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 builder, MCP native and telephony support. https://app.dograh.com
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prabhatlepton 6d1051757c
feat(helm): add HPA for arq-worker + ui, ship a lean k3s prod example (#516)
* feat(helm): add HPA for arq-worker + ui, ship a lean k3s prod example

## Problem

The chart's autoscaling story only covers the `web` tier — one
`web-hpa.yaml` template gated by `autoscaling.web.enabled`. Operators
scaling the `arq-worker` (background jobs) or `ui` (Next.js SSR) tiers
have to write their own HPA manifests out-of-band or fork the chart.

Turning the existing memory-utilization target on for freshly-installed
workloads also silently breaks: idle Python at the chart's default
`128Mi` (workers) / `256Mi` (ui) memory request already sits above
`80%`, so HPA scales every tier to `maxReplicas` on cold start with no
traffic. On a tight node this cascades into "insufficient CPU" and
blocks new-workload scheduling.

## Fix

**New HPA templates** — `templates/arq-worker-hpa.yaml` and
`templates/ui-hpa.yaml`, both mirroring the existing
`templates/web-hpa.yaml` shape (autoscaling/v2, resource metrics,
gated on `.Values.autoscaling.<tier>.enabled`).

**Extended `values.yaml`**:
- `autoscaling.workers` and `autoscaling.ui` blocks with sane defaults
  (`enabled: true`, `minReplicas: 1`, `maxReplicas: 5`,
  `targetCPUUtilizationPercentage: 70`).
- `targetMemoryUtilizationPercentage: null` on both tiers by default,
  with an inline comment explaining why memory-utilization HPA is a
  broken signal at the chart's default request sizes.
- Header comment reworked to (a) document the `metrics-server`
  requirement, (b) note that HPA takes ownership of Deployment
  `replicas` after first sync, (c) call out that CPU is a poor signal
  for the web tier (long-lived WebSockets), and (d) note that CPU is
  a fine signal for workers and ui.

**Example**: `examples/values-k3s-prod.yaml` — a single-node k3s
production override that exercises the new HPA blocks and demonstrates
the paired safety changes (memory targets nulled, sized resource
requests, migration job CPU sized for a tight node). Ship-ready
starting point for the operator flow: hosted-AI only (no local
models), all state on the node's local-path StorageClass, invite-only
signup, TLS terminated at a shared Cloudflare Origin cert.

## Behavior

Fresh install with defaults:
- Workers scale 1 → 5 on CPU 70% target only. No memory-based
  scale-up storm on cold start.
- UI scales 1 → 5 on CPU 70% target only.
- Web autoscaling stays `enabled: false` by default (unchanged) —
  operators opt in per the existing README warning.

Operators who want memory-based HPA back can:
1. Bump `workers.resources.requests.memory` (~256Mi) or
   `ui.resources.requests.memory` (~384Mi).
2. Set `autoscaling.<tier>.targetMemoryUtilizationPercentage: 80`.

* address review: omit replicas when HPA on, suppress empty-metrics HPA, docs

Fixes raised on #516:

- **Worker/UI Replicas Reset On Upgrade** — arq-worker-deployment.yaml and
  ui-deployment.yaml now wrap `replicas:` in `{{- if not .Values.autoscaling.<tier>.enabled }}`,
  mirroring the existing web-deployment guard. With HPA on, Helm no longer
  reapplies the static replicaCount on upgrade and briefly shrink an
  HPA-scaled pool.

- **Empty Metrics Render Invalid HPA** — arq-worker-hpa.yaml and ui-hpa.yaml
  now short-circuit the whole HPA object when both CPU and memory targets
  are null. Previously the template emitted `spec.metrics:` with no items
  (rejected by the k8s API server).

- **`enableSignup: false` removed from examples/values-k3s-prod.yaml** — that
  knob depends on #514 which hasn't landed; unwiring it here avoids
  suggesting a lockdown that isn't in effect until the sibling PR merges.

