diff --git a/examples/06-local-model.ts b/examples/06-local-model.ts
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--- /dev/null
+++ b/examples/06-local-model.ts
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+/**
+ * Example 06 — Local Model + Cloud Model Team (Ollama + Claude)
+ *
+ * Demonstrates mixing a local model served by Ollama with a cloud model
+ * (Claude) in the same task pipeline. The key technique is using
+ * `provider: 'openai'` with a custom `baseURL` pointing at Ollama's
+ * OpenAI-compatible endpoint.
+ *
+ * This pattern works with ANY OpenAI-compatible local server:
+ * - Ollama        → http://localhost:11434/v1
+ * - vLLM          → http://localhost:8000/v1
+ * - LM Studio     → http://localhost:1234/v1
+ * - llama.cpp     → http://localhost:8080/v1
+ * Just change the baseURL and model name below.
+ *
+ * Run:
+ *   npx tsx examples/06-local-model.ts
+ *
+ * Prerequisites:
+ *   1. Ollama installed and running: https://ollama.com
+ *   2. Pull the model: ollama pull llama3.1
+ *   3. ANTHROPIC_API_KEY env var must be set.
+ */
+
+import { OpenMultiAgent } from '../src/index.js'
+import type { AgentConfig, OrchestratorEvent, Task } from '../src/types.js'
+
+// ---------------------------------------------------------------------------
+// Agents
+// ---------------------------------------------------------------------------
+
+/**
+ * Coder — uses Claude (Anthropic) for high-quality code generation.
+ */
+const coder: AgentConfig = {
+  name: 'coder',
+  model: 'claude-sonnet-4-6',
+  provider: 'anthropic',
+  systemPrompt: `You are a senior TypeScript developer. Write clean, well-typed,
+production-quality code. Use the tools to write files to /tmp/local-model-demo/.
+Always include brief JSDoc comments on exported functions.`,
+  tools: ['bash', 'file_write'],
+  maxTurns: 6,
+}
+
+/**
+ * Reviewer — uses a local Ollama model via the OpenAI-compatible API.
+ * The apiKey is required by the OpenAI SDK but Ollama ignores it,
+ * so we pass the placeholder string 'ollama'.
+ */
+const reviewer: AgentConfig = {
+  name: 'reviewer',
+  model: 'llama3.1',
+  provider: 'openai', // 'openai' here means "OpenAI-compatible protocol", not the OpenAI cloud
+  baseURL: 'http://localhost:11434/v1',
+  apiKey: 'ollama',
+  systemPrompt: `You are a code reviewer. You read source files and produce a structured review.
+Your review MUST include these sections:
+- Summary (2-3 sentences)
+- Strengths (bullet list)
+- Issues (bullet list — or "None found" if the code is clean)
+- Verdict: SHIP or NEEDS WORK
+
+Be specific and constructive. Reference line numbers or function names when possible.`,
+  tools: ['file_read'],
+  maxTurns: 4,
+}
+
+// ---------------------------------------------------------------------------
+// Progress handler
+// ---------------------------------------------------------------------------
+
+const taskTimes = new Map<string, number>()
+
+function handleProgress(event: OrchestratorEvent): void {
+  const ts = new Date().toISOString().slice(11, 23)
+
+  switch (event.type) {
+    case 'task_start': {
+      taskTimes.set(event.task ?? '', Date.now())
+      const task = event.data as Task | undefined
+      console.log(`[${ts}] TASK READY    "${task?.title ?? event.task}" → ${task?.assignee ?? '?'}`)
+      break
+    }
+    case 'task_complete': {
+      const elapsed = Date.now() - (taskTimes.get(event.task ?? '') ?? Date.now())
+      console.log(`[${ts}] TASK DONE     task=${event.task} in ${elapsed}ms`)
+      break
+    }
+    case 'agent_start':
+      console.log(`[${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 + Team
+// ---------------------------------------------------------------------------
+
+const orchestrator = new OpenMultiAgent({
+  defaultModel: 'claude-sonnet-4-6',
+  maxConcurrency: 2,
+  onProgress: handleProgress,
+})
+
+const team = orchestrator.createTeam('local-cloud-team', {
+  name: 'local-cloud-team',
+  agents: [coder, reviewer],
+  sharedMemory: true,
+})
+
+// ---------------------------------------------------------------------------
+// Task pipeline: code → review
+// ---------------------------------------------------------------------------
+
+const OUTPUT_DIR = '/tmp/local-model-demo'
+
+const tasks: Array<{
+  title: string
+  description: string
+  assignee?: string
+  dependsOn?: string[]
+}> = [
+  {
+    title: 'Write: retry utility',
+    description: `Write a small but complete TypeScript utility to ${OUTPUT_DIR}/retry.ts.
