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import { Route } from "../route/client"
import { Auth } from "../route/auth"
import { Endpoint } from "../route/endpoint"
import { Framing } from "../route/framing"
import { Protocol } from "../route/protocol"
import {
LLMEvent,
Usage,
type CacheHint,
type FinishReason,
type JsonSchema,
type LLMRequest,
type MediaPart,
type ProviderMetadata,
type ToolCallPart,
type ToolDefinition,
type ToolContent,
type ToolResultPart,
} from "../schema"
import { JsonObject, optionalArray, optionalNull, ProviderShared } from "./shared"
import { isContextOverflow } from "../provider-error"
import * as Cache from "./utils/cache"
import { Lifecycle } from "./utils/lifecycle"
import { ToolSchemaProjection } from "./utils/tool-schema"
import { ToolStream } from "./utils/tool-stream"
const ADAPTER = "anthropic-messages"
export const DEFAULT_BASE_URL = "https://api.anthropic.com/v1"
export const PATH = "/messages"
// =============================================================================
// Request Body Schema
// =============================================================================
const AnthropicCacheControl = Schema.Struct({
type: Schema.tag("ephemeral"),
ttl: Schema.optional(Schema.Literals(["5m", "1h"])),
})
const AnthropicTextBlock = Schema.Struct({
type: Schema.tag("text"),
text: Schema.String,
cache_control: Schema.optional(AnthropicCacheControl),
})
type AnthropicTextBlock = Schema.Schema.Type<typeof AnthropicTextBlock>
const AnthropicImageBlock = Schema.Struct({
type: Schema.tag("image"),
source: Schema.Struct({
type: Schema.tag("base64"),
media_type: Schema.String,
data: Schema.String,
}),
cache_control: Schema.optional(AnthropicCacheControl),
})
type AnthropicImageBlock = Schema.Schema.Type<typeof AnthropicImageBlock>
const AnthropicThinkingBlock = Schema.Struct({
type: Schema.tag("thinking"),
thinking: Schema.String,
signature: Schema.optional(Schema.String),
cache_control: Schema.optional(AnthropicCacheControl),
})
const AnthropicToolUseBlock = Schema.Struct({
type: Schema.tag("tool_use"),
id: Schema.String,
name: Schema.String,
input: Schema.Unknown,
cache_control: Schema.optional(AnthropicCacheControl),
})
type AnthropicToolUseBlock = Schema.Schema.Type<typeof AnthropicToolUseBlock>
const AnthropicServerToolUseBlock = Schema.Struct({
type: Schema.tag("server_tool_use"),
id: Schema.String,
name: Schema.String,
input: Schema.Unknown,
cache_control: Schema.optional(AnthropicCacheControl),
})
type AnthropicServerToolUseBlock = Schema.Schema.Type<typeof AnthropicServerToolUseBlock>
// Server tool result blocks: web_search_tool_result, code_execution_tool_result,
// and web_fetch_tool_result. The provider executes the tool and inlines the
// structured result into the assistant turn — there is no client tool_result
// round-trip. We round-trip the structured `content` payload as opaque JSON so
// the next request can echo it back when continuing the conversation.
const AnthropicServerToolResultType = Schema.Literals([
"web_search_tool_result",
"code_execution_tool_result",
"web_fetch_tool_result",
])
type AnthropicServerToolResultType = Schema.Schema.Type<typeof AnthropicServerToolResultType>
const AnthropicServerToolResultBlock = Schema.Struct({
type: AnthropicServerToolResultType,
tool_use_id: Schema.String,
content: Schema.Unknown,
cache_control: Schema.optional(AnthropicCacheControl),
})
type AnthropicServerToolResultBlock = Schema.Schema.Type<typeof AnthropicServerToolResultBlock>
// Anthropic accepts either a plain string or an ordered array of text/image
// blocks inside `tool_result.content`. The array form is required when a tool
// returns image bytes (screenshot, image search, etc.) so they can be passed
// to the model as proper image inputs instead of being JSON-stringified into
// the prompt — which silently inflates context by megabytes and can push the
// conversation over the model's token limit.