- **Header comment mismatch** — `# HPA: 1 → 5 on CPU 70% / memory 80%` claimed
  memory was on while every tier had `targetMemoryUtilizationPercentage: null`.
  Updated to "CPU 70% only (memory HPA opt-in)".

- **Wrong default in comment** — `values.yaml` said workers default is `128Mi`;
  actual is `256Mi`. Fixed.

- **UI comment said "idle Python"** — UI is Next.js/Node.js. Corrected on the
  UI HPA memory comment and the per-tier comments in values-k3s-prod.yaml
  (web: FastAPI, workers: Python/ARQ, ui: Node.js).

All lints pass; verified with `helm template`:
- Defaults render both HPAs and Deployments without static `replicas:`.
- `--set autoscaling.workers.targetCPUUtilizationPercentage=null --set autoscaling.workers.targetMemoryUtilizationPercentage=null`
  renders only the Deployment (HPA suppressed).
- `--set autoscaling.workers.enabled=false` renders the Deployment with
  static `replicas:` restored.

* address review: align Deployment replicas gate with HPA render gate

Follow-up on #516: my earlier fix guarded `spec.replicas` on only
`autoscaling.<tier>.enabled`, but the HPA-empty-metrics guard I added
suppresses the HPA object when both metric targets are null while
`enabled: true`. That combination produced a Deployment with neither
a `spec.replicas` value nor an HPA owner — a k8s Deployment defaults
to `replicas: 1` in that case, but the chart no longer expresses intent.

Fix: the Deployment `replicas` gate now mirrors the HPA render gate
exactly. Rendered outcomes verified with `helm template`:

| autoscaling.<tier>            | HPA rendered? | Deployment replicas? |
|-------------------------------|---------------|----------------------|
| enabled: true, target set     | yes           | omitted (HPA owns)   |
| enabled: true, both null      | no            | static (kept)        |
| enabled: false                | no            | static (kept)        |

* fix(helm): default worker/ui autoscaling off; ui HPA floor of 2

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* fix(helm): align web replicas/HPA gate with worker/ui pattern

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* docs(helm): document worker/ui HPAs in README; polish k3s example

