feat: add Gemma 4 local model example with tool-calling
Add examples/08-gemma4-local.ts demonstrating a pure-local multi-agent team using Gemma 4 via Ollama — zero API cost. Two agents (researcher + summarizer) collaborate through a task pipeline with bash, file_write, and file_read tools. Verified on gemma4:e2b with Ollama 0.20.0-rc1. Update both READMEs: add example 08 to the examples table and note Gemma 4 as a verified local model with tool-calling support.
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JackChen
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@ -121,6 +121,7 @@ npx tsx examples/01-single-agent.ts
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| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot as an LLM provider |
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| [06 — Local Model](examples/06-local-model.ts) | Ollama + Claude in one pipeline via `baseURL` (works with vLLM, LM Studio, etc.) |
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| [07 — Fan-Out / Aggregate](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 analysts in parallel, then synthesize |
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| [08 — Gemma 4 Local](examples/08-gemma4-local.ts) | Pure-local Gemma 4 agent team with tool-calling — zero API cost |
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## Architecture
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@ -182,6 +183,8 @@ npx tsx examples/01-single-agent.ts
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| GitHub Copilot | `provider: 'copilot'` | `GITHUB_TOKEN` | Verified |
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| Ollama / vLLM / LM Studio | `provider: 'openai'` + `baseURL` | — | Verified |
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Verified local models with tool-calling: **Gemma 4** (see [example 08](examples/08-gemma4-local.ts)).
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Any OpenAI-compatible API should work via `provider: 'openai'` + `baseURL` (DeepSeek, Groq, Mistral, Qwen, MiniMax, etc.). These providers have not been fully verified yet — contributions welcome via [#25](https://github.com/JackChen-me/open-multi-agent/issues/25).
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## Contributing
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@ -125,6 +125,7 @@ npx tsx examples/01-single-agent.ts
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| [05 — Copilot](examples/05-copilot-test.ts) | GitHub Copilot 作为 LLM 提供者 |
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| [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） |
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| [07 — 扇出聚合](examples/07-fan-out-aggregate.ts) | `runParallel()` MapReduce — 3 个分析师并行，然后综合 |
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| [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 |
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## 架构
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@ -186,6 +187,8 @@ npx tsx examples/01-single-agent.ts
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| GitHub Copilot | `provider: 'copilot'` | `GITHUB_TOKEN` | 已验证 |
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| Ollama / vLLM / LM Studio | `provider: 'openai'` + `baseURL` | — | 已验证 |
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已验证支持 tool-calling 的本地模型：**Gemma 4**（见[示例 08](examples/08-gemma4-local.ts)）。
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任何 OpenAI 兼容 API 均可通过 `provider: 'openai'` + `baseURL` 接入（DeepSeek、Groq、Mistral、Qwen、MiniMax 等）。这些 Provider 尚未完整验证——欢迎通过 [#25](https://github.com/JackChen-me/open-multi-agent/issues/25) 贡献验证。
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## 参与贡献
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@ -0,0 +1,203 @@
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/**
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* Example 08 — Gemma 4 Local Agent Team (100% Local, Zero API Cost)
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*
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* Demonstrates a fully local multi-agent team using Google's Gemma 4 via
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* Ollama. No cloud API keys needed — everything runs on your machine.
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*
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* Two agents collaborate through a task pipeline:
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* - researcher: uses bash + file_write to gather system info and write a report
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* - summarizer: uses file_read to read the report and produce a concise summary
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*
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* This pattern works with any Ollama model that supports tool-calling.
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* Gemma 4 (released 2026-04-02) has native tool-calling support.
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*
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* Run:
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* no_proxy=localhost npx tsx examples/08-gemma4-local.ts
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*
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* Prerequisites:
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* 1. Ollama >= 0.20.0 installed and running: https://ollama.com
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* 2. Pull the model: ollama pull gemma4:e2b
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* (or gemma4:e4b for better quality on machines with more RAM)
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* 3. No API keys needed!
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*
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* Note: The no_proxy=localhost prefix is needed if you have an HTTP proxy
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* configured, since the OpenAI SDK would otherwise route Ollama requests
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* through the proxy.
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*/
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import { OpenMultiAgent } from '../src/index.js'
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import type { AgentConfig, OrchestratorEvent, Task } from '../src/types.js'
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// ---------------------------------------------------------------------------
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// Configuration — change this to match your Ollama setup
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// ---------------------------------------------------------------------------
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// See available tags at https://ollama.com/library/gemma4
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const OLLAMA_MODEL = 'gemma4:e2b' // or 'gemma4:e4b', 'gemma4:26b'
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const OLLAMA_BASE_URL = 'http://localhost:11434/v1'
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const OUTPUT_DIR = '/tmp/gemma4-demo'
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// ---------------------------------------------------------------------------
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// Agents — both use Gemma 4 locally
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// ---------------------------------------------------------------------------
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/**
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* Researcher — gathers system information using shell commands.
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*/
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const researcher: AgentConfig = {
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name: 'researcher',
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model: OLLAMA_MODEL,
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provider: 'openai',
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baseURL: OLLAMA_BASE_URL,
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apiKey: 'ollama', // placeholder — Ollama ignores this, but the OpenAI SDK requires a non-empty value
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systemPrompt: `You are a system researcher. Your job is to gather information
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about the current machine using shell commands and write a structured report.
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Use the bash tool to run commands like: uname -a, df -h, uptime, and similar
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non-destructive read-only commands.
