Recommends GPT-5.6 Sol for both OpenAI BYOK and OpenAI subscription.
Also unifies the naming so that we use the same OpenAI model names for
the Zed/OpenAI BYOK and OpenAI subscription providers.
Release Notes:
- N/A
# Objective
- On every startup (and whenever the agent panel initializes), Zed
authenticates all visible language model providers in the background to
populate the model selector. For every other credential-based provider,
"not signed in yet" is reported as
`AuthenticateError::CredentialsNotFound`, which is explicitly treated as
expected and not logged. The ChatGPT Subscription provider instead
returned a generic `anyhow` error, so it logged an `ERROR [agent] Failed
to authenticate provider: ChatGPT Subscription: ...` on every launch,
even for users who have never configured or used it.
- Fixes#60237
> **AI disclosure:** I used Claude (Anthropic) to investigate this
error, write the fix, and draft this PR description, based on a `Failed
to authenticate provider: ChatGPT Subscription` line I noticed in my own
Zed logs. I reviewed the diff and the reasoning below, ran the tests
myself, and understand and take responsibility for the change, per Zed's
[AI
Policy](https://github.com/zed-industries/zed/blob/main/CONTRIBUTING.md#ai-policy).
## Solution
- `OpenAiSubscribedProvider::authenticate` now returns
`AuthenticateError::CredentialsNotFound` (instead of a generic
`anyhow!(...)` wrapped into `AuthenticateError::Other`) when no ChatGPT
credentials are found after the initial load, matching how every sibling
provider (e.g. Anthropic's `ApiKeyState::load_if_needed`) signals "not
configured."
- The user-facing sign-in prompt in the settings UI
(`SUBSCRIPTION_DESCRIPTION`) is unchanged — this only affects the
background authentication sweep's error handling, not what the user sees
when opening provider settings.
## Testing
- `cargo test -p language_models -p openai_subscribed` — all 108 tests
pass, including
`provider::openai_subscribed::tests::test_authenticate_awaits_initial_load`,
which exercises this exact code path.
- `cargo check -p language_models` — compiles cleanly.
- Manually confirmed the log line no longer appears when starting Zed
without ChatGPT Subscription configured.
## Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [ ] 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
Before (this fix): startup log ends with a spurious `ERROR` even though
ChatGPT Subscription was never configured.
```
2026-07-30T21:37:46+05:30 INFO [zed] ========== starting zed version 1.15.0+dev.b90735ff317b83c4ec62ab3d73dd5385bbe1624d, sha b90735f ==========
2026-07-30T21:37:48+05:30 WARN [zed::reliability::hang_detection] debug build, only reporting hangs longer then 5s
2026-07-30T21:37:48+05:30 WARN [zed::reliability] Minidump endpoint not set
2026-07-30T21:37:48+05:30 INFO [zed::reliability] memory usage: resident 98 MiB, virtual 426367 MiB
2026-07-30T21:37:48+05:30 INFO [extension_host] extensions updated. loading 2, reloading 0, unloading 0
2026-07-30T21:37:48+05:30 INFO [prompt_store::rules_to_skills_migration] Rules-to-skills migration already marked done; skipping startup migration
2026-07-30T21:37:49+05:30 INFO [zed::reliability] worktree diagnostics: stores 0, slots 0, live 0, visible 0, strong 0, dead weak 0, loading 0, entries 0, visible entries 0, largest none
2026-07-30T21:37:49+05:30 INFO [workspace] Rendered first frame
2026-07-30T21:37:50+05:30 ERROR [agent] Failed to authenticate provider: ChatGPT Subscription: Sign in with your ChatGPT Plus or Pro subscription to use this provider.
```
After this fix: the same startup sequence completes with no `ERROR`
line.
