feat(llm): cache-policy auto-placement (#26786)

packages/llm/test/cache-policy.test.ts

@@ -0,0 +1,262 @@
+import { describe, expect, test } from "bun:test"
+import { Effect } from "effect"
+import { CacheHint, LLM } from "../src"
+import { LLMClient } from "../src/route"
+import * as AnthropicMessages from "../src/protocols/anthropic-messages"
+import * as BedrockConverse from "../src/protocols/bedrock-converse"
+import * as Gemini from "../src/protocols/gemini"
+import * as OpenAIChat from "../src/protocols/openai-chat"
+import { applyCachePolicy } from "../src/cache-policy"
+import { it } from "./lib/effect"
+
+const anthropicModel = AnthropicMessages.model({
+  id: "claude-sonnet-4-5",
+  baseURL: "https://api.anthropic.test/v1/",
+  headers: { "x-api-key": "test" },
+})
+
+const bedrockModel = BedrockConverse.model({
+  id: "anthropic.claude-3-5-sonnet-20241022-v2:0",
+  credentials: { region: "us-east-1", accessKeyId: "fixture", secretAccessKey: "fixture" },
+})
+
+const openaiModel = OpenAIChat.model({
+  id: "gpt-4o-mini",
+  baseURL: "https://api.openai.test/v1/",
+  headers: { authorization: "Bearer test" },
+})
+
+const geminiModel = Gemini.model({
+  id: "gemini-2.5-flash",
+  baseURL: "https://generativelanguage.test/v1beta/",
+  headers: { "x-goog-api-key": "test" },
+})
+
+describe("applyCachePolicy", () => {
+  it.effect("undefined cache leaves the request untouched (opt-in default)", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          system: "You are concise.",
+          prompt: "hi",
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        system: [{ type: "text", text: "You are concise.", cache_control: undefined }],
+      })
+    }),
+  )
+
+  it.effect("'auto' marks the last tool, last system part, and latest user message on Anthropic", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          system: "Sys A",
+          tools: [{ name: "t1", description: "t1", inputSchema: { type: "object", properties: {} } }],
+          messages: [
+            LLM.user("first user"),
+            LLM.assistant("assistant reply"),
+            LLM.user("latest user message"),
+          ],
+          cache: "auto",
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        tools: [{ name: "t1", cache_control: { type: "ephemeral" } }],
+        system: [{ type: "text", text: "Sys A", cache_control: { type: "ephemeral" } }],
+        messages: [
+          { role: "user", content: [{ type: "text", text: "first user" }] },
+          { role: "assistant", content: [{ type: "text", text: "assistant reply" }] },
+          {
+            role: "user",
+            content: [{ type: "text", text: "latest user message", cache_control: { type: "ephemeral" } }],
+          },
+        ],
+      })
+    }),
+  )
+
+  it.effect("'auto' is a no-op on OpenAI (implicit caching protocol)", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: openaiModel,
+          system: "Sys",
+          prompt: "hi",
+          cache: "auto",
+        }),
+      )
+
+      const body = prepared.body as { messages: Array<{ content: unknown }> }
+      // OpenAI doesn't accept cache_control on messages — policy must skip.
+      const flat = JSON.stringify(body)
+      expect(flat).not.toContain("cache_control")
+      expect(flat).not.toContain("cachePoint")
+    }),
+  )
+
+  it.effect("'auto' is a no-op on Gemini (out-of-band caching protocol)", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: geminiModel,
+          system: "Sys",
+          prompt: "hi",
+          cache: "auto",
+        }),
+      )
+
+      const flat = JSON.stringify(prepared.body)
+      expect(flat).not.toContain("cache_control")
+      expect(flat).not.toContain("cachePoint")
+    }),
+  )
+
+  it.effect("'auto' on Bedrock emits cachePoint markers in the right places", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: bedrockModel,
+          system: "Sys",
+          tools: [{ name: "t1", description: "t1", inputSchema: { type: "object", properties: {} } }],
+          messages: [LLM.user("first user"), LLM.assistant("reply"), LLM.user("latest user")],
+          cache: "auto",
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        toolConfig: {
+          tools: [{ toolSpec: { name: "t1" } }, { cachePoint: { type: "default" } }],
+        },
+        system: [{ text: "Sys" }, { cachePoint: { type: "default" } }],
+        messages: [
+          { role: "user", content: [{ text: "first user" }] },
+          { role: "assistant", content: [{ text: "reply" }] },
+          { role: "user", content: [{ text: "latest user" }, { cachePoint: { type: "default" } }] },
+        ],
+      })
+    }),
+  )
+
+  it.effect("'none' disables auto placement even when manual hints exist", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          system: "Sys",
+          tools: [{ name: "t1", description: "t1", inputSchema: { type: "object", properties: {} } }],
+          prompt: "hi",
+          cache: "none",
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        tools: [{ name: "t1", cache_control: undefined }],
+        system: [{ type: "text", text: "Sys", cache_control: undefined }],
+      })
+    }),
+  )
+
+  it.effect("granular object form: tools-only marks just tools", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          system: "Sys",
+          tools: [{ name: "t1", description: "t1", inputSchema: { type: "object", properties: {} } }],
+          prompt: "hi",
+          cache: { tools: true },
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        tools: [{ name: "t1", cache_control: { type: "ephemeral" } }],
+        system: [{ type: "text", text: "Sys", cache_control: undefined }],
+      })
+    }),
+  )
+
+  it.effect("auto policy preserves manual CacheHints on other parts", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          system: [
+            { type: "text", text: "first system", cache: new CacheHint({ type: "ephemeral", ttlSeconds: 3600 }) },
+            { type: "text", text: "last system" },
+          ],
+          prompt: "hi",
+          cache: "auto",
+        }),
+      )
+
+      const body = prepared.body as { system: Array<{ text: string; cache_control?: unknown }> }
+      expect(body.system[0]?.cache_control).toEqual({ type: "ephemeral", ttl: "1h" })
+      expect(body.system[1]?.cache_control).toEqual({ type: "ephemeral" })
+    }),
+  )
+
+  it.effect("ttlSeconds in the policy flows through to wire markers", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          system: "Sys",
+          prompt: "hi",
+          cache: { system: true, ttlSeconds: 3600 },
+        }),
+      )
+
+      expect(prepared.body).toMatchObject({
+        system: [{ type: "text", text: "Sys", cache_control: { type: "ephemeral", ttl: "1h" } }],
+      })
+    }),
+  )
+
+  it.effect("messages: { tail: 2 } marks the last 2 message boundaries", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          messages: [LLM.user("u1"), LLM.assistant("a1"), LLM.user("u2"), LLM.assistant("a2")],
+          cache: { messages: { tail: 2 } },
+        }),
+      )
+
+      const body = prepared.body as { messages: Array<{ content: Array<{ cache_control?: unknown }> }> }
+      expect(body.messages[0]?.content[0]?.cache_control).toBeUndefined()
+      expect(body.messages[1]?.content[0]?.cache_control).toBeUndefined()
+      expect(body.messages[2]?.content[0]?.cache_control).toEqual({ type: "ephemeral" })
+      expect(body.messages[3]?.content[0]?.cache_control).toEqual({ type: "ephemeral" })
+    }),
+  )
+
+  it.effect("'latest-assistant' marks the last assistant message", () =>
+    Effect.gen(function* () {
+      const prepared = yield* LLMClient.prepare(
+        LLM.request({
+          model: anthropicModel,
+          messages: [LLM.user("u1"), LLM.assistant("a1"), LLM.user("u2")],
+          cache: { messages: "latest-assistant" },
+        }),
+      )
+
+      const body = prepared.body as { messages: Array<{ content: Array<{ cache_control?: unknown }> }> }
+      expect(body.messages[0]?.content[0]?.cache_control).toBeUndefined()
+      expect(body.messages[1]?.content[0]?.cache_control).toEqual({ type: "ephemeral" })
+      expect(body.messages[2]?.content[0]?.cache_control).toBeUndefined()
+    }),
+  )
+
+  test("returns the same request reference when policy is a no-op (pure function)", () => {
+    const request = LLM.request({
+      model: anthropicModel,
+      prompt: "hi",
+    })
+    expect(applyCachePolicy(request)).toBe(request)
+  })
+})

