and schedules batches if one is not already scheduled
Marks campaign as completed if no more tasks pending

runner.py (CampaignRunnerService)

Service layer to handle router requests, like run campaign, pause campaign, resume campaign, get campaign status etc.

call_dispatcher.py (CampaignCallDispatcher)

Ensures rate limit and concurrency limits and dispatches call using telephony provider

campaign_tasks.py

sync campaign from source
process campaign batch

Overview (`api/services/gender/README.md`)

Gender Prediction Service

An internal service for predicting gender from first names using SSA (Social Security Administration) baby names data with GenderAPI fallback for uncertain predictions.

Overview

This service provides gender prediction with:

Local model built from 145 years of SSA data (1880-2024)
104,819 unique names with confidence scores
Compressed storage (2.21 MB model file)
GenderAPI fallback for unknown or low-confidence names

Data Source

The SSA baby names data is already downloaded in the names/ directory from:
https://catalog.data.gov/dataset/baby-names-from-social-security-card-applications-national-data

Building the Model

Prerequisites

Python 3.11+
SSA data files in names/ directory (already included)

Build Steps

bash

# Navigate to the gender service directory
cd dograh/api/services/gender/

# Run the model builder
python build_model.py

This will:

Process all 145 year files (yob1880.txt to yob2024.txt)
Aggregate name counts across all years
Calculate confidence scores based on gender ratios
Generate a compressed model.txt file (~2.21 MB)

Model Output

The builder generates model.txt with:

Version: Model version number
Metadata: Build date, statistics, thresholds
Names: Compressed array format [male_count, female_count, confidence]

Example output:

text

Model saved to: .../services/gender/model.txt
File size: 2.21 MB

Model statistics:
  Total names: 104,819
  High confidence names (≥0.85): 1,711
  Confidence percentage: 1.6%

Using the Service

Basic Usage

python

from services.gender.gender_service import GenderService

# Initialize the service
service = GenderService()

# Predict gender for a single name
result = await service.predict("John")
print(f"Gender: {result.gender}")  # "male"
print(f"Confidence: {result.confidence}")  # 0.996
print(f"Source: {result.source}")  # "model"

# Get salutation for a name
greeting = await service.get_salutation("John")
print(f"Salutation: {greeting}")  # "Mr."

greeting = await service.get_salutation("Mary")
print(f"Salutation: {greeting}")  # "Ms."

greeting = await service.get_salutation("Unknown")
print(f"Salutation: {greeting}")  # "Dear"

# Clean up
await service.close()

Configuration Options

python

# Custom configuration
service = GenderService(
    model_path="custom/path/to/model.txt",  # Default: ./model.txt
    confidence_threshold=0.85,  # Default: 0.85
    gender_api_key="your-api-key",  # Default: from GENDER_API_KEY env
    gender_api_url="https://...",  # Default: GenderAPI v2 endpoint
)

Salutation Generation

python

# Get appropriate salutation based on gender
salutation = await service.get_salutation("John")  # "Mr."
salutation = await service.get_salutation("Mary")  # "Ms."
salutation = await service.get_salutation("Unknown")  # "Dear"

# Custom confidence threshold for salutation
salutation = await service.get_salutation(
    "Taylor",  # Ambiguous name
    confidence_threshold=0.9,  # Higher threshold
)  # Returns "Dear" due to low confidence

# Salutation logic:
# - "Mr." for male with confidence >= threshold
# - "Ms." for female with confidence >= threshold
# - "Dear" for unknown gender or low confidence

Batch Predictions

python

# Predict multiple names at once
names = ["Alice", "Bob", "Charlie", "Diana"]
results = await service.batch_predict(names)

for name, result in zip(names, results):
    print(f"{name}: {result.gender} ({result.confidence:.2f})")

Response Format

python

class GenderPrediction:
    gender: "male" | "female" | "unknown"  # Predicted gender
    confidence: float  # 0.0 to 1.0
    source: "model" | "genderapi"  # Prediction source

Service Statistics

python

# Get service statistics
stats = await service.get_stats()
print(f"Total names: {stats['model']['total_names']:,}")
print(f"High confidence: {stats['model']['high_confidence_names']:,}")
print(f"Cached names in Redis: {stats['cache']['cached_names']}")
print(f"Cache TTL: {stats['cache']['ttl_seconds']} seconds")
print(f"API enabled: {stats['api']['enabled']}")

Cache Management

python

# Clear Redis cache
await service.clear_cache()

Environment Variables

bash

# Required: Redis connection URL
export REDIS_URL=redis://localhost:6379

# Optional: Set GenderAPI key for fallback
export GENDERAPI_API_KEY=your-api-key-here

# Optional: Override confidence threshold (default: 0.85)
export CONFIDENCE_THRESHOLD=0.85

How It Works

Name normalization: Converts to lowercase, strips whitespace
Local model check: Looks up name in pre-built model
Confidence evaluation: If confidence ≥ 0.85, returns local prediction
Redis cache check: Checks Redis for previously fetched API results
API fallback: For unknown/low-confidence names, calls GenderAPI
Redis caching: Stores API responses in Redis with 30-day TTL

Testing

Run the test suite to verify the service:

bash

python test_service.py

This tests:

High-confidence predictions
Ambiguous names
Edge cases (empty strings, special characters)
International names (with API key)
Batch predictions

Model Updates

The model should be rebuilt annually when new SSA data is released:

Download new year file (e.g., yob2025.txt) to names/ directory
Run python build_model.py to rebuild
Test with python test_service.py
Commit the updated model.txt

Performance

Model size: 2.21 MB (compressed JSON)
Load time: < 100ms
Prediction time: < 1ms (local), < 5ms (Redis cache), < 500ms (API)
Memory usage: ~10 MB for model in memory
Cache: Redis-based with 30-day TTL
Scalability: Shared cache across all service instances

Limitations

Based on US SSA data (may not work well for non-US names)
Historical bias in older data
Unisex names have lower confidence
Requires GenderAPI key for comprehensive coverage

Overview (`api/services/telephony/README.md`)

Telephony Provider Implementation

This module implements the telephony provider abstraction for Dograh AI. For user-facing documentation, see the Mintlify docs.

Architecture

text

Business Logic → TelephonyProvider (Interface) → Concrete Provider (Twilio, Vonage, etc.)

Developer Quick Reference

Using the Provider in Code

python

from api.services.telephony.factory import (
    get_default_telephony_provider,
    get_telephony_provider_by_id,
)

# Get the org's default outbound provider
provider = await get_default_telephony_provider(organization_id)

# Or resolve a specific telephony configuration row
provider = await get_telephony_provider_by_id(config_id, organization_id)

# Initiate a call
result = await provider.initiate_call(
    to_number="+1987654321",
    webhook_url="https://your-app.com/webhook",
    workflow_run_id=123,
)

File Structure

text

telephony/
├── __init__.py
├── base.py              # Abstract TelephonyProvider interface
├── factory.py           # Provider creation and config loading
├── providers/
│   ├── __init__.py
│   ├── twilio_provider.py  # Twilio implementation
│   └── vonage_provider.py  # Vonage implementation
├── twilio.py           # Legacy (removed, use factory instead)
└── README.md           # This file

Implementing a New Provider

See the Custom Provider Guide in the documentation for detailed implementation instructions.

Quick checklist:

Create providers/your_provider.py implementing TelephonyProvider
Register the package in providers/__init__.py and add its schemas to api/schemas/telephony_config.py
Write unit tests
Update documentation

Key Interfaces

python

class TelephonyProvider(ABC):
    @abstractmethod
    async def initiate_call(self, to_number: str, webhook_url: str, workflow_run_id: Optional[int] = None, **kwargs: Any) -> Dict[str, Any]
    
    @abstractmethod
    async def get_call_status(self, call_id: str) -> Dict[str, Any]
    
    @abstractmethod
    async def get_available_phone_numbers(self) -> List[str]
    
    @abstractmethod
    def validate_config(self) -> bool
    
    @abstractmethod
    async def verify_webhook_signature(self, url: str, params: Dict[str, Any], signature: str) -> bool
    
    @abstractmethod
    async def get_webhook_response(self, workflow_id: int, organization_id: int, workflow_run_id: int) -> str

Configuration Loading

The factory.py loads configuration from the database:

Both Saas and OSS Modes: Database configuration via UI

python

# Loaded from organization_configuration table
key: "TELEPHONY_CONFIGURATION"
value: {
    "provider": "twilio",  # or "vonage"
    "account_sid": "xxx",  # for Twilio
    "auth_token": "xxx",  # for Twilio
    "application_id": "xxx",  # for Vonage
    "private_key": "xxx",  # for Vonage
    "from_numbers": [...],
}

Testing

Unit Testing with Mock Provider

python

class MockProvider(TelephonyProvider):
    async def initiate_call(self, to_number, webhook_url, **kwargs):
        return {"call_id": "mock_123", "status": "initiated"}

    async def get_call_status(self, call_id):
        return {"call_id": call_id, "status": "completed"}

    # Implement other required methods...


# In tests
@patch("api.services.telephony.factory.get_default_telephony_provider")
async def test_call_initiation(mock_get_provider):
    mock_get_provider.return_value = MockProvider()
    # Test your business logic

Integration Testing

Run against actual providers in development:

Configure your provider through the UI:
- Navigate to Settings → Integrations → Telephony
- Select your provider (Twilio or Vonage)
- Enter test credentials
- Save configuration
Run integration tests:

bash

pytest tests/integration/test_telephony.py

Migration Notes

From Direct TwilioService Usage

Old code:

python

from api.services.telephony.twilio import TwilioService

service = TwilioService(org_id)
await service.initiate_call(...)

New code:

python

from api.services.telephony.factory import get_default_telephony_provider

provider = await get_default_telephony_provider(org_id)
await provider.initiate_call(...)

