[Init] Initial commit - NetMesh terminal manager

infrastructure/ai/harness/compactionPruner.test.ts

@@ -0,0 +1,98 @@
+import assert from 'node:assert/strict';
+import test from 'node:test';
+import type { ModelMessage } from 'ai';
+import { pruneFirstModelMessage, pruneLastModelMessage, pruneUntilFitsCompaction } from './compactionPruner.ts';
+
+test('pruneLastModelMessage removes trailing user and assistant pair', () => {
+  const messages: ModelMessage[] = [
+    { role: 'user', content: 'old' },
+    {
+      role: 'assistant',
+      content: [{
+        type: 'tool-call',
+        toolCallId: 'call-1',
+        toolName: 'terminal_execute',
+        input: { command: 'pwd' },
+      }],
+    },
+    {
+      role: 'tool',
+      content: [{
+        type: 'tool-result',
+        toolCallId: 'call-1',
+        toolName: 'terminal_execute',
+        output: { type: 'text', value: '/tmp' },
+      }],
+    },
+    { role: 'user', content: 'recent' },
+    { role: 'assistant', content: 'acknowledged' },
+  ];
+  const pruned = pruneLastModelMessage(messages);
+  assert.equal(pruned.length, 3);
+  assert.equal(pruned[0]?.content, 'old');
+  assert.equal(pruned.at(-1)?.role, 'tool');
+});
+
+test('message pruning removes complete parallel tool-result batches', () => {
+  const calls = ['a', 'b', 'c'].map((toolCallId) => ({
+    type: 'tool-call' as const,
+    toolCallId,
+    toolName: 'terminal_poll',
+    input: { jobId: toolCallId },
+  }));
+  const results = calls.map((call) => ({
+    role: 'tool' as const,
+    content: [{
+      type: 'tool-result' as const,
+      toolCallId: call.toolCallId,
+      toolName: call.toolName,
+      output: { type: 'text' as const, value: `result ${call.toolCallId}` },
+    }],
+  }));
+  const batch: ModelMessage[] = [
+    { role: 'assistant', content: calls },
+    ...results,
+  ];
+
+  assert.deepEqual(pruneFirstModelMessage([...batch, { role: 'user', content: 'next' }]), [
+    { role: 'user', content: 'next' },
+  ]);
+  assert.deepEqual(pruneLastModelMessage([{ role: 'user', content: 'before' }, ...batch]), [
+    { role: 'user', content: 'before' },
+  ]);
+});
+
+test('pruneUntilFitsCompaction shrinks history to fit budget', () => {
+  const messages: ModelMessage[] = Array.from({ length: 20 }, (_, index) => ({
+    role: index % 2 === 0 ? 'user' : 'assistant',
+    content: 'word '.repeat(500),
+  })) as ModelMessage[];
+
+  const pruned = pruneUntilFitsCompaction({
+    messages,
+    availableForInput: 2_000,
+    providerId: 'openai',
+  });
+  assert.ok(pruned.length < messages.length);
+});
+
+test('pruneUntilFitsCompaction drops oldest messages first', () => {
+  const messages: ModelMessage[] = [
+    { role: 'user', content: `oldest ${'x'.repeat(5_000)}` },
+    ...Array.from({ length: 16 }, (_, index) => ({
+      role: index % 2 === 0 ? 'assistant' : 'user',
+      content: 'middle context',
+    })) as ModelMessage[],
+    { role: 'user', content: 'newest goal for current task' },
+    { role: 'assistant', content: 'latest reply before tail split' },
+  ];
+
+  const pruned = pruneUntilFitsCompaction({
+    messages,
+    availableForInput: 800,
+    providerId: 'openai',
+  });
+  const serialized = JSON.stringify(pruned);
+  assert.match(serialized, /newest goal for current task/);
+  assert.doesNotMatch(serialized, /oldest/);
+});