feat: enhance CommandCode integration with improved message handling

open-sse/translator/request/openai-to-commandcode.js

@@ -1,17 +1,32 @@
 /**
  * OpenAI → CommandCode request translator
  *
- * CommandCode endpoint expects an envelope:
+ * Upstream `/alpha/generate` schema (verified live with curl 2026-05-07):
- *   { threadId, memory, config, params: { model, messages, stream, max_tokens, temperature, tools? } }
+ *  - params.system: STRING at top level (Anthropic-style; system messages NOT allowed in messages[])
- * where `params.messages` are Anthropic-style content blocks ([{type:"text", text}, ...]).
+ *  - params.messages[*].role ∈ {"user","assistant","tool"}
- *
+ *  - params.messages[*].content: Array of content blocks (NEVER a string)
- * The model id received here is the upstream id (e.g. "deepseek/deepseek-v4-pro") thanks to the
+ *  - tool_use blocks (assistant): {type:"tool-call", toolCallId, toolName, input}
- * `provider/model` registration in providerModels.js.
+ *  - tool_result blocks (role=user): {type:"tool-result", toolCallId, toolName, output}
+ *  - tools[*]: Anthropic plain {name, description, input_schema}
  */
 import { register } from "../index.js";
 import { FORMATS } from "../formats.js";
 import { randomUUID } from "crypto";
 
+function flattenText(content) {
+  if (content == null) return "";
+  if (typeof content === "string") return content;
+  if (Array.isArray(content)) {
+    const parts = [];
+    for (const p of content) {
+      if (typeof p === "string") parts.push(p);
+      else if (p && typeof p === "object" && typeof p.text === "string") parts.push(p.text);
+    }
+    return parts.join("\n");
+  }
+  return String(content);
+}
+
 function toContentBlocks(content) {
   if (content == null) return [{ type: "text", text: "" }];
   if (typeof content === "string") return [{ type: "text", text: content }];
@@ -24,8 +39,6 @@ function toContentBlocks(content) {
         if (part.type === "text" && typeof part.text === "string") {
           blocks.push({ type: "text", text: part.text });
         } else if (part.type === "image_url" || part.type === "image") {
-          // CommandCode currently rejects multimodal blocks via this gateway;
-          // collapse to a textual placeholder so the request still validates.
           blocks.push({ type: "text", text: "[image omitted]" });
         } else if (typeof part.text === "string") {
           blocks.push({ type: "text", text: part.text });
@@ -37,25 +50,101 @@ function toContentBlocks(content) {
   return [{ type: "text", text: String(content) }];
 }
 
+function safeParseJson(s) {
+  if (s == null) return {};
+  if (typeof s !== "string") return s;
+  try { return JSON.parse(s); } catch { return {}; }
+}
+
 function convertMessages(messages = []) {
-  return messages.map((m) => {
+  const out = [];
-    const role = m.role === "tool" ? "user" : (m.role || "user");
+  const systemTexts = [];
-    return { role, content: toContentBlocks(m.content) };
+
+  for (const m of messages) {
+    if (!m) continue;
+    const role = m.role;
+
+    if (role === "system") {
+      const t = flattenText(m.content);
+      if (t) systemTexts.push(t);
+      continue;
+    }
+
+    if (role === "tool") {
+      const value = typeof m.content === "string" ? m.content : flattenText(m.content);
+      out.push({
+        role: "tool",
+        content: [{
+          type: "tool-result",
+          toolCallId: m.tool_call_id || "",
+          toolName: m.name || "",
+          output: { type: "text", value },
+        }],
       });
+      continue;
+    }
+
+    if (role === "assistant") {
+      const blocks = [];
+      const text = flattenText(m.content);
+      if (text) blocks.push({ type: "text", text });
+      if (Array.isArray(m.tool_calls)) {
+        for (const tc of m.tool_calls) {
+          const fn = tc.function || {};
+          blocks.push({
+            type: "tool-call",
+            toolCallId: tc.id || "",
+            toolName: fn.name || "",
+            input: safeParseJson(fn.arguments),
+          });
+        }
+      }
+      out.push({ role: "assistant", content: blocks.length ? blocks : [{ type: "text", text: "" }] });
+      continue;
+    }
+
+    out.push({ role: "user", content: toContentBlocks(m.content) });
+  }
+
+  return { messages: out, system: systemTexts.join("\n\n") };
+}
+
+function convertTools(tools) {
+  if (!Array.isArray(tools) || tools.length === 0) return undefined;
+  const result = [];
+  for (const t of tools) {
+    if (!t) continue;
+    if (t.type === "function" && t.function) {
+      result.push({
+        name: t.function.name,
+        description: t.function.description,
+        input_schema: t.function.parameters || { type: "object" },
+      });
+    } else if (t.name && (t.input_schema || t.parameters)) {
+      result.push({
+        name: t.name,
+        description: t.description,
+        input_schema: t.input_schema || t.parameters,
+      });
+    }
+  }
+  return result.length ? result : undefined;
 }
 