Backward Compatibility

Old /api/v1/twilio/* endpoints still work (redirect to /api/v1/telephony/*)
TwilioService class remains for legacy code
Database configuration key TWILIO_CONFIGURATION unchanged

Common Issues

Import Error: Always import from factory, not directly from providers
Config Not Found: Check database configuration via UI
Signature Verification: Ensure auth tokens match between provider and config
WebSocket Issues: Verify audio format compatibility (MULAW for Twilio)

Overview (`deploy/helm/dograh/README.md`)

Dograh Helm chart

Deploys Dograh on Kubernetes with decomposed backend workloads (web,
ARQ workers, telephony singleton, campaign singleton), Next.js UI, and
coturn for WebRTC media relay. Implements the architecture defined in
HELM_DEPLOYMENT_PLAN.md at the repo root.

Status

v1, alpha. Validated with helm lint and helm template. Not yet
exercised against a live cluster.

Quick start

bash

cd deploy/helm/dograh

# Install with defaults (all internal deps, Gateway API exposure).
# The bundled Postgres/Redis/MinIO are in-chart manifests (official Postgres and
# Redis images, dograh's MinIO build) — no `helm dependency` / subchart pull step
# needed.
helm install dograh . \
  --set secrets.ossJwtSecret="$(openssl rand -hex 32)" \
  --set secrets.turnSecret="$(openssl rand -hex 32)" \
  --set exposure.gatewayApi.gatewayClassName=istio

See examples/values-single-node.yaml, examples/values-managed.yaml,
and examples/values-aws.yaml for topology-specific overrides.

Architecture summary

Workload	Replicas	Strategy	Notes
`dograh-web`	2 (HPA opt)	RollingUpdate	Long-lived WS, graceful drain
`dograh-arq-worker`	1 (HPA opt)	RollingUpdate	Stateless
`dograh-ari-manager`	1 fixed	Recreate	Telephony singleton
`dograh-campaign-orchestrator`	1 fixed	Recreate	Campaign singleton (in-memory locks)
`dograh-ui`	2 (HPA opt)	RollingUpdate	Next.js SSR
`dograh-coturn`	1	Recreate	LoadBalancer Service, port-pinned

HTTP traffic: Gateway API (default) or Ingress (fallback).
TURN traffic: dedicated L4 Service of type LoadBalancer.

Decisions log

These are choices the chart made where HELM_DEPLOYMENT_PLAN.md was
silent. Each is exposed in values.yaml for operator override.

terminationGracePeriodSeconds for web: 1275s. Covers a full-length
(20-minute) call so scale-down / rolling updates drain instead of cutting
it, plus 60s after preStop for bounded text-chat finalization and resource
shutdown; tune to your call-length distribution. Shutdown attempts to mark
unfinished chat turns failed before cancellation. If completion already
committed, it hands transcript upload and completion enqueue to an ARQ worker
before cancelling the local task. Both operations share a 5-second deadline;
a failed database write or handoff leaves the local task running for the
remaining shutdown window. This is best effort: dependency outages or forced
termination can still leave a session pending or interrupt work before handoff.
Deploy ARQ workers with finalize_completed_text_chat support before updating
web instances that can enqueue that job.
preStop active-call drain (scripts/drain_web.sh). Waits
preStopSleepSeconds (15s) for the gateway to stop dispatching new
connections, then polls /api/v1/health/active-calls and holds SIGTERM until
the count hits 0 or drainMaxWaitSeconds (1200s). Falls back to a fixed
sleep when the devops secret is unset.
Liveness probes on singletons: exec (pgrep). No HTTP endpoint
exists on ari-manager / campaign-orchestrator; process-alive check is
the simplest correct signal.
HPA on web / workers / ui: CPU(/memory), disabled by default.
Templates exist for all three tiers (web-hpa.yaml,
arq-worker-hpa.yaml, ui-hpa.yaml) but every autoscaling.<tier>
block ships enabled: false — flip on per tier with a knowing eye.
CPU/memory is a poor signal for the WS-heavy web tier and a coarse
one for the IO-bound ARQ workers (plan: queue-depth / active-call
scaling, see TODOs); it is a reasonable signal for the Next.js UI.
Singleton replica counts: hard-coded. No replicaCount knob
exposed on ari-manager / campaign-orchestrator. Prevents accidental
kubectl scale corrupting in-memory dedup state.
MinIO browser exposure: shared host, path prefix /voice-audio/.
Mirrors current nginx behavior. Operators wanting a separate
hostname can override by editing httproute-minio.yaml or
ingress.yaml post-install.
NetworkPolicy: not in v1. TODO below.
ServiceMonitor / Prometheus: not in v1. TODO below.
TURN TLS (turns://): not in v1. Original docker-compose exposed
port 5349 but never wired certs. Chart scopes v1 to plain TURN.

`/tmp` audit (review fix #6)

Resolved. End-of-call artifacts (recordings, transcript) are uploaded to
object storage directly from the web process
(api/services/workflow_run_artifacts.py) before the ARQ completion job
is enqueued; the job carries only the workflow run id. No file handoff
crosses a pod boundary, so web and arq-worker pods need no shared
volume. The remaining /tmp uses (audio_file_cache.py,
knowledge_base_processing.py) write and read within a single process.

Open TODOs (deferred from v1)

Leader election for singletons. Adopt Kubernetes lease-based
leader election so ari-manager / campaign-orchestrator can run
HA. Until then, replicas remain hard-coded to 1.
Connection-count HPA metric. Expose active WS sessions per pod
(Prometheus or KEDA) and replace CPU/memory HPA target.
NetworkPolicy. Add default-deny + explicit egress to Postgres,
Redis, MinIO/S3, and (for ari-manager) Asterisk.
ServiceMonitor. First-class Prometheus integration once
observability stack is selected.
TURN TLS (turns://). Wire certificate paths through coturn config
and document the cert-manager pattern.
MinIO public route via separate hostname. Make /voice-audio/
path-prefix the default but allow operators to opt into a dedicated
hostname.
KEDA for ARQ workers. When a queue-depth or active-calls metric
is available, switch ARQ from CPU HPA to KEDA-driven scaling. Keep
autoscaling.workers.enabled=false when a KEDA ScaledObject owns the
Deployment so the chart doesn't render a competing HPA.

Validation

bash

cd deploy/helm/dograh

helm lint .
helm template test-release . > /tmp/render-default.yaml
helm template test-release . -f examples/values-single-node.yaml > /tmp/render-single.yaml
helm template test-release . -f examples/values-managed.yaml > /tmp/render-managed.yaml
helm template test-release . -f examples/values-aws.yaml > /tmp/render-aws.yaml
helm template test-release . -f examples/values-k3s-prod.yaml > /tmp/render-k3s.yaml

Spot-check expectations:

Deployment/<release>-ari-manager has replicas: 1 and
strategy.type: Recreate.
Deployment/<release>-campaign-orchestrator has replicas: 1 and
strategy.type: Recreate.
Deployment/<release>-web has terminationGracePeriodSeconds: 1275
and a lifecycle.preStop exec hook running ./scripts/drain_web.sh.
Liveness probe on ari-manager / campaign-orchestrator uses exec,
not httpGet.

Layout

text

deploy/helm/dograh/
├── Chart.yaml
├── values.yaml             # heavily commented
├── values.schema.json      # enforces mode enums
├── README.md               # this file
├── examples/
│   ├── values-single-node.yaml
│   ├── values-managed.yaml
│   ├── values-aws.yaml
│   └── values-k3s-prod.yaml
└── templates/
    ├── _helpers.tpl
    ├── NOTES.txt
    ├── serviceaccount.yaml
    ├── configmap.yaml
    ├── secret.yaml
    ├── migrate-job.yaml
    ├── web-deployment.yaml
    ├── web-service.yaml
    ├── web-hpa.yaml
    ├── web-pdb.yaml
    ├── arq-worker-deployment.yaml
    ├── arq-worker-hpa.yaml
    ├── ari-manager-deployment.yaml
    ├── campaign-orchestrator-deployment.yaml
    ├── ui-deployment.yaml
    ├── ui-service.yaml
    ├── ui-hpa.yaml
    ├── ui-pdb.yaml
    ├── coturn-deployment.yaml
    ├── coturn-service.yaml
    ├── coturn-configmap.yaml
    ├── gateway.yaml
    ├── httproute-api.yaml
    ├── httproute-ui.yaml
    ├── httproute-minio.yaml
    └── ingress.yaml

Api Reference Overview (`docs/api-reference/overview.mdx`)

title: "Overview"

Base URL

All API requests are made to:

text

https://your-dograh-instance/api/v1

If you are using the hosted version, the base URL is https://app.dograh.com/api/v1.

Versioning

The current API version is v1. The version is included in the URL path.

Request format

All request bodies must be sent as JSON with the Content-Type: application/json header.

bash

curl -X POST https://your-dograh-instance/api/v1/workflow/create/definition \
  -H "Content-Type: application/json" \
  -H "X-API-Key: dg_your_api_key" \
  -d '{"name": "My Agent", "workflow_definition": {}}'

Response format

All responses are JSON. Successful responses return the relevant resource object. Error responses follow a consistent shape:

json

{
  "detail": "Error message describing what went wrong"
}

For validation errors (422), the detail may be a list:

json

{
  "detail": [
    {
      "loc": ["body", "name"],
      "msg": "field required",
      "type": "value_error.missing"
    }
  ]
}

Health check

Verify your instance is reachable before making API calls.

bash

curl https://your-dograh-instance/api/v1/health

json

{
  "status": "ok",
  "version": "1.0.0",
  "backend_api_endpoint": "https://your-dograh-instance",
  "deployment_mode": "oss",
  "auth_provider": "local"
}

Getting Started Index (`docs/getting-started/index.mdx`)

title: "Introduction"

About Dograh

Dograh is an open-source alternative to platforms like Vapi and Retell - Dograh helps you build voice AI agents with an easy drag-and-drop workflow builder. Unlike proprietary solutions like Vapi, Dograh gives you:

100% open source - no vendor lock-in, full transparency
Self-hostable - deploy anywhere, own your infrastructure
Complete control - every line of code is open and customizable
2-minute setup - from zero to working voice bot in under 2 minutes

Overview

Watch the following video to learn about Dograh’s capabilities.

