Map OpenAI's context_length_exceeded errors (both the in-stream error
events sent by the Responses API / ChatGPT Codex backend and HTTP 400
responses carrying the code) to
LanguageModelCompletionError::PromptTooLarge. This stops pointless
retries and surfaces the dedicated token-limit UI instead of a raw error
string.
Also record a synthetic request token usage when a completion fails with
PromptTooLarge, so the context indicator reports Exceeded rather than
the stale usage of the last successful request, and the next prompt
qualifies for auto-compaction.
Ought to help a bit with
https://github.com/zed-industries/zed/issues/59600
Release Notes:
- N/A or Added/Fixed/Improved ...
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
Co-authored-by: Christopher Biscardi <chris@christopherbiscardi.com>
Add new GPT 5.6 models
Release Notes:
- open_ai: Added support for GPT 5.6 Sol/Terra/Luna
---------
Co-authored-by: Christopher Biscardi <chris@christopherbiscardi.com>
# Objective
Allow zed's language model stack to express OpenAI Responses API custom
tools (freeform text-input tools with an optional lark/regex grammar),
so downstream consumers can offer tools like a freeform `apply_patch` to
GPT models.
## Solution
- `LanguageModelRequestTool` now carries a Function-vs-Custom input
variant; `LanguageModelCustomToolFormat` models text/grammar formats.
- `LanguageModelToolUse.input` becomes a typed
`LanguageModelToolUseInput::{Json, Text}`. Serialization is tagged so
persisted Text inputs round-trip losslessly; legacy plain JSON values
still deserialize as `Json`.
- `open_ai` gains the custom tool wire types (tool definition,
`custom_tool_call`/`custom_tool_call_output` input items with
string-or-content-part outputs, output item, and
`custom_tool_call_input` delta/done stream events). The Responses event
mapper accumulates raw text deltas into `ToolUse` events, and history
replay derives custom-vs-function tool results from the matching
`ToolUse` by id.
- All non-OpenAI providers and the Chat Completions path error
explicitly when a request contains custom tools — no silent drops or
empty-schema coercions.
Release Notes:
- N/A
Adds native support for AWS Bedrock's Mantle endpoint
(`bedrock-mantle`), which serves models with no `Converse`/`Invoke`
support on `bedrock-runtime`, such as GPT-5.5, GPT-5.4, and Grok 4.3 but
more importantly **open-weight** models
Closes#60471
## What's changed
- Renamed the existing `Model` enum in the `bedrock` crate to
`ConverseModel`, and added a new `MantleModel` enum for Mantle-only
models. Mantle models reuse the existing OpenAI-compatible Chat
Completions/Responses request and response plumbing
(`into_open_ai`/`into_open_ai_response`,
`OpenAiEventMapper`/`OpenAiResponseEventMapper`) already used by the
native OpenAI and OpenAI-compatible providers, rather than introducing
new marshalling code.
- Added a `BedrockMantleModel` language model that routes requests to
the `bedrock-mantle` endpoint, dispatching to Chat Completions or the
Responses API depending on the model. Mantle models appear in the model
picker alongside Converse models under the same Bedrock provider.
- Added region gating: `bedrock-mantle` is only available in a subset of
AWS Regions, so using a Mantle model outside of them surfaces a clear
error naming the current Region and the supported ones, instead of an
opaque HTTP failure.
- Implemented Bedrock bearer token authentication for Mantle requests: a
configured Bedrock API key is used as-is, and every other auth method
(IAM credentials, named profile, SSO, automatic) derives a short-term
token by locally SigV4-presigning a `CallWithBearerToken` request. This
requires no extra network round trip and no token caching, since
re-signing locally is cheap.
- Added a specific error for the 403 you get when your credentials have
`bedrock:CallWithBearerToken` but not the separate
`bedrock-mantle:CallWithBearerToken` permission Mantle models require,
since this is the most common misconfiguration.
- Added a `mantle_available_models` setting so custom models served
through `bedrock-mantle` can be configured, the same way other providers
support custom models via `available_models`.
- Documented Mantle models and the new setting in the Amazon Bedrock
section of [Use a
Gateway](https://zed.dev/docs/ai/use-a-gateway#amazon-bedrock).
