dograh-hq/dograh

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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Summary Information

Updated 56 minutes ago
Added to GitGenius on May 31st, 2026
Created on September 9th, 2025
Open Issues & Pull Requests: 24 (+0)
Number of forks: 1,249
Total Stargazers: 5,206 (+0)
Total Subscribers: 41 (+0)

Issue Activity (beta)

Open issues: 18
New in 7 days: 7
Closed in 7 days: 1
Avg open age: 6 days
Stale 30+ days: 1
Stale 90+ days: 0

Recent activity

Opened in 7 days: 7
Closed in 7 days: 1
Comments in 7 days: 1
Events in 7 days: 6

Top labels

  • bug (57)
  • documentation (6)
  • good first issue (1)
  • question (1)

Repository Insights (GitGenius)

Median issue/PR response: 2.5 days
Mean response time: 10.6 days
90th percentile: 21.6 days
Tracked items: 107

Most active contributors

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 15% of issues opened in the past year have been closed. Three people close 89% of everything that gets resolved.

Detailed Description

Dograh is an open-source, self-hosted voice AI platform designed as an alternative to proprietary services like Vapi and Retell. Written primarily in Python, it enables users to build production-grade voice agents through a drag-and-drop workflow builder, with the stated goal of getting from zero to a working bot in under two minutes. The platform is licensed under the BSD 2-Clause License and maintained by YC alumni and exit founders, emphasizing commitment to keeping voice AI open and accessible.

The core value proposition centers on eliminating vendor lock-in through complete openness and local control. Unlike competitors that operate as SaaS-only platforms, Dograh can be self-hosted with a single Docker command, allowing users to maintain full data residency on their own infrastructure. The platform supports bring-your-own-key functionality across speech-to-text, text-to-speech, and large language models, or users can rely on Dograh's built-in stack. Every line of code is open for modification, giving developers source-level customization capabilities unavailable in closed-source alternatives.

The platform supports both inbound and outbound calling scenarios with built-in telephony integration for providers including Twilio, Vonage, Vobiz, and Cloudonix, with straightforward mechanisms to add additional providers. Call transfers to human agents are supported. The architecture emphasizes low-latency voice interactions and real-time processing. A test mode allows agents to be evaluated end-to-end before production deployment, while in-dashboard web calls enable direct interaction with bots during the building phase without requiring telephony setup. A QA node within the workflow builder analyzes prompt quality across other nodes.

The developer experience prioritizes accessibility through zero-configuration startup with auto-generated API keys for immediate testing. The Python-based foundation facilitates customization, while Docker-first containerization ensures consistent deployments across environments. The modular architecture allows component swapping as needed. SDKs are available for both Python and Node.js via PyPI and npm respectively.

Deployment options include local development setup, self-hosted Docker deployment with support for remote servers and HTTPS configuration, and a managed cloud version. The platform ships with anonymous usage telemetry that can be disabled via environment variable. Community engagement occurs through a dedicated Slack workspace, GitHub Discussions for use case sharing and workflow recipes, and GitHub Issues for bug reports and feature requests. Documentation is maintained at docs.dograh.com with localized versions in Chinese and Japanese.

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