Shows concretely how well a frontier model and a small local model actually collaborate under a shared-context orchestrator, and whether that orchestration beats the local model working alone.
That comparison is directly actionable for engineers weighing hybrid cloud/local agent setups.
Traycer Link: https://github.com/traycerai/traycer Timestamps: 00:00 - Intro 00:44 - First Look 01:57 - Technical Look 05:00 - Multi-Agent Collaboration Demo 13:05 - Fable 5 & Local Qwen Agent Info 14:05 - Fable 5 & Qwen 27B Agent Test 18:55 - Qwen 27B Impresses Fable 5 22:16 - Fable 5 Scores Qwen 27B 22:55 - Orchestration Test Result 25:23 - Qwen 27B Solo Test 26:08 - Qwen 27B Test Result 26:44 - Result Comparison 27:26 - Closing Thoughts AI Integration & Consulting: https://bijanbowen.com/ Joi
Transcript
Traycer Link: https://github.com/traycerai/traycer Timestamps: 00:00 - Intro 00:44 - First Look 01:57 - Technical Look 05:00 - Multi-Agent Collaboration Demo 13:05 - Fable 5 & Local Qwen Agent Info 14:05 - Fable 5 & Qwen 27B Agent Test 18:55 - Qwen 27B Impresses Fable 5 22:16 - Fable 5 Scores Qwen 27B 22:55 - Orchestration Test Result 25:23 - Qwen 27B Solo Test 26:08 - Qwen 27B Test Result 26:44 - Result Comparison 27:26 - Closing Thoughts AI Integration & Consulting: https://bijanbowen.com/ Join the Discord: https://discord.gg/hfaR2exy7S In this video, we take a hands-on look at Traycer, an open-source AI orchestration app designed to let multiple AI agents collaborate while sharing context. We connect Claude Fable 5 with a local Qwen 27B agent and test how well they can communicate, review each other’s work, and complete a coding task together. We then compare the orchestrated result against Qwen 27B working alone.
