feat: add Gemma 4 auto-orchestration example (runTeam)

Add examples/09-gemma4-auto-orchestration.ts demonstrating runTeam()
with Gemma 4 as the coordinator — the framework's key feature running
fully local. The coordinator successfully decomposes goals into JSON
task arrays, schedules dependencies, and synthesises results.

Verified on gemma4:e2b (5.1B params) with Ollama 0.20.0-rc1.

README.md

@@ -122,6 +122,7 @@ npx tsx examples/01-single-agent.ts
 | [06 — Local Model](examples/06-local-model.ts) | Ollama + Claude in one pipeline via `baseURL` (works with vLLM, LM Studio, etc.) |
 | [07 — Fan-Out / Aggregate](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 analysts in parallel, then synthesize |
 | [08 — Gemma 4 Local](examples/08-gemma4-local.ts) | Pure-local Gemma 4 agent team with tool-calling — zero API cost |
+| [09 — Gemma 4 Auto-Orchestration](examples/09-gemma4-auto-orchestration.ts) | `runTeam()` with Gemma 4 as coordinator — auto task decomposition, fully local |
 
 ## Architecture
 

README_zh.md

@@ -126,6 +126,7 @@ npx tsx examples/01-single-agent.ts
 | [06 — 本地模型](examples/06-local-model.ts) | Ollama + Claude 混合流水线，通过 `baseURL` 接入（兼容 vLLM、LM Studio 等） |
 | [07 — 扇出聚合](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 个分析师并行，然后综合 |
 | [08 — Gemma 4 本地](examples/08-gemma4-local.ts) | 纯本地 Gemma 4 智能体团队 + tool-calling — 零 API 费用 |
+| [09 — Gemma 4 自动编排](examples/09-gemma4-auto-orchestration.ts) | `runTeam()` 用 Gemma 4 当 coordinator — 自动任务拆解，完全本地 |
 
