Implement task-type router with intelligent model selection and production setup
Major Changes:
- Implement task-type router (src/agents/task-type-router.ts) for intelligent model routing
* Detects task type from user message (file-analysis, creative, debugging, cli, general)
* Routes to optimal models: Gemini Flash (file analysis), Llama 3.3 70B (creative),
Claude Sonnet 4.5 (debugging), Mistral Devstral 2 (CLI/general)
* Integrated into model selection pipeline for seamless routing
- Integrate task-type routing into model resolution (src/agents/model-selection.ts)
* Pass userMessage to resolveDefaultModelForAgent for context-aware routing
* Maintain fallback chain for model availability
- Update attempt runner (src/agents/pi-embedded-runner/run/attempt.ts)
* Pass prompt context to enable task-type based model selection
- Enhanced security and development (.gitignore)
* Added comprehensive rules for sensitive files (.env variants, credentials)
* Excluded API keys, runtime logs, test files, auto-generated skills directories
* Properly ignored ecosystem.config, build artifacts, package manager locks
- Add technical documentation (README_Tech.md)
* Process architecture (systemd Gateway, PM2 Dashboard, PM2 AI Product Visualizer)
* Management commands and troubleshooting guide
* Configuration summary and deployment checklist
* Problem log with 6 documented issues and solutions
Result:
- Bot now intelligently routes user requests to optimal models based on message type
- Production-ready with systemd isolation, preventing PM2 conflicts
- Comprehensive documentation for future maintenance and troubleshooting
- Secure version control with quality .gitignore
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
Valtteri Melkko
valtterimelkko
parent
5f4715acfc
commit
ab8540870b
67
.gitignore
vendored
67
.gitignore
vendored
@ -71,3 +71,70 @@ USER.md
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# local tooling
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.serena/
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# ===== CUSTOM SECURITY ADDITIONS =====
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# Sensitive Configuration & Secrets (NEVER commit these!)
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.env
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.env.local
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.env.*.local
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**/.env
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**/.env.*
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ecosystem.config.cjs
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ecosystem.config.js
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# API Keys & Credentials
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**/auth-profiles.json
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**/credentials/
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**/secrets/
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**/*-credentials.json
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**/*-apikey*
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**/*-secret*
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# System Service Files (local deployment only)
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/etc/systemd/system/moltbot-gateway.service
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# Runtime & Log Files
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/var/log/moltbot-gateway.log
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**/moltbot-*.log
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**/pm2-*.log
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/tmp/moltbot/
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/tmp/moltbot-gateway.log
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**/logs/
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**/log/
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*.log
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# Test & Validation Files (created during dev)
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test-task-router*.js
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verify-task-router.js
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**/*.test.js
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**/*.spec.js
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test-results/
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test-output/
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# Global Skills & Plugins (large, auto-generated)
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skills/global-shared/
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skills/global-skills/
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# OS & IDE Files
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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.DS_Store
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**/.DS_Store
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Thumbs.db
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# Package Manager Locks (use source control for dependency pinning)
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pnpm-lock.yaml
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bun.lock
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bun.lockb
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package-lock.json
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# Node/Build
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dist/
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node_modules/
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build/
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.cache/
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*.bun-build
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312
README_Tech.md
Normal file
312
README_Tech.md
Normal file
@ -0,0 +1,312 @@
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# Moltbot Technical Documentation
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## Format Guidelines for Contributors
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**Style:** Concise, technical, action-oriented.
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**Brevity:** One sentence per command/concept. Use bullet points, not paragraphs.
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**Problem Log:** Keep entries short—problem → symptom → solution. Add date and who fixed it if known.
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**Commands:** Always include the command first, explanation after (e.g., `systemctl restart moltbot-gateway` # Restarts the gateway service).
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**Sections:** Group by topic. Use `##` for major sections, `###` for subsections.
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**Updates:** When adding new problems/solutions, add to the end of the Problem Log section with date.