+
+The module should export:
+1. A \`RetryOptions\` interface with: maxRetries (number), delayMs (number),
+   backoffFactor (optional number, default 2), shouldRetry (optional predicate
+   taking the error and returning boolean).
+2. An async \`retry<T>(fn: () => Promise<T>, options: RetryOptions): Promise<T>\`
+   function that retries \`fn\` with exponential backoff.
+3. A convenience \`withRetry\` wrapper that returns a new function with retry
+   behaviour baked in.
+
+Include JSDoc comments. No external dependencies — use only Node built-ins.
+After writing the file, also create a small test script at ${OUTPUT_DIR}/retry-test.ts
+that exercises the happy path and a failure case, then run it with \`npx tsx\`.`,
+    assignee: 'coder',
+  },
+  {
+    title: 'Review: retry utility',
+    description: `Read the files at ${OUTPUT_DIR}/retry.ts and ${OUTPUT_DIR}/retry-test.ts.
+
+Produce a structured code review covering:
+- Summary (2-3 sentences describing the module)
+- Strengths (bullet list)
+- Issues (bullet list — be specific about what and why)
+- Verdict: SHIP or NEEDS WORK`,
+    assignee: 'reviewer',
+    dependsOn: ['Write: retry utility'],
+  },
+]
+
+// ---------------------------------------------------------------------------
+// Run
+// ---------------------------------------------------------------------------
+
+console.log('Local + Cloud model team')
+console.log(`  coder    → Claude (${coder.model}) via Anthropic API`)
+console.log(`  reviewer → Ollama (${reviewer.model}) at ${reviewer.baseURL}`)
+console.log()
+console.log('Pipeline: coder writes code → local model reviews it')
+console.log('='.repeat(60))
+
+const result = await orchestrator.runTasks(team, tasks)
+
+// ---------------------------------------------------------------------------
+// Summary
+// ---------------------------------------------------------------------------
+
+console.log('\n' + '='.repeat(60))
+console.log('Pipeline complete.\n')
+console.log(`Overall success: ${result.success}`)
+console.log(`Tokens — input: ${result.totalTokenUsage.input_tokens}, output: ${result.totalTokenUsage.output_tokens}`)
+
+console.log('\nPer-agent summary:')
+for (const [name, r] of result.agentResults) {
+  const icon = r.success ? 'OK  ' : 'FAIL'
+  const provider = name === 'coder' ? 'anthropic' : 'ollama (local)'
+  const tools = r.toolCalls.map(c => c.toolName).join(', ')
+  console.log(`  [${icon}] ${name.padEnd(10)} (${provider.padEnd(16)})  tools: ${tools || '(none)'}`)
+}
+
+// Print the reviewer's output
+const review = result.agentResults.get('reviewer')
+if (review?.success) {
+  console.log('\nCode review (from local model):')
+  console.log('─'.repeat(60))
+  console.log(review.output)
+  console.log('─'.repeat(60))
+}
diff --git a/examples/07-fan-out-aggregate.ts b/examples/07-fan-out-aggregate.ts
new file mode 100644
index 0000000..43b2c32
--- /dev/null
+++ b/examples/07-fan-out-aggregate.ts
@@ -0,0 +1,209 @@
+/**
+ * Example 07 — Fan-Out / Aggregate (MapReduce) Pattern
+ *
+ * Demonstrates:
+ * - Fan-out: send the same question to N "analyst" agents in parallel
+ * - Aggregate: a "synthesizer" agent reads all analyst outputs and produces
+ *   a balanced final report
+ * - AgentPool with runParallel() for concurrent fan-out
+ * - No tools needed — pure LLM reasoning to keep the focus on the pattern
+ *
+ * Run:
+ *   npx tsx examples/07-fan-out-aggregate.ts
+ *
+ * Prerequisites:
+ *   ANTHROPIC_API_KEY env var must be set.
