feat(agent): add pi-agent container assets with dynamic MiniMax config and HTTP runner

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-09-07 17:31:27 +08:00
parent 1edf5f60ab
commit fabdb51e65
3 changed files with 431 additions and 0 deletions
@@ -0,0 +1,44 @@
+# =============================================================================
+# Pi Agent Container (MiniMax-M3 + Tavily Key Pool + Runner Service)
+# Requires Node.js 22+ for node:fs globSync support
+# =============================================================================
+FROM node:22-bookworm-slim
+
+# 安装系统运行时依赖 (bash, curl, git, ca-certificates)
+RUN set -eux; \
+    apt-get update; \
+    apt-get install -y --no-install-recommends \
+        ca-certificates \
+        curl \
+        git \
+        bash; \
+    rm -rf /var/lib/apt/lists/*
+
+# 全局安装 pi-coding-agent CLI
+RUN npm install -g @earendil-works/pi-coding-agent@0.85.1 --omit=dev
+
+# 验证 pi 安装
+RUN pi --version
+
+WORKDIR /app
+
+# 复制 runner、扩展工具以及入口脚本
+COPY deploy/pi-agent/extensions/ /app/extensions/
+COPY deploy/pi-agent/runner.mjs /app/runner.mjs
+COPY deploy/pi-agent/entrypoint.sh /app/entrypoint.sh
+
+RUN chmod +x /app/entrypoint.sh /app/runner.mjs
+
+# 默认环境变量配置 (敏感密钥通过运行时 -e 或 docker-compose 传入)
+ENV PORT=18766 \
+    DATA_DIR=/app/data/workspaces \
+    PI_CODING_AGENT_DIR=/root/.pi/agent \
+    MINIMAX_ENDPOINT_URL=https://api.minimaxi.com/v1 \
+    MINIMAX_MODEL_ID=MiniMax-M3 \
+    MINIMAX_API_TYPE=openai-compatible \
+    MINIMAX_PROVIDER=minimax
+
+EXPOSE 18766
+
+ENTRYPOINT ["/app/entrypoint.sh"]
+CMD ["node", "/app/runner.mjs"]
@@ -0,0 +1,92 @@
+#!/bin/bash
+set -e
+
+# =============================================================================
+# Pi Agent Container Entrypoint
+# Dynamically configures MiniMax provider (4 core parameters) & Tavily search extension
+# =============================================================================
+
+# 1. 核心参数配置与规整
+# 1) ENDPOINT URL (默认: https://api.minimaxi.com/v1)
+ENDPOINT_URL="${MINIMAX_ENDPOINT_URL:-https://api.minimaxi.com/v1}"
+
+# 2) API TOKEN (用户自行设定)
+API_TOKEN="${MINIMAX_API_KEY:-${MINIMAX_API_TOKEN:-}}"
+
+# 3) MODEL ID (默认: MiniMax-M3)
+MODEL_ID="${MINIMAX_MODEL_ID:-MiniMax-M3}"
+
+# 4) API 类型 (用户设定 openai-compatible，规整映射至 pi 的 openai-completions)
+RAW_API_TYPE="${MINIMAX_API_TYPE:-openai-compatible}"
+if [ "$RAW_API_TYPE" = "openai-compatible" ] || [ "$RAW_API_TYPE" = "openai" ]; then
+    API_TYPE="openai-completions"
+else
+    API_TYPE="$RAW_API_TYPE"
+fi
+
+echo "================================================================="
+echo "  🚀 Starting Pi-Agent Container"
+echo "  - ENDPOINT URL : ${ENDPOINT_URL}"
+echo "  - MODEL ID     : ${MODEL_ID}"
+echo "  - API TYPE     : ${API_TYPE} (from ${RAW_API_TYPE})"
+if [ -z "$API_TOKEN" ]; then
+    echo "  - API TOKEN    : [WARNING] MINIMAX_API_KEY is not set yet!"
