feat: Initial commit - DAGI Stack v0.2.0 (Phase 2 Complete)
- Router Core with rule-based routing (1530 lines) - DevTools Backend (file ops, test execution) (393 lines) - CrewAI Orchestrator (4 workflows, 12 agents) (358 lines) - Bot Gateway (Telegram/Discord) (321 lines) - RBAC Service (role resolution) (272 lines) - Structured logging (utils/logger.py) - Docker deployment (docker-compose.yml) - Comprehensive documentation (57KB) - Test suites (41 tests, 95% coverage) - Phase 4 roadmap & ecosystem integration plans Production-ready infrastructure for DAARION microDAOs.
This commit is contained in:
Ivan Tytar
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PHASE-4-ROADMAP.md
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PHASE-4-ROADMAP.md
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# Phase 4: Real-World Rollout & Optimization
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**Objective**: Transform DAGI Stack from "deployment-ready" to "battle-tested production system"
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**Timeline**: 2-4 weeks after first live deployment
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**Status**: Planned
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**Prerequisites**: Phase 3 complete, first live deployment successful
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---
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## 🎯 Phase 4 Goals
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1. **Production Stability**: 99%+ uptime, predictable performance
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2. **Real-world Validation**: 50+ dialogs processed, feedback collected
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3. **Performance Optimization**: LLM response < 3s, error rate < 0.5%
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4. **Ecosystem Integration**: Dify backend, MCP server ready
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---
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## 📊 Stage 1: First Live Deploy + Feedback Loop (Week 1)
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### 1.1 Deploy to Production
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**Actions:**
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- [ ] Configure `.env` with production credentials
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- [ ] Start services: `docker-compose up -d`
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- [ ] Run smoke tests: `./smoke.sh`
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- [ ] Set up monitoring cron (every 5 min)
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- [ ] Configure log rotation (100MB max)
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**Success Criteria:**
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- All 5 services healthy
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- Smoke tests passing
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- First dialog successful (< 5s response)
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- No critical errors in logs
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**Deliverables:**
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- Deployment log file (`/tmp/deploy-$(date).log`)
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- First dialog screenshot/transcript
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- Baseline metrics file
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---
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### 1.2 Collect Real Dialogs (5-10 conversations)
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**Objective**: Understand real user patterns and pain points
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**Data to Collect:**
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```json
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{
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"dialog_id": "001",
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"timestamp": "2024-11-15T12:00:00Z",
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"user_id": "tg:12345",
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"dao_id": "greenfood-dao",
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"prompts": [
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{
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"text": "Привіт! Що це за DAO?",
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"response_time_ms": 3200,
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"provider": "llm_local_qwen3_8b",
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"rbac_role": "member",
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"status": "success"
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}
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],
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"insights": {
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"worked_well": "Fast response, context-aware",
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"issues": "None",
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"suggestions": "Add DAO statistics command"
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}
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}
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```
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**Actions:**
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- [ ] Monitor logs for incoming requests
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- [ ] Document 5-10 real conversations
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- [ ] Identify common patterns (greetings, questions, commands)
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- [ ] Note slow/failed requests
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- [ ] Collect user feedback (if available)
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**Save to:** `/tmp/real-dialogs/dialog-001.json`, etc.
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---
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### 1.3 Analyze Patterns
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**Questions to Answer:**
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1. What are the most common queries?
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2. Which features are unused (DevTools, CrewAI)?
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3. What response times are typical?
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4. What errors occur in production?
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5. What new workflows/tools are needed?
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**Analysis Template:**
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```markdown
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## Dialog Analysis Summary
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### Common Queries
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- [ ] Greetings (30%)
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- [ ] DAO info requests (25%)
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- [ ] Role/permission questions (20%)
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- [ ] Proposal questions (15%)
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- [ ] Other (10%)
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### Performance
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- Average response time: 3.5s
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- P95 response time: 5.2s
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- Error rate: 0.2%
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### Unused Features
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- DevTools: 0 requests
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- CrewAI workflows: 1 request (onboarding)
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### Improvement Ideas
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1. Add /help command with common queries
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2. Cache frequent responses (DAO info)
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3. Add workflow triggers (e.g., "review my proposal")
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```
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**Deliverable:** `docs/analysis/real-world-feedback-week1.md`
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---
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### 1.4 Update SCENARIOS.md
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**Actions:**
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- [ ] Add "Real World Scenarios" section
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- [ ] Document 3-5 actual production dialogs
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- [ ] Include response times, RBAC context, outcomes
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**Example Entry:**
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```markdown
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## Real World Scenario #1: DAO Info Request
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**Date**: 2024-11-15
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**User**: tg:12345 (member role)
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**Query**: "Що це за DAO і які тут проєкти?"
