# 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
Closes https://github.com/zed-industries/zed/issues/52576
## Overview
Adds prompt caching support for Anthropic Claude models when accessed
via OpenRouter.
This mirrors Zed's native Anthropic provider by using explicit per-block
`cache_control` breakpoints:
- A long-lived 1-hour breakpoint on the system message's last text
block, covering the tools and system prefix.
- A default 5-minute breakpoint on the last `cache: true` conversation
message.
The implementation intentionally avoids OpenRouter's top-level automatic
`cache_control` field so routing remains available across
Anthropic-compatible providers including Anthropic, Bedrock, and Vertex
AI. It also adds `session_id` sticky routing from the request thread ID
and maps OpenRouter cache usage fields into Zed's token usage
accounting.
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:
- Fixed Anthropic prompt caching when using OpenRouter.
---------
Co-authored-by: Anant Goel <anant@zed.dev>
# 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>
## Summary
Fixes#37815.
`State::fetch_models()` calls `/api/tags` to list models, then calls
`/api/show` for **every** model in that list to get its capabilities,
collecting the results with `collect::<Result<Vec<_>>>()?`. If
`/api/show` errors for even one model, the whole batch fails and
`fetched_models` is never populated. Since `is_authenticated()` is
defined as `!self.fetched_models.is_empty()`, this means a single bad
model permanently breaks both authentication state and the model picker
for the entire Ollama provider - with no error surfaced anywhere (not
the UI, not the logs), which matches the reports in #37815 of "Connect
does nothing" / "no logs, no nothing".
I hit this myself: I had a stale local reference to an Ollama Cloud
model that had been retired server-side. `/api/show` for that one model
returned `410 Gone`, which silently broke Connect and the model picker
for every other model too. Removing the retired model with `ollama rm`
fixed it immediately, which confirmed the root cause.
## Fix
Instead of aborting the whole fetch on the first error, skip individual
models that fail `/api/show` and log a warning, keeping the rest.
Extracted this into a small `skip_failed_models` helper so it's
unit-testable without mocking HTTP.
## Disclosure
I used Claude (Anthropic's Claude Code) to help track down this root
cause (tracing through `fetch_models`/`is_authenticated` in this file)
and draft the fix + tests below. I reviewed and understand the change -
it's a small, targeted fix to a single function plus two unit tests for
the new helper.
## Test plan
- [x] `cargo check -p language_models` passes
- [x] `cargo test -p language_models --lib ollama::` - all 3 tests pass
(the 2 new ones plus the existing
`test_merge_settings_preserves_display_names_for_similar_models`,
unaffected by this change)
- [x] `cargo fmt -p language_models -- --check` - no diff
## Release Notes
Release Notes:
- Fixed Ollama models silently failing to show up in the model picker
(and "Connect" appearing to do nothing) when a single model's details
couldn't be fetched, e.g. a retired Ollama Cloud model
---------
Co-authored-by: MrSubidubi <finn@zed.dev>
Co-authored-by: Ben Brandt <benjamin.j.brandt@gmail.com>
# Objective
Fixes auto-compaction for ChatGPT Subscription / Codex-backed GPT models
by treating the Codex `context_window` as the usable context budget
instead of reserving an inferred `128k` output budget.
- Fixes#59555
## Solution
Zed previously reported `max_output_tokens = 128_000` for
`openai_subscribed` models. Generic agent code subtracts
`max_output_tokens` from `max_token_count` when computing the effective
input budget, so `openai_subscribed` models ended up with:
- `max_token_count = 272_000`
- `max_output_tokens = 128_000`
- effective compaction budget = `144_000`
- default `90%` threshold = `129_600`
## Testing
Tested with a heavy prompt to confirm that the auto compaction is indeed
now triggered at 90% (default)
<img width="385" height="183" alt="image"
src="https://github.com/user-attachments/assets/da569152-f687-4366-868a-a6559b948ce5"
/>
This made Zed compact around the halfway point of the actual Codex
context window.
Codex model metadata exposes `context_window = 272000` and does not
expose a `max_output_tokens` cap. Codex also compacts against the
context window directly, rather than subtracting an output reservation.
The subscribed backend also rejects the `max_output_tokens` request
parameter, so Zed already omits it when serializing requests.
This change makes `openai_subscribed` report no known max output token
cap, which matches also that split token display is disabled for these
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 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:
- agent: Fix compaction happening too early when using the ChatGPT
subscription provider.