Setting up

Get the platform up and running using Docker with a small startup script on your local computer. If you are looking to deploy the platform on a server different than your local machine, please check the Deployment section.

We collect anonymous usage data to improve the product. You can opt out by setting ENABLE_TELEMETRY=false before running the startup script.

```bash macOS/Linux curl -o docker-compose.yaml https://raw.githubusercontent.com/dograh-hq/dograh/main/docker-compose.yaml && curl -o start_docker.sh https://raw.githubusercontent.com/dograh-hq/dograh/main/scripts/start_docker.sh && chmod +x start_docker.sh && ./start_docker.sh ``` ```powershell Windows Invoke-WebRequest -OutFile docker-compose.yaml https://raw.githubusercontent.com/dograh-hq/dograh/main/docker-compose.yaml; Invoke-WebRequest -OutFile start_docker.ps1 https://raw.githubusercontent.com/dograh-hq/dograh/main/scripts/start_docker.ps1; .\start_docker.ps1 ``` **Using an AI coding agent?** If you work in **Claude Code** or **Codex**, install the official Dograh setup skill and let your agent handle installation, configuration, and troubleshooting for you. It orients to your OS, picks the right deploy path, runs Dograh's own setup scripts, and verifies the result. Install it once: ```text Claude Code /plugin marketplace add dograh-hq/dograh-plugins /plugin install dograh@dograh ``` ```text Codex codex plugin marketplace add dograh-hq/dograh-plugins codex plugin add dograh@dograh ```

Then start a new session and ask it to "set up Dograh" (in Claude Code you can also run /dograh-setup). See dograh-hq/dograh-plugins for details.

Please check Prerequisites for the system requirements and Troubleshooting for common issues.

Next Steps

Once the platform is running, build your first agent in Your First Agent in 5 Minutes — no telephony setup required. Or configure an inference provider first if you're bringing your own LLM keys.

Integrations Overview (`docs/integrations/overview.mdx`)

title: "Integrations Overview"

Introduction

Dograh AI provides a flexible integration architecture that allows you to connect with various external services and platforms. Our integration system is designed with modularity and extensibility in mind, making it easy to add new providers while maintaining backward compatibility.

Integration Categories

Telephony Providers

Connect your voice agents with telephony services to make and receive calls.

Configure telephony providers like Twilio and Vonage for voice communication

Future Integration Categories

The integration architecture is designed to support additional categories in the future, such as storage services, analytics platforms, and CRM systems.

Architecture Principles

Our integration system follows these core principles:

Provider Abstraction: All integrations implement a common interface, making it easy to switch between providers
Configuration Flexibility: Database-based configuration through the web interface
Backward Compatibility: New integrations don't break existing implementations
Secure by Default: All credentials are encrypted and never exposed in logs or UI

Getting Started

Choose the integration category you need
Follow the provider-specific setup guide
Configure credentials through the UI
Test your integration with the provided verification tools

Best Practices

Store credentials securely using database configuration
Test integrations in a development environment before production deployment
Use the provider abstraction to maintain clean separation between business logic and provider specifics
Monitor integration health through application logs

Need Help?

Check provider-specific documentation for detailed setup instructions
Visit our GitHub Issues for community support
Join our Slack community for assistance

Integrations Telephony Overview (`docs/integrations/telephony/overview.mdx`)

title: "Telephony Integration"

Overview

Dograh AI's telephony integration system provides a unified interface for connecting with various telephony providers. The same configuration powers both outbound calls (initiated from Dograh) and inbound calls (received on a phone number you own).

Dograh does not sell phone numbers or minutes. The phone service is always
yours — there are two ways to bring it.

Two ways to connect

Open an account with Twilio, Plivo, Telnyx, Vonage or Vobiz, paste the credentials into Dograh, and call using their numbers. **Start here if you don't already run phone infrastructure.** Pick your provider from the list below. Already have a SIP trunk or a PBX? Point it at Dograh and keep your existing carrier and numbers. Every organization is provisioned with a **Dograh Cloudonix SIP** connection for exactly this; self-hosted deployments can use [Asterisk ARI](/integrations/telephony/asterisk-ari) instead.

Supported Providers

Industry-leading cloud communications platform with global reach High-quality voice with 16kHz audio and excellent international coverage Cloud communications platform with programmable voice and global PSTN reach SIP-based telephony with flexible trunk configuration India-focused cloud telephony with Connect Voice AI streaming Cloud-based telephony with global reach and competitive pricing Answer WhatsApp voice calls to your business number via the Business Calling API Connect to your own Asterisk PBX via the Asterisk REST Interface Build your own telephony provider integration

Configuration

All telephony providers are configured from a single page in Dograh:

Navigate to /telephony-configurations and click Add configuration
Select your provider
Enter your credentials and save
Open the new configuration and add at least one phone number
(Optional) Assign an Inbound workflow to a phone number to enable inbound calling

A single org can hold multiple configurations (for example, separate Twilio sub-accounts) and multiple phone numbers per configuration. Mark one configuration as the default outbound to use it for test calls and campaigns by default.

Common Features

The telephony integration in Dograh AI supports:

Outbound Calls: Initiate calls to any phone number from a workflow or campaign
Inbound Calls: Route incoming calls to the right voice agent — see the Inbound Calling guide
Call Transfer: Transfer an in-progress call to a human or another number (provider-dependent)
Call Status Tracking: Monitor call lifecycle events (initiated, ringing, answered, completed, failed)
WebSocket Audio Streaming: Real-time, bidirectional audio between caller and agent
Webhook Signature Verification: Inbound webhooks are verified against the matched configuration's credentials

Inbound Calling

Inbound calls use a single org-wide webhook URL:

text

https://api.dograh.com/api/v1/telephony/inbound/run

Dograh resolves the org from the webhook's account credentials and the agent from the called number's Inbound workflow assignment. See Inbound Calling for the full setup.

Troubleshooting

- Verify credentials are correctly configured - Check phone number format ([E.164](https://en.wikipedia.org/wiki/E.164) — the international standard: a `+`, then country code, then the number, no spaces or dashes, e.g. `+1234567890`) - Ensure webhook URLs are publicly accessible - Review provider-specific error logs - Check network bandwidth and latency - Verify audio codec compatibility - Review WebSocket connection stability - Confirm auth tokens / API secrets match between provider and Dograh configuration - Verify webhook URL matches exactly (including the `/inbound/run` path) - Check for proxy or load balancer modifications

Next Steps

AGENTS (`docs/AGENTS.md`)

Mintlify documentation

Working relationship

You can push back on ideas-this can lead to better documentation. Cite sources and explain your reasoning when you do so
ALWAYS ask for clarification rather than making assumptions
NEVER lie, guess, or make up information

Project context

Format: MDX files with YAML frontmatter
Config: docs.json for navigation, theme, settings
Components: Mintlify components

Content strategy

Document just enough for user success - not too much, not too little
Prioritize accuracy and usability of information
Make content evergreen when possible
Search for existing information before adding new content. Avoid duplication unless it is done for a strategic reason
Check existing patterns for consistency
Start by making the smallest reasonable changes

Frontmatter requirements for pages

title: Clear, descriptive page title
description: Concise summary for SEO/navigation

Writing standards

Second-person voice ("you")
Prerequisites at start of procedural content
Test all code examples before publishing
Match style and formatting of existing pages
Include both basic and advanced use cases
Language tags on all code blocks
Alt text on all images
Relative paths for internal links

Git workflow

NEVER use --no-verify when committing
Ask how to handle uncommitted changes before starting
Create a new branch when no clear branch exists for changes
Commit frequently throughout development
NEVER skip or disable pre-commit hooks

Do not

Skip frontmatter on any MDX file
Use absolute URLs for internal links
Include untested code examples
Make assumptions - always ask for clarification

Docs.Json (`docs/docs.json`)

{
"$schema": "https://mintlify.com/docs.json",
"theme": "mint",
"name": "Dograh AI",
"colors": {
"primary": "#16A34A",
"light": "#07C983",
"dark": "#15803D"
},
"favicon": "/favicon.ico",
"navigation": {
"tabs": [
{
"tab": "Guides",
"icon": "book-open",
"groups": [
{
"group": "Getting started",
"pages": [
{
"group": "Introduction",
"pages": [
"getting-started/index",
"getting-started/prerequisites",
"getting-started/troubleshooting"
]
}
]
},
{
"group": "Build your First Agent",
"pages": [
"getting-started/first-agent",
"getting-started/trigger-calls-from-your-backend",
"getting-started/send-call-data-with-webhooks",
"getting-started/connect-telephony",
"getting-started/add-tools-and-knowledge-base"
]
},
{
"group": "Core Concepts",
"pages": [
"core-concepts/how-dograh-works",
"core-concepts/workflows-and-agents",
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Api Reference Agents (`docs/api-reference/agents.mdx`)

title: "Overview"

In Dograh, a voice agent is called a workflow in the API. A workflow defines the conversation flow, LLM configuration, voice settings, and tools available to your agent.

Method	Endpoint	Quick Link
`POST`	`/workflow/create/definition`	Create from definition
`POST`	`/workflow/create/template`	Create from template
`GET`	`/workflow/fetch`	List agents
`GET`	`/workflow/count`	Get agent count
`GET`	`/workflow/fetch/{workflow_id}`	Get an agent
`PUT`	`/workflow/{workflow_id}`	Update an agent
`PUT`	`/workflow/{workflow_id}/status`	Archive an agent
`POST`	`/workflow/{workflow_id}/validate`	Validate a workflow
`POST`	`/workflow/{workflow_id}/runs`	Create test run
`GET`	`/workflow/{workflow_id}/runs`	List runs
`GET`	`/workflow/{workflow_id}/runs/{run_id}`	Get a run

Api Reference Api Keys (`docs/api-reference/api-keys.mdx`)