```
2026-07-30T22:11:38+05:30 INFO [zed] ========== starting zed version 1.15.0+dev.a8491e63b54bf1881e10fe78bb3e7b1f8a5a2cac, sha a8491e6 ==========
2026-07-30T22:11:38+05:30 WARN [zed::reliability::hang_detection] debug build, only reporting hangs longer then 5s
2026-07-30T22:11:38+05:30 WARN [zed::reliability] Minidump endpoint not set
2026-07-30T22:11:38+05:30 INFO [zed::reliability] memory usage: resident 104 MiB, virtual 426360 MiB
2026-07-30T22:11:38+05:30 INFO [extension_host] extensions updated. loading 2, reloading 0, unloading 0
2026-07-30T22:11:39+05:30 INFO [prompt_store::rules_to_skills_migration] Rules-to-skills migration already marked done; skipping startup migration
2026-07-30T22:11:39+05:30 INFO [zed::reliability] worktree diagnostics: stores 0, slots 0, live 0, visible 0, strong 0, dead weak 0, loading 0, entries 0, visible entries 0, largest none
2026-07-30T22:11:39+05:30 INFO [workspace] Rendered first frame
2026-07-30T22:11:40+05:30 INFO [git::repository] opening git repository at ".../zed/.git" using git binary "/usr/bin/git"
```
Release Notes:
- Fixed a noisy "Failed to authenticate provider: ChatGPT Subscription"
error being logged on every startup for users who have not configured
the ChatGPT Subscription provider
Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
Release Notes:
- copilot: Improve robustness of copilot chat provider by splitting
authorisation paths for edit predictions and chat. Note: Existing users
will have to re-authenticate with Copilot
---------
Co-authored-by: cameron <cameron.studdstreet@gmail.com>
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
<img width="844" height="404" alt="Screenshot 2026-07-24 at 3 47 04 PM"
src="https://github.com/user-attachments/assets/4691a246-bab9-4426-aa0b-6a7cfa2aa486"
/>
action support, and explicit thinking opt-out behavior.
Adds Claude Opus 5 to the Anthropic and Amazon Bedrock BYOK providers,
including its documented context and output limits, regional inference
profiles, fast mode and comp
Release Notes:
- Added Claude Opus 5 support for Anthropic and Amazon Bedrock BYOK
providers.
# Objective
Add explicit conversation compaction for OpenAI Responses API models,
available through both direct OpenAI connections and Zed Cloud models.
Keep finalized replacement context provider-neutral and separate from
the lifecycle events emitted while automatic compaction is in progress.
## Solution
- Add opt-in explicit compaction support to the `LanguageModel` trait.
- Separate streamed `CompactionUpdate` lifecycle events from finalized
`CompactedContext`.
- Represent provider-native compacted context as an opaque, versioned
state owned by the originating provider.
- Construct compact requests through the same OpenAI request conversion
used for completions, then narrow the request to fields supported by
`/responses/compact`.
- Preserve and validate the complete canonical replacement window
returned by OpenAI.
- Replay the replacement window before messages sent after compaction,
without resending the superseded transcript.
- Support explicit compaction through both the direct OpenAI provider
and `CloudLanguageModel`.
- Update automatic OpenAI and Anthropic compaction to use the same
finalized context representation.
- Reject empty, malformed, incorrectly versioned, and incorrectly owned
provider state.
The corresponding Cloud change adds the `/completions/compact` proxy
route: https://github.com/zed-industries/cloud/pull/3099.
The downstream Delta UI integration is
https://github.com/zed-industries/delta/pull/1739.
## Testing
- `cargo nextest run -p anthropic -p open_ai -p language_models_cloud`
- `cargo check -p language_models --tests`
- `cargo fmt --all --check`
- `git diff --check`
The focused tests cover request construction, canonical-window
preservation and replay, direct and Cloud transports, automatic
compaction lifecycle events, malformed provider state, provider
ownership, format compatibility, and HTTP failure handling.
## 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:
- Added: System messages are now sent through the `instructions` field
in OpenAI Responses API requests.
## Problem
AWS Bedrock's Mantle endpoint (used for GPT-5.x models via the
OpenAI-compatible Responses API) has two independent bugs that both
cause visible duplication/corruption in the agent panel:
1. **Duplicated message text.** Mantle streams a reply as a series of
new `message` output items, where each new item's text is a growing
prefix-superset of the previous one, and the full `output_text.delta`
sequence is replayed for each item. Forwarding these events through the
shared `OpenAiResponseEventMapper` unmodified caused the assistant's
reply (and its phase/reasoning metadata) to appear duplicated in the UI.