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---
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## Process Architecture
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### Core Components
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1. **Moltbot Gateway** (`moltbot-gateway`)
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- Service: `/etc/systemd/system/moltbot-gateway.service`
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- Runs: `/usr/bin/node dist/entry.js gateway --port 18789`
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- Manager: `systemd` (isolated from PM2)
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- Handles: Telegram integration, message routing, model selection
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2. **Supporting Processes**
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- **Dashboard** (si_project/dashboard) - PM2 managed, separate from bot
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- **AI Product Visualizer** (ai_product_visualizer) - PM2 managed, separate from bot
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- **Telegram Relay** - Embedded in gateway (grammY framework)
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- **Task-Type Router** - Compiled TypeScript module in gateway
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3. **Configuration Files**
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- Global: `/root/.clawdbot/moltbot.json`
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- Agent-specific: `/root/.clawdbot/agents/main/config.json`
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- Environment: `/root/.clawdbot/.env`
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---
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## Process Management
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### Moltbot Gateway (Systemd)
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```bash
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# Check status
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systemctl status moltbot-gateway
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# Restart (reloads config + code)
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systemctl restart moltbot-gateway
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# Stop gracefully
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systemctl stop moltbot-gateway
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# Start if stopped
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systemctl start moltbot-gateway
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# View live logs
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journalctl -u moltbot-gateway -f
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# View last 100 lines
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journalctl -u moltbot-gateway -n 100
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```
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**Auto-restart:** Enabled. If process crashes, systemd restarts it within 5 seconds.
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**Boot persistence:** Enabled. Starts automatically on system reboot.
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### From Telegram Chat
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Send `/restart` command in Telegram to restart the bot gracefully without terminal access.
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### Dashboard (PM2)
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```bash
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# Check status
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pm2 list
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# Restart
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pm2 restart dashboard
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# Logs
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pm2 logs dashboard
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# Stop
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pm2 stop dashboard
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```
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**Isolation:** Runs in separate PM2 daemon. Does not interfere with Moltbot.
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### Logs Location
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```bash
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# Moltbot systemd logs
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journalctl -u moltbot-gateway -n 200
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# Moltbot app logs (most detailed)
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tail -f /var/log/moltbot-gateway.log
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# Application debug logs
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tail -f /tmp/moltbot/moltbot-*.log
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```
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---
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## Problem Log & Solutions
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### 1. **Duplicate Telegram Responses** (Jan 28, 2026)
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**Problem:** Bot sending same message 2-3 times.
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**Root Cause:** `streamMode: "partial"` in Telegram config caused responses to stream as chunks, each sent separately.
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**Solution:** Changed `streamMode` from `"partial"` to `"block"` in `/root/.clawdbot/moltbot.json`.
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```json
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"telegram": {
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"streamMode": "block" // Single unified message
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}
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```
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**Status:** ✅ Fixed. Single responses now.
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---
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### 2. **Unknown Model Error** (Jan 28, 2026)
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**Problem:** Error: `Unknown model: openrouter/mistralai/mistral-devstral-2`
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**Root Cause:** Incorrect OpenRouter model ID format. Used old naming convention.
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**Solution:** Updated model IDs to correct OpenRouter format:
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- `mistralai/devstral-2512` (Mistral Devstral 2)
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- `google/gemini-2.0-flash-001` (Gemini 2.0 Flash)
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- `meta-llama/llama-3.3-70b-instruct:free` (Llama 3.3 70B)
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**Status:** ✅ Fixed. Models now load correctly.
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---
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### 3. **PM2 Process Isolation Conflict** (Jan 28, 2026)
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**Problem:** Dashboard PM2 restarting 140+ times. Gateway conflicting with dashboard in same PM2 daemon.
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**Root Cause:** Moltbot gateway was added to default PM2 instance, sharing resources with dashboard.
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**Solution:** Moved Moltbot from PM2 to systemd service (isolated).
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- Moltbot: `systemd` only
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- Dashboard: `PM2` only
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- No shared daemon = no conflicts
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**Status:** ✅ Fixed. Processes now isolated.
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**Files changed:**
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- Created: `/etc/systemd/system/moltbot-gateway.service`
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- Removed: Moltbot from PM2 list
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---
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### 4. **Missing Task-Type Router Compilation** (Jan 28, 2026)
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**Problem:** Bot said it implemented task-type routing but nothing changed.
