refactor(llm): colocate per-type factories on their namespaces (#26799)

packages/llm/AGENTS.md

@@ -8,6 +8,10 @@
 - In `Effect.gen`, yield yieldable errors directly (`return yield* new MyError(...)`) instead of `Effect.fail(new MyError(...))`.
 - Use `Effect.void` instead of `Effect.succeed(undefined)` when the successful value is intentionally void.
 
+## Conventions
+
+Per-type constructors live on the type's namespace, not as top-level re-exports. Use `Message.user(...)`, `Message.assistant(...)`, `Message.tool(...)`, `ToolDefinition.make(...)`, `ToolCallPart.make(...)`, `ToolResultPart.make(...)`, `ToolChoice.make(...)`, `ToolChoice.named(...)`, `SystemPart.make(...)`, and `GenerationOptions.make(...)` directly. The top-level `LLM` namespace is reserved for the request-shaped call API: `LLM.request`, `LLM.generate`, `LLM.stream`, `LLM.model`, `LLM.updateRequest`, `LLM.generateObject`. Two ways to construct the same thing is one too many.
+
 ## Tests
 
 - Use `testEffect(...)` from `test/lib/effect.ts` for tests requiring Effect layers.
@@ -166,12 +170,12 @@ If you find yourself copying a 3-to-5-line snippet between two protocols, lift i
 Tool loops are represented in common messages and events:
 
 ```ts
-const call = LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })
-const result = LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } })
+const call = ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })
+const result = Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } })
 
 const followUp = LLM.request({
   model,
-  messages: [LLM.user("Weather?"), LLM.assistant([call]), result],
+  messages: [Message.user("Weather?"), Message.assistant([call]), result],
 })
 ```
 

packages/llm/src/llm.ts

@@ -44,32 +44,8 @@ export type RequestInput = Omit<
 
 export const limits = modelLimits
 
-export const text = Message.text
-
-export const system = SystemPart.make
-
-export const message = Message.make
-
-export const user = Message.user
-
-export const assistant = Message.assistant
-
 export const model = modelRef
 
-export const toolDefinition = ToolDefinition.make
-
-export const toolCall = ToolCallPart.make
-
-export const toolResult = ToolResultPart.make
-
-export const toolMessage = Message.tool
-
-export const toolChoiceName = ToolChoice.named
-
-export const toolChoice = ToolChoice.make
-
-export const generation = GenerationOptions.make
-
 export const generate = LLMClient.generate
 
 export const stream = LLMClient.stream
@@ -95,10 +71,10 @@ export const request = (input: RequestInput) => {
   return new LLMRequest({
     ...rest,
     system: SystemPart.content(requestSystem),
-    messages: [...(messages?.map(message) ?? []), ...(prompt === undefined ? [] : [user(prompt)])],
-    tools: tools?.map(toolDefinition) ?? [],
-    toolChoice: requestToolChoice ? toolChoice(requestToolChoice) : undefined,
-    generation: requestGeneration === undefined ? undefined : generation(requestGeneration),
+    messages: [...(messages?.map(Message.make) ?? []), ...(prompt === undefined ? [] : [Message.user(prompt)])],
+    tools: tools?.map(ToolDefinition.make) ?? [],
+    toolChoice: requestToolChoice ? ToolChoice.make(requestToolChoice) : undefined,
+    generation: requestGeneration === undefined ? undefined : GenerationOptions.make(requestGeneration),
     providerOptions: requestProviderOptions,
     http: requestHttp === undefined ? undefined : HttpOptions.make(requestHttp),
   })