title: "Overview"

API keys authenticate requests from your applications and services. Each key is scoped to your organization — all API calls made with a key create and access resources within that organization.

Method	Endpoint	Quick Link
`POST`	`/user/api-keys`	Create an API key
`GET`	`/user/api-keys`	List API keys
`DELETE`	`/user/api-keys/{api_key_id}`	Archive an API key
`PUT`	`/user/api-keys/{api_key_id}/reactivate`	Reactivate an API key

Best practices

Use one key per environment — separate keys for development, staging, and production make rotation easy and limit blast radius if a key is compromised.
Use one key per service — this allows you to revoke a single service's access without affecting others.
Rotate keys regularly — create a new key, update your secret store, then archive the old key.
Never hardcode keys — use environment variables or a secrets manager. Never commit keys to version control.
Monitor last_used_at — keys with no recent activity may be safe to archive.

Api Reference Authentication (`docs/api-reference/authentication.mdx`)

title: "Authentication"

API key authentication

API keys are the recommended way to authenticate programmatic requests. Pass your key in the X-API-Key request header.

bash

curl https://your-dograh-instance/api/v1/workflow/fetch \
  -H "X-API-Key: dg_your_api_key"

API keys are scoped to an organization. All resources created or accessed using a key belong to that organization.

Create an API key

Create keys in the dashboard under Settings → API Keys. The full key is shown only once at creation time — store it immediately in a secrets manager or environment variable.

For self-hosted deployments using local auth, sign up and log in via the dashboard first, then create an API key there before making API calls.

Manage API keys

Action	Method	Path
List keys	`GET`	`/user/api-keys`
Create key	`POST`	`/user/api-keys`
Archive key	`DELETE`	`/user/api-keys/{api_key_id}`
Reactivate key	`PUT`	`/user/api-keys/{api_key_id}/reactivate`

Archiving a key immediately revokes it. All subsequent requests using that key return 401.

Error responses

Status	Cause
`401 Unauthorized`	Missing, invalid, or expired credentials
`403 Forbidden`	Valid credentials but insufficient permissions for the resource

json

{
  "detail": "Invalid or expired API key"
}

Api Reference Campaigns (`docs/api-reference/campaigns.mdx`)

title: "Overview"

A campaign runs a workflow against a list of contacts. You upload a CSV of phone numbers, configure retry logic and scheduling, then start the campaign. Dograh dials contacts automatically up to your configured concurrency limit.

Method	Endpoint	Quick Link
`POST`	`/s3/presigned-upload-url`	Upload contacts CSV
`POST`	`/campaign/create`	Create a campaign
`GET`	`/campaign/`	List campaigns
`GET`	`/campaign/{campaign_id}`	Get a campaign
`PATCH`	`/campaign/{campaign_id}`	Update a campaign
`POST`	`/campaign/{campaign_id}/start`	Start
`POST`	`/campaign/{campaign_id}/pause`	Pause
`POST`	`/campaign/{campaign_id}/resume`	Resume
`GET`	`/campaign/{campaign_id}/progress`	Get campaign progress
`GET`	`/campaign/{campaign_id}/runs`	Get campaign call runs

Campaign status values

Status	Description
`draft`	Created but not started
`running`	Actively dialing contacts
`paused`	Temporarily stopped; can be resumed
`completed`	All contacts processed
`failed`	Campaign encountered a fatal error

Api Reference Errors (`docs/api-reference/errors.mdx`)

title: "Errors"

Error format

All errors return a JSON body with a detail field:

json

{
  "detail": "Human-readable error message"
}

For request validation failures (status 422), detail is an array of field-level errors:

json

{
  "detail": [
    {
      "loc": ["body", "phone_number"],
      "msg": "field required",
      "type": "value_error.missing"
    }
  ]
}

HTTP status codes

Status	Meaning	Common causes
`400`	Bad Request	Missing required fields, invalid parameter values, unsupported operation
`401`	Unauthorized	Missing auth header, invalid or expired API key or JWT token
`403`	Forbidden	Valid credentials but the resource belongs to a different organization
`404`	Not Found	Resource ID does not exist or is not accessible to your organization
`409`	Conflict	Duplicate resource (e.g., email already registered)
`422`	Unprocessable Entity	Request body or query parameter failed schema validation
`500`	Internal Server Error	Unexpected server-side failure
`501`	Not Implemented	Feature not supported in the current deployment

Workflow validation errors

When validating a workflow (or creating one with invalid nodes), the API returns structured errors that reference specific nodes, edges, or fields:

json

{
  "errors": [
    {
      "kind": "node",
      "id": "agent-1",
      "field": "data.prompt",
      "message": "Prompt cannot be empty"
    },
    {
      "kind": "edge",
      "id": "edge-3",
      "field": null,
      "message": "Edge target node does not exist"
    },
    {
      "kind": "workflow",
      "id": null,
      "field": null,
      "message": "Workflow must have exactly one Start Call node"
    }
  ]
}

Field	Type	Description
`kind`	`"node" \| "edge" \| "workflow"`	What the error applies to
`id`	string or null	Node or edge ID from the workflow definition
`field`	string or null	Dot-notation path to the specific field (e.g. `data.prompt`)
`message`	string	Human-readable description of the problem

Telephony errors

Telephony operations may fail with one of these named error types returned in the detail field:

Error	Description
`PROVIDER_MISMATCH`	The request was routed to the wrong telephony provider
`WORKFLOW_NOT_FOUND`	The workflow ID in the inbound URL does not exist
`ACCOUNT_VALIDATION_FAILED`	Your telephony provider credentials are invalid
`PHONE_NUMBER_NOT_CONFIGURED`	The phone number is not set up in your telephony account
`SIGNATURE_VALIDATION_FAILED`	Webhook signature verification failed (possible spoofed request)
`QUOTA_EXCEEDED`	Your organization has exceeded its usage quota
`GENERAL_AUTH_FAILED`	Generic authentication failure with the telephony provider

Tips for handling errors

Retry on 500 with exponential backoff — transient server errors can resolve on retry.
Do not retry 4xx — these indicate problems with your request that will not self-resolve.
Check detail carefully on 422 — the loc array pinpoints exactly which field failed validation.
Store API keys securely — a 401 on a previously working key likely means the key was archived.

Api Reference Openapi.Json (`docs/api-reference/openapi.json`)

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TelnyxConferenceStrategy then joins the caller\n into this conference at pipeline teardown.\n- call.hangup pre-answer (no conference_id on the context):\n publish TRANSFER_FAILED so the LLM can recover.\n- call.hangup post-answer (conference_id set): the destination\n left a bridged conference; hang up the caller's leg to tear down the\n empty bridge (Telnyx's create_conference doesn't accept\n end_conference_on_exit on the seed leg).\n\nEvent references:\n - call.answered: https://developers.telnyx.com/api-reference/callbacks/call-answered\n - call.hangup: https://developers.telnyx.com/api-reference/callbacks/call-hangup","operationId":"handle_telnyx_transfer_result_api_v1_telephony_telnyx_transfer_result__transfer_id__post","parameters":[{"name":"transfer_id","in":"path","required":true,"schema":{"type":"string","title":"Transfer Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not 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user to create the workflow for","operationId":"create_workflow_api_v1_workflow_create_definition_post","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreateWorkflowRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/WorkflowResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"x-sdk-method":"create_workflow","x-sdk-description":"Create a new workflow from a workflow 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Creates the workflow in the database\n\nArgs:\n request: The template creation request with call_type, use_case, and activity_description\n user: The authenticated user\n\nReturns:\n The created workflow\n\nRaises:\n HTTPException: If MPS API call fails","operationId":"create_workflow_from_template_api_v1_workflow_create_template_post","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreateWorkflowTemplateRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/WorkflowResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/workflow/count":{"get":{"tags":["main"],"summary":"Get Workflow Count","description":"Get workflow counts for the authenticated user's organization.\n\nThis is a lightweight endpoint for checking if the user has workflows,\nuseful for redirect logic without fetching full workflow data.","operationId":"get_workflow_count_api_v1_workflow_count_get","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/WorkflowCountResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/workflow/fetch":{"get":{"tags":["main"],"summary":"Get Workflows","description":"Get all workflows for the authenticated user's organization.\n\nReturns a lightweight response with only essential fields for listing.\nUse GET /workflow/fetch/{workflow_id} to get full workflow details.","operationId":"get_workflows_api_v1_workflow_fetch_get","parameters":[{"name":"status","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by status - 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This is a snapshot,\nnot a reservation of capacity for a future call.","operationId":"get_organization_concurrent_calls_api_v1_organizations_concurrent_calls_get","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OrganizationConcurrentCallsResponse"}}}},"404":{"description":"Not found"},"401":{"description":"Missing or invalid credentials"},"503":{"description":"The current call count is unavailable"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/usage/current-period":{"get":{"tags":["main"],"summary":"Get Current Period Usage","description":"Get current reporting-period usage for the user's organization.","operationId":"get_current_period_usage_api_v1_organizations_usage_current_period_get","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CurrentUsageResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/billing/credits":{"get":{"tags":["main"],"summary":"Get Billing Credits","description":"Return per-key MPS credits (OSS) or the org's paginated billing ledger.","operationId":"get_billing_credits_api_v1_organizations_billing_credits_get","parameters":[{"name":"page","in":"query","required":false,"schema":{"type":"integer","minimum":1,"default":1,"title":"Page"}},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":100,"minimum":1,"default":50,"title":"Limit"}},{"name":"entry_type","in":"query","required":false,"schema":{"anyOf":[{"enum":["credit","purchase","debit","grant","additional_credit"],"type":"string"},{"type":"null"}],"description":"Entry type; credit includes purchases, grants, and additional