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: prabhat pankaj <prabhatiitbhu@gmail.com>
Co-authored-by: Abhishek Kumar <abhishek@a6k.me>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
2026-07-13 14:48:48 +05:30
.agents/skills feat: add Review AGENTS.md Skill 2026-05-20 16:20:07 +05:30
.devcontainer chore: update documentation 2026-06-19 18:11:35 +05:30
.github chore: format release please changelogs 2026-06-02 13:42:55 +05:30
.vscode chore: setup worktree on folder open 2026-06-30 16:15:10 +05:30
api feat: add ElevenLabs realtime STT provider support (#512) (#522) 2026-07-13 14:47:07 +05:30
config/coturn feat: add coturn configurations (#143) 2026-02-03 13:52:50 +05:30
deploy feat(helm): add HPA for arq-worker + ui, ship a lean k3s prod example (#516) 2026-07-13 14:48:48 +05:30
docs feat: add ElevenLabs realtime STT provider support (#512) (#522) 2026-07-13 14:47:07 +05:30
evals chore: upgrade Next.js in evals/visualizer from 16.1.4 to 16.2.6 (#361) 2026-05-27 14:26:08 +05:30
examples feat(examples): add multi-node Workflow SDK example in Python and TypeScript (#440) 2026-06-18 15:13:10 +05:30
nginx feat: add rolling updates for production deployment (#175) 2026-03-02 14:44:04 +05:30
pipecat@cc535a0c86 chore: cleaup mps v1 billing (#507) 2026-07-07 18:38:29 +05:30
scripts feat: add Helm chart for Kubernetes deployment (#365) 2026-07-03 12:39:39 +05:30
sdk fix: fix superadmin impersonation 2026-07-11 15:51:36 +05:30
ui feat: add ElevenLabs realtime STT provider support (#512) (#522) 2026-07-13 14:47:07 +05:30
.dockerignore fix: speed up multi arch build (#372) 2026-05-28 13:43:33 +05:30
.gitignore chore: add Conductor per-worktree dev setup (.conductor/ + .worktreeinclude) 2026-06-29 12:48:33 +05:30
.gitmodules refactor: change pipecat to submodule & add github alerts 2025-09-29 18:17:04 +05:30
.nvmrc Chore/add setup and contributing docs (#90) 2025-12-27 09:25:20 +05:30
.python-version feat: add devcontainer based setup (#352) 2026-05-25 20:44:22 +05:30
.release-please-manifest.json chore(main): release dograh 1.41.0 (#497) 2026-07-06 21:37:02 +05:30
AGENTS.md chore: cleaup mps v1 billing (#507) 2026-07-07 18:38:29 +05:30
CHANGELOG.md chore(main): release dograh 1.41.0 (#497) 2026-07-06 21:37:02 +05:30
CLAUDE.md Chore/add setup and contributing docs (#90) 2025-12-27 09:25:20 +05:30
CONTRIBUTING.md feat: banner if API is not reachable 2026-05-31 13:05:22 +05:30
docker-compose-local.yaml chore: update setup docs 2026-05-12 14:25:34 +05:30
docker-compose.yaml feat(auth): gate OSS signup behind ENABLE_SIGNUP flag (#514) 2026-07-13 14:08:25 +05:30
LICENSE feat: add README, LICENSE, CONTRIBUTING 2025-09-10 09:20:38 +05:30
README.ja-JP.md Docs/add japanese readme (#477) 2026-06-30 09:49:41 +05:30
README.md Docs/add japanese readme (#477) 2026-06-30 09:49:41 +05:30
README.zh-CN.md Docs/add japanese readme (#477) 2026-06-30 09:49:41 +05:30
release-please-config.json chore: format release please changelogs 2026-06-02 13:42:55 +05:30
remote_up.sh feat(scripts): free trusted HTTPS via sslip.io for public-IP remote i… (#460) 2026-06-27 17:19:29 +05:30
SECURITY.md feat: add more issue templates 2025-09-30 15:05:06 +05:30

Dograh AI

The open-source, self-hostable alternative to Vapi & Retell — build production voice agents with a drag-and-drop workflow builder. From zero to a working bot in under 2 minutes.

Try the Cloud   Self-host in 60s   Join Slack

📖 Docs  ·  📜 BSD 2-Clause  ·  🌐 中文  ·  🌐 日本語

Dograh in action — build a workflow, launch a voice agent, talk to it

  • 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

dograh-hq%2Fdograh | Trendshift

Dograh featured by Better Stack
Featured by Better Stack — a hands-on look at Dograh
📺 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

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

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.

# 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

  1. Open http://localhost:3010 in your browser.
  2. Pick Inbound or Outbound, name your bot (e.g. Lead Qualification), and describe the use case in 510 words (e.g. Screen insurance form submissions for purchase intent).
  3. Click Web Call — you're talking to your bot.

🔑 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.

Features

Voice Capabilities

  • Telephony: Built-in telephony integration like Twilio, Vonage, Vobiz, Cloudonix (easily add others), with support for transferring calls to human agents
  • Languages: English support (expandable to other languages)
  • Custom Models: Bring your own TTS/STT models
  • Real-time Processing: Low-latency voice interactions

Developer Experience

  • Zero Config Start: Auto-generated API keys for instant testing
  • Python-Based: Built on Python for easy customization
  • Docker-First: Containerized for consistent deployments
  • Modular Architecture: Swap components as needed

Testing & Quality

  • Test Mode: Try your agent end-to-end before publishing, with no production calls or data affected
  • In-Dashboard Web Calls: Talk to your bot directly while building — no telephony setup required
  • QA Node: A built-in workflow node that analyzes prompt quality across your other nodes

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

🤝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

Dograh 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.




Star us on GitHub | ☁️ Try Cloud Version | 💬 Join Slack