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 the Zed LLM provider to fetch the available models from
the server instead of hard-coding them in the binary.
Release Notes:
- Updated the Zed provider to fetch the list of available language
models from the server.
I noticed the discussion in #28881, and had thought of exactly the same
a few days prior.
This implementation should preserve existing functionality fairly well.
I've added a dependency (serde_with) to allow the deserializer to skip
models which cannot be deserialized, which could occur if a future
provider, for instance, is added. Without this modification, such a
change could break all models. If extra dependencies aren't desired, a
manual implementation could be used instead.
- Closes#29369
Release Notes:
- Dynamically detect available Copilot Chat models, including all models
with tool support
---------
Co-authored-by: AidanV <aidanvanduyne@gmail.com>
Co-authored-by: imumesh18 <umesh4257@gmail.com>
Co-authored-by: Bennet Bo Fenner <bennet@zed.dev>
Co-authored-by: Agus Zubiaga <hi@aguz.me>
This PR makes it so we send up an `x-zed-version` header with the
client's version when making a request to llm.zed.dev for edit
predictions and completions.
Release Notes:
- N/A
This PR adds an error message when the model requests limit has been
hit.
Release Notes:
- N/A
Co-authored-by: Oleksiy Syvokon <oleksiy.syvokon@gmail.com>
This adds a "workspace-hack" crate, see
[mozilla's](https://hg.mozilla.org/mozilla-central/file/3a265fdc9f33e5946f0ca0a04af73acd7e6d1a39/build/workspace-hack/Cargo.toml#l7)
for a concise explanation of why this is useful. For us in practice this
means that if I were to run all the tests (`cargo nextest r
--workspace`) and then `cargo r`, all the deps from the previous cargo
command will be reused. Before this PR it would rebuild many deps due to
resolving different sets of features for them. For me this frequently
caused long rebuilds when things "should" already be cached.
To avoid manually maintaining our workspace-hack crate, we will use
[cargo hakari](https://docs.rs/cargo-hakari) to update the build files
when there's a necessary change. I've added a step to CI that checks
whether the workspace-hack crate is up to date, and instructs you to
re-run `script/update-workspace-hack` when it fails.
Finally, to make sure that people can still depend on crates in our
workspace without pulling in all the workspace deps, we use a `[patch]`
section following [hakari's
instructions](https://docs.rs/cargo-hakari/0.9.36/cargo_hakari/patch_directive/index.html)
One possible followup task would be making guppy use our
`rust-toolchain.toml` instead of having to duplicate that list in its
config, I opened an issue for that upstream: guppy-rs/guppy#481.
TODO:
- [x] Fix the extension test failure
- [x] Ensure the dev dependencies aren't being unified by Hakari into
the main dependencies
- [x] Ensure that the remote-server binary continues to not depend on
LibSSL
Release Notes:
- N/A
---------
Co-authored-by: Mikayla <mikayla@zed.dev>
Co-authored-by: Mikayla Maki <mikayla.c.maki@gmail.com>
Closes#16544
Release Notes:
- Added support for AWS Bedrock to the Assistant.
---------
Co-authored-by: Marshall Bowers <git@maxdeviant.com>
Co-authored-by: Anthony <anthony@zed.dev>
Co-authored-by: Anthony Eid <hello@anthonyeid.me>
This PR removes the dependents of the `language_models` crate.
The following types have been moved from `language_models` to
`language_model` to facilitate this:
- `LlmApiToken`
- `RefreshLlmTokenListener`
- `MaxMonthlySpendReachedError`
- `PaymentRequiredError`
With this change only `zed` now depends on `language_models`.
Release Notes:
- N/A
This PR moves the `report_assistant_event` function from the
`language_models` crate to the `language_model` crate.
This allows us to drop some dependencies on `language_models`.
Release Notes:
- N/A
This PR adds a new `CredentialsProvider` trait that abstracts over
interacting with the system keychain.
We had previously introduced a version of this scoped just to Zed auth
in https://github.com/zed-industries/zed/pull/11505.
However, after landing https://github.com/zed-industries/zed/pull/25123,
we now have a similar issue with the credentials for language model
providers that are also stored in the keychain (and thus also produce a
spam of popups when running a development build of Zed).
This PR takes the existing approach and makes it more generic, such that
we can use it everywhere that we need to read/store credentials in the
keychain.
There are still two credential provider implementations:
- `KeychainCredentialsProvider` will interact with the system keychain
(using the existing GPUI APIs)
- `DevelopmentCredentialsProvider` will use a local file on the file
system
We only use the `DevelopmentCredentialsProvider` when:
1. We are running a development build of Zed
2. The `ZED_DEVELOPMENT_AUTH` environment variable is set
- I am considering removing the need for this and making it the default,
but that will be explored in a follow-up PR.
Release Notes:
- N/A
- Added support for DeepSeek as a new language model provider in Zed
Assistant
- Implemented streaming API support for real-time responses from
DeepSeek models.
- Added a configuration UI for DeepSeek API key management and settings.
- Updated documentation with detailed setup instructions for DeepSeek
integration.
- Added DeepSeek-specific icons and model definitions for seamless
integration into the Zed UI.
- Integrated DeepSeek into the language model registry, making it
available alongside other providers like OpenAI and Anthropic.
Release Notes:
- Added support for DeepSeek to the Assistant.
---------
Co-authored-by: Marshall Bowers <git@maxdeviant.com>
This PR adds a new `language_models` crate to house the various language
model providers.
By extracting the provider definitions out of `language_model`, we're
able to remove `language_model`'s dependency on `editor`, which improves
incremental compilation when changing `editor`.
Release Notes:
- N/A