## Testing
- Added unit tests covering: the local SigV4 bearer-token signing
(including a byte-for-byte cross-check against a reference
implementation), Mantle endpoint URL construction, the
Mantle-supported-regions list, thinking-effort normalization, and the
settings-to-model protocol mapping.
- `cargo test -p bedrock -p language_models -p settings_content -p
settings` passes.
- `./script/clippy` passes with no new warnings.
Release Notes:
- Added native support for AWS Bedrock's Mantle endpoint, enabling
GPT-5.5, GPT-5.4, and Grok 4.3 through the Amazon Bedrock provider.
# Objective
More and more models are supporting the `max` reasoning effort (DeepSeek
V4, GLM 5.2, etc) and that was missing from Zed.
## Solution
Added `ReasoningEffort::Max` level and implemented support for that
across providers. For most providers this is a no-op as they don't
support that (`max` is missing from `supported_reasoning_levels()`) but
for some providers this required tiny changes: for OpenCode there is now
a proper difference between `xhigh` and `max` (this bug was actually
what triggered this whole PR) and for DeepSeek reasoning levels were
migrated from a custom `deepseek::ReasoningEffort`.
## Testing
Tested and confirmed working with a simple _"rename this variable for
me. add a function. delete the function"_ test across a few providers
(OpenCode, GitHub Copilot, and DeepSeek).
## Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [x] The content adheres to Zed's UI standards
([UX/UI](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
and
[icon](https://github.com/zed-industries/zed/blob/main/crates/icons/README.md)
guidelines)
- [ ] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
## Showcase
N/A
---
Release Notes:
- OpenAI-compatible: added support for `max` reasoning levels
- OpenCode: added support for `max` reasoning levels
# Objective
Fix OpenAI-compatible reasoning/thinking support. Previously,
`OpenAiCompatibleLanguageModel` never reported thinking support, so
configured `reasoning_effort` values did not reliably reach the wire and
Responses API reasoning state could miss `include:
["reasoning.encrypted_content"]`.
Fixes#58289
Addresses #59207 for OpenAI-compatible Responses models configured with
`reasoning_effort`.
## Solution
- Treat a non-`none` configured `reasoning_effort` as OpenAI-compatible
thinking support.
- Expose common OpenAI-style effort levels in the agent UI, using the
configured effort as the default.
- Honor selected reasoning effort for OpenAI-compatible chat-completions
requests.
- Send `reasoning_effort: "none"` when thinking is disabled for a
configured reasoning model.
- Preserve native OpenAI behavior by continuing to use native
model-specific supported efforts and `max_completion_tokens`.
- Add an OpenAI-compatible `max_tokens_parameter` capability for
endpoints that expect output limits as `max_tokens`.
- Parse common streamed thinking fields (`reasoning` and
`reasoning_content`).
- Add provider setup UI and docs for configuring OpenAI-compatible
reasoning models.
## Testing
- `cargo fmt -p language_model_core -p agent_ui -p language_models`
- `cargo test -p language_models provider::open_ai_compatible::tests
--lib`
- `cargo test -p language_models provider::open_ai::tests --lib`
- `cargo test -p open_ai completion::tests --lib`
- `cargo test -p agent_ui
agent_configuration::add_llm_provider_modal::tests --lib`
- `git --no-pager diff --check`
## Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [x] The content adheres to Zed's UI standards
([UX/UI](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
and
[icon](https://github.com/zed-industries/zed/blob/main/crates/icons/README.md)
guidelines)
- [x] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
---
Release Notes:
- Improved OpenAI-compatible provider setup for reasoning models.
---------
Co-authored-by: Anant Goel <anant@zed.dev>
Adds provider-side context compaction support to the Anthropic and
OpenAI API clients. Client plumbing only; not wired into the UI.
Release Notes:
- N/A
Add a `custom_headers` setting to each HTTP-based language model
provider
(Anthropic, Bedrock, DeepSeek, Google, LM Studio, Mistral, Ollama,
OpenAI,
OpenAI-compatible, OpenCode, OpenRouter, Vercel AI Gateway, and xAI) so
users
can attach extra headers to every outgoing request. Headers managed by
Zed
(authentication, content-type, etc.) cannot be overridden and are
skipped with
a warning.
Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [x] The content is consistent with the [UI/UX
checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
- [] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
Release Notes:
- Added support for configuring custom HTTP headers on language model
providers via `language_models.<provider>.custom_headers`.
Signed-off-by: Aurabindo Pillai <mail@aurabindo.in>
## Summary
This started from #57636, after we saw ChatGPT subscription/Codex
requests stall over the past week. OpenCode v1.15.11 shipped related
resilience fixes for the same class of Codex subscription endpoint
issues, so this ports the relevant pieces into Zed's native ChatGPT
subscription provider.
When Zed asks ChatGPT/Codex for a response, sometimes the server
connection can get stuck before it even sends the first response
headers. Before this PR, Zed could wait indefinitely, which looks like
OpenCode/Zed “stalling.”
This PR makes Zed:
- Wait up to 10 seconds for the server to start responding.
- If nothing comes back in that window, treat it as a temporary
network/API failure.
- Let the existing retry logic try again instead of leaving the user
stuck.
- Send a stable session-id header so OpenAI’s Codex backend can
associate requests with the same Zed agent thread.
- Add tests to make sure:
- stuck-before-response requests time out,
- normal slow streaming responses are not cut off,
- ChatGPT subscription requests send the right session header,
- the agent retries this kind of failure.
intended user-facing result is: fewer “the assistant is just sitting
there forever” failures when using ChatGPT subscription models.
## Verification
- cargo test -p open_ai responses
- cargo test -p language_models openai_subscribed
- cargo test -p agent test_send_retry_on_http_send_error
- cargo check -p open_ai
- cargo check -p language_models
- cargo check -p agent
Release Notes:
- Fixed ChatGPT subscription requests stalling indefinitely before
response headers arrive.
Same mechanism as for BYOK: `service_tier == priority`. Most of the work
is already done. When validating this in manual testing, I noticed we
get back `service_tier == auto` in the response, unlike in the regular
OpenAI API scenario with BYOK, but apparently [it doesn't mean priority
tier wasn't
applied](https://github.com/openai/codex/issues/14204#issuecomment-4033184620).
It's not a hard confirmation, but the model does seem to respond faster
when I toggle fast mode on.
Release Notes:
- Added Fast Mode (priority service tier) support to OpenAI models used
through the ChatGPT subscription provider.
Maps the existing `Speed::Fast` plumbing to OpenAI's `service_tier:
"priority"`, which matches what "fast mode" in Codex does. Relevant docs
[here](https://platform.openai.com/docs/api-reference/chat/create#chat-create-service_tier).
Like for the existing Anthropic fast mode we have a
`Model::supports_priority` method for the variants on
https://openai.com/api-priority-processing. Pro, nano, and legacy gpt-4
are excluded; Custom defaults to false.
This is gated to staff only for now (not in this diff, but the existing
fast mode feature), until we have the mechanism to require confirmation
before you enable fast mode.
Release Notes:
- Added support for Fast Mode (priority service tier) on the OpenAI API
provider.
For long threads we will spend more and more time cloning the messages
just to save them to the database, as we need a copy of everything to do
so asynchronously. Messages are really expensive to clone though and we
accumulate a lot of them really fast, so even for smaller threads we
start seeing pauses in the millisecond range. The fix to this is fairly
simple though, we never mutate the messages once pushed to the vec, so
just Arc them.
This PR also slightly changes `UserMessage` to be a bit faster to clone
as well.
Release Notes:
- Fixed a cause of stutters when interacting with the agent
I tried to use google cloud to test gemma4 and compare with the result
of ollama. it had response such as
```json
{"choices":[{"delta":{"content":"Hello","reasoning_content":null,"role":null,"tool_calls":null},"finish_reason":null,"index":0,"logprobs":null,"matched_stop":null}],"created":1778081610,"id":"KV_7adz7Ov20xN8Py-angQ8","model":"google/gemma-4-26b-a4b-it-maas","object":"chat.completion.chunk","usage":{"extra_properties":{"google":{"traffic_type":"ON_DEMAND"}}}}
```
(notice that, while "usage" is present, it does not have any of the
usual value)
Eventually, I had some more issue when parsing the response (unrelated
to this), so I decided to try the google ai endpoint, with its own set
of issue.
