GraphCanon updated today · GitHub synced today
Decision brief
Ava is a Python toolkit for developing WhatsApp AI agents with robust speech-to-text and text-to-speech capabilities suited for conversational workflows.
Good fit when
- When you need a specialized agent for WhatsApp interactions
- For projects requiring integration of both STT and TTS technologies
Avoid when
- If your workflow primarily uses platforms other than WhatsApp
- Projects that do not require speech-to-text or text-to-speech capabilities
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (304d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install ava-whatsapp-agent-courseHow it fits your stack(7)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Depends on
Relationship graph
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ai-agents-for-beginners73k★autogen60k★
agentscope29k★
GenAI_Agents24k★
langflow153k★
langchain144k★
whisper107k★
LibreChat41k★
AgentGPT36k★ai-agents-for-beginners73k★autogen60k★agentscope29k★GenAI_Agents24k★langflow153k★langchain144k★whisper107k★LibreChat41k★AgentGPT36k★Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository for creating an AI agent named Ava designed to interact via WhatsApp, leveraging speech-to-text (STT) and text-to-speech (TTS) technologies.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 21, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 21, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 21, 2026
Categories
Tags
README
Getting started
Before you begin the course, there are a few things you need to do.
I'm referring to the virtual environment creation, dependencies installation, .env file creation, etc. I know, it's very boring, but it's a necessary evil! 😅
All of this is detailed in the following doc: GETTING STARTED.md.
Make sure you follow the instructions in the doc, as it's crucial for the course to work.
How much is this going to cost me?
The awesome thing about this project is you can run it on your own computer for free!
The free tiers from Groq, ElevenLabs, Qdrant Cloud, and Together AI are more than enough to get you going.
If you want to try it out on Google Cloud Run, you can get a free account and get $300 in free credits. Even if you've already used up your free credits, Cloud Run is super cheap - so it will take just a buck or two for your experiments.
License
This project is licensed under the MIT License - see the LICENSE file for details.
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For agents
This page has a .md twin and JSON over the API.