Promotes queryParams to a first-class ModelRef field used by Endpoint.baseURL,
so deployment-level URL query params (Azure api-version, OpenAI-compatible
provider knobs) live in a typed home instead of an opaque `native` bag.
Also removes write-only dead fields from `native`:
- openaiCompatibleProvider (set by family helper, never read)
- opencodeProviderID, opencodeModelID (set by opencode bridge + native session
builder, never read)
- npm (set by opencode bridge, never read)
After this commit `model.native` only carries genuinely provider-specific
opaque options that no other adapter cares about (Bedrock's aws_credentials
+ aws_region for SigV4). Drops the now-dead ProviderShared.queryParams
helper. Updates AGENTS.md doc on native is implicit through the new schema
JSDoc.
Add an optional `apiKey` field to `ModelRef` so authentication is no
longer baked into `model.headers` at construction time. Each provider
adapter now passes an `Auth` to `Adapter.fromProtocol` that reads
`request.model.apiKey` per request:
- OpenAI Chat / Responses / OpenAI-compatible Chat: `Auth.bearer`
- Anthropic Messages: `Auth.apiKeyHeader("x-api-key")`
- Gemini: `Auth.apiKeyHeader("x-goog-api-key")`
- Bedrock Converse: custom auth that uses `apiKey` for Bearer auth
and falls back to SigV4 with AWS credentials
The `model()` constructors no longer fold the API key into
`model.headers`. The OpenCode bridge sets `apiKey` directly instead of
building auth headers via the now-deleted `authHeader` helper. Test
assertions move from `headers: { authorization: "Bearer ..." }` to
`apiKey: "..."`.
Closes audit gap #3. The bridge now extracts the encrypted reasoning
blob from `MessageV2.ReasoningPart.metadata` and surfaces it on
`LLM.ReasoningPart.encrypted`, where the Anthropic and Bedrock
adapters lower it to the wire — Anthropic emits `thinking.signature`,
Bedrock emits `reasoningContent.reasoningText.signature`. Without
this, multi-turn sessions with reasoning models would lose the
encrypted state on every step and break the chain.
The encrypted blob originates in three different places depending on
how the session was started:
1. AI-SDK Anthropic sessions store it as
`metadata.anthropic.signature` (per AI SDK provider-keyed
convention).
2. AI-SDK OpenAI sessions store it as
`metadata.openai.reasoningEncryptedContent`.
3. Future LLM-native sessions will store it as a top-level
`metadata.encrypted` string (cleanest shape — provider-agnostic,
matches the LLM IR field name).
The new `encryptedReasoning` helper probes all three locations in
order, so existing OpenCode sessions can be served by the LLM-native
path without re-recording reasoning content. The full `metadata`
record continues to flow through to `LLM.ReasoningPart.metadata`
unchanged, preserving any provider-specific fields adapters might
read in the future.
OpenAI Responses encrypted reasoning round-trip is intentionally out
of scope: the LLM-package adapter doesn't yet model reasoning items
in the request body. That's a separate adapter feature requiring new
input-item schema variants and is deferred until needed.
Tests (5 new in llm-native.test.ts):
- AI-SDK Anthropic signature extracted into LLM.ReasoningPart.encrypted.
- End-to-end Anthropic lowering: bridge \u2192 client.prepare \u2192 target with
`thinking.signature` populated correctly.
- AI-SDK OpenAI reasoningEncryptedContent extracted (forward
compatibility — useful when the OpenAI Responses adapter gains
reasoning-item lowering).
- Top-level metadata.encrypted extracted (LLM-native session shape).
- No known key in metadata leaves `encrypted` undefined.
Verified: 33/0/0 across native + bridge tests (was 28; +5 from the
new reasoning extraction tests).
Closes audit gap #2 (FilePart \u2192 MediaPart not implemented).
The bridge now lowers `MessageV2.FilePart` on user messages into
`LLM.MediaPart`, unblocking image and document inputs. The first
pass supports `data:` URLs only — the inline base64 form most
commonly produced by the OpenCode UI for pasted screenshots and
attached files. `http(s):` and `file:` URLs are explicitly
rejected with a clear error so a future fetch / filesystem-read
path can plug in cleanly without regressing safety.
Implementation:
- New `lowerFilePart` helper extracts the base64 payload from a
data URL via a single regex; failure yields a typed
`UnsupportedContentError` carrying both the partType and a
`reason` that includes the offending URL for debuggability.
- New `lowerUserPart` dispatches user-side parts: text \u2192
`LLM.text`, file \u2192 `MediaPart`. Returns identity-empty
for any unsupported part type the static gate would have caught.
- `userMessage` is now `Effect.fnUntraced` so file conversion can
yield typed errors. `lowerMessage` (the per-message dispatcher,
renamed from `messages` to free the local name) cascades the
Effect through the request flow via `Effect.forEach`.