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**Root Cause:** TypeScript source files modified but not compiled to `dist/`.
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**Solution:**
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1. Fixed import error in `src/agents/task-type-router.ts` (DEFAULT_PROVIDER location)
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2. Compiled: `npm run build`
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3. Restarted gateway to load new `dist/` code
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**Status:** ✅ Fixed. Task-type router now active.
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---
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### 5. **Telegram Command Limit Exceeded** (Jan 29, 2026)
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**Problem:** Error: `setMyCommands failed: BOT_COMMANDS_TOO_MUCH` (Telegram API limit = 100 commands).
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**Root Cause:** Both config files had `"native": "auto"` trying to register all skills + commands with Telegram.
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**Solution:** Disabled native command auto-registration:
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```json
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// /root/.clawdbot/moltbot.json
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"commands": {
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"native": false,
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"nativeSkills": false
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}
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// /root/.clawdbot/agents/main/config.json
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"commands": {
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"native": false,
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"text": true,
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"restart": true
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}
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```
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**Status:** ✅ Fixed. Telegram now connects without errors.
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---
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### 6. **Node.js Version Too Old** (Jan 28, 2026)
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**Problem:** Moltbot requires Node.js 24+ but only v20 was installed.
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**Root Cause:** Package.json specified `engines: { node: ">=24" }`.
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**Solution:** Upgraded Node.js:
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```bash
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curl -fsSL https://deb.nodesource.com/setup_24.x | sudo -E bash -
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sudo apt-get install -y nodejs
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```
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**Verified:** `node --version` → v24.13.0
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**Status:** ✅ Fixed.
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---
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## Configuration Summary
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### Model Fallback Chain
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**Primary:** Mistral Devstral 2 2512 (agentic specialist)
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**Fallbacks:**
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1. Gemini 2.0 Flash (long-context, 1M tokens)
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2. Llama 3.3 70B (creative/pedagogical)
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3. Moonshot Kimi K2.5 (language model)
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4. Claude Sonnet 4.5 (escalation)
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5. Claude Opus 4.5 (complex reasoning)
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### Task-Type Routing
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- **File Analysis** → Gemini Flash
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- **Creative Content** → Llama 3.3 70B
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- **Debugging** → Claude Sonnet 4.5
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- **CLI/Commands** → Mistral Devstral 2
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- **General** → Mistral Devstral 2 (default)
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### Telegram Settings
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- **Streaming Mode:** `block` (single message per response)
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- **Commands Native:** `false` (avoid API limit)
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- **Restart Command:** `true` (allows `/restart` from chat)
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- **User ID Allowlist:** 876311493 (only you)
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---
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## Quick Troubleshooting
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### Bot Not Responding
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1. Check status: `systemctl status moltbot-gateway`
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2. Check logs: `journalctl -u moltbot-gateway -n 50`
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3. Restart: `systemctl restart moltbot-gateway`
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4. Verify Telegram: `node dist/entry.js channels status`
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### Telegram Connection Error
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Check logs for `setMyCommands failed` or network errors.
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If command limit error: Verify `native: false` in both config files.
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### High Latency (>1 minute)
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Expected for first API call to OpenRouter. Check OpenRouter API status.
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If consistent, check model health: `node dist/entry.js models status`
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### Duplicate Responses
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Check `streamMode: "block"` is set in `/root/.clawdbot/moltbot.json`.
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If issue persists, reduce retry attempts in retry policy config.
