Major changes: - Add autoconfigure_models tool for intelligent model assignment - Implement LLM-based model selection using openclaw agent - Improve onboarding flow with better model access checks - Update README with clearer installation and onboarding instructions Technical improvements: - Add model-fetcher utility to query authenticated models - Add smart-model-selector for LLM-driven model assignment - Use session context for LLM calls during onboarding - Suppress logging from openclaw models list calls Documentation: - Add prerequisites section to README - Add conversational onboarding example - Improve quick start flow Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
194 lines
7.2 KiB
TypeScript
194 lines
7.2 KiB
TypeScript
/**
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* onboarding.ts — Conversational onboarding context templates.
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*
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* Provides context templates for the onboard tool.
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*/
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import fs from "node:fs/promises";
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import path from "node:path";
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import { DEFAULT_MODELS } from "./tiers.js";
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// ---------------------------------------------------------------------------
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// Detection
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// ---------------------------------------------------------------------------
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export function isPluginConfigured(
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pluginConfig?: Record<string, unknown>,
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): boolean {
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const models = (pluginConfig as { models?: Record<string, string> })?.models;
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return !!models && Object.keys(models).length > 0;
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}
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export async function hasWorkspaceFiles(
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workspaceDir?: string,
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): Promise<boolean> {
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if (!workspaceDir) return false;
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try {
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const content = await fs.readFile(
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path.join(workspaceDir, "AGENTS.md"),
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"utf-8",
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);
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return content.includes("DevClaw") && content.includes("work_start");
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} catch {
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return false;
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}
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}
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// ---------------------------------------------------------------------------
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// Context templates
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// ---------------------------------------------------------------------------
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function buildModelTable(pluginConfig?: Record<string, unknown>): string {
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const cfg = (
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pluginConfig as {
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models?: { dev?: Record<string, string>; qa?: Record<string, string> };
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}
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)?.models;
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const lines: string[] = [];
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for (const [role, levels] of Object.entries(DEFAULT_MODELS)) {
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for (const [level, defaultModel] of Object.entries(levels)) {
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const model = cfg?.[role as "dev" | "qa"]?.[level] || defaultModel;
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lines.push(
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` - **${role} ${level}**: ${model} (default: ${defaultModel})`,
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);
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}
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}
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return lines.join("\n");
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}
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export function buildReconfigContext(
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pluginConfig?: Record<string, unknown>,
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): string {
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const modelTable = buildModelTable(pluginConfig);
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return `# DevClaw Reconfiguration
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The user wants to reconfigure DevClaw. Current model configuration:
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${modelTable}
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## What can be changed
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1. **Model levels** — call \`setup\` with a \`models\` object containing only the levels to change
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2. **Workspace files** — \`setup\` re-writes AGENTS.md, HEARTBEAT.md (backs up existing files)
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3. **Register new projects** — use \`project_register\`
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Ask what they want to change, then call the appropriate tool.
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\`setup\` is safe to re-run — it backs up existing files before overwriting.
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`;
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}
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export function buildOnboardToolContext(): string {
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// Build the model table dynamically from DEFAULT_MODELS
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const rows: string[] = [];
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const purposes: Record<string, string> = {
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junior: "Typos, single-file fixes",
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medior: "Features, bug fixes",
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senior: "Architecture, refactoring",
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reviewer: "Code review",
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tester: "Testing",
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};
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for (const [role, levels] of Object.entries(DEFAULT_MODELS)) {
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for (const [level, model] of Object.entries(levels)) {
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rows.push(`| ${role} | ${level} | ${model} | ${purposes[level] ?? ""} |`);
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}
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}
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const modelTable = rows.join("\n");
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return `# DevClaw Onboarding
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## What is DevClaw?