2. **Colliding tool call ids.** Mantle assigns raw `tool_use` ids as a
simple per-request counter (`call_1`, `call_2`, ...) that resets to
`call_1` on every request/response cycle to the model — including
separate cycles within a single agent turn as it performs tool calls
sequentially. Since `acp::ToolCallId` was built directly from that raw
id, and `AcpThread::upsert_tool_call` matches tool calls by id across a
thread's entire entry list, a later, unrelated tool call reusing a raw
id would silently overwrite an earlier, already-completed one instead of
appearing as a new entry — visible as "old tool calls getting replaced
by new ones."
Both were confirmed against real raw Mantle SSE traces and a real
persisted thread (`call_1` recurring across 10+ separate request cycles
within a single turn).
## Fix
**For message duplication:** `MantleResponseEventMapper` wraps
`OpenAiResponseEventMapper` and collapses adjacent, same-phase,
strict-prefix message extensions into a single logical message,
forwarding only the replayed suffix as ordinary incremental text.
- Only adjacent, same-phase, strict-prefix extensions are collapsed.
Equal, shrinking, divergent, or different-phase messages are still shown
as-is.
- Any non-message output item (reasoning, tool call) is a hard boundary:
it's forwarded unchanged and clears the merge state, so a message after
a tool call is never merged with the one before it.
- When a message is merged into the previous one, its `StartMessage` and
`ReasoningDetails` metadata events are suppressed, since they'd
otherwise duplicate what was already emitted for the first message.
**For tool call id collisions:** `scoped_tool_call_id(message_ix,
tool_use_id)` combines the raw provider id with the index of the
`AgentMessage` it belongs to in `Thread::messages`. That index is stable
for the lifetime of one request/response cycle (`flush_pending_message`
only runs on the next `StartMessage` event), so ids stay unique across
the whole thread without needing the raw provider id itself to be
globally unique. The index is threaded through explicitly from where
each tool call is created (`handle_tool_use_event`/`run_tool`) to where
it's later referenced (`process_tool_result`), rather than recomputed
later by re-reading `Thread::messages.len()`.
We deliberately did not rewrite the raw `tool_use` id itself at the
provider layer to make it globally unique, since that id round-trips
back to Mantle verbatim as `call_id` in `function_call_output` items on
the next request — rewriting it would require a fragile reverse-mapping
on every code path that replays tool results.
## Testing
- `cargo test -p language_models --lib bedrock` — 23/23 pass (11 tests
covering coalescing, independent messages, phase boundaries,
tool/reasoning boundaries, chained cumulative extensions, and metadata
deduplication).
- `cargo test -p agent --lib` — 699/699 pass (2 new regression tests for
tool call id scoping: one via live streaming across two request cycles
in one turn, one via persistence/`Thread::replay()` across two separate
turns).
- `script/clippy -p language_models -p agent` — clean.
Release Notes:
- Fixed duplicated assistant replies and colliding tool calls when using
GPT-5.x models on Amazon Bedrock's Mantle endpoint
# Objective
Follow-up to #60898 (as suggested by @tomhoule ). That PR marked
`mistral-medium-latest` as supporting thinking, but the flag is
currently a no-op: unlike Magistral, whose reasoning was native, Mistral
Medium 3.5 and Small 4 only emit thinking chunks when the request sets
`reasoning_effort: "high"` (see [Mistral's reasoning
docs](https://docs.mistral.ai/studio-api/conversations/reasoning)), and
the `Request` struct had no such field.
Related: #57581
## Solution
- Add a `ReasoningEffort` enum and an optional `reasoning_effort` field
to the Mistral `Request`. The field is skipped during serialization when
`None`, so requests for non-reasoning models (Codestral, Large,
Ministrals) are byte-identical to before.
- Set `reasoning_effort: "high"` in `into_mistral()` when the model
supports thinking and the request allows it. The rest of the pipeline
already works: `MistralEventMapper` handles `MessagePart::Thinking`
deltas, and `into_mistral` replays thinking parts into history, matching
Mistral's requirement to replay the reasoning trace across turns.