Those simple change should only loosen the accepted format, so no new
compatibility error are expected (but I haven’t tried with other
provider)
Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [ ] The content is consistent with the [UI/UX
checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
(no change)
- [ ] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
Release Notes:
- Improved open-ai compatibility when token usage info is absent
Adds a new language model provider that lets users authenticate with
their ChatGPT Plus/Pro subscription and use OpenAI models
(codex-mini-latest, o4-mini, o3) directly in the Zed agent — without
needing a separate API key.
## How it works
1. **OAuth 2.0 + PKCE sign-in**: Uses OpenAI's official Codex CLI client
ID to run an authorization code flow. A local HTTP server on
`127.0.0.1:1455` captures the callback, exchanges the code for tokens,
and stores them in the system keychain.
2. **Token refresh**: Access tokens are automatically refreshed when
they're within 5 minutes of expiry, using the stored refresh token.
3. **Responses API**: Requests go to
`https://chatgpt.com/backend-api/codex/responses` using the existing
`open_ai::responses` client (Responses API format, not Chat Completions
which was deprecated for this endpoint in Feb 2026).
4. **Required headers**: `originator: zed`, `OpenAI-Beta:
responses=experimental`, `ChatGPT-Account-Id` (extracted from JWT),
`store: false` in the body.
## Files changed
- `crates/open_ai/src/responses.rs`: Add `store: Option<bool>` field to
`Request`; add `extra_headers` param to `stream_response` for
per-provider header injection
- `crates/language_models/src/provider/openai_subscribed.rs`: New
provider (sign-in UI, OAuth flow, token storage/refresh, model list)
- `crates/language_models/src/provider/open_ai.rs`,
`open_ai_compatible.rs`, `opencode.rs`: Pass `vec![]` for new
`extra_headers` param
- `crates/language_models/src/language_models.rs`: Register the new
provider
- `crates/language_models/Cargo.toml`: Add `rand` and `sha2` deps for
PKCE
## Open questions / known gaps
- [ ] **Terms of service**: Usage appears to be within OpenAI's ToS
(interactive use via their official CLI client ID), but needs legal
sign-off before shipping
- [ ] **Redirect URI**: Currently `http://localhost:1455/auth/callback`
— may need to match exactly what OpenAI's Codex CLI uses
- [ ] **UI polish**: The sign-in card is functional but minimal; needs
design review
- [ ] **Error messages**: OAuth error responses from the callback URL
aren't surfaced to the user yet
- [ ] **`o3` availability**: o3 may require a higher subscription tier;
consider gating it
## Testing
Sign-in flow was designed to match the Copilot Chat provider pattern.
Manual testing against the live OAuth endpoint is needed.
Release Notes:
- Added ChatGPT subscription provider, allowing users to use their
ChatGPT Plus/Pro subscription with the Zed agent
---------
Co-authored-by: Zed Zippy <234243425+zed-zippy[bot]@users.noreply.github.com>
Co-authored-by: Richard Feldman <richard@zed.dev>
Co-authored-by: Richard Feldman <oss@rtfeldman.com>
Co-authored-by: Agus Zubiaga <agus@zed.dev>
Release Notes:
- Fixed: DashScope (Aliyun) tool calls now preserve id and name across
streaming delta chunks
---------------------------------------------------------------------------------------------------
Aliyun (DashScope) SSE streaming sends id="" and name="" in
subsequent tool_calls delta chunks after the first chunk. Previously
these empty strings would unconditionally overwrite the accumulated
id and name values, causing tool calls to lose identity and fail.
Add is_empty() guards so id and name are only updated when the
delta provides a non-empty value (falsy guard pattern), matching how
Hermes Agent and OpenAI SDK handle this provider edge case.
Test stream_maps_preserves_tool_id_and_name_across_empty_deltas
simulates DashScope's actual streaming behavior and asserts that
the completed ToolUse retains the correct id, name, and arguments.
Files changed: 1 (+148/-2)
- crates/open_ai/src/completion.rs
CLA signed.
