chore: Spring AI 重构
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abel533
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-61
@@ -2,7 +2,7 @@
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`s01 > s02 > s03 > s04 > s05 > s06 | s07 > s08 > [ s09 ] s10 > s11 > s12`
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> *"When the task is too big for one, delegate to teammates"* -- persistent teammates + async mailboxes.
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> *"When the task is too big for one, delegate to teammates"* -- persistent teammates + JSONL mailboxes.
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>
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> **Harness layer**: Team mailboxes -- multiple models, coordinated through files.
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@@ -10,7 +10,7 @@
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Subagents (s04) are disposable: spawn, work, return summary, die. No identity, no memory between invocations. Background tasks (s08) run shell commands but can't make LLM-guided decisions.
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Real teamwork needs: (1) persistent agents that outlive a single prompt, (2) identity and lifecycle management, (3) a communication channel between agents.
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Real teamwork needs three things: (1) persistent agents that outlive a single prompt, (2) identity and lifecycle management, (3) a communication channel between agents.
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## Solution
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@@ -37,89 +37,137 @@ Communication:
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## How It Works
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1. TeammateManager maintains config.json with the team roster.
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1. TeammateManager maintains the team roster via config.json.
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```python
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class TeammateManager:
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def __init__(self, team_dir: Path):
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self.dir = team_dir
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self.dir.mkdir(exist_ok=True)
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self.config_path = self.dir / "config.json"
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self.config = self._load_config()
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self.threads = {}
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```java
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// src/main/java/io/mybatis/learn/s09/TeammateManager.java
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public class TeammateManager {
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private final ChatModel chatModel;
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private final MessageBus bus;
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private final Path configPath;
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private final ObjectMapper mapper = new ObjectMapper();
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private Map<String, Object> config;
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// Python uses threading.Thread + dict; Java uses ConcurrentHashMap for natural thread safety
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private final Map<String, Thread> threads = new ConcurrentHashMap<>();
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public TeammateManager(ChatModel chatModel, MessageBus bus, Path teamDir) {
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this.chatModel = chatModel;
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this.bus = bus;
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this.configPath = teamDir.resolve("config.json");
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Files.createDirectories(teamDir);
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this.config = loadConfig();
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}
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```
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2. `spawn()` creates a teammate and starts its agent loop in a thread.
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```python
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def spawn(self, name: str, role: str, prompt: str) -> str:
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member = {"name": name, "role": role, "status": "working"}
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self.config["members"].append(member)
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self._save_config()
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thread = threading.Thread(
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target=self._teammate_loop,
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args=(name, role, prompt), daemon=True)
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thread.start()
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return f"Spawned teammate '{name}' (role: {role})"
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```java
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// Python uses threading.Thread; Java uses Thread.startVirtualThread() for virtual threads
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public synchronized String spawn(String name, String role, String prompt) {
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Map<String, Object> member = new LinkedHashMap<>();
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member.put("name", name);
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member.put("role", role);
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member.put("status", "working");
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((List<Map<String, Object>>) config.get("members")).add(member);
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saveConfig();
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// Virtual thread: lightweight, JVM-scheduled, doesn't occupy OS threads
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Thread thread = Thread.startVirtualThread(
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() -> teammateLoop(name, role, prompt));
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threads.put(name, thread);
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return "Spawned '" + name + "' (role: " + role + ")";
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}
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```
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3. MessageBus: append-only JSONL inboxes. `send()` appends a JSON line; `read_inbox()` reads all and drains.