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

@@ -1,6 +1,6 @@
 import { describe, expect, test } from "bun:test"
 import { Effect } from "effect"
-import { CacheHint, LLM } from "../src"
+import { CacheHint, LLM, Message } from "../src"
 import { LLMClient } from "../src/route"
 import * as AnthropicMessages from "../src/protocols/anthropic-messages"
 import * as BedrockConverse from "../src/protocols/bedrock-converse"
@@ -59,7 +59,7 @@ describe("applyCachePolicy", () => {
           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")],
+          messages: [Message.user("first user"), Message.assistant("assistant reply"), Message.user("latest user message")],
           cache: "auto",
         }),
       )
@@ -122,7 +122,7 @@ describe("applyCachePolicy", () => {
           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")],
+          messages: [Message.user("first user"), Message.assistant("reply"), Message.user("latest user")],
           cache: "auto",
         }),
       )
@@ -221,7 +221,7 @@ describe("applyCachePolicy", () => {
       const prepared = yield* LLMClient.prepare(
         LLM.request({
           model: anthropicModel,
-          messages: [LLM.user("u1"), LLM.assistant("a1"), LLM.user("u2"), LLM.assistant("a2")],
+          messages: [Message.user("u1"), Message.assistant("a1"), Message.user("u2"), Message.assistant("a2")],
           cache: { messages: { tail: 2 } },
         }),
       )
@@ -239,7 +239,7 @@ describe("applyCachePolicy", () => {
       const prepared = yield* LLMClient.prepare(
         LLM.request({
           model: anthropicModel,
-          messages: [LLM.user("u1"), LLM.assistant("a1"), LLM.user("u2")],
+          messages: [Message.user("u1"), Message.assistant("a1"), Message.user("u2")],
           cache: { messages: "latest-assistant" },
         }),
       )

packages/llm/test/llm.test.ts

@@ -1,6 +1,6 @@
 import { describe, expect, test } from "bun:test"
 import { LLM, LLMResponse } from "../src"
-import { LLMRequest, Message, ModelRef, ToolChoice, ToolDefinition } from "../src/schema"
+import { LLMRequest, Message, ModelRef, ToolCallPart, ToolChoice, ToolDefinition, ToolResultPart } from "../src/schema"
 
 describe("llm constructors", () => {
   test("builds canonical schema classes from ergonomic input", () => {
@@ -28,7 +28,7 @@ describe("llm constructors", () => {
     })
     const updated = LLM.updateRequest(base, {
       generation: { maxTokens: 20 },
-      messages: [...base.messages, LLM.assistant("Hi.")],
+      messages: [...base.messages, Message.assistant("Hi.")],
     })
 
     expect(updated).toBeInstanceOf(LLMRequest)
@@ -70,7 +70,7 @@ describe("llm constructors", () => {
       model: LLM.model({ id: "fake-model", provider: "fake", route: "openai-chat", baseURL: "https://fake.local" }),
       prompt: "Say hello.",
     })
-    const updated = LLMRequest.update(base, { messages: [...base.messages, LLM.assistant("Hi.")] })
+    const updated = LLMRequest.update(base, { messages: [...base.messages, Message.assistant("Hi.")] })
 
     expect(updated).toBeInstanceOf(LLMRequest)
     expect(updated.id).toBe("req_1")
@@ -91,18 +91,18 @@ describe("llm constructors", () => {
   })
 
   test("builds tool choices from names and tools", () => {
-    const tool = LLM.toolDefinition({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })
+    const tool = ToolDefinition.make({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })
 
     expect(tool).toBeInstanceOf(ToolDefinition)
-    expect(LLM.toolChoice("lookup")).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
-    expect(LLM.toolChoiceName("required")).toEqual(new ToolChoice({ type: "tool", name: "required" }))
-    expect(LLM.toolChoice(tool)).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
+    expect(ToolChoice.make("lookup")).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
+    expect(ToolChoice.named("required")).toEqual(new ToolChoice({ type: "tool", name: "required" }))
+    expect(ToolChoice.make(tool)).toEqual(new ToolChoice({ type: "tool", name: "lookup" }))
   })
 
   test("builds tool choice modes from reserved strings", () => {
-    expect(LLM.toolChoice("auto")).toEqual(new ToolChoice({ type: "auto" }))
-    expect(LLM.toolChoice("none")).toEqual(new ToolChoice({ type: "none" }))
-    expect(LLM.toolChoice("required")).toEqual(new ToolChoice({ type: "required" }))
+    expect(ToolChoice.make("auto")).toEqual(new ToolChoice({ type: "auto" }))
+    expect(ToolChoice.make("none")).toEqual(new ToolChoice({ type: "none" }))
+    expect(ToolChoice.make("required")).toEqual(new ToolChoice({ type: "required" }))
     expect(
       LLM.request({
         model: LLM.model({ id: "fake-model", provider: "fake", route: "openai-chat", baseURL: "https://fake.local" }),
@@ -113,11 +113,11 @@ describe("llm constructors", () => {
   })
 
   test("builds assistant tool calls and tool result messages", () => {
-    const call = LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })
-    const result = LLM.toolResult({ id: "call_1", name: "lookup", result: { temperature: 72 } })
+    const call = ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })
+    const result = ToolResultPart.make({ id: "call_1", name: "lookup", result: { temperature: 72 } })
 