`OpenAiCompatibleLanguageModel::stream_completion` (used for custom
OpenAI-compatible providers) forwarded `request.speed` straight into
`into_open_ai`/`into_open_ai_response`, which translate `Speed::Fast`
into OpenAI's `service_tier` field. Custom OpenAI-compatible endpoints
don't recognize that field and reject the request.
`speed` can end up set even for a provider that doesn't support fast
mode: `Thread::inherit_parent_settings` copies `speed` from a parent
thread to a subagent without checking whether the subagent's model
supports it.
Every other provider sharing this conversion code already guards against
this (`open_ai.rs`, `anthropic.rs`, `anthropic_compatible.rs`,
`language_models_cloud.rs`). This adds the same guard for
`OpenAiCompatibleLanguageModel`.
Release Notes:
- Fixed requests to OpenAI-compatible providers (e.g. Baseten) sometimes
including an unsupported `service_tier` parameter
Manage various LLM settings (previously in agent panel) in settings UI.
Behind a feature flag, not staff shipped
Release Notes:
- N/A or Added/Fixed/Improved ...
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
When a Zed Business organization hits its token spend limit, the agent
panel told the user to "check that your API key has access to this
model", and did not display the error message returned by the server.
The message about checking your API key was the generic 401 and 403
error for all language model providers. This is also incorrect in
general since there are no API keys involved in the Zed and ChatGPT
Subscription providers.
So this commit changes the message for authentication errors (401s) to
be provider-specific, with the current message about invalid API key as
the default, and overrides for the providers that don't use API keys.
And the authorization error messages (403s) now include the server error
message. So the spend-limit case now reads "Permission Denied —
Token-based spending limit reached." instead of talking about API keys.
<img width="1888" height="426" alt="grafik"
src="https://github.com/user-attachments/assets/cf9e07cd-66f0-4f8d-828c-79b875edcf2d"
/>
Telemetry event names (`invalid_api_key`, `no_api_key`) are kept
unchanged to avoid breaking existing dashboards.
Release Notes:
- Fixed agent panel errors telling users to check their API key when the
provider doesn't use one (Zed account, ChatGPT subscription). Permission
errors also now show the provider's actual message.
Claude Fable 5 always thinks and cannot honor a request with thinking
disabled, but the cloud models listing gives clients no way to tell it
apart from models where thinking is optional (e.g. Claude Opus 4.6):
both report `supports_thinking: true` plus the same adaptive effort
levels. As a result, the agent panel shows a thinking toggle for Fable
even though turning it off isn't actually supported.
zed-industries/cloud#2789 adds a `supports_disabling_thinking` field to
the models listing. This PR mirrors it through
`cloud_llm_client::LanguageModel` (serde-defaulted to `false`, so a
server without the field is treated as "don't claim thinking can be
turned off") and exposes it as
`LanguageModel::supports_disabling_thinking()`, forwarded from the
listing by `CloudLanguageModel`. The agent panel now hides the thinking
toggle for models that report `false`, showing only the effort selector.
The trait default is `true`: every non-cloud provider in the tree treats
thinking as toggleable today, and only the cloud listing knows about
always-thinking models.
Draft until zed-industries/cloud#2789 lands and deploys.
Release Notes:
- Fixed the agent panel offering a thinking toggle for models that
cannot run with thinking disabled.
<img width="325" height="201" alt="Screenshot 2026-06-09 at 1 38 32 PM"
src="https://github.com/user-attachments/assets/a6518073-1e17-41ff-a8fc-cb279fcd4436"
/>
Adds support for Anthropic's Claude Fable 5 model when using your own
Anthropic API key. Because Fable 5 cannot be offered under Zero Data
Retention (Anthropic retains inference logs for 30 days), this gates the
model behind an explicit data-retention consent: a new
telemetry.anthropic_retention setting (default off, surfaced in the
Privacy section of the settings UI), and a hard, non-retryable check in
the cloud completion path that raises a typed error when consent is
missing.
When Fable 5 declines a request, it transparently falls back to Claude
Opus 4.8 (matching Anthropic's server-side behavior), and the agent
panel shows a callout for the consent error with "Switch to Opus 4.8" /
"Accept" actions that resume the failed turn so the user's message
continues without retyping.
Closes AI-382
Release Notes:
- Add Claude Fable 5 to Anthropic BYOK
---------
Co-authored-by: Mikayla Maki <mikayla@zed.dev>
This PR makes it so we always pass up an organization ID when creating
an LLM token.