+else
+    MASKED_KEY="${API_TOKEN:0:4}...${API_TOKEN: -4}"
+    echo "  - API TOKEN    : configured (${MASKED_KEY})"
+fi
+if [ -n "$TAVILY_API_KEYS" ] || [ -n "$TAVILY_API_KEY" ]; then
+    echo "  - TAVILY SEARCH: configured (API Key Pool active)"
+else
+    echo "  - TAVILY SEARCH: not configured (set TAVILY_API_KEYS to enable)"
+fi
+echo "================================================================="
+
+# 2. 准备 Pi Agent 运行时目录结构
+PI_DIR="${PI_CODING_AGENT_DIR:-/root/.pi/agent}"
+mkdir -p "${PI_DIR}/extensions"
+mkdir -p "${DATA_DIR:-/app/data/workspaces}"
+
+# 3. 注入 Tavily Web Search 扩展
+if [ -f "/app/extensions/tavily-search.mjs" ]; then
+    cp -f "/app/extensions/tavily-search.mjs" "${PI_DIR}/extensions/tavily-search.mjs"
+fi
+
+# 4. 动态生成 models.json
+cat <<EOF > "${PI_DIR}/models.json"
+{
+  "providers": {
+    "minimax": {
+      "name": "MiniMax",
+      "baseUrl": "${ENDPOINT_URL}",
+      "api": "${API_TYPE}",
+      "apiKey": "${API_TOKEN}",
+      "models": [
+        {
+          "id": "${MODEL_ID}",
+          "name": "${MODEL_ID}",
+          "reasoning": true,
+          "input": ["text"],
+          "contextWindow": 1000000,
+          "maxTokens": 131072
+        }
+      ]
+    }
+  }
+}
+EOF
+
+# 5. 生成 settings.json 设置默认 Provider 和 Model
+cat <<EOF > "${PI_DIR}/settings.json"
+{
+  "defaultProvider": "minimax",
+  "defaultModel": "${MODEL_ID}"
+}
+EOF
+
+# 6. 执行容器命令
+if [ "$#" -eq 0 ]; then
+    exec node /app/runner.mjs
+else
+    exec "$@"
+fi
@@ -0,0 +1,295 @@
+#!/usr/bin/env node
+/**
+ * Pi Agent Container HTTP Runner Server
+ * Listens for task run requests, executes pi agent in RPC mode within workspace,
+ * and returns generated artifact patches.
+ */
+
+import http from "node:http";
+import { spawn } from "node:child_process";
+import { readFileSync, writeFileSync, existsSync, mkdirSync } from "node:fs";
+import { resolve, relative, isAbsolute } from "node:path";
+import { TavilyKeyPool, searchTavily, formatTavilyResults } from "./extensions/tavily-search.mjs";
+
+const PORT = parseInt(process.env.PORT || process.env.PI_AGENT_RUNNER_PORT || "18766", 10);
+const DATA_DIR = process.env.DATA_DIR || "/app/data/workspaces";
+const DEFAULT_MODEL = process.env.MINIMAX_MODEL_ID || "MiniMax-M3";
+const DEFAULT_PROVIDER = process.env.MINIMAX_PROVIDER || "minimax";
+const tavilyPool = TavilyKeyPool.fromEnv();
+
+function safeRel(workspace, target) {
+  const abs = isAbsolute(target) ? target : resolve(workspace, target);
+  const rel = relative(workspace, abs);
+  if (!rel || rel.startsWith("..") || isAbsolute(rel)) {
+    return null;
+  }
+  return rel.replace(/\\/g, "/");
+}
+
+function resolveWorkspace(rawPath) {
+  if (!rawPath) return DATA_DIR;
+  if (existsSync(rawPath)) return rawPath;
+
+  // If path contains 'workspaces/', extract the relative part
+  const wsIndex = rawPath.indexOf("workspaces/");
+  if (wsIndex !== -1) {
+    const sub = rawPath.slice(wsIndex + "workspaces/".length);
+    return resolve(DATA_DIR, sub);
+  }
+
+  // If relative path
+  if (!isAbsolute(rawPath)) {
+    return resolve(DATA_DIR, rawPath);
+  }
+
+  return rawPath;
+}
+
+function parseJsonBody(req) {
+  return new Promise((resolvePromise, rejectPromise) => {
+    const chunks = [];
+    req.on("data", (chunk) => chunks.push(chunk));
+    req.on("end", () => {
+      const raw = Buffer.concat(chunks).toString("utf8");
+      if (!raw.trim()) {
+        resolvePromise({});
+        return;
+      }
+      try {
+        resolvePromise(JSON.parse(raw));
+      } catch (err) {