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DevClaw turns each Telegram group into an autonomous development team:
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- An **orchestrator** that manages backlogs and delegates work
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- **DEV workers** (junior/medior/senior levels) that write code in isolated sessions
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- **QA workers** that review code and run tests
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- Atomic tools for label transitions, session dispatch, state management, and audit logging
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## Setup Steps
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**Step 1: Agent Selection**
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Ask: "Do you want to configure DevClaw for the current agent, or create a new dedicated agent?"
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- Current agent → no \`newAgentName\` needed
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- New agent → ask for:
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1. Agent name
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2. **Channel binding**: "Which channel should this agent listen to? (telegram/whatsapp/none)"
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- If telegram/whatsapp selected:
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a) Check openclaw.json for existing channel bindings
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b) If channel not configured/enabled → warn and recommend skipping binding for now
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c) If channel-wide binding exists on another agent → ask: "Migrate binding from {agentName}?"
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d) Collect migration decision
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- If none selected, user can add bindings manually later via openclaw.json
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**Step 2: Model Configuration**
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1. **Call \`autoconfigure_models\`** to automatically discover and assign models:
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- Discovers all authenticated models in OpenClaw
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- Uses AI to intelligently assign them to DevClaw roles
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- Returns a ready-to-use model configuration
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2. **Handle the result**:
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- If \`success: false\` and \`modelCount: 0\`:
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- **BLOCK setup** - show the authentication instructions from the message
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- **DO NOT proceed** - exit onboarding until user configures API keys
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- If \`success: true\`:
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- Present the model assignment table to the user
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- Store the \`models\` object for Step 3
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3. **Optional: Prefer specific provider**
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- If user wants only models from one provider (e.g., "only use Anthropic"):
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- Call \`autoconfigure_models({ preferProvider: "anthropic" })\`
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4. **Confirm with user**
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- Ask: "Does this look good, or would you like to customize any roles?"
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- If approved → proceed to Step 3 with the \`models\` configuration
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- If they want changes → ask which specific roles to modify
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- If they want different provider → go back to step 3
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**Step 3: Run Setup**
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Call \`setup\` with the collected answers:
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- Current agent: \`setup({})\` or \`setup({ models: { dev: { ... }, qa: { ... } } })\`
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- New agent: \`setup({ newAgentName: "<name>", channelBinding: "telegram"|"whatsapp"|null, migrateFrom: "<agentId>"|null, models: { ... } })\`
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- \`migrateFrom\`: Include if user wants to migrate an existing channel-wide binding
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**Step 4: Telegram Group Setup (IMPORTANT)**
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After setup completes, explain project isolation best practices:
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📱 **Telegram Group Guidance:**
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DevClaw uses **one Telegram group per project** for isolation and clean backlogs.
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**Recommended Setup:**
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1. **Create a new Telegram group** for each project
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2. **Add your bot** to the group
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3. **Use mentions** to interact: "@botname status", "@botname pick up #42"
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4. Each group gets its own queue, workers, and audit log
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**Why separate groups?**
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- Clean issue backlogs per project
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- Isolated worker state (no cross-project confusion)
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- Clear audit trails
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- Team-specific access control
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**Single-project mode:**
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If you REALLY want all projects in one group (not recommended):
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- You can register multiple projects to the same group ID
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- ⚠️ WARNING: Shared queues, workers will see all issues
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- Only use this for personal/solo projects
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Ask: "Do you understand the group-per-project model, or do you want single-project mode?"
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- Most users should proceed with the recommended approach
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- Only force single-project if they insist
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**Step 5: Project Registration**
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Ask: "Would you like to register a project now?"
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If yes, collect: project name, repo path, Telegram group ID, group name, base branch.
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Then call \`project_register\`.
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💡 **Tip**: For the Telegram group ID:
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- Add the bot to your group
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- Send any message with the bot mentioned
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- Bot can tell you the group ID
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## Guidelines
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- Be conversational and friendly. Ask one question at a time.
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- Show defaults so the user can accept them quickly.
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- After setup, summarize what was configured (including channel binding if applicable).
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`;
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}
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