const AnthropicToolResultContent = Schema.Union([AnthropicTextBlock, AnthropicImageBlock])
const AnthropicToolResultBlock = Schema.Struct({
type: Schema.tag("tool_result"),
tool_use_id: Schema.String,
content: Schema.Union([Schema.String, Schema.Array(AnthropicToolResultContent)]),
is_error: Schema.optional(Schema.Boolean),
cache_control: Schema.optional(AnthropicCacheControl),
})
const AnthropicUserBlock = Schema.Union([AnthropicTextBlock, AnthropicImageBlock, AnthropicToolResultBlock])
type AnthropicUserBlock = Schema.Schema.Type<typeof AnthropicUserBlock>
const AnthropicAssistantBlock = Schema.Union([
AnthropicTextBlock,
AnthropicThinkingBlock,
AnthropicToolUseBlock,
AnthropicServerToolUseBlock,
AnthropicServerToolResultBlock,
])
type AnthropicAssistantBlock = Schema.Schema.Type<typeof AnthropicAssistantBlock>
type AnthropicToolResultBlock = Schema.Schema.Type<typeof AnthropicToolResultBlock>
const AnthropicMessage = Schema.Union([
Schema.Struct({ role: Schema.Literal("user"), content: Schema.Array(AnthropicUserBlock) }),
Schema.Struct({ role: Schema.Literal("assistant"), content: Schema.Array(AnthropicAssistantBlock) }),
Schema.Struct({ role: Schema.Literal("system"), content: Schema.Array(AnthropicTextBlock) }),
]).pipe(Schema.toTaggedUnion("role"))
type AnthropicMessage = Schema.Schema.Type<typeof AnthropicMessage>
const AnthropicTool = Schema.Struct({
name: Schema.String,
description: Schema.String,
input_schema: JsonObject,
cache_control: Schema.optional(AnthropicCacheControl),
})
type AnthropicTool = Schema.Schema.Type<typeof AnthropicTool>
const AnthropicToolChoice = Schema.Union([
Schema.Struct({ type: Schema.Literals(["auto", "any"]) }),
Schema.Struct({ type: Schema.tag("tool"), name: Schema.String }),
])
const AnthropicThinking = Schema.Struct({
type: Schema.tag("enabled"),
budget_tokens: Schema.Number,
})
const AnthropicBodyFields = {
model: Schema.String,
system: optionalArray(AnthropicTextBlock),
messages: Schema.Array(AnthropicMessage),
tools: optionalArray(AnthropicTool),
tool_choice: Schema.optional(AnthropicToolChoice),
stream: Schema.Literal(true),
max_tokens: Schema.Number,
temperature: Schema.optional(Schema.Number),
top_p: Schema.optional(Schema.Number),
top_k: Schema.optional(Schema.Number),
stop_sequences: optionalArray(Schema.String),
thinking: Schema.optional(AnthropicThinking),
}
const AnthropicMessagesBody = Schema.Struct(AnthropicBodyFields)
export type AnthropicMessagesBody = Schema.Schema.Type<typeof AnthropicMessagesBody>
const AnthropicUsage = Schema.Struct({
input_tokens: Schema.optional(Schema.Number),
output_tokens: Schema.optional(Schema.Number),
cache_creation_input_tokens: optionalNull(Schema.Number),
cache_read_input_tokens: optionalNull(Schema.Number),
})
type AnthropicUsage = Schema.Schema.Type<typeof AnthropicUsage>
const AnthropicStreamBlock = Schema.Struct({
type: Schema.String,
id: Schema.optional(Schema.String),
name: Schema.optional(Schema.String),
text: Schema.optional(Schema.String),
thinking: Schema.optional(Schema.String),
signature: Schema.optional(Schema.String),
input: Schema.optional(Schema.Unknown),
// *_tool_result blocks arrive whole as content_block_start (no streaming
// delta) with the structured payload in `content` and the originating
// server_tool_use id in `tool_use_id`.