+ */
+
+import { Agent, AgentPool, ToolRegistry, ToolExecutor, registerBuiltInTools } from '../src/index.js'
+import type { AgentConfig, AgentRunResult } from '../src/types.js'
+
+// ---------------------------------------------------------------------------
+// Analysis topic
+// ---------------------------------------------------------------------------
+
+const TOPIC = `Should a solo developer build a SaaS product that uses AI agents
+for automated customer support? Consider the current state of AI technology,
+market demand, competition, costs, and the unique constraints of being a solo
+founder with limited time (~6 hours/day of productive work).`
+
+// ---------------------------------------------------------------------------
+// Analyst agent configs — three perspectives on the same question
+// ---------------------------------------------------------------------------
+
+const optimistConfig: AgentConfig = {
+  name: 'optimist',
+  model: 'claude-sonnet-4-6',
+  systemPrompt: `You are an optimistic technology analyst who focuses on
+opportunities, upside potential, and emerging trends. You see possibilities
+where others see obstacles. Back your optimism with concrete reasoning —
+cite market trends, cost curves, and real capabilities. Keep your analysis
+to 200-300 words.`,
+  maxTurns: 1,
+  temperature: 0.4,
+}
+
+const skepticConfig: AgentConfig = {
+  name: 'skeptic',
+  model: 'claude-sonnet-4-6',
+  systemPrompt: `You are a skeptical technology analyst who focuses on risks,
+challenges, failure modes, and hidden costs. You stress-test assumptions and
+ask "what could go wrong?" Back your skepticism with concrete reasoning —
+cite failure rates, technical limitations, and market realities. Keep your
+analysis to 200-300 words.`,
+  maxTurns: 1,
+  temperature: 0.4,
+}
+
+const pragmatistConfig: AgentConfig = {
+  name: 'pragmatist',
+  model: 'claude-sonnet-4-6',
+  systemPrompt: `You are a pragmatic technology analyst who focuses on practical
+feasibility, execution complexity, and resource requirements. You care about
+what works today, not what might work someday. You think in terms of MVPs,
+timelines, and concrete tradeoffs. Keep your analysis to 200-300 words.`,
+  maxTurns: 1,
+  temperature: 0.4,
+}
+
+const synthesizerConfig: AgentConfig = {
+  name: 'synthesizer',
+  model: 'claude-sonnet-4-6',
+  systemPrompt: `You are a senior strategy advisor who synthesizes multiple
+perspectives into a balanced, actionable recommendation. You do not simply
+summarise — you weigh the arguments, identify where they agree and disagree,
+and produce a clear verdict with next steps. Structure your output as:
+
+1. Key agreements across perspectives
+2. Key disagreements and how you weigh them
+3. Verdict (go / no-go / conditional go)
+4. Recommended next steps (3-5 bullet points)
+
+Keep the final report to 300-400 words.`,
+  maxTurns: 1,
+  temperature: 0.3,
+}
+
+// ---------------------------------------------------------------------------
+// Build agents — no tools needed for pure reasoning
+// ---------------------------------------------------------------------------
+
+function buildAgent(config: AgentConfig): Agent {
+  const registry = new ToolRegistry()
+  registerBuiltInTools(registry) // not needed here, but safe if tools are added later
+  const executor = new ToolExecutor(registry)
+  return new Agent(config, registry, executor)
+}
+
+const optimist = buildAgent(optimistConfig)
+const skeptic = buildAgent(skepticConfig)
+const pragmatist = buildAgent(pragmatistConfig)
+const synthesizer = buildAgent(synthesizerConfig)
+
+// ---------------------------------------------------------------------------
+// Set up the pool
+// ---------------------------------------------------------------------------
+
+const pool = new AgentPool(3) // 3 analysts can run simultaneously
+pool.add(optimist)
+pool.add(skeptic)
+pool.add(pragmatist)
+pool.add(synthesizer)
+
+console.log('Fan-Out / Aggregate (MapReduce) Pattern')
+console.log('='.repeat(60))
+console.log(`\nTopic: ${TOPIC.replace(/\n/g, ' ').trim()}\n`)
+
+// ---------------------------------------------------------------------------
+// Step 1: Fan-out — run all 3 analysts in parallel
+// ---------------------------------------------------------------------------
+
+console.log('[Step 1] Fan-out: 3 analysts running in parallel...\n')
+
+const analystResults: Map<string, AgentRunResult> = await pool.runParallel([
+  { agent: 'optimist',   prompt: TOPIC },
+  { agent: 'skeptic',    prompt: TOPIC },
+  { agent: 'pragmatist', prompt: TOPIC },
+])
+
+// Print each analyst's output (truncated)
+const analysts = ['optimist', 'skeptic', 'pragmatist'] as const
+for (const name of analysts) {
+  const result = analystResults.get(name)!