- `supportsPart` static gate now allows `file` parts on user
messages. Assistant messages still reject file parts (the LLM
IR's MediaPart isn't valid in assistant content for any
adapter we ship today).
- `UnsupportedContentError` gains an optional `reason` field that
appends to the canonical message as `<base>: <reason>`. Existing
static-gate failures keep the same shape (no reason).
Tests (3 new, 1 rewritten):
- Image data URL with filename round-trips to MediaPart with
base64-stripped data.
- PDF data URL preserves filename and base64 payload.
- `https:` URL rejected with an error mentioning both the file
partType, the message ID, and the offending URL.
- The pre-existing "fails instead of dropping unsupported native
parts" test now uses a reasoning part on a user message
(reasoning is valid for assistants only) since file parts with
data URLs are no longer rejected by the static gate.
Out of scope, intentional follow-ups:
- HTTP/HTTPS URL fetching (would need HttpClient.HttpClient and a
decision on caching, retries, size limits).
- File path / file:// URL reading (would need FileSystem.FileSystem
and a permission check against the session's working directory).
- File parts on assistant messages (LLM IR doesn't model
assistant-side media; defer until we hit a provider that needs it).
- text/plain and application/x-directory file parts that the
AI-SDK path converts to text inline at message-v2.ts:791 — for
the bridge, those should be converted upstream before reaching
LLMNative.request rather than handled here.
Verified: bun typecheck clean, 28/0/0 across native + bridge
tests (was 21; +7 from the FilePart additions plus the rewritten
unsupported-parts test).
Lift the prompt-cache policy out of OpenCode's bridge and into the
LLM package as a typed, gated patch. The policy mirrors the AI-SDK
applyCaching path (packages/opencode/src/provider/transform.ts:229):
mark the first 2 system parts and the last 2 messages with an
ephemeral cache hint, gated on `model.capabilities.cache.prompt`.
Adapters lower the hint structurally — Anthropic emits
`cache_control: { type: "ephemeral" }` on the marked block,
Bedrock emits a positional `cachePoint: { type: "default" }`
after the marked block (added in 9d7d518ac). The capability gate
keeps non-cache adapters (OpenAI Responses, Gemini, OpenAI-compat
Chat) hint-free.
Why a Patch and not bridge code:
- packages/llm/AGENTS.md TODO explicitly calls for cache hint patches
- Other consumers of @opencode-ai/llm get caching for free
- The bridge stays focused on shape conversion (MessageV2 \u2192 LLMRequest)
- Patches compose via ProviderPatch.defaults (now includes this one)
- The capability gate is a typed predicate, not provider-name matching
Implementation:
- New `cachePromptHints` patch in provider/patch.ts. The
`withCacheOnLastText` helper uses Array.findLastIndex (codebase
idiom) and short-circuits when no text part exists so messages
with only tool-result content are returned identity-equal.
- `EPHEMERAL_CACHE` is a single shared CacheHint instance — no
per-request allocation, preserves `instanceof` for any consumer
that checks class identity.
- Added to `ProviderPatch.defaults` so existing callers that pass
`defaults` get cache support automatically.
Tests (5 new in patch.test.ts):
- Marks first 2 system parts on cache-capable models.
- Marks last text part of last 2 messages.
- Targets the last text part when a message has trailing
non-text content (assistant text + tool-call).
- Returns content unchanged (identity-equal) when no text part
exists, so pure tool-result messages don't allocate.
- No-op when the model does not advertise prompt caching.
Bridge cleanup:
- Removed `applyCachePolicy`, `withCacheOnLastText`,
`updateMessageContent`, `EPHEMERAL_CACHE` from llm-native.ts
(-30 lines of bridge-side cache code).
- Dropped now-unused `CacheHint`, `LLMRequest`, `Message` imports.
- The bridge's only responsibility is now MessageV2 lowering;
callers wire `patches: ProviderPatch.defaults` at client
construction.
OpenCode tests rewritten:
- Old: assert on `request.system[N].cache` (bridge internals).
- New: assert on `prepared.target` after running through
`LLMClient.make({ adapters, patches: ProviderPatch.defaults })
.prepare(request)` — verifies the full lowering end-to-end.
- Anthropic: target.system[0..1] carry `cache_control: ephemeral`,
target.messages[1..2] carry it on the final text block.
- Bedrock: target has `cachePoint` markers after each cached block.
- Non-cache (OpenAI Responses): JSON.stringify(target) contains
none of `cache_control` / `cachePoint` / `ephemeral`.
Verified: bun typecheck clean across both packages, 120/0/0 in LLM
package (was 113; +7 from new patch tests counting parameter
variations), 21/0/0 in OpenCode native+bridge tests.
Kit Langton