We should have an organization ID in all cases.
Release Notes:
- N/A
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)
- [ ] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
## Summary
Adding an OpenRouter API key did not populate the model list until the
app was
restarted. `set_api_key` stored the credential but never triggered a
model
fetch, so until re-authentication happened (on restart or a settings
change)
the picker showed only the user-configured `settings.available_models`,
not the
models returned by the OpenRouter API.
This makes `set_api_key` refresh the models right after the key is
stored,
mirroring how `authenticate` already does it.
## Changes
- `set_api_key` awaits the credential store and then calls
`restart_fetch_models_task`, so the API model list loads immediately
after a
key is entered (and is cleared when the key is removed).
- `fetch_models` now maps `list_models` errors via
`LanguageModelCompletionError::from`, preserving the real OpenRouter
error
message instead of wrapping it in a generic `Other(...)`.
Release Notes:
- Fixed OpenRouter models not appearing until restart after adding an
API key
Signed-off-by: Zhiwei Liang <zhiwei.liang@zliang.me>
Closes#58305
OpenAI sunset GPT-5.2 and GPT-5.3-Codex in Codex for ChatGPT account
logins on June 2nd, so sending to them via the ChatGPT Subscription
provider now fails with error. This PR removes those models from ChatGPT
Subscription model list.
Release Notes:
- Removed the retired GPT-5.2 and GPT-5.3 Codex models from the ChatGPT
Subscription provider.
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>
This primarily
- requires components to have a description as well as a preview
(especially having no preview makes no sense)
- implements some basic previews where missing
- adds a scrollbar to the preview navigation
with a sadly large diff due to reformatting (less indentation 🎉 ), but
very little changes at its core.
Release Notes:
- N/A
<img width="627" height="752" alt="Screenshot 2026-05-28 at 1 20 22 PM"
src="https://github.com/user-attachments/assets/0a7825f0-73c5-49e9-b59a-83924a45de98"
/>
Adds Claude Opus 4.8 for BYOK providers, including Anthropic fast-mode
handling and Bedrock/OpenCode model definitions.
Closes AI-336
Release Notes:
- Added Claude Opus 4.8 BYOK support
## 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.
Until now, the cloud-hosted model list was only refreshed in response to
events that exercise the LLM token (a `UserUpdated` push, an
organization change, or `PrivateUserInfoUpdated`). If a user wasn't
actively using AI features around the time we shipped new models, the
list could stay stale until they restarted Zed.
This is the second step toward fixing that, after #57078 made the cloud
websocket reconnect on its own. We now treat each successful (re)connect
as a hint that the server state may have changed, so possibly new model
definitions will be available, and trigger a model list refresh.
The trigger is a new `Client::cloud_connection_id()` watch that bumps a
counter each time the websocket handshake completes.
`CloudLanguageModelProvider::State` subscribes to it and, on every tick
after the initial `0`, schedules a debounced refresh (with jitter, so we
don't have all active clients trying to reconnect at the same time after
we deploy in cloud).
Closes CLO-713.
Release Notes:
- The list of Zed hosted models is now refreshed automatically, without
requiring a restart
It look like this:
<img width="1698" height="688" alt="grafik"
src="https://github.com/user-attachments/assets/02a37271-63d3-42da-887f-e17b31e8d9ca"
/>
The idea is to avoid people turning on fast mode without understanding
the financial implications. It also clarifiers (in the BYOK case) why
they might not see a difference between fast mode enabled and disabled.
Release Notes:
- N/A
---------
Co-authored-by: Danilo Leal <daniloleal09@gmail.com>
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
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
Also makes sure we are properly catching and processing thinking events.
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:
- google: Support thinking levels for Google models.
We were storing reasoning output inside `RedactedThinking` which causes
issues when switching mid-turn from an OpenAI to an Anthropic model.