credits","title":"Entry Type"},"description":"Entry type; credit includes purchases, grants, and additional credits"},{"name":"start_date","in":"query","required":false,"schema":{"anyOf":[{"type":"string","format":"date"},{"type":"null"}],"description":"Inclusive calendar date in timezone","title":"Start Date"},"description":"Inclusive calendar date in timezone"},{"name":"end_date","in":"query","required":false,"schema":{"anyOf":[{"type":"string","format":"date"},{"type":"null"}],"description":"Inclusive calendar date in timezone","title":"End Date"},"description":"Inclusive calendar date in timezone"},{"name":"timezone","in":"query","required":false,"schema":{"type":"string","description":"IANA timezone for the selected calendar dates","default":"UTC","title":"Timezone"},"description":"IANA timezone for the selected calendar dates"},{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/MPSBillingCreditsResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/usage/mps-credits/purchase-url":{"post":{"tags":["main"],"summary":"Create Mps Credit Purchase Url","description":"Create a checkout URL for purchasing organization credits.","operationId":"create_mps_credit_purchase_url_api_v1_organizations_usage_mps_credits_purchase_url_post","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/MPSCreditPurchaseUrlResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/usage/runs":{"get":{"tags":["main"],"summary":"Get Usage History","description":"Get paginated workflow runs with usage for the organization.","operationId":"get_usage_history_api_v1_organizations_usage_runs_get","parameters":[{"name":"start_date","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"ISO 8601 date-time string (UTC). Lower bound (inclusive) on created_at.","examples":["2026-04-01T00:00:00Z"],"title":"Start Date"},"description":"ISO 8601 date-time string (UTC). Lower bound (inclusive) on created_at."},{"name":"end_date","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"ISO 8601 date-time string (UTC). Upper bound (inclusive) on created_at.","examples":["2026-05-01T00:00:00Z"],"title":"End Date"},"description":"ISO 8601 date-time string (UTC). Upper bound (inclusive) on created_at."},{"name":"page","in":"query","required":false,"schema":{"type":"integer","minimum":1,"default":1,"title":"Page"}},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":100,"minimum":1,"default":50,"title":"Limit"}},{"name":"filters","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"JSON-encoded array of filter objects. Each object has the shape:\n\njson\n{ \"attribute\": \"<name>\", \"type\": \"<type>\", \"value\": <value> }\n\n\nSupported attribute / type / value combinations:\n\n| attribute | type | value shape | matches |\n|-----------------|---------------|----------------------------------------------|------------------------------------------------------|\n| runId | number | { \"value\": 12345 } | exact run id |\n| workflowId | number | { \"value\": 42 } | exact agent (workflow) id |\n| campaignId | number | { \"value\": 7 } | exact campaign id |\n| callerNumber | text | { \"value\": \"415555\" } | substring match on initial_context.caller_number |\n| calledNumber | text | { \"value\": \"9911848\" } | substring match on initial_context.called_number |\n| dispositionCode | multiSelect | { \"codes\": [\"XFER\", \"DNC\"] } | any of the codes in gathered_context.mapped_call_disposition |\n| duration | numberRange | { \"min\": 60, \"max\": 300 } | call duration (seconds), inclusive bounds |\n| callDirection | radio | { \"status\": \"inbound\" } | inbound or outbound; any other value matches all |\n| callChannel | radio | { \"status\": \"telephony\" } | telephony, web, or chat \u2014 the group of run modes for that channel |\n\nUnknown attributes and unsupported type values are silently ignored.\n\nDate filtering on this endpoint is done via the dedicated start_date / end_date query params, not via a dateRange filter object.\n","examples":["[{"attribute":"callerNumber","type":"text","value":{"value":"415555"}}]","[{"attribute":"campaignId","type":"number","value":{"value":7}},{"attribute":"duration","type":"numberRange","value":{"min":60,"max":300}}]","[{"attribute":"dispositionCode","type":"multiSelect","value":{"codes":["XFER","DNC"]}}]"],"title":"Filters"},"description":"JSON-encoded array of filter objects. Each object has the shape:\n\njson\n{ \"attribute\": \"<name>\", \"type\": \"<type>\", \"value\": <value> }\n\n\nSupported attribute / type / value combinations:\n\n| attribute | type | value shape | matches |\n|-----------------|---------------|----------------------------------------------|------------------------------------------------------|\n| runId | number | { \"value\": 12345 } | exact run id |\n| workflowId | number | { \"value\": 42 } | exact agent (workflow) id |\n| campaignId | number | { \"value\": 7 } | exact campaign id |\n| callerNumber | text | { \"value\": \"415555\" } | substring match on initial_context.caller_number |\n| calledNumber | text | { \"value\": \"9911848\" } | substring match on initial_context.called_number |\n| dispositionCode | multiSelect | { \"codes\": [\"XFER\", \"DNC\"] } | any of the codes in gathered_context.mapped_call_disposition |\n| duration | numberRange | { \"min\": 60, \"max\": 300 } | call duration (seconds), inclusive bounds |\n| callDirection | radio | { \"status\": \"inbound\" } | inbound or outbound; any other value matches all |\n| callChannel | radio | { \"status\": \"telephony\" } | telephony, web, or chat \u2014 the group of run modes for that channel |\n\nUnknown attributes and unsupported type values are silently ignored.\n\nDate filtering on this endpoint is done via the dedicated start_date / end_date query params, not via a dateRange filter object.\n"},{"name":"sort_by","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Field to sort by ('duration'). Defaults to created_at.","examples":["duration"],"title":"Sort By"},"description":"Field to sort by ('duration'). Defaults to created_at."},{"name":"sort_order","in":"query","required":false,"schema":{"type":"string","pattern":"^(asc|desc)$","description":"Sort order ('asc' or 'desc').","default":"desc","title":"Sort Order"},"description":"Sort order ('asc' or 'desc')."},{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/UsageHistoryResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/usage/runs/report":{"get":{"tags":["main"],"summary":"Download Usage Runs Report","description":"Download a CSV of runs matching the same filters as /usage/runs.","operationId":"download_usage_runs_report_api_v1_organizations_usage_runs_report_get","parameters":[{"name":"start_date","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"ISO 8601 date-time string (UTC). Lower bound (inclusive) on created_at.","title":"Start Date"},"description":"ISO 8601 date-time string (UTC). Lower bound (inclusive) on created_at."},{"name":"end_date","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"ISO 8601 date-time string (UTC). Upper bound (inclusive) on created_at.","title":"End Date"},"description":"ISO 8601 date-time string (UTC). Upper bound (inclusive) on created_at."},{"name":"filters","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"JSON-encoded array of filter objects. Each object has the shape:\n\njson\n{ \"attribute\": \"<name>\", \"type\": \"<type>\", \"value\": <value> }\n\n\nSupported attribute / type / value combinations:\n\n| attribute | type | value shape | matches |\n|-----------------|---------------|----------------------------------------------|------------------------------------------------------|\n| runId | number | { \"value\": 12345 } | exact run id |\n| workflowId | number | { \"value\": 42 } | exact agent (workflow) id |\n| campaignId | number | { \"value\": 7 } | exact campaign id |\n| callerNumber | text | { \"value\": \"415555\" } | substring match on initial_context.caller_number |\n| calledNumber | text | { \"value\": \"9911848\" } | substring match on initial_context.called_number |\n| dispositionCode | multiSelect | { \"codes\": [\"XFER\", \"DNC\"] } | any of the codes in gathered_context.mapped_call_disposition |\n| duration | numberRange | { \"min\": 60, \"max\": 300 } | call duration (seconds), inclusive bounds |\n| callDirection | radio | { \"status\": \"inbound\" } | inbound or outbound; any other value matches all |\n| callChannel | radio | { \"status\": \"telephony\" } | telephony, web, or chat \u2014 the group of run modes for that channel |\n\nUnknown attributes and unsupported type values are silently ignored.\n\nDate filtering on this endpoint is done via the dedicated start_date / end_date query params, not via a dateRange filter object.\n","title":"Filters"},"description":"JSON-encoded array of filter objects. Each object has the shape:\n\njson\n{ \"attribute\": \"<name>\", \"type\": \"<type>\", \"value\": <value> }\n\n\nSupported attribute / type / value combinations:\n\n| attribute | type | value shape | matches |\n|-----------------|---------------|----------------------------------------------|------------------------------------------------------|\n| runId | number | { \"value\": 12345 } | exact run id |\n| workflowId | number | { \"value\": 42 } | exact agent (workflow) id |\n| campaignId | number | { \"value\": 7 } | exact campaign id |\n| callerNumber | text | { \"value\": \"415555\" } | substring match on initial_context.caller_number |\n| calledNumber | text | { \"value\": \"9911848\" } | substring match on initial_context.called_number |\n| dispositionCode | multiSelect | { \"codes\": [\"XFER\", \"DNC\"] } | any of the codes in gathered_context.mapped_call_disposition |\n| duration | numberRange | { \"min\": 60, \"max\": 300 } | call duration (seconds), inclusive bounds |\n| callDirection | radio | { \"status\": \"inbound\" } | inbound or outbound; any other value matches all |\n| callChannel | radio | { \"status\": \"telephony\" } | telephony, web, or chat \u2014 the group of run modes for that channel |\n\nUnknown attributes and unsupported type values are silently ignored.\n\nDate filtering on this endpoint is done via the dedicated start_date / end_date query params, not via a dateRange filter object.\n"},{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/usage/daily-breakdown":{"get":{"tags":["main"],"summary":"Get Daily Usage Breakdown","description":"Get daily usage breakdown for the last N days. Only available for organizations with pricing.","operationId":"get_daily_usage_breakdown_api_v1_organizations_usage_daily_breakdown_get","parameters":[{"name":"days","in":"query","required":false,"schema":{"type":"integer","maximum":30,"minimum":1,"description":"Number of days to include","default":7,"title":"Days"},"description":"Number of days to include"},{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DailyUsageBreakdownResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/reports/daily":{"get":{"tags":["main"],"summary":"Get Daily Report","description":"Get daily report for the specified date and timezone.