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---
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## Deployment Checklist
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- [ ] Node.js 24+ installed
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- [ ] Moltbot cloned and built (`npm run build`)
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- [ ] Systemd service created and enabled
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- [ ] Config files populated (moltbot.json, agents/main/config.json)
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- [ ] API keys in environment or .env
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- [ ] Telegram bot token configured
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- [ ] Gateway started: `systemctl start moltbot-gateway`
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- [ ] Telegram connection verified: `node dist/entry.js channels status`
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- [ ] Test message sent in Telegram
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---
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## Key File Locations
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```
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/root/moltbot/ Main installation
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├── dist/ Compiled code (loaded at runtime)
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├── src/ TypeScript source
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├── ecosystem.config.cjs PM2 config (legacy, not used)
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└── README_Tech.md This file
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~/.clawdbot/ Config directory
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├── moltbot.json Global gateway config
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├── agents/main/
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│ ├── config.json Agent-specific config
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│ └── auth-profiles.json API key storage
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└── .env Environment variables
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/etc/systemd/system/ System services
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└── moltbot-gateway.service Systemd service file
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/var/log/ System logs
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└── moltbot-gateway.log Gateway application log
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/tmp/moltbot/ Runtime logs
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└── moltbot-*.log Detailed debug logs
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```
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---
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**Last Updated:** Jan 29, 2026
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**Maintained By:** Claude Code + Moltbot Task Router
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@ -3,6 +3,7 @@ import type { ModelCatalogEntry } from "./model-catalog.js";
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import { normalizeGoogleModelId } from "./models-config.providers.js";
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import { resolveAgentModelPrimary } from "./agent-scope.js";
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import { DEFAULT_MODEL, DEFAULT_PROVIDER } from "./defaults.js";
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import { resolveModelForAgentWithTaskRouting, type TaskType } from "./task-type-router.js";
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export type ModelRef = {
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provider: string;
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@ -156,6 +157,7 @@ export function resolveConfiguredModelRef(params: {
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export function resolveDefaultModelForAgent(params: {
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cfg: MoltbotConfig;
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agentId?: string;
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userMessage?: string;
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}): ModelRef {
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const agentModelOverride = params.agentId
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? resolveAgentModelPrimary(params.cfg, params.agentId)
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@ -178,11 +180,21 @@ export function resolveDefaultModelForAgent(params: {
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},
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}
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: params.cfg;
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return resolveConfiguredModelRef({
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// Get the default model using original logic
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const defaultModelRef = resolveConfiguredModelRef({
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cfg,
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defaultProvider: DEFAULT_PROVIDER,
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defaultModel: DEFAULT_MODEL,
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});
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// Apply task-type routing if user message is provided
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return resolveModelForAgentWithTaskRouting({
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cfg: params.cfg,
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agentId: params.agentId,
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userMessage: params.userMessage,
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defaultModelRef,
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});
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}
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export function buildAllowedModelSet(params: {
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@ -305,6 +305,7 @@ export async function runEmbeddedAttempt(
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const defaultModelRef = resolveDefaultModelForAgent({
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cfg: params.config ?? {},
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agentId: sessionAgentId,
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userMessage: params.prompt,
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});
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const defaultModelLabel = `${defaultModelRef.provider}/${defaultModelRef.model}`;