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On macOS you can also use: sw_vers, sysctl -n hw.memsize.
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On Linux you can also use: cat /etc/os-release, free -h.
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Then use file_write to save a Markdown report to ${OUTPUT_DIR}/system-report.md.
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The report should have sections: OS, Hardware, Disk, and Uptime.
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Be concise — one or two lines per section is enough.`,
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tools: ['bash', 'file_write'],
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maxTurns: 8,
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}
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/**
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* Summarizer — reads the report and writes a one-paragraph executive summary.
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*/
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const summarizer: AgentConfig = {
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name: 'summarizer',
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model: OLLAMA_MODEL,
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provider: 'openai',
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baseURL: OLLAMA_BASE_URL,
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apiKey: 'ollama',
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systemPrompt: `You are a technical writer. Read the system report file provided,
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then produce a concise one-paragraph executive summary (3-5 sentences).
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Focus on the key highlights: what OS, how much RAM, disk status, and uptime.`,
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tools: ['file_read'],
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maxTurns: 4,
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}
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// ---------------------------------------------------------------------------
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// Progress handler
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// ---------------------------------------------------------------------------
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const taskTimes = new Map<string, number>()
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function handleProgress(event: OrchestratorEvent): void {
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const ts = new Date().toISOString().slice(11, 23)
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switch (event.type) {
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case 'task_start': {
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taskTimes.set(event.task ?? '', Date.now())
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const task = event.data as Task | undefined
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console.log(`[${ts}] TASK START "${task?.title ?? event.task}" → ${task?.assignee ?? '?'}`)
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break
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}
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case 'task_complete': {
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const elapsed = Date.now() - (taskTimes.get(event.task ?? '') ?? Date.now())
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console.log(`[${ts}] TASK DONE "${event.task}" in ${(elapsed / 1000).toFixed(1)}s`)
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break
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}
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case 'agent_start':
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console.log(`[${ts}] AGENT START ${event.agent}`)
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break
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case 'agent_complete':
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console.log(`[${ts}] AGENT DONE ${event.agent}`)
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break
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case 'error':
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console.error(`[${ts}] ERROR ${event.agent ?? ''} task=${event.task ?? '?'}`)
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break
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}
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}
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// ---------------------------------------------------------------------------
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// Orchestrator + Team
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// ---------------------------------------------------------------------------
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const orchestrator = new OpenMultiAgent({
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defaultModel: OLLAMA_MODEL,
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maxConcurrency: 1, // run agents sequentially — local model can only serve one at a time
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onProgress: handleProgress,
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})
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const team = orchestrator.createTeam('gemma4-team', {
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name: 'gemma4-team',
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agents: [researcher, summarizer],
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sharedMemory: true,
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})
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// ---------------------------------------------------------------------------
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// Task pipeline: research → summarize
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// ---------------------------------------------------------------------------
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const tasks: Array<{
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title: string
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description: string
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assignee?: string
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dependsOn?: string[]
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}> = [
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{
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title: 'Gather system information',
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description: `Use bash to run system info commands (uname -a, sw_vers, sysctl, df -h, uptime).
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Then write a structured Markdown report to ${OUTPUT_DIR}/system-report.md with sections:
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OS, Hardware, Disk, and Uptime.`,
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assignee: 'researcher',
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},
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{
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title: 'Summarize the report',
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description: `Read the file at ${OUTPUT_DIR}/system-report.md.
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Produce a concise one-paragraph executive summary of the system information.`,
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assignee: 'summarizer',
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dependsOn: ['Gather system information'],
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},
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]
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// ---------------------------------------------------------------------------
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// Run
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// ---------------------------------------------------------------------------
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console.log('Gemma 4 Local Agent Team — Zero API Cost')
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console.log('='.repeat(60))
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console.log(` model → ${OLLAMA_MODEL} via Ollama`)
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console.log(` researcher → bash + file_write`)
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console.log(` summarizer → file_read`)
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console.log(` output dir → ${OUTPUT_DIR}`)
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console.log()
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console.log('Pipeline: researcher gathers info → summarizer writes summary')
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console.log('='.repeat(60))
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const start = Date.now()
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const result = await orchestrator.runTasks(team, tasks)
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const totalTime = Date.now() - start
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// ---------------------------------------------------------------------------
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// Summary
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// ---------------------------------------------------------------------------
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console.log('\n' + '='.repeat(60))
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console.log('Pipeline complete.\n')
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console.log(`Overall success: ${result.success}`)
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console.log(`Total time: ${(totalTime / 1000).toFixed(1)}s`)
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console.log(`Tokens — input: ${result.totalTokenUsage.input_tokens}, output: ${result.totalTokenUsage.output_tokens}`)
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console.log('\nPer-agent results:')
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for (const [name, r] of result.agentResults) {
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const icon = r.success ? 'OK ' : 'FAIL'
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const tools = r.toolCalls.map(c => c.toolName).join(', ')
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console.log(` [${icon}] ${name.padEnd(12)} tools: ${tools || '(none)'}`)
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}
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// Print the summarizer's output
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const summary = result.agentResults.get('summarizer')
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if (summary?.success) {
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console.log('\nExecutive Summary (from local Gemma 4):')
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console.log('-'.repeat(60))
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console.log(summary.output)
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console.log('-'.repeat(60))
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}
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console.log('\nAll processing done locally. $0 API cost.')
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