Cline SDK v0.0.69Cline SDK v0.0.69

Cline SDK v0.0.69 introduces a small but practical change for developers who pair the coding agent with locally hosted language models: the response-start timeout for Ollama now defaults to five minutes rather than 30 seconds. The adjustment targets a common failure mode in which large models that must load into memory before serving their first token were being cut off prematurely, and it matters because local inference has become a mainstream option for teams that want to keep code and prompts off third-party servers.

The core issue is what happens during a "cold load." When a model is not already resident in memory, Ollama has to read its weights from disk and place them into RAM or GPU memory before it can begin generating. For larger models, that initial load can take considerably longer than a warm request that reuses an already-loaded model. Under the previous default, the agent would wait only 30 seconds for the response to begin, which was often too short for a big model starting from cold. The request would fail before the model had even finished loading, producing an error that looked like a timeout even though the server was working normally. Raising the default to five minutes gives those models room to finish loading and start responding.

Importantly, the change is scoped to the response-start window and is described as preserving the other safeguards that were already in place. Unreachable servers still fail immediately, so a wrong address, a stopped Ollama process, or a refused connection does not force the user to wait out the longer window before seeing an error. Requests remain cancelable, meaning a developer who no longer wants to wait can stop the operation rather than being locked into the extended timeout. And an explicit requestTimeoutMs is still honored, so anyone who sets a specific timeout value keeps full control and can override the new default in either direction. In other words, the longer default only applies where a value has not been specified, and it does not weaken the fast-fail behavior for genuinely broken connections.

For readers less familiar with the tooling, Cline is an open-source AI coding agent that operates inside the editor and can read files, run commands, and make multi-step changes under user supervision. It is model-agnostic, connecting to a range of providers, and its support for Ollama is what makes fully local workflows possible. Ollama is a widely used runtime for running open-weight models such as those in the Llama, Qwen, Mistral, and Gemma families on a developer's own machine. The combination appeals to users who want privacy, offline capability, or freedom from per-token API costs, though it comes with the tradeoff of local hardware limits and the load-time behavior that this release addresses.

The distinction between a response-start timeout and an overall request timeout is worth underlining, because conflating the two is a frequent source of confusion. A response-start timeout governs how long the client waits for the first sign of output, while an overall or per-request timeout can cap the total duration of a generation. A model that takes minutes to load but then streams quickly is very different from one that hangs indefinitely, and setting the start window generously while keeping cancellation and explicit limits available is a reasonable way to accommodate the former without ignoring the latter.

This kind of tuning reflects a broader pattern across local-inference tooling, where projects such as LM Studio, llama.cpp, and Ollama itself continue to refine defaults around model loading, memory management, and keep-alive behavior so that already-loaded models stay warm. As open-weight models grow in size and more developers run them on laptops and workstations, the gap between cold and warm performance becomes more visible, and client defaults that assume cloud-like response times can misfire. Adjusting a default from 30 seconds to five minutes is not a headline feature, but it appears aimed squarely at reducing spurious failures for users of larger local models. Developers relying on Cline with Ollama are likely to see fewer premature timeouts after upgrading, while retaining the ability to configure the behavior to their own environment.