+        rejectPromise(new Error("Invalid JSON: " + err.message));
+      }
+    });
+    req.on("error", rejectPromise);
+  });
+}
+
+function sendJson(res, statusCode, data) {
+  res.writeHead(statusCode, { "Content-Type": "application/json; charset=utf-8" });
+  res.end(JSON.stringify(data, null, 2) + "\n");
+}
+
+async function handleRun(body) {
+  const rawWs = body.workspace || "";
+  const workspace = resolveWorkspace(rawWs);
+  const model = body.model || DEFAULT_MODEL;
+  const provider = body.provider || DEFAULT_PROVIDER;
+  const prompt = body.prompt || "";
+  const instructions = body.instructions || "";
+  const targets = Array.isArray(body.targets) ? body.targets : [];
+  const timeoutMs = Number(body.timeout_ms) || 30 * 60 * 1000;
+
+  if (!existsSync(workspace)) {
+    mkdirSync(workspace, { recursive: true });
+  }
+
+  if (!prompt.trim()) {
+    return { ok: false, fallback_reason: "prompt_empty", patches: [] };
+  }
+
+  const allowed = new Set();
+  for (const t of targets) {
+    const rel = safeRel(workspace, t);
+    if (rel) allowed.add(rel);
+  }
+
+  // 1. Write persona instructions into workspace
+  if (instructions.trim()) {
+    try {
+      writeFileSync(resolve(workspace, "AGENTS.md"), instructions, "utf8");
+      writeFileSync(resolve(workspace, "INSTRUCTIONS.md"), instructions, "utf8");
+    } catch (e) {
+      console.warn("[runner] Failed to write instructions to workspace:", e);
+    }
+  }
+
+  // 2. Build pi arguments
+  const piBin = process.env.PI_AGENT_BIN || "pi";
+  const args = [
+    "--mode", "rpc",
+    "--no-session",
+    "--provider", provider,
+    "--model", model,
+  ];
+
+  const sysPromptFile = resolve(workspace, "INSTRUCTIONS.md");
+  if (existsSync(sysPromptFile)) {
+    args.push("--append-system-prompt", "INSTRUCTIONS.md");
+  }
+
+  args.push(
+    "--tools", "read,write,edit,tavily_search,web_search",
+    "--approve",
+  );
+
+  const env = { ...process.env };
+  env.PI_AGENT_MODEL = model;
+  env.PI_AGENT_PROVIDER = provider;
+  env.PI_AGENT_SYSTEM_PROMPT_FILE = "AGENTS.md";
+
+  let agentId = "";
+  let runId = "";
+  let lastErr = "";
+  let succeeded = false;
+
+  console.log(`[runner] Starting pi run in workspace: ${workspace}, model: ${model}`);
+
+  const child = spawn(piBin, args, {
+    cwd: workspace,
+    env,
+    stdio: ["pipe", "pipe", "pipe"],
+  });
+
+  const timer = setTimeout(() => {
+    lastErr = `Execution timed out after ${timeoutMs}ms`;
+    child.kill("SIGKILL");
+  }, timeoutMs);
+
+  const cmdId = "req-" + Date.now();
+  child.stdin.write(
+    JSON.stringify({
+      id: cmdId,
+      type: "prompt",
+      message: instructions ? `[Persona & System Instructions]:\n${instructions}\n\n[User Task]:\n${prompt}` : prompt,
+    }) + "\n",
+  );
+
+  let stdoutBuf = "";
+  child.stdout.on("data", (chunk) => {
+    stdoutBuf += chunk.toString("utf8");
+    let nl;
+    while ((nl = stdoutBuf.indexOf("\n")) >= 0) {
+      const line = stdoutBuf.slice(0, nl).trim();
+      stdoutBuf = stdoutBuf.slice(nl + 1);
+      if (!line) continue;
+      let ev;
+      try { ev = JSON.parse(line); } catch { continue; }
+      if (!ev || typeof ev !== "object") continue;
+
+      const t = ev.type || "";
+      if (t === "session" && ev.id) agentId = ev.id;
+      if (t === "agent_settled") {
+        succeeded = true;
+        child.stdin.end();
+      }
+      if (t === "agent_error") {
+        lastErr = ev.error?.message || ev.message || "agent_error";
+      }
+    }
+  });
+
+  let stderrBuf = "";