- Add `mistral-small-latest` to `supports_thinking()`: it now points to
Mistral Small 4, a hybrid reasoning model like Medium 3.5.
- Cover the Ministral variants in `Model::from_id()`, which previously
rejected `ministral-3b-latest` / `ministral-8b-latest` /
`ministral-14b-latest` as invalid ids even though the enum defines them.
## Testing
- Added `test_into_mistral_reasoning_effort` covering the three cases:
thinking model with thinking allowed (sends `high`), thinking model with
thinking disallowed (omits the field), and non-thinking model (omits the
field).
- Existing Mistral provider tests pass (`cargo test -p language_models
mistral`); both touched crates are clippy-clean.
- Reviewers can test by configuring a Mistral API key, selecting
`mistral-medium-latest` or `mistral-small-latest` in the agent panel,
and confirming thinking blocks stream in; with a non-reasoning model
like Codestral, requests are unchanged.
## Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [ ] Unsafe blocks (if any) have justifying comments
- [ ] 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:
- Fixed thinking not being triggered for Mistral Medium 3.5 and Mistral
Small 4, and fixed custom Mistral configurations rejecting Ministral
model ids
# Objective
- Adds the three GPT-5.6 models (Sol, Terra, and Luna) that OpenAI now
serves through AWS Bedrock's `bedrock-mantle` endpoint, so they can be
selected as built-in models under the Amazon Bedrock provider.
- Follows up on the native Bedrock Mantle support added in #60480.
- Addresses the feature request in
https://github.com/zed-industries/zed/discussions/61003.
## Solution
- Adds `Gpt5_6Sol`, `Gpt5_6Terra`, and `Gpt5_6Luna` variants to
`MantleModel` in `crates/bedrock/src/models.rs`, with per-model
`id`/`request_id`/`display_name` and shared capability arms.
- All three are Responses-API, `bedrock-mantle`-only models, so they
reuse the existing Mantle request/response plumbing, region gating,
bearer-token auth, and model-picker wiring (`MantleModel::iter()`) with
no other changes required.
- Metadata mirrors the AWS Bedrock model cards
([Sol](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-sol.html),
[Terra](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-terra.html),
[Luna](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-openai-gpt-56-luna.html)):
272K context window, image input, tool and thinking support, and
`openai.gpt-5.6-{sol,terra,luna}` request IDs.
- Updates the Amazon Bedrock section of "Use a Gateway" to mention the
GPT-5.6 family.
## Testing
- `cargo test -p bedrock` — adds `test_gpt_5_6_mantle_model_metadata`
and extends `test_builtin_mantle_models_use_responses_protocol`; all
pass.
- `cargo test -p language_models mantle` — the consumer crate compiles
and all Mantle tests pass.
- `./script/clippy -p bedrock` passes with no new warnings; docs pass
`prettier --check`.
## Self-Review Checklist:
- [x] I've reviewed my own diff for quality, security, and reliability
- [x] Unsafe blocks (if any) have justifying comments — N/A, no unsafe
code
- [x] The content adheres to Zed's UI standards — N/A, no UI change
- [x] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
---
Release Notes:
- Added GPT-5.6 Sol, Terra, and Luna models to the Amazon Bedrock
provider via the `bedrock-mantle` endpoint.
---------
Co-authored-by: Anant Goel <anant@zed.dev>
Split off from #57105 per @maxdeviant's review.
`language_model_core::ModelMode` previously had only `Default` and
`Thinking` variants. As a result, `available_model_to_anthropic_model`
in the Anthropic provider had a dead branch for `AdaptiveThinking` that
was never reachable from settings, and users manually configuring Claude
models via `available_models` could only toggle extended thinking on/off
— they could not opt into adaptive thinking and its
low/medium/high/xhigh/max effort selector. This matters most for users
on third-party Anthropic-compatible proxies, where the `/v1/models`
endpoint may not report a `capabilities` block, leaving
`available_models` as the only path to enable adaptive thinking.