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```python
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class MessageBus:
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def send(self, sender, to, content, msg_type="message", extra=None):
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msg = {"type": msg_type, "from": sender,
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"content": content, "timestamp": time.time()}
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if extra:
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msg.update(extra)
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with open(self.dir / f"{to}.jsonl", "a") as f:
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f.write(json.dumps(msg) + "\n")
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```java
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// src/main/java/io/mybatis/learn/core/team/MessageBus.java
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// Python relies on GIL for implicit thread safety; Java uses synchronized for explicit safety
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public class MessageBus {
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private final Path inboxDir;
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private final ObjectMapper mapper = new ObjectMapper();
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def read_inbox(self, name):
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path = self.dir / f"{name}.jsonl"
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if not path.exists(): return "[]"
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msgs = [json.loads(l) for l in path.read_text().strip().splitlines() if l]
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path.write_text("") # drain
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return json.dumps(msgs, indent=2)
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public synchronized String send(String sender, String to, String content,
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String msgType, Map<String, Object> extra) {
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Map<String, Object> msg = new LinkedHashMap<>();
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msg.put("type", msgType);
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msg.put("from", sender);
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msg.put("content", content);
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msg.put("timestamp", System.currentTimeMillis() / 1000.0);
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if (extra != null) msg.putAll(extra);
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Path inbox = inboxDir.resolve(to + ".jsonl");
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Files.writeString(inbox, mapper.writeValueAsString(msg) + "\n",
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StandardOpenOption.CREATE, StandardOpenOption.APPEND);
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return "Sent " + msgType + " to " + to;
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}
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public synchronized List<Map<String, Object>> readInbox(String name) {
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Path inbox = inboxDir.resolve(name + ".jsonl");
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if (!Files.exists(inbox)) return List.of();
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List<Map<String, Object>> messages = new ArrayList<>();
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for (String line : Files.readAllLines(inbox)) {
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if (!line.isBlank())
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messages.add(mapper.readValue(line, new TypeReference<>() {}));
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}
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Files.writeString(inbox, ""); // drain
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return messages;
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}
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}
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```
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4. Each teammate checks its inbox before every LLM call, injecting received messages into context.
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4. Each teammate checks its inbox between `call()` invocations, injecting messages into context. ChatClient's `call()` is equivalent to Python's full tool loop (looping until `stop_reason != "tool_use"`).
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```python
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def _teammate_loop(self, name, role, prompt):
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messages = [{"role": "user", "content": prompt}]
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for _ in range(50):
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inbox = BUS.read_inbox(name)
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if inbox != "[]":
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messages.append({"role": "user",
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"content": f"<inbox>{inbox}</inbox>"})
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messages.append({"role": "assistant",
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"content": "Noted inbox messages."})
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response = client.messages.create(...)
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if response.stop_reason != "tool_use":
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break
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# execute tools, append results...
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self._find_member(name)["status"] = "idle"
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```java
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// Python teammates check inbox before each LLM call; Java checks between each call()
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protected void teammateLoop(String name, String role, String initialPrompt) {
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String sysPrompt = String.format(
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"You are '%s', role: %s. Use send_message to communicate.",
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name, role);
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var messageTool = new TeammateMessageTool(bus, name);
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ChatClient client = ChatClient.builder(chatModel)
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.defaultSystem(sysPrompt)
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.defaultTools(new BashTool(), new ReadFileTool(),
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new WriteFileTool(), new EditFileTool(), messageTool)
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.build();
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// Initial work (call() = full tool chain, equivalent to Python loop until stop_reason != "tool_use")
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String response = client.prompt(initialPrompt).call().content();
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// Check inbox between each call() (vs. Python's between each LLM call)
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for (int round = 0; round < 50; round++) {
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Thread.sleep(2000);
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var inbox = bus.readInbox(name);
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if (inbox.isEmpty()) break;
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String inboxJson = mapper.writeValueAsString(inbox);
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response = client.prompt("<inbox>" + inboxJson + "</inbox>").call().content();
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}
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setStatus(name, "idle");
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}
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```
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## What Changed From s08
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| Component | Before (s08) | After (s09) |
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|----------------|------------------|----------------------------|
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| Tools | 6 | 9 (+spawn/send/read_inbox) |
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| Agents | Single | Lead + N teammates |
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| Persistence | None | config.json + JSONL inboxes|
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| Threads | Background cmds | Full agent loops per thread|
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| Lifecycle | Fire-and-forget | idle -> working -> idle |
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| Communication | None | message + broadcast |
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| Component | Before (s08) | After (s09) |
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|----------------|------------------|------------------------------------|
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| Tools | 6 | 9 (+spawn/send/read_inbox) |
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| Agents | Single | Lead + N teammates |
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| Persistence | None | config.json + JSONL inboxes |
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| Threads | Background cmds | Full agent loops per thread |
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| Lifecycle | Fire-and-forget | idle -> working -> idle |
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| Communication | None | message + broadcast |
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## Try It
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```sh
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cd learn-claude-code
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python agents/s09_agent_teams.py
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mvn exec:java -Dexec.mainClass=io.mybatis.learn.s09.S09AgentTeams
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```
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Try these prompts (English prompts work better with LLMs, but Chinese also works):
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1. `Spawn alice (coder) and bob (tester). Have alice send bob a message.`
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2. `Broadcast "status update: phase 1 complete" to all teammates`
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3. `Check the lead inbox for any messages`
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