+  const status = result.success ? 'OK' : 'FAILED'
+  console.log(`  ${name} [${status}] — ${result.tokenUsage.output_tokens} output tokens`)
+  console.log(`  ${result.output.slice(0, 150).replace(/\n/g, ' ')}...`)
+  console.log()
+}
+
+// Check all analysts succeeded
+for (const name of analysts) {
+  if (!analystResults.get(name)!.success) {
+    console.error(`Analyst '${name}' failed: ${analystResults.get(name)!.output}`)
+    process.exit(1)
+  }
+}
+
+// ---------------------------------------------------------------------------
+// Step 2: Aggregate — synthesizer reads all 3 analyses
+// ---------------------------------------------------------------------------
+
+console.log('[Step 2] Aggregate: synthesizer producing final report...\n')
+
+const synthesizerPrompt = `Three analysts have independently evaluated the same question.
+Read their analyses below and produce your synthesis report.
+
+--- OPTIMIST ---
+${analystResults.get('optimist')!.output}
+
+--- SKEPTIC ---
+${analystResults.get('skeptic')!.output}
+
+--- PRAGMATIST ---
+${analystResults.get('pragmatist')!.output}
+
+Now synthesize these three perspectives into a balanced recommendation.`
+
+const synthResult = await pool.run('synthesizer', synthesizerPrompt)
+
+if (!synthResult.success) {
+  console.error('Synthesizer failed:', synthResult.output)
+  process.exit(1)
+}
+
+// ---------------------------------------------------------------------------
+// Final output
+// ---------------------------------------------------------------------------
+
+console.log('='.repeat(60))
+console.log('SYNTHESIZED REPORT')
+console.log('='.repeat(60))
+console.log()
+console.log(synthResult.output)
+console.log()
+console.log('-'.repeat(60))
+
+// ---------------------------------------------------------------------------
+// Token usage comparison
+// ---------------------------------------------------------------------------
+
+console.log('\nToken Usage Summary:')
+console.log('-'.repeat(60))
+
+let totalInput = 0
+let totalOutput = 0
+
+for (const name of analysts) {
+  const r = analystResults.get(name)!
+  totalInput += r.tokenUsage.input_tokens
+  totalOutput += r.tokenUsage.output_tokens
+  console.log(`  ${name.padEnd(12)} — input: ${r.tokenUsage.input_tokens}, output: ${r.tokenUsage.output_tokens}`)
+}
+
+totalInput += synthResult.tokenUsage.input_tokens
+totalOutput += synthResult.tokenUsage.output_tokens
+console.log(`  ${'synthesizer'.padEnd(12)} — input: ${synthResult.tokenUsage.input_tokens}, output: ${synthResult.tokenUsage.output_tokens}`)
+console.log('-'.repeat(60))
+console.log(`  ${'TOTAL'.padEnd(12)} — input: ${totalInput}, output: ${totalOutput}`)
+
+console.log('\nDone.')