Add an `Adaptive` variant to `ModelMode` and map it to
`AnthropicModelMode::AdaptiveThinking` in the Anthropic provider's
settings conversion. Users can now write:
```json
{
"language_models": {
"anthropic": {
"available_models": [
{
"name": "claude-opus-4-7",
"display_name": "Claude Opus 4.7",
"max_tokens": 1000000,
"max_output_tokens": 128000,
"mode": { "type": "adaptive" }
}
]
}
}
}
```
and get the full adaptive-thinking UX in the agent panel.
### Impact on other providers
`ModelMode` is re-exported by Google (`GoogleModelMode`) and OpenRouter.
Both already use non-exhaustive `matches!` checks for `Thinking`, so the
new variant naturally falls through to "no thinking" without compile
breakage. Bedrock defines its own local `ModelMode` enum and is
unaffected. `cargo check --workspace --all-targets` passes.
### Tests
Three new tests in `crates/language_models/src/provider/anthropic.rs`
covering Adaptive / Thinking / Default settings → model mapping:
- `cargo test -p language_models --lib provider::anthropic::`: 3/3
passing.
- `./script/clippy --package language_models --package
language_model_core`: clean.
Release Notes:
- agent: Added support for `"mode": { "type": "adaptive" }` for custom
anthropic to enable adaptive thinking
---------
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
LM Studio doesn't show the context token wheel (#53790) because token
usage is
never reported in streaming responses.
Causes:
1. `stream_options` was missing from the request. Without
`stream_options: { include_usage: true }`, the LM Studio API omits
`usage`
from every streaming chunk entirely.
2. The event mapper discarded usage data in the final chunk.
OpenAI-compatible
servers send the usage summary in a trailing chunk that has an empty
`choices`
array. The old guard treated that as an error, so even when usage was
present
it was thrown away before emitting a `UsageUpdate` event.
Fix:
- Add `StreamOptions { include_usage: bool }` and `stream_options` to
`ChatCompletionRequest`, and always set it to `true` for streaming
requests.
- Move usage handling in `LmStudioEventMapper::map_event` to run
*before* the
empty-choices guard, mirroring the OpenAI provider's approach.
- Add four unit tests for `map_event` covering the fixed behavior.
Release Notes:
- Fixed LM Studio not showing the context window usage wheel.
<img width="1184" height="1080" alt="Screenshot_20260527_130449"
src="https://github.com/user-attachments/assets/97eb8500-39dd-4824-aaf8-f0422b62119d"
/>
---------
Co-authored-by: Gabriele Ancillai <gabriele.ancillai@sofka.com.co>
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
Adds support for enabling Anthropic fast mode on configured models.
Previously, Anthropic models configured through
`language_models.anthropic.available_models` were always marked as not
supporting fast mode, so `speed: "fast"` would be stripped before
sending requests even when the configured model supported Anthropic fast
mode.
This adds an optional `supports_fast_mode` field to configured Anthropic
models. When enabled, the model is marked as supporting fast mode and
the required Anthropic beta header is added automatically. Built-in
fast-mode model detection remains the fallback when the setting is
omitted.
Testing:
- `cargo fmt --package anthropic --package language_models --package
settings_content`
- `cargo test -p language_models available_model --lib`
- `cargo test -p anthropic from_listed_enables_fast_mode --lib`
- `cargo check -p language_models`
- Manual: verified in a local build that a configured Anthropic model
can send fast mode requests correctly.
Release Notes:
- agent: Allow specifying if fast mode is supported for custom anthropic
models
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
Release Notes:
- agent: Added GPT 5.6 Sol & Terra for ChatGPT subscription. Note: GPT
5.6 Luna is not available yet, since OpenAI has not unlocked access for
third-party clients
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.
**TL;DR**: model updates + reasoning levels + fixes discovered when
working on https://github.com/zed-industries/zed/pull/60373
# Objective
Since the model auto-discovery PR was
[cancelled](https://github.com/zed-industries/zed/pull/60373#issuecomment-4886521448),
here is a manual model list update! I also copied the stand-alone
bugfixes/enhancements from that PR.