 export function openaiToCommandCode(model, body, stream /* , credentials */) {
+  const { messages, system } = convertMessages(body.messages);
   const params = {
     model,
-    messages: convertMessages(body.messages),
+    messages,
     stream: stream !== false,
     max_tokens: body.max_tokens ?? body.max_output_tokens ?? 64000,
     temperature: body.temperature ?? 0.3,
   };
 
-  if (Array.isArray(body.tools) && body.tools.length > 0) {
+  if (system) params.system = system;
-    params.tools = body.tools;
+
-  }
+  const tools = convertTools(body.tools);
+  if (tools) params.tools = tools;
   if (body.top_p != null) params.top_p = body.top_p;
 
   const today = new Date().toISOString().slice(0, 10);

open-sse/translator/response/commandcode-to-openai.js

@@ -5,8 +5,9 @@
  *   {"type":"start"} {"type":"start-step", ...}
  *   {"type":"reasoning-start","id":"..."} {"type":"reasoning-delta","text":"..."}
  *   {"type":"text-start","id":"..."}     {"type":"text-delta","text":"..."}
- *   {"type":"tool-input-start","toolCallId","toolName"}
+ *   {"type":"tool-input-start","id","toolName"}
- *   {"type":"tool-input-delta","toolCallId","inputTextDelta"}
+ *   {"type":"tool-input-delta","id","delta"}
+ *   {"type":"tool-input-end","id"}
  *   {"type":"tool-call","toolCallId","toolName","input"}
  *   {"type":"finish-step","finishReason","usage": {...}, ...}
  *   {"type":"finish",...}
@@ -104,7 +105,7 @@ export function convertCommandCodeToOpenAI(chunk, state) {
       break;
     }
     case "tool-input-start": {
-      const id = event.toolCallId || `call_${Date.now()}_${state.toolIndex}`;
+      const id = event.id || event.toolCallId || `call_${Date.now()}_${state.toolIndex}`;
       let idx = state.toolIndexById.get(id);
       if (idx == null) {
         idx = state.toolIndex++;
@@ -125,13 +126,13 @@ export function convertCommandCodeToOpenAI(chunk, state) {
       break;
     }
     case "tool-input-delta": {
-      const id = event.toolCallId;
+      const id = event.id || event.toolCallId;
       const idx = state.toolIndexById.get(id);
       if (idx == null) break;
       const delta = {
         tool_calls: [{
           index: idx,
-          function: { arguments: event.inputTextDelta || event.delta || "" },
+          function: { arguments: event.delta || event.inputTextDelta || "" },
         }],
       };
       out.push(makeChunk(state, delta));
@@ -178,7 +179,9 @@ export function convertCommandCodeToOpenAI(chunk, state) {
     }
     case "error": {
       state.finishReason = "stop";
-      out.push(makeChunk(state, { content: `\n\n[CommandCode error: ${event.error || event.message || "unknown"}]` }));
+      const errVal = event.error ?? event.message ?? "unknown";
+      const errStr = typeof errVal === "string" ? errVal : JSON.stringify(errVal);
+      out.push(makeChunk(state, { content: `\n\n[CommandCode error: ${errStr}]` }));
       out.push(makeChunk(state, {}, "stop"));
       break;
     }

src/app/api/providers/validate/route.js

@@ -2,7 +2,8 @@ import { NextResponse } from "next/server";
 import { getProviderNodeById } from "@/models";
 import { isOpenAICompatibleProvider, isAnthropicCompatibleProvider, isCustomEmbeddingProvider, AI_PROVIDERS } from "@/shared/constants/providers";
 import { getDefaultModel } from "open-sse/config/providerModels.js";
-import { resolveOllamaLocalHost } from "open-sse/config/providers.js";
+import { resolveOllamaLocalHost, PROVIDERS } from "open-sse/config/providers.js";
+import { openaiToCommandCode } from "open-sse/translator/request/openai-to-commandcode.js";
 import { PROVIDER_ENDPOINTS } from "@/shared/constants/config";
 