packages/llm/test/provider/anthropic-messages-cache.recorded.test.ts

@@ -28,7 +28,12 @@ const recorded = recordedTests({
   provider: "anthropic",
   protocol: "anthropic-messages",
   requires: ["ANTHROPIC_API_KEY"],
-  options: { redactor: Redactor.defaults({ requestHeaders: { allow: ["content-type", "anthropic-version"] } }) },
+  // Two identical requests in one cassette — match by recording order so the
+  // second call replays the cached-hit interaction.
+  options: {
+    dispatch: "sequential",
+    redactor: Redactor.defaults({ requestHeaders: { allow: ["content-type", "anthropic-version"] } }),
+  },
 })
 
 describe("Anthropic Messages cache recorded", () => {

packages/llm/test/provider/bedrock-converse-cache.recorded.test.ts

@@ -35,6 +35,9 @@ const recorded = recordedTests({
   provider: "amazon-bedrock",
   protocol: "bedrock-converse",
   requires: ["AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY"],
+  // Two identical requests in one cassette — match by recording order so the
+  // second call replays the cached-hit interaction.
+  options: { dispatch: "sequential" },
 })
 
 describe("Bedrock Converse cache recorded", () => {

packages/llm/test/provider/gemini-cache.recorded.test.ts

@@ -8,7 +8,7 @@ import { recordedTests } from "../recorded-test"
 
 const model = Gemini.model({
   id: "gemini-2.5-flash",
-  apiKey: process.env.GEMINI_API_KEY ?? "fixture",
+  apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY ?? process.env.GEMINI_API_KEY ?? "fixture",
 })
 
 // Gemini does implicit prefix caching on 2.5+ models above ~1024 tokens. The
@@ -28,7 +28,10 @@ const recorded = recordedTests({
   prefix: "gemini-cache",
   provider: "google",
   protocol: "gemini",
-  requires: ["GEMINI_API_KEY"],
+  requires: ["GOOGLE_GENERATIVE_AI_API_KEY"],
+  // Two identical requests in one cassette — match by recording order so the
+  // second call replays the cached-hit interaction.
+  options: { dispatch: "sequential" },
 })
 
 describe("Gemini cache recorded", () => {

packages/llm/test/provider/openai-responses-cache.recorded.test.ts

@@ -29,6 +29,9 @@ const recorded = recordedTests({
   provider: "openai",
   protocol: "openai-responses",
   requires: ["OPENAI_API_KEY"],
+  // Two identical requests in one cassette — match by recording order so the
+  // second call replays the cached-hit interaction, not the cold-miss one.
+  options: { dispatch: "sequential" },
 })
 
 describe("OpenAI Responses cache recorded", () => {