This pull request adds full integration with OpenRouter, allowing users
to access a wide variety of language models through a single API key.
**Implementation Details:**
* **Provider Registration:** Registers OpenRouter as a new language
model provider within the application's model registry. This includes UI
for API key authentication, token counting, streaming completions, and
tool-call handling.
* **Dedicated Crate:** Adds a new `open_router` crate to manage
interactions with the OpenRouter HTTP API, including model discovery and
streaming helpers.
* **UI & Configuration:** Extends workspace manifests, the settings
schema, icons, and default configurations to surface the OpenRouter
provider and its settings within the UI.
* **Readability:** Reformats JSON arrays within the settings files for
improved readability.
**Design Decisions & Discussion Points:**
* **Code Reuse:** I leveraged much of the existing logic from the
`openai` provider integration due to the significant similarities
between the OpenAI and OpenRouter API specifications.
* **Default Model:** I set the default model to `openrouter/auto`. This
model automatically routes user prompts to the most suitable underlying
model on OpenRouter, providing a convenient starting point.
* **Model Population Strategy:**
* <strike>I've implemented dynamic population of available models by
querying the OpenRouter API upon initialization.
* Currently, this involves three separate API calls: one for all models,
one for tool-use models, and one for models good at programming.
* The data from the tool-use API call sets a `tool_use` flag for
relevant models.
* The data from the programming models API call is used to sort the
list, prioritizing coding-focused models in the dropdown.</strike>
* <strike>**Feedback Welcome:** I acknowledge this multi-call approach
is API-intensive. I am open to feedback and alternative implementation
suggestions if the team believes this can be optimized.</strike>
* **Update: Now this has been simplified to one api call.**
* **UI/UX Considerations:**
* <strike>Authentication Method: Currently, I've implemented the
standard API key input in settings, similar to other providers like
OpenAI/Anthropic. However, OpenRouter also supports OAuth 2.0 with PKCE.
This could offer a potentially smoother, more integrated setup
experience for users (e.g., clicking a button to authorize instead of
copy-pasting a key). Should we prioritize implementing OAuth PKCE now,
or perhaps add it as an alternative option later?</strike>(PKCE is not
straight forward and complicated so skipping this for now. So that we
can add the support and work on this later.)
* <strike>To visually distinguish models better suited for programming,
I've considered adding a marker (e.g., `</>` or `🧠`) next to their
names. Thoughts on this proposal?</strike>. (This will require a changes
and discussion across model provider. This doesn't fall under the scope
of current PR).
* OpenRouter offers 300+ models. The current implementation loads all of
them. **Feedback Needed:** Should we refine this list or implement more
sophisticated filtering/categorization for better usability?
**Motivation:**
This integration directly addresses one of the most highly upvoted
feature requests/discussions within the Zed community. Adding OpenRouter
support significantly expands the range of AI models accessible to
users.
I welcome feedback from the Zed team on this implementation and the
design choices made. I am eager to refine this feature and make it
available to users.
ISSUES: https://github.com/zed-industries/zed/discussions/16576
Release Notes:
- Added support for OpenRouter as a language model provider.
---------
Signed-off-by: Umesh Yadav <umesh4257@gmail.com>
Co-authored-by: Marshall Bowers <git@maxdeviant.com>
This PR updates how we handle Ollama responses, leveraging the new
[v0.9.0](https://github.com/ollama/ollama/releases/tag/v0.9.0) release.
Previously, thinking text was embedded within the model's main content,
leading to it appearing directly in the agent's response. Now, thinking
content is provided as a separate parameter, allowing us to display it
correctly within the agent panel, similar to other providers. I have
tested this with qwen3:8b and works nicely. ~~We can release this once
the ollama is release is stable.~~ It's released now as stable.
<img width="433" alt="image"
src="https://github.com/user-attachments/assets/2983ef06-6679-4033-82c2-231ea9cd6434"
/>
Release Notes:
- Add thinking support for ollama
---------
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
https://github.com/zed-industries/zed/pull/31668 renamed
`CompletionMode::Max` to `CompletionMode::Burn` which is a good change,
but this broke the deserialization for threads whose completion mode was
stored in LMDB. This adds a deserialization alias so that both values
work.
We could make a full new `SerializedThread` version which migrates this
value, but that seems overkill for this single change, we can batch that
with more changes later. Also, people in nightly already have some v1
threads with `burn` stored, so it wouldn't quite work for everybody.
Release Notes:
- N/A
Follow up to https://github.com/zed-industries/zed/pull/31470.
I started looking at config and changed preferred_completion_mode to
burn to only find its max so made changes to align it better with
rebrand. As this is in preview build now.
This doesn't touch zed_llm_client. Only the Zed changes the code and doc
to match the new UI of burn mode. There are still more things to be
renamed, though.
Release Notes:
- N/A
---------
Signed-off-by: Umesh Yadav <git@umesh.dev>
Co-authored-by: Danilo Leal <daniloleal09@gmail.com>
