Ask Sage Tool Calling

core/llm/llms/Asksage.ts

@@ -8,6 +8,7 @@ import {
   ToolCallDelta,
 } from "../../index.js";
 import { BaseLLM } from "../index.js";
+import { LlmApiRequestType } from "../openaiTypeConverters.js";
 import {
   AskSageTool,
   AskSageToolChoice,
@@ -63,6 +64,11 @@ class Asksage extends BaseLLM {
     model: "gpt-4o",
   };
 
+  protected useOpenAIAdapterFor: (LlmApiRequestType | "*")[] = [
+    "chat",
+    "streamChat",
+  ];
+
   private sessionTokenPromise: Promise<string> | null = null;
   private tokenTimestamp: number = 0;
   private email?: string;
@@ -383,21 +389,44 @@ class Asksage extends BaseLLM {
 
       const data = (await response.json()) as AskSageResponse;
 
-      // Extract tool calls from response (check both top-level and choices format)
+      // Extract tool calls from response, preferring the unified format
       const rawToolCalls =
-        data.tool_calls || data.choices?.[0]?.message?.tool_calls;
-
-      // Convert to ToolCallDelta format if present
-      const toolCalls: ToolCallDelta[] | undefined = rawToolCalls?.map(
-        (tc) => ({
-          id: tc.id,
-          type: tc.type,
-          function: {
-            name: tc.function.name,
-            arguments: tc.function.arguments,
-          },
-        }),
-      );
+        data.tool_calls_unified ||
+        data.tool_calls ||
+        data.choices?.[0]?.message?.tool_calls;
+
+      // Normalize to ToolCallDelta format (handles OpenAI, Anthropic, and unified formats)
+      const toolCalls: ToolCallDelta[] | undefined = rawToolCalls
+        ?.map((tc): ToolCallDelta | undefined => {
+          if ("function" in tc && tc.function?.name) {
+            // OpenAI or unified format
+            const args = tc.function.arguments;
+            return {
+              id: tc.id,
+              type: "function" as const,
+              function: {
+                name: tc.function.name,
+                arguments:
+                  typeof args === "string" ? args : JSON.stringify(args ?? {}),
+              },
+            };
+          } else if ("name" in tc && typeof tc.name === "string") {
+            // Anthropic format
+            const args =
+              tc.text ?? (tc.input ? JSON.stringify(tc.input) : "{}");
+            return {
+              id: tc.id,
+              type: "function" as const,
+              function: {
+                name: tc.name,
+                arguments:
+                  typeof args === "string" ? args : JSON.stringify(args),
+              },
+            };
+          }
+          return undefined;
+        })
+        .filter((tc): tc is ToolCallDelta => tc !== undefined);
 
       const assistantMessage: ChatMessage = {
         role: "assistant",

core/llm/toolSupport.ts

@@ -506,6 +506,10 @@ export const PROVIDER_TOOL_SUPPORT: Record<string, (model: string) => boolean> =
 
       return false;
     },
+    askSage: (_model) => {
+      // Ask Sage proxies to tool-capable models (GPT-4o, Claude, Gemini, etc.)
+      return true;
+    },
   };
 
 export function isRecommendedAgentModel(modelName: string): boolean {

manual-testing-sandbox/test.py

@@ -10,6 +10,16 @@ def subtract(self, number):
         self.result -= number
         return self
 
+    def multiply(self, number):
+        self.result *= number
+        return self
+
+    def divide(self, number):
+        if number == 0:
+            raise ValueError("Cannot divide by zero")
+        self.result /= number
+        return self
+
     def reset(self):
         self.result = 0
         return self