-    expect(LLM.assistant([call]).content).toEqual([call])
-    expect(LLM.toolMessage(result).content).toEqual([
+    expect(Message.assistant([call]).content).toEqual([call])
+    expect(Message.tool(result).content).toEqual([
       { type: "tool-result", id: "call_1", name: "lookup", result: { type: "json", value: { temperature: 72 } } },
     ])
   })

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

@@ -1,7 +1,7 @@
 import { Redactor } from "@opencode-ai/http-recorder"
 import { describe, expect } from "bun:test"
 import { Effect } from "effect"
-import { LLM, LLMError } from "../../src"
+import { LLM, LLMError, Message, ToolCallPart } from "../../src"
 import { LLMClient } from "../../src/route"
 import * as AnthropicMessages from "../../src/protocols/anthropic-messages"
 import { weatherToolName } from "../recorded-scenarios"
@@ -16,12 +16,12 @@ const malformedToolOrderRequest = LLM.request({
   id: "recorded_anthropic_malformed_tool_order",
   model,
   messages: [
-    LLM.assistant([
-      LLM.toolCall({ id: "call_1", name: weatherToolName, input: { city: "Paris" } }),
+    Message.assistant([
+      ToolCallPart.make({ id: "call_1", name: weatherToolName, input: { city: "Paris" } }),
       { type: "text", text: "I will check the weather." },
     ]),
-    LLM.toolMessage({ id: "call_1", name: weatherToolName, result: { temperature: "72F" } }),
-    LLM.user("Use that result to answer briefly."),
+    Message.tool({ id: "call_1", name: weatherToolName, result: { temperature: "72F" } }),
+    Message.user("Use that result to answer briefly."),
   ],
   tools: [{ name: weatherToolName, description: "Get weather", inputSchema: { type: "object", properties: {} } }],
 })

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

@@ -1,6 +1,6 @@
 import { describe, expect } from "bun:test"
 import { Effect } from "effect"
-import { CacheHint, LLM, LLMError, Usage } from "../../src"
+import { CacheHint, LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
 import { LLMClient } from "../../src/route"
 import * as AnthropicMessages from "../../src/protocols/anthropic-messages"
 import { it } from "../lib/effect"
@@ -47,9 +47,9 @@ describe("Anthropic Messages route", () => {
           id: "req_tool_result",
           model,
           messages: [
-            LLM.user("What is the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
-            LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
+            Message.user("What is the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
+            Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
           ],
           cache: "none",
         }),
@@ -77,7 +77,7 @@ describe("Anthropic Messages route", () => {
         LLM.request({
           model,
           messages: [
-            LLM.assistant([
+            Message.assistant([
               { type: "reasoning", text: "thinking", providerMetadata: { anthropic: { signature: "sig_1" } } },
             ]),
           ],
@@ -304,8 +304,8 @@ describe("Anthropic Messages route", () => {
           id: "req_round_trip",
           model,
           messages: [
-            LLM.user("Search for something."),
-            LLM.assistant([
+            Message.user("Search for something."),
+            Message.assistant([
               {
                 type: "tool-call",
                 id: "srvtoolu_abc",
@@ -322,7 +322,7 @@ describe("Anthropic Messages route", () => {
               },
               { type: "text", text: "Found it." },
             ]),
-            LLM.user("Thanks."),
+            Message.user("Thanks."),
           ],
         }),
       )
@@ -355,7 +355,7 @@ describe("Anthropic Messages route", () => {
           id: "req_unknown_server_tool",
           model,
           messages: [
-            LLM.assistant([
+            Message.assistant([
               {
                 type: "tool-result",
                 id: "srvtoolu_abc",
@@ -378,7 +378,7 @@ describe("Anthropic Messages route", () => {
         LLM.request({
           id: "req_media",
           model,
-          messages: [LLM.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
+          messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
         }),
       ).pipe(Effect.flip)
 