\nIf workflow_id is provided, filters results to that specific workflow.\nIf workflow_id is None, includes all workflows for the organization.","operationId":"get_daily_report_api_v1_organizations_reports_daily_get","parameters":[{"name":"date","in":"query","required":true,"schema":{"type":"string","description":"Date in YYYY-MM-DD format","title":"Date"},"description":"Date in YYYY-MM-DD format"},{"name":"timezone","in":"query","required":true,"schema":{"type":"string","description":"IANA timezone (e.g., 'America/New_York')","title":"Timezone"},"description":"IANA timezone (e.g., 'America/New_York')"},{"name":"workflow_id","in":"query","required":false,"schema":{"anyOf":[{"type":"integer"},{"type":"null"}],"description":"Optional workflow ID to filter by","title":"Workflow Id"},"description":"Optional workflow ID to filter by"},{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DailyReportResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/reports/workflows":{"get":{"tags":["main"],"summary":"Get Workflow Options","description":"Get all workflows for the user's organization.\nUsed to populate the workflow selector dropdown in the reports page.","operationId":"get_workflow_options_api_v1_organizations_reports_workflows_get","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/WorkflowOption"},"title":"Response Get Workflow Options Api V1 Organizations Reports Workflows Get"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/organizations/reports/daily/runs":{"get":{"tags":["main"],"summary":"Get Daily Runs Detail","description":"Get detailed workflow runs for the specified date.\nUsed for CSV export functionality.","operationId":"get_daily_runs_detail_api_v1_organizations_reports_daily_runs_get","parameters":[{"name":"date","in":"query","required":true,"schema":{"type":"string","description":"Date in YYYY-MM-DD format","title":"Date"},"description":"Date in YYYY-MM-DD format"},{"name":"timezone","in":"query","required":true,"schema":{"type":"string","description":"IANA timezone (e.g., 'America/New_York')","title":"Timezone"},"description":"IANA timezone (e.g., 'America/New_York')"},{"name":"workflow_id","in":"query","required":false,"schema":{"anyOf":[{"type":"integer"},{"type":"null"}],"description":"Optional workflow ID to filter by","title":"Workflow Id"},"description":"Optional workflow ID to filter by"},{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/WorkflowRunDetail"},"title":"Response Get Daily Runs Detail Api V1 Organizations Reports Daily Runs Get"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/turn/credentials":{"get":{"tags":["main","turn"],"summary":"Get Turn Credentials","description":"Get time-limited TURN credentials for WebRTC connections.\n\nThis endpoint generates ephemeral TURN credentials that are:\n- Valid for the configured TTL (default: 24 hours)\n- Cryptographically bound to the user via HMAC\n- Compatible with coturn's use-auth-secret mode\n\nReturns:\n TurnCredentialsResponse with username, password, ttl, and TURN URIs","operationId":"get_turn_credentials_api_v1_turn_credentials_get","parameters":[{"name":"authorization","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Authorization"}},{"name":"X-API-Key","in":"header","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"X-Api-Key"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/TurnCredentialsResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/public/embed/init":{"post":{"tags":["main"],"summary":"Initialize Embed Session","operationId":"initialize_embed_session_api_v1_public_embed_init_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/InitEmbedRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/InitEmbedResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"options":{"tags":["main"],"summary":"Options Init","description":"Fallback OPTIONS handler for init endpoint.","operationId":"options_init_api_v1_public_embed_init_options","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not found"}}}},"/api/v1/public/embed/init/stream":{"post":{"tags":["main"],"summary":"Stream Initialize Embed Chat","operationId":"stream_initialize_embed_chat_api_v1_public_embed_init_stream_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/InitEmbedRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"text/event-stream":{"schema":{"oneOf":[{"$ref":"#/components/schemas/PublicEmbedChatSessionStreamEvent"},{"$ref":"#/components/schemas/PublicEmbedChatMessageStreamEvent"},{"$ref":"#/components/schemas/PublicEmbedChatStreamError"}],"type":"string","title":"Response 200 Stream Initialize Embed Chat Api V1 Public Embed Init Stream Post","discriminator":{"propertyName":"type","mapping":{"session":"#/components/schemas/PublicEmbedChatSessionStreamEvent","complete":"#/components/schemas/PublicEmbedChatSessionStreamEvent","message":"#/components/schemas/PublicEmbedChatMessageStreamEvent","error":"#/components/schemas/PublicEmbedChatStreamError"}}}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"options":{"tags":["main"],"summary":"Options Init","description":"Fallback OPTIONS handler for init endpoint.","operationId":"options_init_api_v1_public_embed_init_stream_options","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not found"}}}},"/api/v1/public/embed/config/{token}":{"options":{"tags":["main"],"summary":"Options Embed Config","description":"Fallback OPTIONS handler for the embed config endpoint.\n\nBrowser preflights include Access-Control-Request-Method and are handled by\nPublicEmbedCORSMiddleware before global CORS. This keeps non-conformant\nOPTIONS requests on the same validation path.","operationId":"options_embed_config_api_v1_public_embed_config__token__options","parameters":[{"name":"token","in":"path","required":true,"schema":{"type":"string","title":"Token"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"get":{"tags":["main"],"summary":"Get Embed Config","description":"Get embed configuration without creating a session.\n\nThis endpoint is used to fetch widget configuration for display purposes\nwithout actually starting a call session.","operationId":"get_embed_config_api_v1_public_embed_config__token__get","parameters":[{"name":"token","in":"path","required":true,"schema":{"type":"string","title":"Token"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/EmbedConfigResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/public/embed/turn-credentials/{session_token}":{"get":{"tags":["main"],"summary":"Get Public Turn Credentials","description":"Get TURN credentials for an embed session.\n\nThis endpoint allows embedded widgets to obtain TURN server credentials\nfor WebRTC connections without requiring authentication.\n\nArgs:\n session_token: The session token from embed initialization\n\nReturns:\n TurnCredentialsResponse with username, password, ttl, and TURN URIs","operationId":"get_public_turn_credentials_api_v1_public_embed_turn_credentials__session_token__get","parameters":[{"name":"session_token","in":"path","required":true,"schema":{"type":"string","title":"Session Token"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/TurnCredentialsResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"options":{"tags":["main"],"summary":"Options Turn Credentials","description":"Fallback OPTIONS handler for TURN credentials endpoint.","operationId":"options_turn_credentials_api_v1_public_embed_turn_credentials__session_token__options","parameters":[{"name":"session_token","in":"path","required":true,"schema":{"type":"string","title":"Session Token"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/public/embed/chat/{session_token}":{"get":{"tags":["main"],"summary":"Get Public Chat Session","description":"Current transcript for an embed chat session (used for 409 resync).","operationId":"get_public_chat_session_api_v1_public_embed_chat__session_token__get","parameters":[{"name":"session_token","in":"path","required":true,"schema":{"type":"string","title":"Session Token"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PublicEmbedChatSessionResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"options":{"tags":["main"],"summary":"Options Public Chat Session","description":"Fallback OPTIONS handler; browser preflights hit PublicEmbedCORSMiddleware.","operationId":"options_public_chat_session_api_v1_public_embed_chat__session_token__options","parameters":[{"name":"session_token","in":"path","required":true,"schema":{"type":"string","title":"Session Token"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/public/embed/chat/{session_token}/end":{"post":{"tags":["main"],"summary":"End Public Chat Session","operationId":"end_public_chat_session_api_v1_public_embed_chat__session_token__end_post","parameters":[{"name":"session_token","in":"path","required":true,"schema":{"type":"string","title":"Session Token"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/PublicEmbedChatEndRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PublicEmbedChatSessionResponse"}}}},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"options":{"tags":["main"],"summary":"Options Public Chat End","description":"Fallback OPTIONS handler; 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The webhook verify\ntoken is deliberately not declared here: it is minted by the provider's\nsave hook and carried forward on update, never read from a request (see\npreprocess_credentials_on_save`` in the package)."},"WidgetTexts":{"properties":{"endChatText":{"type":"string","title":"Endchattext","default":"End chat"},"endChatConfirmText":{"type":"string","title":"Endchatconfirmtext","default":"End this chat?"},"endChatCancelText":{"type":"string","title":"Endchatcanceltext","default":"Cancel"},"endingChatText":{"type":"string","title":"Endingchattext","default":"Ending\u2026"},"conversationEndedText":{"type":"string","title":"Conversationendedtext","default":"Conversation ended."},"startNewChatText":{"type":"string","title":"Startnewchattext","default":"Start new chat"},"chatRetryText":{"type":"string","title":"Chatretrytext","default":"Retry"},"chatInputPlaceholder":{"type":"string","title":"Chatinputplaceholder","default":"Type a message\u2026"},"sendMessageLabel":{"type":"string","title":"Sendmessagelabel","default":"Send message"},"closeChatLabel":{"type":"string","title":"Closechatlabel","default":"Close chat"},"voiceConnectingText":{"type":"string","title":"Voiceconnectingtext","default":"Connecting\u2026"},"voiceEndCallText":{"type":"string","title":"Voiceendcalltext","default":"End Call"},"voiceRetryText":{"type":"string","title":"Voiceretrytext","default":"Retry"},"voiceReadyTitle":{"type":"string","title":"Voicereadytitle","default":"Ready to Connect"},"voiceConnectingSubtext":{"type":"string","title":"Voiceconnectingsubtext","default":"Please wait while we establish the connection"},"voiceConnectedTitle":{"type":"string","title":"Voiceconnectedtitle","default":"Connected"},"voiceConnectedSubtext":{"type":"string","title":"Voiceconnectedsubtext","default":"Your voice call is now active"},"voiceCallEndedTitle":{"type":"string","title":"Voicecallendedtitle","default":"Call ended"},"voiceCallEndedSubtext":{"type":"string","title":"Voicecallendedsubtext","default":"Click below to start a new call"},"voiceConnectionFailedTitle":{"type":"string","title":"Voiceconnectionfailedtitle","default":"Connection failed"},"voiceConnectionFailedSubtext":{"type":"string","title":"Voiceconnectionfailedsubtext","default":"Please check your microphone and try again"},"voiceConnectionLostTitle":{"type":"string","title":"Voiceconnectionlosttitle","default":"Connection lost"},"voiceConnectionLostSubtext":{"type":"string","title":"Voiceconnectionlostsubtext","default":"The call has been disconnected"}},"type":"object","title":"WidgetTexts","description":"Every visitor-facing string the embed widget can render."