tool_use_id: Schema.optional(Schema.String),
content: Schema.optional(Schema.Unknown),
})
const AnthropicStreamDelta = Schema.Struct({
type: Schema.optional(Schema.String),
text: Schema.optional(Schema.String),
thinking: Schema.optional(Schema.String),
partial_json: Schema.optional(Schema.String),
signature: Schema.optional(Schema.String),
stop_reason: optionalNull(Schema.String),
stop_sequence: optionalNull(Schema.String),
})
const AnthropicEvent = Schema.Struct({
type: Schema.String,
index: Schema.optional(Schema.Number),
message: Schema.optional(Schema.Struct({ usage: Schema.optional(AnthropicUsage) })),
content_block: Schema.optional(AnthropicStreamBlock),
delta: Schema.optional(AnthropicStreamDelta),
usage: Schema.optional(AnthropicUsage),
// `type` and `message` are both required per Anthropic's spec, but
// OpenAI-compatible proxies and gateway translations occasionally drop one
// or the other; mark them optional so a partial payload still parses and
// the parser can fall back to whichever field is populated.
error: Schema.optional(
Schema.Struct({ type: Schema.optional(Schema.String), message: Schema.optional(Schema.String) }),
),
})
type AnthropicEvent = Schema.Schema.Type<typeof AnthropicEvent>
interface ParserState {
readonly tools: ToolStream.State<number>
readonly usage?: Usage
readonly lifecycle: Lifecycle.State
}
const invalid = ProviderShared.invalidRequest
// =============================================================================
// Request Lowering
// =============================================================================
// Anthropic accepts at most 4 explicit cache_control breakpoints per request,
// across `tools`, `system`, and `messages`. Beyond the cap the API returns a
// 400 — so the lowering layer counts emitted markers and silently drops any
// that exceed it.
const ANTHROPIC_BREAKPOINT_CAP = 4
const EPHEMERAL_5M = { type: "ephemeral" as const }
const EPHEMERAL_1H = { type: "ephemeral" as const, ttl: "1h" as const }
const cacheControl = (breakpoints: Cache.Breakpoints, cache: CacheHint | undefined) => {
if (cache?.type !== "ephemeral" && cache?.type !== "persistent") return undefined
if (breakpoints.remaining <= 0) {
breakpoints.dropped += 1
return undefined
}
breakpoints.remaining -= 1
return Cache.ttlBucket(cache.ttlSeconds) === "1h" ? EPHEMERAL_1H : EPHEMERAL_5M
}
const anthropicMetadata = (metadata: Record<string, unknown>): ProviderMetadata => ({ anthropic: metadata })
const signatureFromMetadata = (metadata: ProviderMetadata | undefined): string | undefined => {
const anthropic = metadata?.anthropic
if (!ProviderShared.isRecord(anthropic)) return undefined
return typeof anthropic.signature === "string" ? anthropic.signature : undefined
}
const lowerTool = (breakpoints: Cache.Breakpoints, tool: ToolDefinition, inputSchema: JsonSchema): AnthropicTool => ({
name: tool.name,
description: tool.description,
input_schema: inputSchema,
cache_control: cacheControl(breakpoints, tool.cache),
})
const lowerToolChoice = (toolChoice: NonNullable<LLMRequest["toolChoice"]>) =>
ProviderShared.matchToolChoice("Anthropic Messages", toolChoice, {
auto: () => ({ type: "auto" as const }),
none: () => undefined,
required: () => ({ type: "any" as const }),
tool: (name) => ({ type: "tool" as const, name }),
})
const lowerToolCall = (part: ToolCallPart): AnthropicToolUseBlock => ({
type: "tool_use",
id: part.id,
name: part.name,
input: part.input,
})
const lowerServerToolCall = (part: ToolCallPart): AnthropicServerToolUseBlock => ({
type: "server_tool_use",
id: part.id,
name: part.name,
input: part.input,
})
// Server tool result blocks are typed by name. Anthropic ships three today;
// extend this list when new server tools land. The block content is the
// structured payload returned by the provider, which we round-trip as-is.
