justinmoon · October 27, 2025 15:04
diff --git a/maple-bug-summary.md b/maple-bug-summary.md
diff --git a/maple-tool-calling-bug-repro.ts b/maple-tool-calling-bug-repro.ts
 #!/usr/bin/env bun
 /**
 * Bug Reproduction: Maple AI models don't use OpenAI tool calling format
 * 
 * Testing: gpt-oss-120b (which works with tool calling on Cerebras)
 * Expected: Model should return tool_calls in response
 * Actual: Model returns empty response or text instead of tool calls
 * 
 * Setup:
 *   bun install ai @ai-sdk/openai-compatible
 *   export MAPLE_API_KEY="your-key"
 *   bun run maple-tool-calling-bug-repro.ts
 */

 import { streamText } from "ai"
 import { createOpenAICompatible } from "@ai-sdk/openai-compatible"
 import { createMapleFetch } from "./src/maple"

 const MAPLE_API_KEY = process.env.MAPLE_API_KEY || ""

 if (!MAPLE_API_KEY) {
  console.error("❌ Missing MAPLE_API_KEY environment variable")
  console.error("   Get one at: https://trymaple.ai")
  process.exit(1)
 }

 console.log("🐛 Maple AI Tool Calling Bug Reproduction\n")
 console.log("📋 Test Setup:")
 console.log("   Model: gpt-oss-120b")
 console.log("   API: https://enclave.trymaple.ai")
 console.log("   Task: Write 'Hello World' to /tmp/test.txt")
 console.log("   Expected: Model calls write() tool")
 console.log("   Actual: ???\n")

 // Create Maple's encrypted fetch
 const mapleFetch = await createMapleFetch({
  apiUrl: "https://enclave.trymaple.ai",
  apiKey: MAPLE_API_KEY,
  pcr0Values: [
    "79e7bd1e7df09fdb5b7098956a2268c278cc88be323c11975e2a2d080d65f30f8e0efe690edd450493c833b46f40ae1a",
    "ed9109c16f30a470cf0ea2251816789b4ffa510c990118323ce94a2364b9bf05bdb8777959cbac86f5cabc4852e0da71",
    "4f2bcdf16c38842e1a45defd944d24ea58bb5bcb76491843223022acfe9eb6f1ff79b2cb9a6b2a9219daf9c7bf40fa37",
    "b8ee4b511ef2c9c6ab3e5c0840c5df2218fbb4d9df88254ece7af9462677e55aa5a03838f3ae432d86ca1cb6f992eee7",
  ],
 })

 // Create provider with Maple's fetch
 const maple = createOpenAICompatible({
  name: "maple",
  baseURL: "https://enclave.trymaple.ai/v1",
  fetch: mapleFetch,
 })

 const tools = {
  write: {
    description: "Write content to a file on disk",
    parameters: {
      type: "object" as const,
      properties: {
        path: {
          type: "string" as const,
          description: "The file path to write to",
        },
        content: {
          type: "string" as const,
          description: "The content to write to the file",
        },
      },
      required: ["path", "content"],
    },
    execute: async ({ path, content }: { path: string; content: string }) => {
      try {
        await Bun.write(path, content)
        return `Written to ${path}`
      } catch (err: any) {
        return `Error: ${err.message}`
      }
    },
  },
 }

 try {
  console.log("📤 Sending request with tool definitions...\n")
  
  const result = streamText({
    model: maple("gpt-oss-120b"),
    messages: [
      {
        role: "user",
        content: "Write 'Hello World from Maple!' to /tmp/maple-test.txt",
      },
    ],
    tools,
    maxSteps: 3,
  })

  let hasToolCalls = false
  let hasText = false
  let textContent = ""
  let toolCallsFound: string[] = []

  for await (const chunk of result.fullStream) {
    if (chunk.type === "tool-call") {
      hasToolCalls = true
      toolCallsFound.push(chunk.toolName)
      console.log(`✅ Tool call: ${chunk.toolName}`)
      console.log(`   Args: ${JSON.stringify(chunk.args)}`)
    } else if (chunk.type === "text-delta") {
      hasText = true
      const delta = String(chunk.textDelta || "")
      textContent += delta
      process.stdout.write(delta)
    } else if (chunk.type === "finish") {
      console.log("\n\n📊 Result:")
      console.log(`   Tool calls: ${hasToolCalls ? "✅ YES" : "❌ NO"}`)
      console.log(`   Text output: ${hasText ? "YES" : "NO"}`)
      console.log(`   Text length: ${textContent.length} chars`)
      