 // Probe a webSearch/webFetch provider using its searchConfig/fetchConfig.
@@ -413,6 +414,28 @@ export async function POST(request) {
           break;
         }
 
+        case "commandcode": {
+          const cfg = PROVIDERS.commandcode;
+          const model = getDefaultModel("commandcode");
+          const payload = openaiToCommandCode(model, {
+            messages: [{ role: "user", content: "ping" }],
+            max_tokens: 1,
+            stream: false,
+          }, false);
+          const res = await fetch(cfg.baseUrl, {
+            method: "POST",
+            headers: {
+              "Content-Type": "application/json",
+              ...(cfg.headers || {}),
+              "x-session-id": crypto.randomUUID(),
+              "Authorization": `Bearer ${apiKey}`,
+            },
+            body: JSON.stringify(payload),
+          });
+          isValid = res.status !== 401 && res.status !== 403;
+          break;
+        }
+
         case "deepgram": {
           const res = await fetch("https://api.deepgram.com/v1/projects", {
             headers: { "Authorization": `Token ${apiKey}` },

tests/unit/commandcode-to-openai.test.js

@@ -0,0 +1,127 @@
+/**
+ * Unit tests for open-sse/translator/response/commandcode-to-openai.js
+ *
+ * Verified live against upstream stream (curl, 2026-05-07):
+ *  - tool-input-start: { id, toolName }   (id, NOT toolCallId)
+ *  - tool-input-delta: { id, delta }      (id, NOT toolCallId; delta, NOT inputTextDelta)
+ *  - tool-input-end:   { id }
+ *  - tool-call (final): { toolCallId, toolName, input }
+ */
+
+import { describe, it, expect } from "vitest";
+import { convertCommandCodeToOpenAI } from "../../open-sse/translator/response/commandcode-to-openai.js";
+
+function feed(events) {
+  const state = {};
+  const all = [];
+  for (const e of events) {
+    const out = convertCommandCodeToOpenAI(JSON.stringify(e), state);
+    if (out) for (const c of out) all.push(c);
+  }
+  return { state, chunks: all };
+}
+
+describe("commandcode-to-openai — text-delta", () => {
+  it("emits assistant role on first delta then content-only", () => {
+    const { chunks } = feed([
+      { type: "text-delta", text: "Hello" },
+      { type: "text-delta", text: " world" },
+    ]);
+    expect(chunks[0].choices[0].delta.role).toBe("assistant");
+    expect(chunks[0].choices[0].delta.content).toBe("Hello");
+    expect(chunks[1].choices[0].delta.role).toBeUndefined();
+    expect(chunks[1].choices[0].delta.content).toBe(" world");
+  });
+});
+
+describe("commandcode-to-openai — reasoning-delta", () => {
+  it("maps reasoning-delta to reasoning_content delta", () => {
+    const { chunks } = feed([
+      { type: "reasoning-delta", text: "thinking..." },
+    ]);
+    expect(chunks[0].choices[0].delta.reasoning_content).toBe("thinking...");
+  });
+});
+
+describe("commandcode-to-openai — tool-input-* with id field (live schema)", () => {
+  it("registers tool index using event.id (NOT toolCallId)", () => {
+    const { chunks } = feed([
+      { type: "tool-input-start", id: "call_X", toolName: "Bash" },
+      { type: "tool-input-delta", id: "call_X", delta: "{\"cmd" },
+      { type: "tool-input-delta", id: "call_X", delta: "\":\"ls\"}" },