const serverToolResultType = (name: string): AnthropicServerToolResultType | undefined => {
if (name === "web_search") return "web_search_tool_result"
if (name === "code_execution") return "code_execution_tool_result"
if (name === "web_fetch") return "web_fetch_tool_result"
return undefined
}
const lowerServerToolResult = Effect.fn("AnthropicMessages.lowerServerToolResult")(function* (part: ToolResultPart) {
const wireType = serverToolResultType(part.name)
if (!wireType)
return yield* invalid(`Anthropic Messages does not know how to round-trip server tool result for ${part.name}`)
return { type: wireType, tool_use_id: part.id, content: part.result.value } satisfies AnthropicServerToolResultBlock
})
const lowerImage = Effect.fn("AnthropicMessages.lowerImage")(function* (part: MediaPart) {
const media = yield* ProviderShared.validateMedia(
"Anthropic Messages",
part,
new Set<string>(ProviderShared.IMAGE_MIMES),
)
return {
type: "image" as const,
source: {
type: "base64" as const,
media_type: media.mime,
data: media.base64,
},
} satisfies AnthropicImageBlock
})
// Tool results may carry structured text/images. Keep media as provider-native
// content instead of JSON-stringifying base64 into a prompt string.
const lowerToolResultContentItem = Effect.fn("AnthropicMessages.lowerToolResultContentItem")(function* (
item: ToolContent,
) {
if (item.type === "text") return { type: "text" as const, text: item.text } satisfies AnthropicTextBlock
const media = yield* ProviderShared.validateToolFile(
"Anthropic Messages",
item,
new Set<string>(ProviderShared.IMAGE_MIMES),
)
return {
type: "image" as const,
source: {
type: "base64" as const,
media_type: media.mime,
data: media.base64,
},
} satisfies AnthropicImageBlock
})
const lowerToolResultContent = Effect.fn("AnthropicMessages.lowerToolResultContent")(function* (part: ToolResultPart) {
// Text / json / error results stay as a string for backward compatibility
// with existing cassettes and provider expectations.
if (part.result.type !== "content") return ProviderShared.toolResultText(part)
// Preserve the narrowed array element type when compiled through a consumer package.
const content: ReadonlyArray<ToolContent> = part.result.value
return yield* Effect.forEach(content, lowerToolResultContentItem)
})
// Mid-conversation system messages are a native Claude API feature only for
// Opus 4.8. Other Anthropic models intentionally use the same visible wrapped-
// user fallback as non-Anthropic routes rather than sending a role they reject.
const supportsNativeSystemUpdates = (request: LLMRequest) => String(request.model.id) === "claude-opus-4-8"
const endsInServerToolUse = (message: LLMRequest["messages"][number]) => {
const last = message.content.at(-1)
return message.role === "assistant" && last?.type === "tool-call" && last.providerExecuted === true
}
const canUseNativeSystemUpdate = (messages: LLMRequest["messages"], index: number) => {
const previous = messages[index - 1]
const next = messages[index + 1]
return (
previous !== undefined &&
previous.role !== "system" &&
(previous.role === "user" || previous.role === "tool" || endsInServerToolUse(previous)) &&
next?.role !== "system" &&
(next === undefined || next.role === "assistant")
)
}
const splitsLocalToolResults = (messages: LLMRequest["messages"], index: number) => {
const pending = new Set<string>()
for (const message of messages.slice(0, index)) {
for (const part of message.content) {
if (message.role === "assistant" && part.type === "tool-call" && part.providerExecuted !== true)
pending.add(part.id)
if (message.role === "tool" && part.type === "tool-result") pending.delete(part.id)
}
}
return pending.size > 0
}
const lowerNativeSystemUpdate = Effect.fn("AnthropicMessages.lowerNativeSystemUpdate")(function* (
message: LLMRequest["messages"][number],
breakpoints: Cache.Breakpoints,
) {
const content = yield* ProviderShared.systemUpdateText("Anthropic Messages", message)
return {
role: "system" as const,
content: content.map((part) => ({
type: "text" as const,
text: part.text,
cache_control: cacheControl(breakpoints, part.cache),
})),
}
})
const lowerMessages = Effect.fn("AnthropicMessages.lowerMessages")(function* (