@@ -416,9 +416,9 @@ describe("Anthropic Messages route", () => {
             },
           ],
           messages: [
-            LLM.user("What's the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: {} })]),
-            LLM.toolMessage({
+            Message.user("What's the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })]),
+            Message.tool({
               id: "call_1",
               name: "lookup",
               result: { temp: 72 },
@@ -501,7 +501,7 @@ describe("Anthropic Messages route", () => {
             },
           ],
           system: [{ type: "text", text: "system-tail", cache: hint }],
-          messages: [LLM.user([{ type: "text", text: "message-tail", cache: hint }])],
+          messages: [Message.user([{ type: "text", text: "message-tail", cache: hint }])],
         }),
       )
 

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

@@ -2,7 +2,7 @@ import { EventStreamCodec } from "@smithy/eventstream-codec"
 import { fromUtf8, toUtf8 } from "@smithy/util-utf8"
 import { describe, expect } from "bun:test"
 import { Effect } from "effect"
-import { CacheHint, LLM } from "../../src"
+import { CacheHint, LLM, Message, ToolCallPart, ToolChoice } from "../../src"
 import { LLMClient } from "../../src/route"
 import * as BedrockConverse from "../../src/protocols/bedrock-converse"
 import { it } from "../lib/effect"
@@ -94,7 +94,7 @@ describe("Bedrock Converse route", () => {
               inputSchema: { type: "object", properties: { query: { type: "string" } }, required: ["query"] },
             },
           ],
-          toolChoice: LLM.toolChoice({ type: "required" }),
+          toolChoice: ToolChoice.make({ type: "required" }),
         }),
       )
 
@@ -124,9 +124,9 @@ describe("Bedrock Converse route", () => {
           id: "req_history",
           model,
           messages: [
-            LLM.user("What is the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "tool_1", name: "lookup", input: { query: "weather" } })]),
-            LLM.toolMessage({ id: "tool_1", name: "lookup", result: { forecast: "sunny" } }),
+            Message.user("What is the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "tool_1", name: "lookup", input: { query: "weather" } })]),
+            Message.tool({ id: "tool_1", name: "lookup", result: { forecast: "sunny" } }),
           ],
           cache: "none",
         }),
@@ -294,8 +294,8 @@ describe("Bedrock Converse route", () => {
           model,
           system: [{ type: "text", text: "System prefix.", cache }],
           messages: [
-            LLM.user([{ type: "text", text: "User prefix.", cache }]),
-            LLM.assistant([{ type: "text", text: "Assistant prefix.", cache }]),
+            Message.user([{ type: "text", text: "User prefix.", cache }]),
+            Message.assistant([{ type: "text", text: "Assistant prefix.", cache }]),
           ],
           generation: { maxTokens: 16, temperature: 0 },
         }),
@@ -335,7 +335,7 @@ describe("Bedrock Converse route", () => {
           id: "req_image",
           model,
           messages: [
-            LLM.user([
+            Message.user([
               { type: "text", text: "What is in this image?" },
               { type: "media", mediaType: "image/png", data: "AAAA" },
               { type: "media", mediaType: "image/jpeg", data: "BBBB" },
@@ -371,7 +371,7 @@ describe("Bedrock Converse route", () => {
         LLM.request({
           id: "req_image_bytes",
           model,
-          messages: [LLM.user([{ type: "media", mediaType: "image/png", data: new Uint8Array([1, 2, 3, 4, 5]) }])],
+          messages: [Message.user([{ type: "media", mediaType: "image/png", data: new Uint8Array([1, 2, 3, 4, 5]) }])],
         }),
       )
 
@@ -394,7 +394,7 @@ describe("Bedrock Converse route", () => {
           id: "req_doc",
           model,
           messages: [
-            LLM.user([
+            Message.user([
               { type: "media", mediaType: "application/pdf", data: "PDFDATA", filename: "report.pdf" },
               { type: "media", mediaType: "text/csv", data: "CSVDATA" },
             ]),
@@ -424,7 +424,7 @@ describe("Bedrock Converse route", () => {
         LLM.request({
           id: "req_bad_image",
           model,
-          messages: [LLM.user([{ type: "media", mediaType: "image/svg+xml", data: "x" }])],
+          messages: [Message.user([{ type: "media", mediaType: "image/svg+xml", data: "x" }])],
         }),
       ).pipe(Effect.flip)
 
