fix(langchain/middleware): allow model strings in summarization middleware

Author: hntrl

.changeset/chatty-spies-shine.md

@@ -0,0 +1,5 @@
+---
+"langchain": patch
+---
+
+allow for model strings in summarization middleware

libs/langchain/src/agents/middleware/summarization.ts

@@ -18,6 +18,7 @@ import { REMOVE_ALL_MESSAGES } from "@langchain/langgraph";
 import { createMiddleware } from "../middleware.js";
 import { countTokensApproximately } from "./utils.js";
 import { hasToolCalls } from "../utils.js";
+import { initChatModel } from "../../chat_models/universal.js";
 
 const DEFAULT_SUMMARY_PROMPT = `<role>
 Context Extraction Assistant
@@ -57,7 +58,7 @@ const SEARCH_RANGE_FOR_TOOL_PAIRS = 5;
 type TokenCounter = (messages: BaseMessage[]) => number | Promise<number>;
 
 const contextSchema = z.object({
-  model: z.custom<BaseLanguageModel>(),
+  model: z.custom<string | BaseLanguageModel>(),
   maxTokensBeforeSummary: z.number().optional(),
   messagesToKeep: z.number().default(DEFAULT_MESSAGES_TO_KEEP),
   tokenCounter: z
@@ -148,6 +149,11 @@ export function summarizationMiddleware(
       } as InferInteropZodOutput<typeof contextSchema>;
       const { messages } = state;
 
+      const model =
+        typeof config.model === "string"
+          ? await initChatModel(config.model)
+          : config.model;
+
       // Ensure all messages have IDs
       ensureMessageIds(messages);
 
@@ -180,7 +186,7 @@ export function summarizationMiddleware(
 
       const summary = await createSummary(
         messagesToSummarize,
-        config.model,
+        model,
         config.summaryPrompt,
         tokenCounter
       );

libs/langchain/src/agents/middleware/tests/summarization.test.ts

@@ -11,6 +11,36 @@ import { countTokensApproximately } from "../utils.js";
 import { createAgent } from "../../index.js";
 import { FakeToolCallingChatModel } from "../../tests/utils.js";
 
+// Mock @langchain/anthropic to test model string usage without requiring the built package
+vi.mock("@langchain/anthropic", async () => {
+  const { AIMessage } = await import("@langchain/core/messages");
+  return {
+    ChatAnthropic: class MockChatAnthropic {
+      lc_kwargs: Record<string, any>;
+
+      constructor(params?: any) {
+        this.lc_kwargs = params || {};
+      }
+
+      async invoke() {
+        return new AIMessage({ content: "Mocked response" });
+      }
+
+      getName() {
+        return "ChatAnthropic";
+      }
+
+      get _modelType() {
+        return "chat-anthropic";
+      }
+
+      get lc_runnable() {
+        return true;
+      }
+    },
+  };
+});
+
 describe("summarizationMiddleware", () => {
   // Mock summarization model
   function createMockSummarizationModel() {
@@ -335,4 +365,27 @@ describe("summarizationMiddleware", () => {
     expect(nonSystemMessages.length).toBeGreaterThanOrEqual(messagesToKeep);
     expect(nonSystemMessages.length).toBeLessThanOrEqual(messagesToKeep + 3); // Some buffer for safety
   });
+
+  it("can be created using a model string", async () => {
+    // Verify the mocked ChatAnthropic exists
+    const { ChatAnthropic } = await import("@langchain/anthropic");
+    expect(ChatAnthropic).toBeDefined();
+    expect(typeof ChatAnthropic).toBe("function");
+
+    const model = "anthropic:claude-sonnet-4-20250514";
+
+    const middleware = summarizationMiddleware({
+      model,
+      maxTokensBeforeSummary: 100,
+      messagesToKeep: 2,
+    });
+
+    const agent = createAgent({
+      model,
+      middleware: [middleware],
+    });
+
+    const result = await agent.invoke({ messages: [] });
+    expect(result.messages.length).toBeGreaterThan(0);
+  });
 });