This implementation fixes this by storing it inside `reasoning_details`,
which matches our responses implementation in `open_ai.rs`
See
https://github.com/microsoft/vscode-copilot-chat/blob/main/src/platform/endpoint/node/responsesApi.ts
For whatever reason the copilot chat extension sets `summary: []`, this
is what our implementation does too
Closes#56385
Release Notes:
- Fixed an issue where the agent would error when using Copilot as a
provider and switching between OpenAI and Anthropic 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)
- [x] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
Closes: #56857 Title generation failing on agent threads with OpenAI
models from subscription
Currently title generation requests are made without any system prompt
which causes instructions to be not set, and the request fails. This
change makes sure there is always instructions set (api does not seem to
care if its empty or not)
Release Notes:
- N/A
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: #56844
Release Notes:
- Added support for specifying effort level when using OpenAI models via
ChatGPT subscription
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>
#56472 broke Copilot chat
> Failed to connect to API: 400 Bad Request {"message":"cache_control:
Extra inputs are not permitted"}
This PR makes it so that we still use the legacy caching approach for
Copilot
Release Notes:
- N/A
The Bedrock Converse API supports placing `CachePoint` blocks inside the
`system` field, but we were sending the system prompt as a single
`SystemContentBlock::Text`, which leaves the system tokens dependent on
whatever message-level breakpoint happens to fall within the 20-block
lookback window.
This widens `bedrock::Request.system` from `Option<String>` to
`Vec<BedrockSystemContentBlock>` and has `into_bedrock` emit
`[Text(system), CachePoint(Default)]` whenever the model supports prompt
caching. The system prompt now anchors its own cache prefix, on top of
the existing tool-list anchor and per-message breakpoint, so a stable
system prompt keeps producing cache hits even when earlier conversation
turns change.
Bedrock does not support automatic caching or the 1-hour TTL, so the
default 5-minute ephemeral cache is the only option for this provider.
Release Notes:
- Improved Bedrock prompt cache utilization by anchoring the system
prompt as its own cache prefix
Code had been assuming (erroneously, but understandably) that
LlmApiToken::acquire would give them a valid token.
This is not true, as those tokens expire and you must call refresh
explicitly.
Add some helpers to do the retry for you, and rename acquire to cached
to be
clearer about the intent.
Closes #ISSUE
Release Notes:
- Fixed some rare cases where API requests would fail with Unauthorized
## Background: Amazon Bedrock Guardrails
AWS Bedrock Guardrails enables configurable safety and compliance
controls for generative AI applications:
- Evaluates both user inputs and model responses against policies.
:contentReference[oaicite:8]{index=8}
- Can block or filter content based on harmful categories, denied
topics, PII, or hallucination criteria.
:contentReference[oaicite:9]{index=9}
- Guardrails are applied during inference API calls by specifying
`guardrailIdentifier` and `guardrailVersion` in the request.
:contentReference[oaicite:10]{index=10}
Relevant AWS documentation:
- User Guide:
https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html
- How Guardrails Works:
https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-how.html
- API Reference (GuardrailConfiguration):
https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GuardrailConfiguration.html
Some AWS environments enforce IAM policies that require a guardrail to
be specified on every Bedrock API call (via a `StringEquals` condition
on `bedrock:GuardrailIdentifier`). Without this, Zed returns
`AccessDenied` and Bedrock models are completely unusable in those
environments.
This adds two optional settings, `guardrail_identifier` and
`guardrail_version`, to the Bedrock provider config. When set, a
`GuardrailStreamConfiguration` is attached to every `converse_stream`
request. When unset, behaviour is identical to before.
```json
{
"language_models": {
"bedrock": {
"guardrail_identifier": "arn:aws:bedrock:us-east-1:123456789012:guardrail/abc123",
"guardrail_version": "DRAFT"
}
}
}
```
`guardrail_version` defaults to `"DRAFT"` if omitted.
Release Notes:
- agent: Added `guardrail_identifier` and `guardrail_version` settings
for AWS Bedrock, enabling use in environments where IAM policies require
a guardrail on all model requests
---------
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
Most compelling reason to make this change is that we don't have to ship
a new Zed binary if Anthropic releases a new model
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:
- anthropic: Dynamically fetch available models from Anthropic API
---------
Co-authored-by: Ben Brandt <benjamin.j.brandt@gmail.com>
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#55998
Release Notes:
- ollama: Fixed issue where specifying `auto_discover: false` would
still auto discover models
"msg.string_contents();" return more than just content. It also return
tool result (which need special handling, and should be emitted from the
tool role) and thinking (which need already implemented special
handling).
This resulted in thinking being sent as content, which apparently
ollama’s gemma4 parser didn’t handled well, and caused a number of issue
that manifested by 1. outputting end of though token where unappropriate
and 2. repeating things endlessly
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 UI/UX impact)
- [ ] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable (a quick
review, without benchmark, show that this should be at least as fast as
the previous code. This is called only exceptionally anyway.)
Closes#55537
Release Notes:
- Fixed "thinking" text being badly formatted when sent to Ollama
Tom Houlé