request: LLMRequest,
breakpoints: Cache.Breakpoints,
) {
const messages: AnthropicMessage[] = []
for (const [index, message] of request.messages.entries()) {
if (message.role === "system") {
if (splitsLocalToolResults(request.messages, index))
return yield* invalid("Anthropic Messages system updates cannot split a local tool call from its tool result")
if (supportsNativeSystemUpdates(request) && canUseNativeSystemUpdate(request.messages, index)) {
messages.push(yield* lowerNativeSystemUpdate(message, breakpoints))
continue
}
const part = yield* ProviderShared.wrappedSystemUpdate("Anthropic Messages", message)
const block = { type: "text" as const, text: part.text, cache_control: cacheControl(breakpoints, part.cache) }
const previous = messages.at(-1)
if (previous?.role === "user")
messages[messages.length - 1] = { role: "user", content: [...previous.content, block] }
else messages.push({ role: "user", content: [block] })
continue
}
if (message.role === "user") {
const content: AnthropicUserBlock[] = []
for (const part of message.content) {
if (part.type === "text") {
content.push({ type: "text", text: part.text, cache_control: cacheControl(breakpoints, part.cache) })
continue
}
if (part.type === "media") {
content.push(yield* lowerImage(part))
continue
}
return yield* ProviderShared.unsupportedContent("Anthropic Messages", "user", ["text", "media"])
}
messages.push({ role: "user", content })
continue
}
if (message.role === "assistant") {
const content: AnthropicAssistantBlock[] = []
for (const part of message.content) {
if (part.type === "text") {
content.push({ type: "text", text: part.text, cache_control: cacheControl(breakpoints, part.cache) })
continue
}
if (part.type === "reasoning") {
content.push({
type: "thinking",
thinking: part.text,
signature: part.encrypted ?? signatureFromMetadata(part.providerMetadata),
})
continue
}
if (part.type === "tool-call") {
content.push(part.providerExecuted ? lowerServerToolCall(part) : lowerToolCall(part))
continue
}
if (part.type === "tool-result" && part.providerExecuted) {
content.push(yield* lowerServerToolResult(part))
continue
}
return yield* invalid(
`Anthropic Messages assistant messages only support text, reasoning, and tool-call content for now`,
)
}
messages.push({ role: "assistant", content })
continue
}
const content: AnthropicToolResultBlock[] = []
for (const part of message.content) {
if (!ProviderShared.supportsContent(part, ["tool-result"]))
return yield* ProviderShared.unsupportedContent("Anthropic Messages", "tool", ["tool-result"])
content.push({
type: "tool_result",
tool_use_id: part.id,
content: yield* lowerToolResultContent(part),
is_error: part.result.type === "error" ? true : undefined,
cache_control: cacheControl(breakpoints, part.cache),
})
}
messages.push({ role: "user", content })
}
return messages
})
const anthropicOptions = (request: LLMRequest) => request.providerOptions?.anthropic
const lowerThinking = Effect.fn("AnthropicMessages.lowerThinking")(function* (request: LLMRequest) {
const thinking = anthropicOptions(request)?.thinking
if (!ProviderShared.isRecord(thinking) || thinking.type !== "enabled") return undefined
const budget =
typeof thinking.budgetTokens === "number"
? thinking.budgetTokens
: typeof thinking.budget_tokens === "number"
? thinking.budget_tokens
: undefined
if (budget === undefined) return yield* invalid("Anthropic thinking provider option requires budgetTokens")
return { type: "enabled" as const, budget_tokens: budget }
})
const fromRequest = Effect.fn("AnthropicMessages.fromRequest")(function* (request: LLMRequest) {
const toolChoice = request.toolChoice ? yield* lowerToolChoice(request.toolChoice) : undefined
const generation = request.generation
const toolSchemaCompatibility = request.model.compatibility?.toolSchema
const outputLimit = request.model.defaults?.limits?.output ?? request.model.route.defaults.limits?.output ?? 4096
// Allocate the 4-breakpoint budget in invalidation order: tools → system →
// messages. Tools live highest in the cache hierarchy, so when callers
// over-mark we keep their tool hints and shed the message-tail ones first.
