EchoFire
Realistic Voice Agent Testing
Automate end-to-end evaluation of voice agents with human-like audio simulation.
flowchart LR
A[Record Session With Voice Agent] --> B[Write Tests w/ LLM Judge + Built-in Functions]
B --> C{Parallel Test Execution}
C -->|Multiple Iterations| D1[Run Test 1]
C -->|Multiple Iterations| D2[Run Test 2]
C -->|Multiple Iterations| D3[Run Test ...]
D1 --> E[Share Results & Collaborate]
D2 --> E
D3 --> E
E --> F[Build Datasets for Fine-Tuning]
F --> G[Improve Models & Prompts]
G --> A
Why EchoFire?
- 🚫 Manual testing sucks: Listening to every agent response isn't scalable.
- 🤖 Synthetic TTS isn't real: Simulate actual human speech patterns, background noise, and ASR edge cases.
- 🔥 Test everything: Validate ASR accuracy, intent logic, and agent responses in one flow.
- 💻 Works locally: Run tests on your machine with a simple CLI - no cloud deployment needed.
- 🔄 CI/CD ready: Integrate voice agent testing into your continuous integration pipeline.
1 maintainer
Metadata
1 maintainer
Release files for echofire 0.1.18
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| echofire-0.1.18.tar.gz | 201.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| echofire-0.1.18-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 409.2 kB
Release files / echofire-0.1.18.tar.gz
| Download URL | echofire-0.1.18.tar.gz |
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| Size | 201.2 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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| Size | 208.0 kB |
| Tags | Python 3 |
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