      if (toolCallsFound.length > 0) {
        console.log(`   Tools called: ${toolCallsFound.join(", ")}`)
      }
      
      if (hasText) {
        const sample = textContent.trim().substring(0, 200)
        console.log(`   Text content: "${sample}${sample.length < textContent.trim().length ? '...' : ''}"`)
      }

      console.log("\n🔍 Diagnosis:")
      if (hasToolCalls) {
        console.log("   ✅ SUCCESS: Model correctly used tool calling!")
      } else if (hasText) {
        console.log("   ❌ BUG: Model returned text instead of calling tools")
        console.log("   Expected: model.tool_calls = [{ name: 'write', args: {...} }]")
        console.log("   Actual: model.content = text explaining what to do")
      } else {
        console.log("   ❌ BUG: Model returned empty response")
        console.log("   Expected: model.tool_calls = [{ name: 'write', args: {...} }]")
        console.log("   Actual: No tool calls, no text")
      }
    }
  }

  console.log("\n📝 Notes:")
  console.log("   • This same model (gpt-oss-120b) DOES support tool calling on Cerebras")
  console.log("   • See: https://inference-docs.cerebras.ai/models/openai-oss")
  console.log("   • OpenCode Zen models (grok-code, etc) work fine for tool calling")
  console.log("   • All Maple models tested show this issue (qwen, llama, deepseek, gpt-oss)")

 } catch (error: any) {
  console.error("\n❌ Error:", error.message)
  if (error.cause) {
    console.error("   Cause:", error.cause)
  }
  if (error.stack) {
    console.error("\n Stack trace:")
    console.error(error.stack)
  }
  process.exit(1)
 }
diff --git a/WHY_OPENCODE_WORKS_MAPLE_DOESNT.md b/WHY_OPENCODE_WORKS_MAPLE_DOESNT.md
Model	Cerebras API	Maple API
gpt-oss-120b	✅ `tool_call: true`	❌ Empty response
qwen3-coder-480b	✅ `tool_call: true`	❌ Not offered
qwen2.5-72b	✅ Available with tools	❌ Returns text
	#!/usr/bin/env bun
	/**
	* Bug Reproduction: Maple AI models don't use OpenAI tool calling format
	*
	* Testing: gpt-oss-120b (which works with tool calling on Cerebras)
	* Expected: Model should return tool_calls in response
	* Actual: Model returns empty response or text instead of tool calls
	*
	* Setup:
	* bun install ai @ai-sdk/openai-compatible
	* export MAPLE_API_KEY="your-key"
	* bun run maple-tool-calling-bug-repro.ts
	*/

	import { streamText } from "ai"
	import { createOpenAICompatible } from "@ai-sdk/openai-compatible"
	import { createMapleFetch } from "./src/maple"

	const MAPLE_API_KEY = process.env.MAPLE_API_KEY \|\| ""

	if (!MAPLE_API_KEY) {
	console.error("❌ Missing MAPLE_API_KEY environment variable")
	console.error(" Get one at: https://trymaple.ai")
	process.exit(1)
	}

	console.log("🐛 Maple AI Tool Calling Bug Reproduction\n")
	console.log("📋 Test Setup:")
	console.log(" Model: gpt-oss-120b")
	console.log(" API: https://enclave.trymaple.ai")
	console.log(" Task: Write 'Hello World' to /tmp/test.txt")
	console.log(" Expected: Model calls write() tool")
	console.log(" Actual: ???\n")