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
<img width="980" height="1392" alt="CleanShot 2026-04-26 at 00 03 20@2x"
src="https://github.com/user-attachments/assets/428a845b-82a0-44eb-9e43-1a351de6ca6a"
/>
After FIx
<img width="900" height="1398" alt="CleanShot 2026-04-26 at 00 02 15@2x"
src="https://github.com/user-attachments/assets/604e36fd-bf90-4549-9e60-8a927033d3e9"
/>
---------
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [x] The content is consistent with the [UI/UX
checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
- [x] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
Closes #ISSUE
Release Notes:
- N/A
From the
[docs](https://developers.openai.com/api/docs/guides/migrate-to-responses#responses-benefits):
> Better performance: Using reasoning models, like GPT-5, with Responses
will result in better model intelligence when compared to Chat
Completions. Our internal evals reveal a 3% improvement in SWE-bench
with same prompt and setup.
Agentic by default: The Responses API is an agentic loop, allowing the
model to call multiple tools, like web_search, image_generation,
file_search, code_interpreter, remote MCP servers, as well as your own
custom functions, within the span of one API request.
Lower costs: Results in lower costs due to improved cache utilization
(40% to 80% improvement when compared to Chat Completions in internal
tests).
Stateful context: Use store: true to maintain state from turn to turn,
preserving reasoning and tool context from turn-to-turn.
Flexible inputs: Pass a string with input or a list of messages; use
instructions for system-level guidance.
Encrypted reasoning: Opt-out of statefulness while still benefiting from
advanced reasoning.
Future-proof: Future-proofed for upcoming models.
Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [x] The content is consistent with the [UI/UX
checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
- [ ] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
Closes #ISSUE
Release Notes:
- Always use Responses API for OpenAI models
## Summary
Users who add custom OpenAI models under
`language_models.openai.available_models` can set `capabilities.images:
true` to declare that the endpoint accepts image inputs. Today, that
setting is silently ignored: the Agent panel's image-attach button stays
disabled regardless, and the only workaround is to switch to a built-in
OpenAI model, attach the image, and switch back.
Root cause: `Model::Custom` does not carry a `supports_images` field,
and the OpenAI provider's `supports_images()` for the `Custom` arm
hardcodes `false`.
## Changes
1. `crates/settings_content/src/language_model.rs`: add `images: bool`
to `OpenAiModelCapabilities` with `#[serde(default)]` so existing
settings.json files keep working unchanged.
2. `crates/open_ai/src/open_ai.rs`: add `supports_images: bool` to
`Model::Custom` with a matching serde default.
3. `crates/language_models/src/provider/open_ai.rs`: pass
`model.capabilities.images` into the `Model::Custom` variant in
`provided_models`, and return the stored value from `supports_images()`
for `Custom`.
Existing `Model::Custom { .. }` match sites (`completion.rs:829`,
various in `open_ai.rs`) all use `..` so they continue to compile
without change.
## Testing
- `cargo check -p settings_content -p open_ai -p language_models`:
clean.
- I was not able to complete `./script/clippy` locally: the build
stalled on the first-time `webrtc-sys` download for livekit-rust-sdks
(TLS close_notify failure on docs.rs mirror). Happy to rerun once CI has
cached artifacts.
- Manually verified the capability plumbing by tracing: settings.json ->
`OpenAiModelCapabilities.images` -> `Model::Custom { supports_images }`
-> `supports_images()` -> `Thread::prompt_capabilities` ->
`SessionCapabilities.supports_images()` -> `build_add_context_menu` gate
in `thread_view.rs`.
## Related Issues
Closes#50752
Release Notes:
- Fixed custom OpenAI models ignoring the `capabilities.images` setting
in `language_models.openai.available_models`.
This contribution was developed with AI assistance (Codex).
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Drop the `count_tokens` API and related implementations across
providers, and remove the unused `tiktoken-rs` dependency.
I was going to update the dependency becuase they finally released a fix
we needed. But then I realized we only used this api in one place, the
Rules library. And for most models it would have been wildly incorrect
becuase we use tiktoken, i.e. OpenAI tokenizers, for almost every model,
which is going to give incorrect results.
Given that, I just removed these because the difference in how we get
these has caused plenty of confusion in the past.
Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments
- [x] The content is consistent with the [UI/UX
checklist](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#uiux-checklist)
- [x] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
Release Notes:
- N/A
Release Notes:
- Added interleaved_reasoning option to openai compatible models
---
This PR adds the interleaved_reasoning option for OpenAI-compatible
models, addressing the issue described in
https://github.com/ggml-org/llama.cpp/issues/20837.
In my testing, enabling interleaved_reasoning not only resolved the
tool-calling issues encountered by Qwen3.5 models in llama.cpp, but also
appeared to improve the model's coding capabilities. I have also
verified the outgoing requests using a proxy to ensure the parameter is
being sent correctly.It is also likely that this change will benefit
other models and providers as well.
Note: While I used AI to assist with the implementation, I have reviewed
and tested the changes. As I am relatively new to Rust and the Zed
codebase, I would appreciate any feedback or suggestions for
improvement. I am happy to make further adjustments if needed.
Thank you all for building such an amazing editor!
Co-authored-by: Oleksiy Syvokon <oleksiy@zed.dev>
- `language_model` no longer depends on provider-specific crates such as
`anthropic` and `open_ai` (inverted dependency)
- `language_model_core` was extracted from `language_model` which
contains the types for the provider-specific crates to convert to/from.
- `gpui::SharedString` has been extracted into its own crate (still
exposed by `gpui`), so `language_model_core` and provider API crates
don't have to depend on `gpui`.
- Removes some unnecessary `&'static str` | `SharedString` -> `String`
-> `SharedString` conversions across the codebase.
- Extracts the core logic of the cloud `LanguageModelProvider` into its
own crate with simpler dependencies.
Release Notes:
- N/A
---------
Co-authored-by: John Tur <john-tur@outlook.com>
Allow function call outputs to carry either plain text or a list
of input content items, so image tool results are serialized as
image content instead of a raw base64 string.
Release Notes:
- N/A
## Summary
This PR enables token usage reporting in streaming responses for
OpenAI-compatible APIs (OpenAI, xAI/Grok, OpenRouter, etc).
## Problem
Currently, the token counter UI in the Agent Panel doesn't display usage
for some OpenAI-compatible providers because they don't return usage
data during streaming by default. According to OpenAI's API
documentation, the `stream_options.include_usage` parameter must be set
to `true` to receive usage statistics in streaming responses.
## Solution
- Added StreamOptions struct with `include_usage` field to the open_ai
crate
- Added `stream_options` field to the Request struct
- Automatically set `stream_options: { include_usage: true }` when
`stream: true`
- Updated edit_prediction requests with `stream_options: None`
(non-streaming)
## Testing
Tested with xAI Grok models - token counter now correctly shows usage
after sending a message.
## References
- [OpenAI Chat Completions API -
stream_options](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stream_options)
- [xAI API Documentation](https://docs.x.ai/api)
Add GPT-5.4 and GPT-5.4-pro as Bring Your Own Key model options for the
OpenAI provider.
**GPT-5.4** (`gpt-5.4`):
- 1,050,000 token context window, 128K max output
- Supports chat completions, images, parallel tool calls
- Default reasoning effort: none
**GPT-5.4-pro** (`gpt-5.4-pro`):
- 1,050,000 token context window, 128K max output
- Responses API only (no chat completions)
- Default reasoning effort: medium (supports medium/high/xhigh)
Also fixes context window sizes for GPT-5 mini and GPT-5 nano (272K →
400K) to match current OpenAI docs.
Closes AI-78
Release Notes:
- Added GPT-5.4 and GPT-5.4-pro as available models when using your own
OpenAI API key.
Adds `gpt-5.3-codex` as a built-in model under the OpenAI provider for
BYOK usage.
Model specs:
- 400,000 context window
- 128,000 max output tokens
- Reasoning token support (default medium effort)
- Uses the Responses API (like other codex models)
- Token counting falls back to the gpt-5 tokenizer
Closes AI-59
Release Notes:
- Added support for GPT-5.3-Codex as a bring-your-own-key model in the
OpenAI provider.
Remove GPT-4o, GPT-4.1, GPT-4.1-mini, and o4-mini from BYOK model
options in Zed before OpenAI retires these models.