 ## 架构
 

examples/09-gemma4-auto-orchestration.ts

@@ -0,0 +1,162 @@
+/**
+ * Example 09 — Gemma 4 Auto-Orchestration (runTeam, 100% Local)
+ *
+ * Demonstrates the framework's key feature — automatic task decomposition —
+ * powered entirely by a local Gemma 4 model. No cloud API needed.
+ *
+ * What happens:
+ * 1. A Gemma 4 "coordinator" receives the goal + agent roster
+ * 2. It outputs a structured JSON task array (title, description, assignee, dependsOn)
+ * 3. The framework resolves dependencies, schedules tasks, and runs agents
+ * 4. The coordinator synthesises all task results into a final answer
+ *
+ * This is the hardest test for a local model — it must produce valid JSON
+ * for task decomposition AND do tool-calling for actual task execution.
+ * Gemma 4 e2b (5.1B params) handles both reliably.
+ *
+ * Run:
+ *   no_proxy=localhost npx tsx examples/09-gemma4-auto-orchestration.ts
+ *
+ * Prerequisites:
+ *   1. Ollama >= 0.20.0 installed and running: https://ollama.com
+ *   2. Pull the model: ollama pull gemma4:e2b
+ *   3. No API keys needed!
+ *
+ * Note: The no_proxy=localhost prefix is needed if you have an HTTP proxy
+ * configured, since the OpenAI SDK would otherwise route Ollama requests
+ * through the proxy.
+ */
+
+import { OpenMultiAgent } from '../src/index.js'
+import type { AgentConfig, OrchestratorEvent, Task } from '../src/types.js'
+
+// ---------------------------------------------------------------------------
+// Configuration
+// ---------------------------------------------------------------------------
+
+// See available tags at https://ollama.com/library/gemma4
+const OLLAMA_MODEL = 'gemma4:e2b'      // or 'gemma4:e4b', 'gemma4:26b'
+const OLLAMA_BASE_URL = 'http://localhost:11434/v1'
+
+// ---------------------------------------------------------------------------
+// Agents — the coordinator is created automatically by runTeam()
+// ---------------------------------------------------------------------------
+
+const researcher: AgentConfig = {
+  name: 'researcher',
+  model: OLLAMA_MODEL,
+  provider: 'openai',
+  baseURL: OLLAMA_BASE_URL,
+  apiKey: 'ollama',
+  systemPrompt: `You are a system researcher. Use bash to run non-destructive,
+read-only commands and report the results concisely.`,
+  tools: ['bash'],
+  maxTurns: 4,
+}
+
+const writer: AgentConfig = {
+  name: 'writer',
+  model: OLLAMA_MODEL,
+  provider: 'openai',
+  baseURL: OLLAMA_BASE_URL,
+  apiKey: 'ollama',
+  systemPrompt: `You are a technical writer. Use file_write to create clear,
+structured Markdown reports based on the information provided.`,
+  tools: ['file_write'],
+  maxTurns: 4,
+}
+
+// ---------------------------------------------------------------------------
+// Progress handler
+// ---------------------------------------------------------------------------
+
+function handleProgress(event: OrchestratorEvent): void {
+  const ts = new Date().toISOString().slice(11, 23)
+  switch (event.type) {
+    case 'task_start': {
+      const task = event.data as Task | undefined
+      console.log(`[${ts}] TASK START    "${task?.title ?? event.task}" → ${task?.assignee ?? '?'}`)
+      break
+    }
+    case 'task_complete':
+      console.log(`[${ts}] TASK DONE     "${event.task}"`)
+      break
+    case 'agent_start':
+      console.log(`[${ts}] AGENT START   ${event.agent}`)
+      break
+    case 'agent_complete':
+      console.log(`[${ts}] AGENT DONE    ${event.agent}`)
+      break
+    case 'error':
+      console.error(`[${ts}] ERROR         ${event.agent ?? ''}  task=${event.task ?? '?'}`)
+      break
+  }
+}
+
+// ---------------------------------------------------------------------------
+// Orchestrator — defaultModel is used for the coordinator agent
+// ---------------------------------------------------------------------------
+
+const orchestrator = new OpenMultiAgent({
+  defaultModel: OLLAMA_MODEL,
+  defaultProvider: 'openai',
+  defaultBaseURL: OLLAMA_BASE_URL,
+  defaultApiKey: 'ollama',
+  maxConcurrency: 1, // local model serves one request at a time
+  onProgress: handleProgress,
+})
+
+const team = orchestrator.createTeam('gemma4-auto', {
+  name: 'gemma4-auto',
+  agents: [researcher, writer],
+  sharedMemory: true,
+})
+
+// ---------------------------------------------------------------------------
+// Give a goal — the framework handles the rest
+// ---------------------------------------------------------------------------
+
+const goal = `Check this machine's Node.js version, npm version, and OS info,
+then write a short Markdown summary report to /tmp/gemma4-auto/report.md`
+
+console.log('Gemma 4 Auto-Orchestration — Zero API Cost')
+console.log('='.repeat(60))
+console.log(`  model        → ${OLLAMA_MODEL} via Ollama (all agents + coordinator)`)
+console.log(`  researcher   → bash`)
+console.log(`  writer       → file_write`)
+console.log(`  coordinator  → auto-created by runTeam()`)
+console.log()
+console.log(`Goal: ${goal.replace(/\n/g, ' ').trim()}`)
+console.log('='.repeat(60))
+
+const start = Date.now()
+const result = await orchestrator.runTeam(team, goal)
+const totalTime = Date.now() - start
+
+// ---------------------------------------------------------------------------
+// Results
+// ---------------------------------------------------------------------------
+
+console.log('\n' + '='.repeat(60))
+console.log('Pipeline complete.\n')
+console.log(`Overall success: ${result.success}`)
+console.log(`Total time: ${(totalTime / 1000).toFixed(1)}s`)
+console.log(`Tokens — input: ${result.totalTokenUsage.input_tokens}, output: ${result.totalTokenUsage.output_tokens}`)
+
+console.log('\nPer-agent results:')
+for (const [name, r] of result.agentResults) {
+  const icon = r.success ? 'OK  ' : 'FAIL'
+  const tools = r.toolCalls.length > 0 ? r.toolCalls.map(c => c.toolName).join(', ') : '(none)'
+  console.log(`  [${icon}] ${name.padEnd(24)} tools: ${tools}`)
+}
+
+// Print the coordinator's final synthesis
+const coordResult = result.agentResults.get('coordinator')
+if (coordResult?.success) {
+  console.log('\nFinal synthesis (from local Gemma 4 coordinator):')
+  console.log('-'.repeat(60))
+  console.log(coordResult.output)
+  console.log('-'.repeat(60))
+}
+
+console.log('\nAll processing done locally. $0 API cost.')