## Solution
A lot of manual work 😅
**OpenCode Zen**:
- added Fable 5 and Sonnet 5
- added models that were previously only available on OpenCode Go: GLM
5.2, Kimi K2.7 Code, and Minimax M3
- added reasoning levels for all models. I started from the data on
[`Models.dev`](https://models.dev) (the `/api.json` raw data), and then
I matched that with what is shown in the OpenCode CLI and what I know to
be true
**OpenCode Go**:
- added reasoning levels for GLM 5.2
**OpenCode in general**:
- added `protocol` validation for the settings, by moving from a random
`String` to an `enum`, for both nicer error messages (random strings or
typos will get an error instead of using `openai_chat` by default) and
to avoid issues like [folks saying non-existent protocols are a
thing](https://github.com/zed-industries/zed/issues/56869#issuecomment-4550154554)
- enabled parallel tool calls by default. As per [OpenCode developer on
Discord](https://discord.com/channels/1391832426048651334/1471233160993050918/1472020924881702912),
_"almost all models worth using support parallel tool calling
natively"_. Manual tests confirmed all OpenCode Go models support this
correctly (and was enabled by default on the OpenCode side for all but 1
model). I initially wanted to skip this from the release notes, but I
added it so folks are aware of it in case any issues are caused by this
being enabled for all models
- allegedly fixed Google thinking since reasoning levels / thinking
effort levels were added for Google models and an auto-checker LLM
highlighted that was not properly configured
- added support for the new-ish `supports_disabling_thinking` so
thinking-only models don't get a no-impact toggle to disable thinking
I have no idea if any of the Free models will disappear in 2 days or
not, so I did not update those 🤷 (as per decision in
https://github.com/zed-industries/zed/issues/56869#issuecomment-4466637648)
## Testing
The Zen and Google changes were not tested as I don't have a Zen
subscription and I stubbornly refuse to get one.
The Free&Go changes were tested by running a "_rename this variable for
me. add a function. delete the function_" test with a few different
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)
- [ ] Tests cover the new/changed behavior
- [x] Performance impact has been considered and is acceptable
---
Release Notes:
- Agent: OpenCode settings now validate `protocol` values
- Agent: OpenCode only shows the "Disable thinking" toggle if thinking
can indeed be disabled/enabled
- Agent: OpenCode models now enable parallel tool calls by default
- Agent: Updated OpenCode Zen models (added Fable 5, Sonnet 5, GLM 5.2,
Kimi K2.7 Code, and Minimax M3)
- Agent: Added OpenCode Go GLM 5.2 reasoning effort levels
- Agent: Added reasoning effort levels for all OpenCode Zen models
- Agent: Fixed thinking for OpenCode Zen Google models
This PR improves the onboarding experience when using the agent panel.
Previously we would pick a fallback model in case the provider failed to
authenticate/was slow to resolve models. However, this code had a race
condition (for providers that resolve models dynamically like
Anthropic/GitHub Copilot), since we pick a fallback immediately after
all providers have been authenticated. However, at that point the
configured provider might not have resolved its models, so we would fall
back to a different provider. At some point we added a workaround for
the zed.dev provider.
We landed on a much simpler approach that eliminates the race condition:
We only pick a fallback model in case the user actually has no model
configured in his settings. In case the user has a model configured, we
won't fallback and show an actionable error message:
1. Model is not set and no fallback available
<img width="862" height="78" alt="image"
src="https://github.com/user-attachments/assets/e8e7472a-1c05-4cd2-8efc-49e6d921b0a4"
/>
2. Model is set, but provider is not authenticated
<img width="865" height="65" alt="image"
src="https://github.com/user-attachments/assets/3c8cbf1d-7dd5-4b2e-809a-61e6662721a3"
/>
3. Model is set, provider is authenticated, but model is not in model
list
<img width="863" height="63" alt="image"
src="https://github.com/user-attachments/assets/b97defa2-3fb4-4ec0-b3c6-26878d1c815c"
/>
4. Model is set, but provider is not recognised
<img width="865" height="67" alt="image"
src="https://github.com/user-attachments/assets/ecff1e3f-4f6b-47eb-a25d-125a104baafc"
/>
This plays well with the reason why we have the fallback model in the
first place: We only want to pick a fallback for users that open Zed for
a first time and e.g. have an Anthropic API key present in their
environment. As soon as the user manually changes his provider/model we
won't apply the fallback anymore.