const breakpoints = Cache.newBreakpoints(ANTHROPIC_BREAKPOINT_CAP)
const tools =
request.tools.length === 0 || request.toolChoice?.type === "none"
? undefined
: request.tools.map((tool) =>
lowerTool(
breakpoints,
tool,
ToolSchemaProjection.modelCompatibility(tool.inputSchema, toolSchemaCompatibility),
),
)
const system =
request.system.length === 0
? undefined
: request.system.map((part) => ({
type: "text" as const,
text: part.text,
cache_control: cacheControl(breakpoints, part.cache),
}))
const messages = yield* lowerMessages(request, breakpoints)
if (breakpoints.dropped > 0) {
yield* Effect.logWarning(
`Anthropic Messages: dropped ${breakpoints.dropped} cache breakpoint(s); the API allows at most ${ANTHROPIC_BREAKPOINT_CAP} per request.`,
)
}
return {
model: request.model.id,
system,
messages,
tools,
tool_choice: toolChoice,
stream: true as const,
max_tokens: generation?.maxTokens ?? outputLimit,
temperature: generation?.temperature,
top_p: generation?.topP,
top_k: generation?.topK,
stop_sequences: generation?.stop,
thinking: yield* lowerThinking(request),
}
})
// =============================================================================
// Stream Parsing
// =============================================================================
const mapFinishReason = (reason: string | null | undefined): FinishReason => {
if (reason === "end_turn" || reason === "stop_sequence" || reason === "pause_turn") return "stop"
if (reason === "max_tokens") return "length"
if (reason === "tool_use") return "tool-calls"
if (reason === "refusal") return "content-filter"
return "unknown"
}
// Anthropic reports the non-overlapping breakdown natively — its
// `input_tokens` is the *non-cached* count per the Messages API docs, with
// cache reads and writes as separate fields. We sum them to derive the
// inclusive `inputTokens` the rest of the contract expects. Extended
// thinking tokens are *not* broken out by Anthropic — they're billed as
// part of `output_tokens`, so `reasoningTokens` stays `undefined` and
// `outputTokens` carries the combined total.
const mapUsage = (usage: AnthropicUsage | undefined): Usage | undefined => {
if (!usage) return undefined
const nonCached = usage.input_tokens
const cacheRead = usage.cache_read_input_tokens ?? undefined
const cacheWrite = usage.cache_creation_input_tokens ?? undefined
const inputTokens = ProviderShared.sumTokens(nonCached, cacheRead, cacheWrite)
return new Usage({
inputTokens,
outputTokens: usage.output_tokens,
nonCachedInputTokens: nonCached,
cacheReadInputTokens: cacheRead,
cacheWriteInputTokens: cacheWrite,
totalTokens: ProviderShared.totalTokens(inputTokens, usage.output_tokens, undefined),
providerMetadata: { anthropic: usage },
})
}
// Anthropic emits usage on `message_start` and again on `message_delta` — the
// final delta carries the authoritative totals. Right-biased merge: each
// field prefers `right` when defined, falls back to `left`. `inputTokens` is
// recomputed from the merged breakdown so the inclusive total stays
// consistent with `nonCached + cacheRead + cacheWrite`.
const mergeUsage = (left: Usage | undefined, right: Usage | undefined) => {
if (!left) return right
if (!right) return left
const nonCachedInputTokens = right.nonCachedInputTokens ?? left.nonCachedInputTokens
const cacheReadInputTokens = right.cacheReadInputTokens ?? left.cacheReadInputTokens
const cacheWriteInputTokens = right.cacheWriteInputTokens ?? left.cacheWriteInputTokens
const inputTokens = ProviderShared.sumTokens(nonCachedInputTokens, cacheReadInputTokens, cacheWriteInputTokens)
const outputTokens = right.outputTokens ?? left.outputTokens
return new Usage({
inputTokens,
outputTokens,
nonCachedInputTokens,
cacheReadInputTokens,
cacheWriteInputTokens,
totalTokens: ProviderShared.totalTokens(inputTokens, outputTokens, undefined),
providerMetadata: {
anthropic: {
...left.providerMetadata?.["anthropic"],
...right.providerMetadata?.["anthropic"],
},
},
})
}
// Server tool result blocks come whole in `content_block_start` (no streaming
// delta sequence). We convert the payload to a `tool-result` event with
// `providerExecuted: true`. The runtime appends it to the assistant message
// for round-trip; downstream consumers can inspect `result.value` for the
// structured payload.