	// Create Maple's encrypted fetch
	const mapleFetch = await createMapleFetch({
	apiUrl: "https://enclave.trymaple.ai",
	apiKey: MAPLE_API_KEY,
	pcr0Values: [
	"79e7bd1e7df09fdb5b7098956a2268c278cc88be323c11975e2a2d080d65f30f8e0efe690edd450493c833b46f40ae1a",
	"ed9109c16f30a470cf0ea2251816789b4ffa510c990118323ce94a2364b9bf05bdb8777959cbac86f5cabc4852e0da71",
	"4f2bcdf16c38842e1a45defd944d24ea58bb5bcb76491843223022acfe9eb6f1ff79b2cb9a6b2a9219daf9c7bf40fa37",
	"b8ee4b511ef2c9c6ab3e5c0840c5df2218fbb4d9df88254ece7af9462677e55aa5a03838f3ae432d86ca1cb6f992eee7",
	],
	})

	// Create provider with Maple's fetch
	const maple = createOpenAICompatible({
	name: "maple",
	baseURL: "https://enclave.trymaple.ai/v1",
	fetch: mapleFetch,
	})

	const tools = {
	write: {
	description: "Write content to a file on disk",
	parameters: {
	type: "object" as const,
	properties: {
	path: {
	type: "string" as const,
	description: "The file path to write to",
	},
	content: {
	type: "string" as const,
	description: "The content to write to the file",
	},
	},
	required: ["path", "content"],
	},
	execute: async ({ path, content }: { path: string; content: string }) => {
	try {
	await Bun.write(path, content)
	return `Written to ${path}`
	} catch (err: any) {
	return `Error: ${err.message}`
	}
	},
	},
	}

	try {
	console.log("📤 Sending request with tool definitions...\n")

	const result = streamText({
	model: maple("gpt-oss-120b"),
	messages: [
	{
	role: "user",
	content: "Write 'Hello World from Maple!' to /tmp/maple-test.txt",
	},
	],
	tools,
	maxSteps: 3,
	})

	let hasToolCalls = false
	let hasText = false
	let textContent = ""
	let toolCallsFound: string[] = []

	for await (const chunk of result.fullStream) {
	if (chunk.type === "tool-call") {
	hasToolCalls = true
	toolCallsFound.push(chunk.toolName)
	console.log(`✅ Tool call: ${chunk.toolName}`)
	console.log(` Args: ${JSON.stringify(chunk.args)}`)
	} else if (chunk.type === "text-delta") {
	hasText = true
	const delta = String(chunk.textDelta \|\| "")
	textContent += delta
	process.stdout.write(delta)
	} else if (chunk.type === "finish") {
	console.log("\n\n📊 Result:")
	console.log(` Tool calls: ${hasToolCalls ? "✅ YES" : "❌ NO"}`)
	console.log(` Text output: ${hasText ? "YES" : "NO"}`)
	console.log(` Text length: ${textContent.length} chars`)

	if (toolCallsFound.length > 0) {
	console.log(` Tools called: ${toolCallsFound.join(", ")}`)
	}

	if (hasText) {
	const sample = textContent.trim().substring(0, 200)
	console.log(` Text content: "${sample}${sample.length < textContent.trim().length ? '...' : ''}"`)
	}

	console.log("\n🔍 Diagnosis:")
	if (hasToolCalls) {
	console.log(" ✅ SUCCESS: Model correctly used tool calling!")
	} else if (hasText) {
	console.log(" ❌ BUG: Model returned text instead of calling tools")
	console.log(" Expected: model.tool_calls = [{ name: 'write', args: {...} }]")
	console.log(" Actual: model.content = text explaining what to do")
	} else {
	console.log(" ❌ BUG: Model returned empty response")
	console.log(" Expected: model.tool_calls = [{ name: 'write', args: {...} }]")
	console.log(" Actual: No tool calls, no text")
	}
	}
	}

	console.log("\n📝 Notes:")
	console.log(" • This same model (gpt-oss-120b) DOES support tool calling on Cerebras")
	console.log(" • See: https://inference-docs.cerebras.ai/models/openai-oss")
	console.log(" • OpenCode Zen models (grok-code, etc) work fine for tool calling")
	console.log(" • All Maple models tested show this issue (qwen, llama, deepseek, gpt-oss)")

	} catch (error: any) {
	console.error("\n❌ Error:", error.message)
	if (error.cause) {
	console.error(" Cause:", error.cause)
	}
	if (error.stack) {
	console.error("\n Stack trace:")
	console.error(error.stack)
	}
	process.exit(1)
	}