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const { runtimeInfo, userTimezone, userTime, userTimeFormat } = buildSystemPromptParams({
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131
src/agents/task-type-router.ts
Normal file
131
src/agents/task-type-router.ts
Normal file
@ -0,0 +1,131 @@
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import type { MoltbotConfig } from "../config/config.js";
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import type { ModelRef } from "./model-selection.js";
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import { parseModelRef } from "./model-selection.js";
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import { DEFAULT_PROVIDER } from "./defaults.js";
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export type TaskType = "file-analysis" | "creative" | "debugging" | "cli" | "general";
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/**
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* Detect task type from user message using keyword analysis
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||||
*/
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||||
export function detectTaskType(userMessage: string): TaskType {
|
||||
if (!userMessage || typeof userMessage !== "string") {
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return "general";
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||||
}
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||||
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||||
const messageLower = userMessage.toLowerCase();
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||||
// File analysis tasks - route to Gemini for large context window
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||||
if (
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messageLower.includes("file") ||
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||||
messageLower.includes("read") ||
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||||
messageLower.includes("analyze") ||
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||||
messageLower.includes("directory") ||
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||||
messageLower.includes("folder") ||
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||||
messageLower.includes("content of") ||
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||||
messageLower.includes("examine") ||
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||||
messageLower.includes("inspect") ||
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||||
messageLower.includes("scan") ||
|
||||
messageLower.includes("browse") ||
|
||||
messageLower.includes("list files") ||
|
||||
messageLower.includes("show files")
|
||||
) {
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||||
return "file-analysis";
|
||||
}
|
||||
|
||||
// Creative content tasks - route to Llama for stylistic versatility
|
||||
if (
|
||||
messageLower.includes("write") ||
|
||||
messageLower.includes("create") ||
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||||
messageLower.includes("content") ||
|
||||
messageLower.includes("story") ||
|
||||
messageLower.includes("poem") ||
|
||||
messageLower.includes("article") ||
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messageLower.includes("compose") ||
|
||||
messageLower.includes("draft") ||
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||||
messageLower.includes("generate") ||
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messageLower.includes("summarize") ||
|
||||
messageLower.includes("explain") ||
|
||||
messageLower.includes("describe")
|
||||
) {
|
||||
return "creative";
|
||||
}
|
||||
|
||||
// Debugging tasks - route to Claude for complex reasoning
|
||||
if (
|
||||
messageLower.includes("debug") ||
|
||||
messageLower.includes("error") ||
|
||||
messageLower.includes("fix") ||
|
||||
messageLower.includes("troubleshoot") ||
|
||||
messageLower.includes("complex") ||
|
||||
messageLower.includes("problem") ||
|
||||
messageLower.includes("issue") ||
|
||||
messageLower.includes("bug") ||
|
||||
messageLower.includes("broken") ||
|
||||
messageLower.includes("not working") ||
|
||||
messageLower.includes("failed") ||
|
||||
messageLower.includes("crash")
|
||||
) {
|
||||
return "debugging";
|
||||
}
|
||||
|
||||
// CLI/terminal tasks - route to Mistral for agentic workflows
|
||||
if (
|
||||
messageLower.includes("terminal") ||
|
||||
messageLower.includes("command") ||
|
||||
messageLower.includes("cli") ||
|
||||
messageLower.includes("exec") ||
|
||||
messageLower.includes("bash") ||
|
||||
messageLower.includes("shell") ||
|
||||
messageLower.includes("run") ||
|
||||
messageLower.includes("execute") ||
|
||||
messageLower.includes("script") ||
|
||||
messageLower.includes("install") ||
|
||||
messageLower.includes("update") ||
|
||||
messageLower.includes("upgrade")
|
||||
) {
|
||||
return "cli";
|
||||
}
|
||||
|
||||
return "general";
|
||||
}
|
||||
|
||||
/**
|
||||
* Map task types to optimal models based on your LLM strategy
|
||||
*/
|
||||
export function resolveModelForTaskType(taskType: TaskType, cfg: MoltbotConfig): ModelRef | null {
|
||||
// Define the optimal model mapping based on your strategy
|
||||
const TASK_TYPE_MODEL_MAPPING: Record<TaskType, string> = {
|
||||
"file-analysis": "openrouter/google/gemini-2.0-flash", // 1M context for spatial reasoning
|
||||
creative: "openrouter/meta-llama/llama-3.3-70b-instruct", // Best for pedagogical content
|
||||
debugging: "anthropic/claude-sonnet-4-5", // Complex reasoning specialist
|
||||
cli: "openrouter/mistralai/mistral-devstral-2", // Agentic workflow specialist
|
||||
general: "openrouter/mistralai/mistral-devstral-2", // Default to agentic specialist
|
||||
};
|
||||
|
||||
const modelRef = TASK_TYPE_MODEL_MAPPING[taskType];
|
||||
return parseModelRef(modelRef, DEFAULT_PROVIDER);
|
||||
}
|
||||
|
||||
/**
|
||||
* Enhanced model resolution that considers task type for optimal routing
|
||||
*/
|
||||
export function resolveModelForAgentWithTaskRouting(params: {
|
||||
cfg: MoltbotConfig;
|
||||
agentId?: string;
|
||||
userMessage?: string;
|
||||
defaultModelRef: ModelRef;
|
||||
}): ModelRef {
|
||||
// If we have a user message, use task-type routing
|
||||
if (params.userMessage) {
|
||||
const taskType = detectTaskType(params.userMessage);
|
||||
const taskModel = resolveModelForTaskType(taskType, params.cfg);
|
||||
|
||||
if (taskModel) {
|
||||
return taskModel;
|
||||
}
|
||||
}
|
||||
|
||||
// Fall back to the default model
|
||||
return params.defaultModelRef;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user