dograh-hq/dograh
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 builder, MCP native and telephony support.
Usage guide
dograh is an open-source project around ai-calling, asterisk-ari, conversational-ai with 4,033 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the BSD-2-Clause repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating dograh for Python AI workflows.
- Comparing a GitHub project with 4,033 stars and current repository activity.
Pros
- dograh has visible GitHub traction with 4,033 stars. Topics: ai-calling, asterisk-ari, conversational-ai.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the BSD-2-Clause terms fit your use case.
Production readiness
dograh should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
BSD-2-Clause is reported by GitHub; review the repository license before redistribution or commercial use.
dograh architecture preview
dograh's main path starts at the entry surface, runs through Coding agent runtime, combines LLM / model client, Runtime context, GitHub / MCP tools, and returns Assistant response / action result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://app.dograh.com
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
GitHub / MCP tools
Tool adapters let the runtime act outside the model through GitHub / MCP tools.
GitHub, MCP tools
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
dograh depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/dograh-hq/dograh.gitInstall or build dependencies
No extra setup command was detected. Check the README before adding custom configuration.
Adoption guidance and sources
Practical use cases
Open source voice AI platform. Self-hosted alternative to Vapi and Ret
This is one of the documented reasons to evaluate dograh before choosing a stack.
Focus area: ai-calling
This is one of the documented reasons to evaluate dograh before choosing a stack.
Speech project comparison
Compare dograh with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official dograh setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented dograh example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is dograh?
dograh is an open-source speech project. 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.
How do I install dograh?
Start with the official README. The first detected setup step is: git clone https://github.com/dograh-hq/dograh.git.
Is dograh beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can dograh be used commercially?
GitHub detected the BSD-2-Clause repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does dograh need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt dograh?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.