},"WorkflowConfigurationDefaults":{"properties":{"model_configuration_override":{"anyOf":[{"$ref":"#/components/schemas/ModelConfigurationOverride"},{"type":"null"}]},"ambient_noise_configuration":{"$ref":"#/components/schemas/AmbientNoiseConfigurationDefaults"},"max_call_duration":{"type":"integer","maximum":1200.0,"exclusiveMinimum":0.0,"title":"Max Call Duration","default":300},"max_user_idle_timeout":{"type":"number","title":"Max User Idle Timeout","default":10.0},"smart_turn_stop_secs":{"type":"number","title":"Smart Turn Stop Secs","default":2.0},"turn_start_strategy":{"type":"string","enum":["default","min_words"],"title":"Turn Start Strategy","default":"min_words"},"turn_start_min_words":{"type":"integer","minimum":1.0,"title":"Turn Start Min Words","default":2},"turn_stop_strategy":{"type":"string","enum":["transcription","turn_analyzer"],"title":"Turn Stop Strategy","default":"transcription"},"dictionary":{"type":"string","title":"Dictionary","default":""},"context_compaction_enabled":{"type":"boolean","title":"Context Compaction Enabled","default":false},"tts_cache_enabled":{"type":"boolean","title":"Tts Cache Enabled","description":"Reuse generated speech for repeated phrases. 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Api Reference Runs (`docs/api-reference/runs.mdx`)

title: "Overview"

Method	Endpoint	Quick Link
`POST`	`/public/agent/{uuid}`	Trigger an outbound agent run by API Trigger node
`POST`	`/public/agent/workflow/{workflow_uuid}`	Trigger an outbound agent run by Agent UUID
`GET`	`/workflow/{workflow_id}/runs/{run_id}`	Retrieve agent run details
`GET`	`/public/download/workflow/{token}/{artifact_type}`	Download recordings and transcripts
`POST`	`/telephony/inbound/{workflow_id}`	Inbound run webhook

Choose the right public run route

Dograh exposes two public outbound agent run route families. They are not
interchangeable, even though both path parameters look like UUIDs.

Use this when	Production route	Test route	Identifier you pass
You added an API Trigger node to the workflow and want to execute that trigger	`/public/agent/{uuid}`	`/public/agent/test/{uuid}`	The trigger UUID (`trigger_path`) from the API Trigger node
You want to execute the workflow by its stable Agent UUID instead of a trigger node	`/public/agent/workflow/{workflow_uuid}`	`/public/agent/test/workflow/{workflow_uuid}`	The workflow UUID from the agent's Agent UUID field

Do not pass a workflow UUID to `/public/agent/{uuid}` and do not pass a trigger UUID to `/public/agent/workflow/{workflow_uuid}`. Dograh treats these as different identifier types and the request will fail if you mix them up.

Once Dograh resolves the target agent, both route families behave the same:

They accept the same request body
They return the same response shape
They validate the same X-API-Key organization boundary
They use the same telephony configuration selection rules

If you specifically need the API Trigger route, see Trigger an outbound agent run by API Trigger node. To execute by Agent UUID, see Trigger an outbound agent run by Agent UUID.

Using initial context

initial_context passes runtime data into the agent at run time. Values are available as template variables in your agent's prompt using double-brace syntax.

json

{
  "initial_context": {
    "customer_name": "Jane",
    "appointment_date": "March 15"
  }
}

Your agent prompt can then reference {{customer_name}} and {{appointment_date}} and they will be substituted when the run starts.

Run status values

Status	Description
`pending`	Run queued but not yet connected
`in_progress`	Run is live
`completed`	Run ended normally
`failed`	Run failed before or during execution

Api Reference Telephony Configs (`docs/api-reference/telephony-configs.mdx`)

title: "Overview"

A telephony configuration binds an organization to a telephony provider (Twilio, Vonage, Plivo, Telnyx, etc.) along with the credentials needed to place and receive calls. Each configuration owns a set of phone numbers; campaigns and inbound routing pin to a specific configuration.

One configuration in your organization can be marked as the default outbound — campaigns and ad-hoc calls that omit telephony_configuration_id fall back to it.

Method	Endpoint	Quick Link
`GET`	`/organizations/telephony-providers/metadata`	List supported providers
`GET`	`/organizations/telephony-configs`	List configurations
`POST`	`/organizations/telephony-configs`	Create a configuration
`GET`	`/organizations/telephony-configs/{config_id}`	Get a configuration
`PUT`	`/organizations/telephony-configs/{config_id}`	Update a configuration
`DELETE`	`/organizations/telephony-configs/{config_id}`	Delete a configuration
`POST`	`/organizations/telephony-configs/{config_id}/set-default-outbound`	Set as default outbound

Phone numbers

Phone numbers are nested under a configuration:

Method	Endpoint	Quick Link
`GET`	`/organizations/telephony-configs/{config_id}/phone-numbers`	List phone numbers
`POST`	`/organizations/telephony-configs/{config_id}/phone-numbers`	Add a phone number
`GET`	`/organizations/telephony-configs/{config_id}/phone-numbers/{phone_number_id}`	Get a phone number
`PUT`	`/organizations/telephony-configs/{config_id}/phone-numbers/{phone_number_id}`	Update a phone number
`DELETE`	`/organizations/telephony-configs/{config_id}/phone-numbers/{phone_number_id}`	Delete a phone number
`POST`	`/organizations/telephony-configs/{config_id}/phone-numbers/{phone_number_id}/set-default-caller`	Set as default caller

Sensitive credentials

Provider credentials (API keys, auth tokens, etc.) are masked in GET responses. When updating a configuration, you can re-submit the masked value as-is — Dograh detects the mask and preserves the original secret.

Configurations Agent Uuid (`docs/configurations/agent-uuid.mdx`)

title: "Agent UUID"

The Agent UUID is the workflow's stable identifier. In API routes and payloads you may also see it called workflow_uuid.

Unlike numeric workflow IDs, the Agent UUID is portable across environments and does not change when you publish new versions of the same workflow.

Where to find it

You can copy the Agent UUID from two places in the dashboard.

From the workflow editor

Open your agent in the workflow editor
Click the ⋮ menu in the top-right of the header
Click Copy Agent UUID

From the agent's Settings page

Open the agent and go to Settings
Scroll to the Agent UUID section
Click the UUID code block, or use Copy UUID

Where you use it

Use the Agent UUID for workflow-level routes such as:

POST /api/v1/public/agent/workflow/{workflow_uuid}
POST /api/v1/public/agent/test/workflow/{workflow_uuid}
wss://<your-host>/api/v1/agent-stream/{provider}/{agent_uuid}

Do not confuse the Agent UUID with an API Trigger node UUID (`trigger_path`).

Use the Agent UUID for workflow-level routes like /public/agent/workflow/{workflow_uuid}
Use the API Trigger node UUID for trigger routes like /public/agent/{uuid}
The API Trigger node path can be edited if you want something more descriptive
The Agent UUID is generated by Dograh and cannot be changed

See API Trigger for trigger-node URLs.

Configurations Api Keys (`docs/configurations/api-keys.mdx`)

title: "API Keys and Service Keys"

The option to create the Keys are in https://app.dograh.com/api-keys if you are using hosted version, or http://localhost:3010/api-keys if you are using the self hosted version.

API Keys

API keys can be used to trigger a voice agent from an external system, like n8n or programatically from your other workflows. In order to generate that, you can go to /api-keys and create a new key.

Please note that you must copy and keep the API key secretly, since this is the only time that you would be able to copy it. If you lose it, you can always delete that, if its not being used anywhere, and create a new API key.

Service Keys

Service Keys are the keys which you generate to be used in Model Configurations. In order to generate that, you can go to /api-keys and create a new key.

You can use a Service Key created in Dograh Cloud (`https://app.dograh.com/api-keys`) in your self-hosted Dograh deployment. Create the Service Key from your Dograh Cloud account, then paste it into **Model Configurations** in your self-hosted instance to use Dograh-managed inference providers. You can purchase Dograh credits in Dograh Cloud; billing happens on the Cloud account that owns the Service Key. Service Keys are scoped to the Dograh Cloud account that created them. You cannot use a Service Key from one cloud-hosted account in another cloud-hosted account; create a new Service Key from the account where you want to use it.

Configurations Call Events (`docs/configurations/call-events.mdx`)

title: "Call events"

You need an existing BigQuery table with the schema below and a Google identity
that can read its schema and insert rows. Call event export is disabled until
you configure it in Settings → Call events.

Select BigQuery as the destination.
Enter the table as project.dataset.table.
Choose a service account and enter its email and PEM private key. On a
self-hosted installation, you can instead select Deployment identity to
use the Google credentials configured on your Dograh server.
Select Test connection to check authentication and table compatibility.
This reads table metadata without inserting events; successful exports also
require write permission.
Enable Export call events and select Save.

Each organization has its own destination and credentials. Saved private keys
are masked in the UI. Keep the masked value to retain your existing key, or
replace it to rotate the key.

BigQuery schema

Use these scalar columns with nullable fields; ts and event can also be
required. The adapter does not create or migrate tables.

Column	Type
`ts`	TIMESTAMP
`event`	STRING
`run_id`, `org_id`, `workflow_id`, `turn`	INTEGER
`severity`	STRING
`value_ms`	FLOAT
`node_id`, `node_name`	STRING
`detail`	JSON (STRING also supported)

You receive one row per diagnostic event. ts is when the event happened, and
detail contains JSON, stored as a native JSON value or serialized text according
to the column type. Events cover speech, accepted user turns,
mute state, tool execution, silence warnings, stage latencies, pipeline errors
and the final call summary. The transcription event records character counts
