aduermael/langdag

langdag is a functional but nascent project (24 stars) attempting to solve LLM orchestration using a DAG model in Go. Its primary value proposition—performance—is a common differentiator for Go-based tools in a Python-dominated ecosystem. However, it faces a massive defensibility hurdle: the LLM orchestration space is saturated with high-velocity projects like LangChain (LangGraph), PydanticAI, and Haystack, which benefit from the vast Python ecosystem of AI libraries. While Go provides better raw execution speed for the DAG logic, the bottleneck in LLM workflows is almost always the network latency of the API calls themselves, rendering the 'high performance' claim marginal for most users. With zero velocity and minimal forks, the project lacks the community momentum required to survive against frontier lab offerings like OpenAI's Assistants API or established enterprise workflow tools like Temporal. The risk of platform domination is high as cloud providers (AWS Step Functions, Azure Logic Apps) and LLM providers increasingly bake DAG-like state management directly into their SDKs.