Release Notes:
- agent: Improved error messaging when provider is not configured
- agent: Improve fallback model selection
---------
Co-authored-by: cameron <cameron.studdstreet@gmail.com>
I encountered an issue where I couldn't sign in to ChatGPT Subscription
on Windows:
```
ChatGPT subscription sign-in failed to persist credentials:
Failed to write credentials to Windows Credential Manager:
占位程序接收到错误数据。 (0x800706F7)
```
Interestingly, only one of my three ChatGPT accounts had this problem —
the other two signed in successfully.
## What I Found
After investigating, I noticed that the OAuth scope in
`openai_subscribed.rs` requests 6 permissions:
```
openid profile email offline_access api.connectors.read api.connectors.invoke
```
I wrote a test script to measure token sizes and found that:
- With 6 scopes: my problematic account's token was **2578 bytes**
- With 4 scopes (removing `api.connectors.read` and
`api.connectors.invoke`): the same token was **2516 bytes**
Since Windows Credential Manager has a 2560-byte limit
(`CRED_MAX_CREDENTIAL_BLOB_SIZE`), this could explain why some accounts
fail while others succeed — it depends on the base token size.
## My Hypothesis
The issue seems to be that:
- Error code `0x800706F7` = `RPC_X_BAD_STUB_DATA` (size limit, not
format)
- Accounts with larger base tokens exceed the limit when the extra 48
chars are added
- Accounts with smaller base tokens still fit, which is why this isn't
universally reproducible
I'm not 100% certain this is the root cause, but the evidence seems to
point in this direction.
## Proposed Fix
I removed `api.connectors.read` and `api.connectors.invoke` from the
OAuth scope, since:
- These scopes don't appear to be used by Zed's current ChatGPT
integration
- OpenCode (another tool using the same OAuth provider) successfully
uses only 4 scopes
- This fix allowed my problematic account to sign in
However, I'd like to ask the maintainers:
- Are there plans to use OpenAI Connectors API in the future?
- Are there security or compliance reasons for requesting these extra
scopes?
If the answer is yes to either, we'd need a different approach (e.g.,
splitting credentials, using encrypted file storage, or checking token
size before storage).
## Testing
- [x] Verified my problematic account now signs in successfully
- [x] Verified my other accounts still work
- [x] Ran `cargo check -p language_models` (compilation successful)
- [ ] Would appreciate help testing on macOS/Linux
## Questions
1. Does this analysis make sense?
2. Are there any other considerations I'm missing?
3. Would it be helpful to add a size check with a clearer error message
for future cases?
I'm happy to adjust this approach based on your feedback.
Release Notes:
- Fixed ChatGPT Subscription sign-in failing on Windows for some
accounts by removing unused OAuth scopes (`api.connectors.read`,
`api.connectors.invoke`) that pushed JWT tokens over Windows Credential
Manager's limit.
---------
Co-authored-by: Smit Barmase <heysmitbarmase@gmail.com>
When prompt caching is enabled, `into_bedrock` pushes a `CachePoint`
block
onto a message's content whenever `message.cache && supports_caching` is
true.
This push happens before the `if bedrock_message_content.is_empty() {
continue; }`
guard. As a result, a message whose content filters down to empty (for
example,
a message that contained only content stripped during conversion) still
gets
appended to the request carrying nothing but a `cachePoint`. Bedrock
rejects such
a message with a `ValidationException`, breaking the whole request.
The Anthropic path is already internally consistent here: in
`crates/language_models/src/provider/anthropic.rs` (around the message
assembly
in `completion.rs`) the empty-content check runs first, so an empty
message is
dropped before any cache marker is attached. The Bedrock path should
behave the
same way.
This change gates the `CachePoint` push on non-empty content
(`&& !bedrock_message_content.is_empty()`), so an empty message is left
empty and
then skipped by the existing `continue`, exactly mirroring the Anthropic
ordering.
The fix is minimal and touches only the cache-point condition.