+    ]);
+
+    // First chunk emits tool_calls with id
+    const startChunk = chunks[0].choices[0].delta.tool_calls[0];
+    expect(startChunk.id).toBe("call_X");
+    expect(startChunk.function.name).toBe("Bash");
+
+    // Subsequent deltas accumulate arguments
+    expect(chunks[1].choices[0].delta.tool_calls[0].function.arguments).toBe("{\"cmd");
+    expect(chunks[2].choices[0].delta.tool_calls[0].function.arguments).toBe("\":\"ls\"}");
+  });
+
+  it("ignores tool-input-delta when id is unknown (no prior start)", () => {
+    const { chunks } = feed([
+      { type: "tool-input-delta", id: "unknown", delta: "x" },
+    ]);
+    expect(chunks.length).toBe(0);
+  });
+});
+
+describe("commandcode-to-openai — final tool-call event", () => {
+  it("does NOT re-emit tool_calls when tool-input-* deltas already fired", () => {
+    const { chunks } = feed([
+      { type: "tool-input-start", id: "call_Y", toolName: "Write" },
+      { type: "tool-input-delta", id: "call_Y", delta: "{\"file\":\"a\"}" },
+      { type: "tool-call", toolCallId: "call_Y", toolName: "Write", input: { file: "a" } },
+    ]);
+    // Should be exactly 2 chunks (start + delta), no duplicate from final tool-call
+    expect(chunks.length).toBe(2);
+  });
+
+  it("emits a consolidated tool_calls when only the final tool-call event arrives", () => {
+    const { chunks } = feed([
+      { type: "tool-call", toolCallId: "call_Z", toolName: "Read", input: { path: "/x" } },
+    ]);
+    expect(chunks.length).toBe(1);
+    const tc = chunks[0].choices[0].delta.tool_calls[0];
+    expect(tc.id).toBe("call_Z");
+    expect(tc.function.name).toBe("Read");
+    expect(tc.function.arguments).toBe(JSON.stringify({ path: "/x" }));
+  });
+});
+
+describe("commandcode-to-openai — finish", () => {
+  it("emits a final chunk with finish_reason=tool_calls when finishReason is tool-calls", () => {
+    const { chunks } = feed([
+      { type: "tool-input-start", id: "call_F", toolName: "Bash" },
+      { type: "tool-input-delta", id: "call_F", delta: "{}" },
+      { type: "finish-step", finishReason: "tool-calls" },
+      { type: "finish" },
+    ]);
+    const last = chunks[chunks.length - 1];
+    expect(last.choices[0].finish_reason).toBe("tool_calls");
+  });
+
+  it("includes usage on the final chunk when totalUsage provided", () => {
+    const { chunks } = feed([
+      { type: "text-delta", text: "hi" },
+      { type: "finish-step", finishReason: "stop", usage: { inputTokens: 10, outputTokens: 5, totalTokens: 15 } },
+      { type: "finish", totalUsage: { inputTokens: 10, outputTokens: 5, totalTokens: 15 } },
+    ]);
+    const last = chunks[chunks.length - 1];
+    expect(last.usage).toEqual({ prompt_tokens: 10, completion_tokens: 5, total_tokens: 15 });
+  });
+});
+
+describe("commandcode-to-openai — error event", () => {
+  it("stringifies object errors so client sees readable message", () => {
+    const { chunks } = feed([
+      { type: "error", error: { type: "server_error", message: "Boom" } },
+    ]);
+    const text = chunks[0].choices[0].delta.content;
+    expect(text).toContain("Boom");
+    expect(text).not.toContain("[object Object]");
+  });
+});