const SERVER_TOOL_RESULT_NAMES: Record<AnthropicServerToolResultType, string> = {
web_search_tool_result: "web_search",
code_execution_tool_result: "code_execution",
web_fetch_tool_result: "web_fetch",
}
const isServerToolResultType = (type: string): type is AnthropicServerToolResultType => type in SERVER_TOOL_RESULT_NAMES
const serverToolResultEvent = (block: NonNullable<AnthropicEvent["content_block"]>): LLMEvent | undefined => {
if (!block.type || !isServerToolResultType(block.type)) return undefined
const errorPayload =
typeof block.content === "object" && block.content !== null && "type" in block.content
? String((block.content as Record<string, unknown>).type)
: ""
const isError = errorPayload.endsWith("_tool_result_error")
return LLMEvent.toolResult({
id: block.tool_use_id ?? "",
name: SERVER_TOOL_RESULT_NAMES[block.type],
result: isError ? { type: "error", value: block.content } : { type: "json", value: block.content },
providerExecuted: true,
providerMetadata: anthropicMetadata({ blockType: block.type }),
})
}
type StepResult = readonly [ParserState, ReadonlyArray<LLMEvent>]
const NO_EVENTS: StepResult["1"] = []
const onMessageStart = (state: ParserState, event: AnthropicEvent): StepResult => {
const usage = mapUsage(event.message?.usage)
return [usage ? { ...state, usage: mergeUsage(state.usage, usage) } : state, NO_EVENTS]
}
const onContentBlockStart = (state: ParserState, event: AnthropicEvent): StepResult => {
const block = event.content_block
if (!block) return [state, NO_EVENTS]
if ((block.type === "tool_use" || block.type === "server_tool_use") && event.index !== undefined) {
const events: LLMEvent[] = []
const lifecycle = Lifecycle.stepStart(state.lifecycle, events)
return [
{
...state,
lifecycle,
tools: ToolStream.start(state.tools, event.index, {
id: block.id ?? String(event.index),
name: block.name ?? "",
providerExecuted: block.type === "server_tool_use",
}),
},
[...events, LLMEvent.toolInputStart({ id: block.id ?? String(event.index), name: block.name ?? "" })],
]
}
if (block.type === "text" && block.text) {
const events: LLMEvent[] = []
return [
{ ...state, lifecycle: Lifecycle.textDelta(state.lifecycle, events, `text-${event.index ?? 0}`, block.text) },
events,
]
}
if (block.type === "thinking" && block.thinking) {
const events: LLMEvent[] = []
return [
{
...state,
lifecycle: Lifecycle.reasoningDelta(state.lifecycle, events, `reasoning-${event.index ?? 0}`, block.thinking),
},
events,
]
}
const result = serverToolResultEvent(block)
if (!result) return [state, NO_EVENTS]
const events: LLMEvent[] = []
return [{ ...state, lifecycle: Lifecycle.stepStart(state.lifecycle, events) }, [...events, result]]
}
const onContentBlockDelta = Effect.fn("AnthropicMessages.onContentBlockDelta")(function* (
state: ParserState,
event: AnthropicEvent,
) {
const delta = event.delta
if (delta?.type === "text_delta" && delta.text) {
const events: LLMEvent[] = []
return [
{ ...state, lifecycle: Lifecycle.textDelta(state.lifecycle, events, `text-${event.index ?? 0}`, delta.text) },
events,
] satisfies StepResult
}
if (delta?.type === "thinking_delta" && delta.thinking) {
const events: LLMEvent[] = []
return [
{
...state,
lifecycle: Lifecycle.reasoningDelta(state.lifecycle, events, `reasoning-${event.index ?? 0}`, delta.thinking),
},
events,
] satisfies StepResult
}
if (delta?.type === "signature_delta" && delta.signature) {
const events: LLMEvent[] = []
return [
{
...state,
lifecycle: Lifecycle.reasoningEnd(
state.lifecycle,
events,
`reasoning-${event.index ?? 0}`,
anthropicMetadata({ signature: delta.signature }),
),
},
events,
] satisfies StepResult
}
if (delta?.type === "input_json_delta" && event.index !== undefined) {
if (!delta.partial_json) return [state, NO_EVENTS] satisfies StepResult
const result = ToolStream.appendExisting(
ADAPTER,
state.tools,
event.index,
delta.partial_json,