Release Notes:
- Fixed Bedrock requests failing with ValidationException when the last
message filtered to empty content while prompt caching was enabled.
---------
Signed-off-by: Yi LIU <yi@quantstamp.com>
Co-authored-by: Smit Barmase <heysmitbarmase@gmail.com>
This PR removes/simplifies some APIs that were leftover after moving the
agent settings from the agent panel into the agent settings UI.
Behavior/UI should be identical.
- Removed `ConfigurationViewTargetAgent` since only a single variant was
used
- Removed `configuration_view` and `configuration_view_v2` and replaced
it with `settings_view`
- Removed all the custom configuration views that were replaced by the
API key view abstraction
- Removed `intitial_title` and `initial_description` and moved it to
`ProviderSettingsView`
Release Notes:
- N/A
Closes AI-159
Closes AI-434
Closes AI-435
Release Notes:
- Key agent-related settings now live in the settings editor, close to
all other settings available in Zed. This specifically includes the move
of LLM providers, external agents, and MCP servers to the settings
editor.
---------
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
Co-authored-by: Bennet Bo Fenner <bennetbo@gmx.de>
Hi there, I'm Celina from Hugging Face! Opening this PR to add
[llama.cpp](https://llama.app) as a model provider
# Objective
Today Zed users running llama.cpp have to fall back to the generic
OpenAI-compatible provider, which means no auto-discovery (the router
mode (`llama serve`) discovers models from the cache and loads them on
demand) and manual configuration of every model and its capabilities.
This PR makes `llama.cpp` a first-class provider with the same
auto-discovery experience.
## Solution
- Add a `llama_cpp` client crate with the OpenAI-compatible chat types
(`/v1/chat/completions`, including `reasoning_content`) and the
discovery types (`/v1/models`, `/props`), mirroring the existing
`ollama` crate.
- Add the provider in
`crates/language_models/src/provider/llama_cpp.rs`, modeled on the
Ollama provider (settings, configuration view, event mapping).
- Auto discover served models and their context length and tool/vision
support from `/props`. Set `auto_discover: false` to list models
manually instead.
- An unloaded model can't be inspected without loading it, so it is
listed with optimistic defaults (large context, tools enabled) and is
usable from the first message; its real context length and tool support
are filled in once it loads. These live behind a shared map, so a model
already selected in an open conversation picks them up without being
re-selected.
- Show load progress. The provider subscribes to `/models/sse` and
surfaces each model's load progress (e.g. "Loading weights 42%") in its
display name, reconciling stale labels against `/v1/models`. Builds
without `/models/sse` degrade gracefully - no progress, and no
capability refresh after the initial discovery.
- Add settings (`api_url`, `auto_discover`, `available_models` with
per-model `max_tokens` / `supports_tools` / `supports_images`,
`context_window`, `custom_headers`), the provider icon, a `default.json`
entry, and documentation under "Use a Local Model".
No new dependencies: the crate reuses existing workspace dependencies,
and shared state uses `std::sync::RwLock`.
## Testing
- Unit tests in both new crates cover wire/response parsing, model
discovery for single-model and router shapes, the cold-start optimistic
defaults, the in-place capability refresh once a model loads, and the
`/models/sse` event handling (state changes, load failure, load
progress).
- Built Zed locally and ran it against a local `llama serve` router:
confirmed models are discovered without manual configuration, that the
first message works before a model has finished loading, that load
progress is shown in the model's display name while it loads, and that
the reported context length and tool support refine to the model's real
values once it finishes loading.
- Platforms: tested on macOS (Apple Silicon).
## 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
## Showcase
The generation speed (tokens/sec) depends on the machine you're running
the model, here it's a Apple M3 Max 64GB running a 4-bit quant of
https://huggingface.co/Qwen/Qwen3.5-35B-A3B. For the load progress
status, make sure to upgrade your llama.cpp version to the latest build.
https://github.com/user-attachments/assets/0254f6ef-abe9-42ed-810b-ef1a5b8fa3bd
---
Release Notes:
- Added llama.cpp as a language model provider
---------
Co-authored-by: Ben Brandt <benjamin.j.brandt@gmail.com>
# 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>
Neel