feat(ai): upgrade pi-ai from @mariozechner 0.54 to @earendil-works 0.74

- Replace @mariozechner/pi-ai and pi-agent-core with @earendil-works scope
- Adapt to AgentTool.execute() params type change (Record → unknown)
- Migrate Agent constructor to use initialState for systemPrompt/tools/messages
- Update onPayload callback signature to include model parameter
- Filter finalMessages with isPiMessage() for new AgentMessage union type
- Use state.errorMessage instead of deprecated state.error

Co-authored-by: Cursor <cursoragent@cursor.com>

apps/cli/package.json

@@ -2,6 +2,7 @@
   "name": "chatlab-cli",
   "version": "0.0.0",
   "description": "ChatLab CLI & Service - Chat history analysis tool",
+  "type": "module",
   "bin": {
     "chatlab": "bin/chatlab.mjs"
   },
@@ -46,8 +47,8 @@
   "dependencies": {
     "@fastify/multipart": "^10.0.0",
     "@fastify/static": "^9.1.3",
-    "@mariozechner/pi-agent-core": "0.54.2",
-    "@mariozechner/pi-ai": "0.54.2",
+    "@earendil-works/pi-agent-core": "0.74.2",
+    "@earendil-works/pi-ai": "0.74.2",
     "@modelcontextprotocol/sdk": "^1.12.1",
     "better-sqlite3": "^12.4.6",
     "commander": "^13.1.0",

apps/cli/src/ai/tool-adapter.ts

@@ -1,7 +1,7 @@
 /**
  * 工具适配层
  *
- * 将 @openchatlab/tools 的 ToolDefinition 适配为 @mariozechner/pi-agent-core 的 AgentTool 格式。
+ * 将 @openchatlab/tools 的 ToolDefinition 适配为 @earendil-works/pi-agent-core 的 AgentTool 格式。
  * 消息类工具返回 rawMessages 时自动执行预处理管道（清洗、去噪、脱敏、截断、格式化）。
  */
 
@@ -63,7 +63,8 @@ export function adaptToolsForAgent(
     label: tool.name,
     description: tool.description,
     parameters: convertJsonSchemaToParameters(tool.inputSchema) as any,
-    async execute(_toolCallId: string, params: Record<string, unknown>): Promise<AgentToolResult<unknown>> {
+    async execute(_toolCallId: string, params: unknown): Promise<AgentToolResult<unknown>> {
+      const toolParams = (params && typeof params === 'object' ? params : {}) as Record<string, unknown>
       const ctx = getContext()
       const execCtx: ToolExecutionContext = {
         db: ctx.db,
@@ -73,7 +74,7 @@ export function adaptToolsForAgent(
         segmentText: (texts, locale, options) => batchSegmentWithFrequency(texts, locale as any, options as any),
       }
       try {
-        const result = await tool.handler(params, execCtx)
+        const result = await tool.handler(toolParams, execCtx)
 
         if (result.rawMessages && result.rawMessages.length > 0) {
           const pipelineResult = applyPreprocessingPipeline({