# 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without resending the superseded transcript.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.
# 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
# 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
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.
Stop the Zed cloud LLM provider from funneling completion failures
through anyhow::Error and collapsing them into
LanguageModelCompletionError::Other (which surfaced as a generic
"Request failed.").
- perform_llm_completion now returns a typed
LanguageModelCompletionError, mapping each failure to its real variant
(SerializeRequest, HttpSend, ApiReadResponseError, and ApiError-derived
status variants).
- response_lines yields a typed ResponseStreamError so mid-stream read/
deserialize failures become ApiReadResponseError/DeserializeResponse
without a runtime downcast.
- Add a first-class PaymentRequired variant for HTTP 402 and remove the
now-dead PaymentRequiredError struct and its anyhow downcast checks.
Self-Review Checklist:
- [ ] 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)
- [ ] Tests cover the new/changed behavior
- [ ] Performance impact has been considered and is acceptable
Closes #ISSUE
Release Notes:
- N/A
---------
Co-authored-by: Piotr Osiewicz <24362066+osiewicz@users.noreply.github.com>
Co-authored-by: zed-zippy[bot] <234243425+zed-zippy[bot]@users.noreply.github.com>
<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>
#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
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
No, sadly, the title is not a typo. See
https://www.githubstatus.com/incidents/zsg1lk7w13cf for the context.
I'll read with joy and popcorn through that root cause analysis.
It makes literally zero sense what happened here, but for some completly
bonkers reason GitHub completely messed up the merge queue with
https://github.com/zed-industries/zed/pull/54632.
I have no idea how it happened. It makes literally zero sense. A PR
going into the merge queue should have the same LoC when getting out of
it. GitHub obviously does not check this. GitHub causes extra work with
a feature that is supposed to save time.
Thanks, I guess.
Release Notes:
- N/A
---------
Co-authored-by: Danilo Leal <daniloleal09@gmail.com>
This PR brings back the button to filter remote branches when accessing
the title bar's branch picker with the mouse. It was unintentionally
removed when we introduced the new worktree picker.
Release Notes:
- N/A
Starting with Claude Opus 4.7, Anthropic omits thinking content from
responses by default; callers must pass `display: "summarized"` to keep
seeing thinking summaries. Without opting in, the agent UI shows a long
pause with no visible thinking, and users get no progress indication
during extended reasoning.
This extends the adaptive-thinking wire type with an optional `display`
field and requests `Summarized` from every call site that builds an
adaptive thinking request (direct Anthropic, Copilot Chat proxy, Zed
Cloud, and Bedrock).
## Notes
- Applied at the adaptive-thinking layer rather than special-casing Opus
4.7. The `display` parameter is accepted by every
adaptive-thinking-capable model, and the previous behavior (visible
summaries) is what users already see on Opus 4.6 / Sonnet 4.6, so there
is no behavior change for those models.
Release Notes:
- Restored thinking summaries for Claude Opus 4.7.
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>
Fixes#54140
When `RulesLibrary::count_tokens` calls
`CloudLanguageModel::count_tokens` for Google cloud models, it does so
inside a `cx.update` closure, which holds a mutable borrow on the global
`AppCell`. The Google provider branch then called
`token_provider.auth_context(&cx.to_async())`, which created a new
`AsyncApp` handle and tried to take a shared borrow on the same
`RefCell` — causing a "RefCell already mutably borrowed" panic.
This only affects Google models because they are the only provider that
counts tokens server-side via an HTTP request (requiring
authentication). The other providers (Anthropic, OpenAI, xAI) count
tokens locally using tiktoken, so they never call `auth_context` during
`count_tokens`.
The fix makes `CloudLlmTokenProvider::auth_context` generic over `impl
AppContext` instead of requiring `&AsyncApp`. This allows the
`count_tokens` call site to pass `&App` directly (which reads entities
without re-borrowing the `RefCell`), while all other call sites that
already pass `&AsyncApp` (e.g. `stream_completion`, `refresh_models`)
continue to work unchanged.
Release Notes:
- Fixed a crash ("RefCell already mutably borrowed") that could occur
when counting tokens with Google cloud language models.
<img width="767" height="428" alt="Screenshot 2026-04-16 at 11 29 13 AM"
src="https://github.com/user-attachments/assets/e8b450fa-aefc-4dec-a286-b211bd492011"
/>
Add Claude Opus 4.7 (`claude-opus-4-7`) to the anthropic, bedrock, and
opencode provider crates.
Key specs:
- 1M token context window
- 128k max output tokens
- Adaptive thinking support
- AWS Bedrock cross-region inference (global, US, EU, AU)
Release Notes:
- Added Claude Opus 4.7 as an available language model
- `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>
