Transportation has greatly benefited the cities' development in the modern civilization process. Intelligent transportation, leveraging advanced computer algorithms, further increases people's daily commuting efficiency, however, as a cross-discipline, it often requires practitioners to comprehend complicated algorithms and obscure neural networks, bringing a challenge for the advanced techniques to be trusted and deployed in practical industries. Recognizing the expressiveness of the pre-trained large language models, especially the potential of being augmented to understand and execute intricate commands, we introduce Open Traffic Intelligence, Open-TI. As a bridge to mitigate the industry-academic gap, Open-TI is an innovative language agent augmented with the capability to harness external analysis packages according to conversations. Marking its distinction, Open-TI is the first method capable of conducting exhaustive traffic analysis from scratch - spanning from map data acquisition to the eventual execution in complex simulations. Besides, Open-TI can conduct task-specific embodiment like training and adapting the traffic signal control policies (TSC), explore demand optimizations, etc. Furthermore, we explored the viability of LLMs directly serving as control agents, by understanding the expected intentions from Open-TI, we designed an agent-to-agent communication mode to support Open-TI conveying messages to ChatZero (control agent), and then the control agent would choose from the action space to proceed the execution. We provide the code including the implementation structure and will invite further community-driven enhancements, the demo video is available: https://youtu.be/QLiMvyXaQeM.