@@ -438,7 +438,7 @@ describe("Bedrock Converse route", () => {
         LLM.request({
           id: "req_bad_doc",
           model,
-          messages: [LLM.user([{ type: "media", mediaType: "application/x-tar", data: "x", filename: "a.tar" }])],
+          messages: [Message.user([{ type: "media", mediaType: "application/x-tar", data: "x", filename: "a.tar" }])],
         }),
       ).pipe(Effect.flip)
 
@@ -471,9 +471,9 @@ describe("Bedrock Converse route", () => {
           model,
           tools: [{ name: "lookup", description: "lookup", inputSchema: { type: "object", properties: {} }, cache }],
           messages: [
-            LLM.user("What's the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: {} })]),
-            LLM.toolMessage({ id: "call_1", name: "lookup", result: { temp: 72 }, cache }),
+            Message.user("What's the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })]),
+            Message.tool({ id: "call_1", name: "lookup", result: { temp: 72 }, cache }),
           ],
           cache: "none",
         }),
@@ -583,7 +583,7 @@ describe("Bedrock Converse recorded", () => {
           system: "Call tools exactly as requested.",
           prompt: "Call get_weather with city exactly Paris.",
           tools: [weatherTool],
-          toolChoice: LLM.toolChoice(weatherTool),
+          toolChoice: ToolChoice.make(weatherTool),
           cache: "none",
           generation: { maxTokens: 80, temperature: 0 },
         }),

packages/llm/test/provider/gemini.test.ts

@@ -1,6 +1,6 @@
 import { describe, expect } from "bun:test"
 import { Effect } from "effect"
-import { LLM, LLMError, Usage } from "../../src"
+import { LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
 import { LLMClient } from "../../src/route"
 import * as Gemini from "../../src/protocols/gemini"
 import { it } from "../lib/effect"
@@ -49,12 +49,12 @@ describe("Gemini route", () => {
           ],
           toolChoice: { type: "tool", name: "lookup" },
           messages: [
-            LLM.user([
+            Message.user([
               { type: "text", text: "What is in this image?" },
               { type: "media", mediaType: "image/png", data: "AAECAw==" },
             ]),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
-            LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
+            Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
           ],
         }),
       )
@@ -353,7 +353,7 @@ describe("Gemini route", () => {
         LLM.request({
           id: "req_media",
           model,
-          messages: [LLM.assistant({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
+          messages: [Message.assistant({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
         }),
       ).pipe(Effect.flip)
 

packages/llm/test/provider/openai-chat.test.ts

@@ -1,7 +1,7 @@
 import { describe, expect } from "bun:test"
 import { Effect, Schema, Stream } from "effect"
 import { HttpClientRequest } from "effect/unstable/http"
-import { LLM, LLMError, Usage } from "../../src"
+import { LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
 import * as Azure from "../../src/providers/azure"
 import * as OpenAI from "../../src/providers/openai"
 import * as OpenAIChat from "../../src/protocols/openai-chat"
@@ -149,9 +149,9 @@ describe("OpenAI Chat route", () => {
           id: "req_tool_result",
           model,
           messages: [
-            LLM.user("What is the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
-            LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
+            Message.user("What is the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
+            Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
           ],
         }),
       )
@@ -185,7 +185,7 @@ describe("OpenAI Chat route", () => {
         LLM.request({
           id: "req_media",
           model,
-          messages: [LLM.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
+          messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
         }),
       ).pipe(Effect.flip)
 
