GraphCanon updated 3w · GitHub synced 3w
Decision brief
Parlor is an all-in-one multimodal AI solution running entirely on device for real-time voice and vision interactions.
Good fit when
- Need to run complex, interactive AI locally without relying on cloud services.
- Specific hardware requirements like Apple Silicon or Linux with supported GPUs do not pose a barrier.
Avoid when
- Limited by Python 3.12 requirement and need for specialized hardware such as Apple Silicon.
- Insufficient ~3 GB RAM makes it unsuitable for environments with tight memory constraints.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/fikrikarim/parlorSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Parlor enables local running of natural voice and vision conversations using Gemma 4 E2B and Kokoro models, focusing on real-time interactivity with minimal external dependencies.
Capability facts
- Languages
- html
Source: github.language · Jul 29, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Requirements
- Python 3.12+
- macOS with Apple Silicon, or Linux with a supported GPU
- ~3 GB free RAM for the model
Quick start
git clone https://github.com/fikrikarim/parlor.git
cd parlor
---
# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh
cd src
uv sync
uv run server.py
Open http://localhost:8000, grant camera and microphone access, and start talking.
Models are downloaded automatically on first run (~2.6 GB for Gemma 4 E2B, plus TTS models).
For agents
This page has a .md twin and JSON over the API.