and empty/finalized flags; it does not contain transcript text or audio.

Delivery behavior

Events are sent after a call finishes, with their original timestamps.
Capture and export are separate from the real-time feedback logs.
Dograh stores the destination settings in its organization configuration;
event payloads are not persisted in Postgres or Redis.

Exports use bounded retries for retryable row and HTTP errors. BigQuery's
insertion IDs provide best-effort deduplication; duplicates can still occur.
Capture and pending export queues are bounded, and unsent events can be lost
on a process crash, queue overflow or exhausted retries. There is no durable
replay queue in this version. Export failures and dropped-event counts appear
in the API worker logs.

Table changes apply to new calls. An active call's batch is discarded if its
destination is changed or disabled before export, so it cannot accidentally
land in a different table. Credentials are read again before each delivery
attempt to support rotation. Requests already sent to BigQuery cannot be
recalled when you disable export.

Configurations Inference Providers (`docs/configurations/inference-providers.mdx`)

title: "Model Configurations"

Provider connections and model configurations

Use Providers in the sidebar to connect provider accounts, and Models to create named model configurations. Providers lists each added connection in its own row. You can connect multiple accounts for the same provider and reuse them across configurations.

Item	What it contains
Provider Connection	A provider account's credentials and connection settings, such as an endpoint, cloud project, or region.
Named Model Configuration	A reusable choice of connections and model settings, such as the LLM model, temperature, and TTS voice.
Organization Default	The named setup agents inherit when they have no model override.

Connections and configurations belong to your organization. Dograh provisions an initial Dograh connection and default setup during signup. You can add more Dograh connections with different Service Keys, or connect external providers.

When you upgrade an existing deployment, the database migration converts your organization's saved model settings into connections and an Organization default configuration, and gives each agent that had its own model settings a named configuration of its own. Agents without their own settings keep inheriting the default.

Add a provider connection

You need the credentials and any connection settings required by your provider. To create a Dograh Service Key, see API Keys and Service Keys.

Open Providers in the sidebar and click Add Provider to open the connection editor page.
Choose the provider and name the connection so you can distinguish its account or environment.
Enter its credentials and any required connection settings, then save. Where key pools are supported, use Add API Key to enter each key in its own row.

Click Edit on a provider row to open its editor page. Use All provider connections to return to the list.

You choose which services use the connection when editing a named model configuration in Models.

You can repeat these steps for multiple accounts at the same provider. Editing a connection lets you keep or replace configured credentials; saved secrets are not returned to the browser. Model settings reference the connection instead of copying its key.

Saving a new connection, replacing credentials, or changing connection settings runs the provider's available credential checks before saving. Every API key in a pool is checked. A rejected key leaves the existing connection unchanged. Renaming a connection does not repeat provider checks. The checks vary by provider; some validate field structure or token format rather than making a live request.

A provider's realtime and standard services may require separate connection types, even when they accept the same key. The available roles come from the provider definitions.

Create a named configuration

Open Models in the sidebar, click Add Configuration, and give it a name.
Choose Realtime, Dograh, or BYOK mode. In BYOK and Realtime, each service has its own tab where you select a provider connection. To connect a new account without leaving the page, choose Add a provider at the end of a provider list.
Adjust the available settings, then save.
Click Make org default on its row in Named Model Configurations if agents should inherit it. The current default has an Org default badge.

Mode	Provider selection
Dograh	One Dograh connection manages LLM, STT, TTS, and embeddings. You can select the LLM model and adjust temperature, voice, speed, language, and language hints.
BYOK	Separate compatible external connections for LLM, STT/transcriber, and TTS/voice.
Realtime	An external realtime speech-to-speech connection and a separate external LLM for tasks such as extraction and QA.

In BYOK and Realtime modes, you can also select an external embedding connection on the Embedding tab. Its model must match the indexes of the knowledge-base documents you use. Changing the configuration does not rebuild existing indexes. Dograh mode manages embeddings automatically and has no separate embedding controls. Dograh connections are selected only through Dograh mode in the editor.

Use Duplicate to create a variation, or Copy UUID to reference a saved configuration or connection from your API integration. Names are labels; UUIDs identify references.

Creating, editing, and duplicating model configurations opens a dedicated page. Use All model configurations to return to the list. In Dograh mode, choose the managed tier under LLM model, and click Voice to browse, filter, and play voice samples before selecting Use this voice. For Dograh's Flux-supported languages and Deepgram Flux multilingual, select Language hints with the language checkboxes. Model and language dropdowns offer Enter Custom Value where the provider supports it.

In BYOK mode, ElevenLabs, Cartesia, and Deepgram voices load using the selected provider connection's saved API key. Rime uses its public voice catalog, and Sarvam offers model-specific speaker lists. Voice choices refresh when you change the connection, model, or language. If a catalog cannot load, you can retry or enter a voice ID manually.

Saving a named model configuration checks its structure and supported settings without repeating provider credential checks. A run's final configuration is validated before billing and quota authorization.

For one run, all selected Dograh services must share a service key. You can keep multiple Dograh connections in the catalog and use them in different configurations.

Configure an individual agent

Open the agent's Settings and its model configuration section.
Inherit the organization default or select a named configuration.
Save and publish the agent.

Create and edit configurations in Models. For a variation, duplicate a named configuration there and select it in your workflow. Override individual fields such as temperature or voice through the API trigger for a single run.

If an existing workflow has custom model settings, they remain in use until you explicitly select and save a named configuration or the organization default. Workflow settings show that existing override without offering individual field editing.

A named selection follows edits to that configuration for future runs. A run that has already started keeps its saved settings and credential selection.

Override a single run

The effective order is organization default, published workflow override (or draft for test calls), API trigger override, then pre-call data fetch override. The pre-call data fetch runs once the call is connected, so it can only change the services the agent owns: llm, llm_fallback, and tts. The mode, stt, realtime, and embeddings are fixed by the first three layers.

An override can select a named configuration by UUID, select a provider connection for a service, or change a field such as temperature or voice. Selecting a different named configuration replaces earlier model-setting patches; that override's own explicit fields then apply. Changing to a connection for the same provider preserves model settings. Changing provider starts that service from the new provider's defaults plus your explicit settings.

See API Trigger for request examples and Pre-call Data Fetch for response examples. Overrides do not edit your saved configurations.

Archive unused setups

Archive a named configuration or connection when you no longer need it. Dograh rejects archiving references still needed by a default, active configuration, workflow, or pending campaign. Change those references first. Started runs keep their resolved model settings and load the connection's current credentials. Credential updates replace the stored values; previous credentials are not retained.

Expand Archived at the bottom of Models or Providers to see archived items and select Restore to use them again. Restoring preserves the item's UUID and settings without changing your org default. If a model configuration uses archived provider connections, restore those providers first. Archived items do not appear in configuration selectors.

When you use external providers, Dograh sends the d

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Overview (`README.md`)

🎥 Featured

⚖️ Dograh vs Vapi vs Retell

🚀 Get Started

🎙️ Your First Voice Bot

Build Agents with MCP

Features

Voice Agent Builder

Voice & Telephony

Developer Experience

Deployment Options

Local Development

Self-Hosted Deployment

Cloud Version

📚Documentation

📦 SDKs

🤝Community & Support

🙌 Contributing

Getting Started

⭐ Star History

📄 License

🏢 About

Overview (`docs/README.md`)

Local Setup

Overview (`examples/README.md`)

Shared environment variables

Python

TypeScript

Overview (`ui/README.md`)

Getting Started

Login Flow

Sentry and PostHog

Overview (`deploy/hostinger/README.md`)

Files

What the app stack needs from Traefik

WebRTC media (coturn) is NOT proxied by Traefik

Deploy on Hostinger

Test on a generic VPS (self-managed stand-in Traefik)

Overview (`evals/stt/README.md`)

Providers

Setup

Usage

CLI Options

Directory Structure

How It Works

Output Example

Adding New Providers

API Documentation

Overview (`evals/visualizer/README.md`)

Getting Started

Learn More

Deploy on Vercel

Overview (`sdk/python/README.md`)

Install

Usage

What gets validated at the call site

Environment

License

Overview (`sdk/typescript/README.md`)

Install

Usage

Client-side validation

Environment

License

Overview (`api/services/campaign/readme.md`)

campaign_orchestrator.py (CampaignOrchestrator)

runner.py (CampaignRunnerService)

call_dispatcher.py (CampaignCallDispatcher)

campaign_tasks.py

Overview (`api/services/gender/README.md`)

Overview

Data Source

Building the Model

Prerequisites

Build Steps

Model Output

Using the Service

Basic Usage

Configuration Options

Salutation Generation