+  child.stderr.on("data", (chunk) => {
+    stderrBuf += chunk.toString("utf8");
+    lastErr = (stderrBuf + " " + lastErr).slice(-400);
+  });
+
+  await new Promise((resolveDone) => {
+    child.on("close", () => {
+      clearTimeout(timer);
+      resolveDone();
+    });
+    child.on("error", (err) => {
+      clearTimeout(timer);
+      lastErr = err && err.message ? err.message : String(err);
+      resolveDone();
+    });
+  });
+
+  if (!succeeded) {
+    return {
+      ok: false,
+      agent_id: agentId,
+      run_id: runId,
+      fallback_reason: lastErr || "agent_not_settled",
+      stderr: stderrBuf ? stderrBuf.slice(0, 400) : undefined,
+      patches: [],
+    };
+  }
+
+  // 3. Read generated files
+  const patches = [];
+  for (const rel of allowed) {
+    const abs = resolve(workspace, rel);
+    if (!existsSync(abs)) continue;
+    const content = readFileSync(abs, "utf8");
+    patches.push({ rel_path: rel, content });
+  }
+
+  return {
+    ok: true,
+    agent_id: agentId,
+    run_id: runId,
+    patches,
+    fallback_reason: "",
+  };
+}
+
+const server = http.createServer(async (req, res) => {
+  const url = new URL(req.url, `http://${req.headers.host || "localhost"}`);
+  const method = req.method.toUpperCase();
+
+  // 1. Healthcheck
+  if (method === "GET" && (url.pathname === "/health" || url.pathname === "/")) {
+    sendJson(res, 200, {
+      ok: true,
+      service: "shuimu-pi-agent-runner",
+      endpoint_url: process.env.MINIMAX_ENDPOINT_URL || "https://api.minimaxi.com/v1",
+      model_id: DEFAULT_MODEL,
+      api: process.env.MINIMAX_API_TYPE || "openai-completions",
+      has_token: Boolean(process.env.MINIMAX_API_KEY || process.env.MINIMAX_API_TOKEN),
+      tavily: {
+        active: tavilyPool.size() > 0,
+        keys_count: tavilyPool.size(),
+      },
+      data_dir: DATA_DIR,
+      uptime: process.uptime(),
+    });
+    return;
+  }
+
+  // 2. Direct search test endpoint
+  if (method === "POST" && url.pathname === "/v1/search") {
+    try {
+      const body = await parseJsonBody(req);
+      const query = (body.query || "").trim();
+      if (!query) {
+        sendJson(res, 400, { error: "Missing query parameter" });
+        return;
+      }
+      const data = await tavilyPool.executeWithPool(async (apiKey) => {
+        return await searchTavily(apiKey, query, {
+          search_depth: body.search_depth,
+          max_results: body.max_results,
+        });
+      });
+      const markdown = formatTavilyResults(data, query);
+      sendJson(res, 200, { ok: true, data, markdown });
+    } catch (err) {
+      sendJson(res, 500, { ok: false, error: err.message });
+    }
+    return;
+  }
+
+  // 3. Agent Task Run Endpoint
+  if (method === "POST" && url.pathname === "/v1/run") {
+    try {
+      const body = await parseJsonBody(req);
+      const result = await handleRun(body);
+      sendJson(res, 200, result);
+    } catch (err) {
+      sendJson(res, 500, { ok: false, error: err.message, patches: [] });
+    }
+    return;
+  }
+
+  sendJson(res, 404, { error: "Not Found", path: url.pathname });
+});
+
+server.listen(PORT, "0.0.0.0", () => {
+  console.log(`[pi-agent-runner] Listening on 0.0.0.0:${PORT}`);
+  console.log(`[pi-agent-runner] Endpoint: ${process.env.MINIMAX_ENDPOINT_URL || "https://api.minimaxi.com/v1"}`);
+  console.log(`[pi-agent-runner] Model: ${DEFAULT_MODEL}, Provider: ${DEFAULT_PROVIDER}`);
+  console.log(`[pi-agent-runner] Tavily API keys pool size: ${tavilyPool.size()}`);
+});