tests/unit/openai-to-commandcode.test.js

@@ -0,0 +1,181 @@
+/**
+ * Unit tests for open-sse/translator/request/openai-to-commandcode.js
+ *
+ * Verified live against upstream `/alpha/generate` (curl, 2026-05-07):
+ *  - params.system: STRING at top level (Anthropic-style; "system" role NOT in messages[])
+ *  - params.messages[*].role ∈ {"user","assistant","tool"}
+ *  - params.messages[*].content: Array<content_block> (NEVER string)
+ *  - tools[*]: Anthropic plain {name, description, input_schema}
+ */
+
+import { describe, it, expect } from "vitest";
+import { openaiToCommandCode } from "../../open-sse/translator/request/openai-to-commandcode.js";
+
+const MODEL = "moonshotai/Kimi-K2.6";
+
+describe("openaiToCommandCode — basic envelope", () => {
+  it("returns the expected top-level envelope shape", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [{ role: "user", content: "hi" }],
+    }, true);
+
+    expect(out).toHaveProperty("threadId");
+    expect(out).toHaveProperty("memory");
+    expect(out).toHaveProperty("config");
+    expect(out).toHaveProperty("params");
+    expect(out.params.model).toBe(MODEL);
+    expect(out.params.stream).toBe(true);
+  });
+});
+
+describe("openaiToCommandCode — system handling", () => {
+  it("hoists system messages to params.system (string), not messages[]", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [
+        { role: "system", content: "You are concise." },
+        { role: "user", content: "hi" },
+      ],
+    }, true);
+
+    expect(typeof out.params.system).toBe("string");
+    expect(out.params.system).toBe("You are concise.");
+    const roles = out.params.messages.map((m) => m.role);
+    expect(roles).not.toContain("system");
+  });
+
+  it("joins multiple system messages with blank line", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [
+        { role: "system", content: "A" },
+        { role: "system", content: "B" },
+        { role: "user", content: "hi" },
+      ],
+    }, true);
+
+    expect(out.params.system).toBe("A\n\nB");
+  });
+
+  it("omits params.system when no system messages", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [{ role: "user", content: "hi" }],
+    }, true);
+    expect(out.params.system).toBeUndefined();
+  });
+});
+
+describe("openaiToCommandCode — content shape", () => {
+  it("MUST always emit content as Array (never string) for user", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [{ role: "user", content: "hello" }],
+    }, true);
+
+    const u = out.params.messages[0];
+    expect(Array.isArray(u.content)).toBe(true);
+    expect(u.content[0]).toEqual({ type: "text", text: "hello" });
+  });
+
+  it("MUST always emit content as Array for assistant", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [
+        { role: "user", content: "a" },
+        { role: "assistant", content: "b" },
+      ],
+    }, true);
+    const a = out.params.messages[1];
+    expect(Array.isArray(a.content)).toBe(true);
+    expect(a.content[0]).toEqual({ type: "text", text: "b" });
+  });
+});
+
+describe("openaiToCommandCode — tool role / tool-result (AI SDK)", () => {
+  it("converts role:\"tool\" to role:\"tool\" with tool-result block; output is {type:\"text\",value}", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [
+        { role: "user", content: "run X" },
+        {
+          role: "assistant",
+          content: null,
+          tool_calls: [
+            { id: "call_1", type: "function", function: { name: "do_x", arguments: "{\"a\":1}" } },
+          ],
+        },
+        { role: "tool", tool_call_id: "call_1", name: "do_x", content: "RESULT_OK" },
+      ],
+    }, true);
+
+    const toolMsg = out.params.messages[out.params.messages.length - 1];
+    expect(toolMsg.role).toBe("tool");
+    const block = toolMsg.content[0];
+    expect(block.type).toBe("tool-result");
+    expect(block.toolCallId).toBe("call_1");
+    expect(block.toolName).toBe("do_x");
+    expect(block.output).toEqual({ type: "text", value: "RESULT_OK" });
+  });
+});
+
+describe("openaiToCommandCode — assistant tool_calls / tool-call", () => {
+  it("converts assistant.tool_calls[] into content blocks of type tool-call", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [
+        { role: "user", content: "go" },
+        {
+          role: "assistant",
+          content: null,
+          tool_calls: [
+            { id: "call_42", type: "function", function: { name: "search", arguments: "{\"q\":\"hi\"}" } },
+          ],
+        },
+      ],
+    }, true);
+
+    const asst = out.params.messages[1];
+    expect(asst.role).toBe("assistant");
+    const tc = asst.content.find((b) => b.type === "tool-call");
+    expect(tc).toBeDefined();
+    expect(tc.toolCallId).toBe("call_42");
+    expect(tc.toolName).toBe("search");
+    expect(tc.input).toEqual({ q: "hi" });
+  });
+});
+
+describe("openaiToCommandCode — tools schema conversion", () => {
+  it("converts OpenAI {type:\"function\", function:{...}} to Anthropic plain {name, input_schema}", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [{ role: "user", content: "hi" }],
+      tools: [
+        {
+          type: "function",
+          function: {
+            name: "weather",
+            description: "Get weather",
+            parameters: { type: "object", properties: { city: { type: "string" } }, required: ["city"] },
+          },
+        },
+      ],
+    }, true);
+
+    const t = out.params.tools[0];
+    expect(t.name).toBe("weather");
+    expect(t.input_schema).toBeDefined();
+    expect(t.input_schema.type).toBe("object");
+    expect(t.function).toBeUndefined();
+    expect(t.parameters).toBeUndefined();
+  });
+
+  it("preserves description on converted tool", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [{ role: "user", content: "hi" }],
+      tools: [
+        { type: "function", function: { name: "ping", description: "Ping the server", parameters: { type: "object" } } },
+      ],
+    }, true);
+    expect(out.params.tools[0].description).toBe("Ping the server");
+  });
+
+  it("does not include tools field when input has none", () => {
+    const out = openaiToCommandCode(MODEL, {
+      messages: [{ role: "user", content: "hi" }],
+    }, true);
+    expect(out.params.tools).toBeUndefined();
+  });
+});