@@ -199,7 +199,7 @@ describe("OpenAI Chat route", () => {
         LLM.request({
           id: "req_reasoning",
           model,
-          messages: [LLM.assistant({ type: "reasoning", text: "hidden" })],
+          messages: [Message.assistant({ type: "reasoning", text: "hidden" })],
         }),
       ).pipe(Effect.flip)
 

packages/llm/test/provider/openai-compatible-chat.test.ts

@@ -1,7 +1,7 @@
 import { describe, expect } from "bun:test"
 import { Effect, Schema } from "effect"
 import { HttpClientRequest } from "effect/unstable/http"
-import { LLM } from "../../src"
+import { LLM, Message, ToolCallPart } from "../../src"
 import { LLMClient } from "../../src/route"
 import * as OpenAICompatible from "../../src/providers/openai-compatible"
 import * as OpenAICompatibleChat from "../../src/protocols/openai-compatible-chat"
@@ -157,9 +157,9 @@ describe("OpenAI-compatible Chat route", () => {
           ],
           toolChoice: "lookup",
           messages: [
-            LLM.user("What is the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
-            LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
+            Message.user("What is the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
+            Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
           ],
         }),
       )

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

@@ -1,7 +1,7 @@
 import { describe, expect } from "bun:test"
 import { ConfigProvider, Effect, Layer, Stream } from "effect"
 import { Headers, HttpClientRequest } from "effect/unstable/http"
-import { LLM, LLMError, Usage } from "../../src"
+import { LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
 import { Auth, LLMClient, RequestExecutor, WebSocketExecutor } from "../../src/route"
 import * as Azure from "../../src/providers/azure"
 import * as OpenAI from "../../src/providers/openai"
@@ -251,9 +251,9 @@ describe("OpenAI Responses route", () => {
           id: "req_tool_result",
           model,
           messages: [
-            LLM.user("What is the weather?"),
-            LLM.assistant([LLM.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
-            LLM.toolMessage({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
+            Message.user("What is the weather?"),
+            Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
+            Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
           ],
         }),
       )
@@ -508,7 +508,7 @@ describe("OpenAI Responses route", () => {
         LLM.request({
           id: "req_media",
           model,
-          messages: [LLM.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
+          messages: [Message.user({ type: "media", mediaType: "image/png", data: "AAECAw==" })],
         }),
       ).pipe(Effect.flip)
 

packages/llm/test/recorded-scenarios.ts

@@ -1,6 +1,6 @@
 import { expect } from "bun:test"
 import { Effect, Schema, Stream } from "effect"
-import { LLM, LLMEvent, LLMResponse, type LLMRequest, type ModelRef } from "../src"
+import { LLM, LLMEvent, LLMResponse, ToolChoice, ToolDefinition, type LLMRequest, type ModelRef } from "../src"
 import { LLMClient } from "../src/route"
 import { tool } from "../src/tool"
 
@@ -18,7 +18,7 @@ export const LARGE_CACHEABLE_SYSTEM = (() => {
   return sentence.repeat(250)
 })()
 
-export const weatherTool = LLM.toolDefinition({
+export const weatherTool = ToolDefinition.make({
   name: weatherToolName,
   description: "Get current weather for a city.",
   inputSchema: {
@@ -70,7 +70,7 @@ export const weatherToolRequest = (input: {
     system: "Call tools exactly as requested.",
     prompt: "Call get_weather with city exactly Paris.",
     tools: [weatherTool],
-    toolChoice: LLM.toolChoice(weatherTool),
+    toolChoice: ToolChoice.make(weatherTool),
     cache: "none",
     generation:
       input.temperature === false

packages/llm/test/tool-runtime.test.ts

@@ -1,6 +1,6 @@
 import { describe, expect } from "bun:test"
 import { Effect, Schema, Stream } from "effect"
-import { LLM, LLMEvent, LLMRequest, LLMResponse } from "../src"
+import { GenerationOptions, LLM, LLMEvent, LLMRequest, LLMResponse, ToolChoice } from "../src"
 import { LLMClient } from "../src/route"
 import * as AnthropicMessages from "../src/protocols/anthropic-messages"
 import * as OpenAIChat from "../src/protocols/openai-chat"
@@ -78,8 +78,8 @@ describe("LLMClient tools", () => {
 
       yield* TestToolRuntime.runTools({
         request: LLMRequest.update(baseRequest, {
-          generation: LLM.generation({ maxTokens: 50 }),
-          toolChoice: LLM.toolChoice("auto"),
+          generation: GenerationOptions.make({ maxTokens: 50 }),
+          toolChoice: ToolChoice.make("auto"),
         }),
         tools: { get_weather },
       }).pipe(Stream.runCollect, Effect.provide(layer))