AUTOMATION
Local-First Multimodal File Search with Agent Integration
Index and search text, PDF, image, audio, video locally for agent retrieval
Updated: 6/6/2026
Difficulty
hard
Time
varies
Use Case
Providing agents with fast, accurate multimodal search over local files for context retrieval
Popularity
0 views
About this automation
Omni indexes text, PDF, image, audio, and video files locally using SOTA omni embedding model. Exposes HTTP server for agents like OpenClaw and Hermes to query. Search is near-instant; indexing is slower (10K-300 tps depending on file type).
How to implement
1
Build SOTA omni embedding model for multimodal content
2
Implement Swift-native UI with mlx-swift-transformer core
3
Create indexing pipeline for text, PDF, image, audio, video
4
Expose HTTP server for agent queries
5
Optimize for recall (agent refines results)
6
Test on various Mac hardware (M3 Pro, M3 Ultra, M4 Pro)