These models are being retired by OpenAI (ChatGPT workspace support ends
April 3, 2026), so they have been removed from the available models list
in Zed's BYOK provider.
Closes AI-4
Release Notes:
- Removed deprecated GPT-4o, GPT-4.1, GPT-4.1-mini, and o4-mini models
from OpenAI BYOK provider
Support for Kimi K2 Thinking
Release Notes:
- Added support for thinking traces when using OpenAI-API-compatible AI providers
---------
Co-authored-by: Bennet Bo Fenner <bennet@zed.dev>
This PR makes it so we use a proper type for the Responses API `input`
rather than a `serde_json::Value`.
It should have never used `serde_json::Value` to begin with.
Release Notes:
- N/A
Add support for OpenAI's /responses endpoint for models that don't
support /chat/completions API. This enables compatibility with newer
model variants (`gpt-5-codex`, `gpt-5-pro`, `o3-pro`, etc) while
maintaining compatibility with existing configs
Changes:
- Add `supports_chat_completions` flag to model capabilities that
defaults to true for existing behavior
- Implement responses API client with streaming support as per [OpenAI
documentation](https://app.stainless.com/api/spec/documented/openai/openapi.documented.yml).
- Add `ResponseEventMapper` to convert responses events to completion
events for maintainer simplicity
- Update UI to allow toggling `chat_completions` capability
- Add `gpt-5-codex` model
Closes#38858
Release Notes:
- Added support for `gpt-5-codex` model
---------
Co-authored-by: Bennet Bo Fenner <bennet@zed.dev>
Automatically retry the agent's LLM completion requests when the
provider returns 429 Too Many Requests. Uses the Retry-After header to
determine the retry delay if it is available.
Many providers are frequently overloaded or have low rate limits. These
providers are essentially unusable without automatic retries.
Tested with Cerebras configured via openai_compatible.
Related: #31531
Release Notes:
- Added automatic retries for OpenAI-compatible LLM providers
---------
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
I am using an Azure OpenAI instance since that is what is provided at
work and with how they have it setup not all responses contain a delta,
which lead to errors and truncated responses. This is related to how
they are filtering potentially offensive requests and responses. I don't
believe this filter was made in-house, instead I believe it is provided
by Microsoft/Azure, so I suspect this fix may help other users.
Release Notes:
- N/A
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
We've been considering removing workspace-hack for a couple reasons:
- Lukas ran into a situation where its build script seemed to be causing
spurious rebuilds. This seems more likely to be a cargo bug than an
issue with workspace-hack itself (given that it has an empty build
script), but we don't necessarily want to take the time to hunt that
down right now.
- Marshall mentioned hakari interacts poorly with automated crate
updates (in our case provided by rennovate) because you'd need to have
`cargo hakari generate && cargo hakari manage-deps` after their changes
and we prefer to not have actions that make commits.
Currently removing workspace-hack causes our workspace to grow from
~1700 to ~2000 crates being built (depending on platform), which is
mainly a problem when you're building the whole workspace or running
tests across the the normal and remote binaries (which is where
feature-unification nets us the most sharing). It doesn't impact
incremental times noticeably when you're just iterating on `-p zed`, and
we'll hopefully get these savings back in the future when
rust-lang/cargo#14774 (which re-implements the functionality of hakari)
is finished.
Release Notes:
- N/A
Co-Authored-By: Ben K <ben@zed.dev>
Co-Authored-By: Anthony <anthony@zed.dev>
Co-Authored-By: Mikayla <mikayla@zed.dev>
Release Notes:
- settings: Major internal changes to settings. The primary user-facing
effect is that some settings which did not make sense in project
settings files are no-longer read from there. (For example the inline
blame settings)
---------
Co-authored-by: Ben Kunkle <ben@zed.dev>
Co-authored-by: Mikayla Maki <mikayla.c.maki@gmail.com>
Co-authored-by: Anthony <anthony@zed.dev>
Follow up: #34532Closes#35434
Mostly fixes a issue were when the tool_choice is none it was getting
serialised as null. This was fixed for openrouter just wanted to follow
up and cleanup for other providers which might have this issue as this
is against the spec.
Release Notes:
- N/A