"Anthropic Messages tool argument delta is missing its tool call",
)
if (ToolStream.isError(result)) return yield* result
const events: LLMEvent[] = []
const lifecycle = result.events.length ? Lifecycle.stepStart(state.lifecycle, events) : state.lifecycle
events.push(...result.events)
return [{ ...state, lifecycle, tools: result.tools }, events] satisfies StepResult
}
return [state, NO_EVENTS] satisfies StepResult
})
const onContentBlockStop = Effect.fn("AnthropicMessages.onContentBlockStop")(function* (
state: ParserState,
event: AnthropicEvent,
) {
if (event.index === undefined) return [state, NO_EVENTS] satisfies StepResult
const result = yield* ToolStream.finish(ADAPTER, state.tools, event.index)
const events: LLMEvent[] = []
const resultEvents = result.events ?? []
const lifecycle = resultEvents.length
? Lifecycle.stepStart(state.lifecycle, events)
: Lifecycle.reasoningEnd(
Lifecycle.textEnd(state.lifecycle, events, `text-${event.index}`),
events,
`reasoning-${event.index}`,
)
events.push(...resultEvents)
return [{ ...state, lifecycle, tools: result.tools }, events] satisfies StepResult
})
const onMessageDelta = (state: ParserState, event: AnthropicEvent): StepResult => {
const usage = mergeUsage(state.usage, mapUsage(event.usage))
const events: LLMEvent[] = []
const lifecycle = Lifecycle.finish(state.lifecycle, events, {
reason: mapFinishReason(event.delta?.stop_reason),
usage,
providerMetadata: event.delta?.stop_sequence
? anthropicMetadata({ stopSequence: event.delta.stop_sequence })
: undefined,
})
return [{ ...state, lifecycle, usage }, events]
}
// Prefix `error.type` so overloads, rate limits, and quota errors are visible
// even when the provider message is generic or empty.
const providerErrorMessage = (event: AnthropicEvent): string => {
const type = event.error?.type
const message = event.error?.message
if (type && message) return `${type}: ${message}`
return message || type || "Anthropic Messages stream error"
}
const onError = (state: ParserState, event: AnthropicEvent): StepResult => [
state,
[
LLMEvent.providerError({
message: providerErrorMessage(event),
classification: isContextOverflow(event.error?.message ?? "") ? "context-overflow" : undefined,
}),
],
]
const step = (state: ParserState, event: AnthropicEvent) => {
if (event.type === "message_start") return Effect.succeed(onMessageStart(state, event))
if (event.type === "content_block_start") return Effect.succeed(onContentBlockStart(state, event))
if (event.type === "content_block_delta") return onContentBlockDelta(state, event)
if (event.type === "content_block_stop") return onContentBlockStop(state, event)
if (event.type === "message_delta") return Effect.succeed(onMessageDelta(state, event))
if (event.type === "error") return Effect.succeed(onError(state, event))
return Effect.succeed<StepResult>([state, NO_EVENTS])
}
// =============================================================================
// Protocol And Anthropic Route
// =============================================================================
/**
* The Anthropic Messages protocol — request body construction, body schema,
* and the streaming-event state machine. Used by native Anthropic Cloud and
* (once registered) Vertex Anthropic / Bedrock-hosted Anthropic passthrough.
*/
export const protocol = Protocol.make({
id: ADAPTER,
body: {
schema: AnthropicMessagesBody,
from: fromRequest,
},
stream: {
event: Protocol.jsonEvent(AnthropicEvent),
initial: () => ({ tools: ToolStream.empty<number>(), lifecycle: Lifecycle.initial() }),
step,
},
})
export const route = Route.make({
id: ADAPTER,
provider: "anthropic",
protocol,
endpoint: Endpoint.path(PATH, { baseURL: DEFAULT_BASE_URL }),
auth: Auth.none,
framing: Framing.sse,
headers: () => ({ "anthropic-version": "2023-06-01" }),
})
export * as AnthropicMessages from "./anthropic-messages"
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