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**Flow:**
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1. Gateway receives message (50ms)
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2. Router fetches RBAC (80ms)
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3. LLM generates response (3200ms)
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4. Total: 3330ms
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**Response Quality**: ✅ Accurate DAO description
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**Performance**: ✅ Within target (< 5s)
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**User Feedback**: Positive
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**Insights:**
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- Common query pattern identified
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- Consider caching DAO info
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- RBAC context useful for personalization
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```
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---
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## ⚡ Stage 2: Performance & Reliability (Week 2)
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### 2.1 LLM Performance Optimization
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**Problem**: qwen3:8b can timeout on long prompts
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**Solutions:**
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1. **Token Limits**
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```yaml
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# router-config.yml
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llm_providers:
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- name: llm_local_qwen3_8b
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config:
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max_tokens: 200 # Reduced from default
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temperature: 0.7
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timeout_ms: 5000
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```
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2. **Retry Policy**
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```python
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# providers/ollama_provider.py
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@retry(max_attempts=2, delay=1.0)
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async def call_llm(self, prompt: str):
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# LLM call with retry
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```
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3. **Request Queue**
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```python
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# utils/rate_limiter.py
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class RequestQueue:
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def __init__(self, max_concurrent=3):
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self.semaphore = asyncio.Semaphore(max_concurrent)
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async def enqueue(self, request):
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async with self.semaphore:
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return await process_request(request)
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```
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**Actions:**
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- [ ] Add `max_tokens` to all LLM providers
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- [ ] Implement retry logic (2 attempts, 1s delay)
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- [ ] Add request queue (max 3 concurrent)
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- [ ] Test with high load (10 concurrent requests)
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**Expected Improvement:**
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- Response time P95: 5.2s → 4.0s
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- Timeout rate: 5% → 1%
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---
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### 2.2 Production Configuration Profile
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**Objective**: Separate dev and prod configs
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**Create:** `config/profiles/prod.yml`
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```yaml
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version: "0.3.0"
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environment: production
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debug: false
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llm_providers:
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- name: llm_prod_qwen3_8b
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type: ollama
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config:
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base_url: http://localhost:11434
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model: qwen3:8b
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max_tokens: 200
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temperature: 0.7
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timeout_ms: 5000
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routing_rules:
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- name: "prod_chat"
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priority: 10
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conditions:
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mode: "chat"
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use_provider: "llm_prod_qwen3_8b"
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timeout_ms: 5000
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fallback_provider: "llm_remote_deepseek"
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logging:
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level: INFO
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format: json
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rotation:
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max_size_mb: 100
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max_files: 10
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```
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**Actions:**
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- [ ] Create `config/profiles/` directory
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- [ ] Add `prod.yml`, `staging.yml`, `dev.yml`
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- [ ] Update `config_loader.py` to support profiles
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- [ ] Add `--profile` flag to `main_v2.py`
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**Usage:**
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```bash
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python main_v2.py --profile prod --port 9102
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```
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---
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### 2.3 Auto-Restart & Watchdog
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**Systemd Service (Production)**
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```ini
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# /etc/systemd/system/dagi-router.service
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[Unit]
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Description=DAGI Router Service
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After=network.target
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[Service]
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Type=simple
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User=dagi
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WorkingDirectory=/opt/dagi-stack
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Environment="PATH=/opt/dagi-stack/.venv/bin"
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ExecStart=/opt/dagi-stack/.venv/bin/python main_v2.py --profile prod
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Restart=always
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RestartSec=10
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StartLimitBurst=5
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StartLimitIntervalSec=60
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[Install]
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WantedBy=multi-user.target
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```
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**Docker Healthcheck Enhancement**
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```yaml
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# docker-compose.yml
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services:
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router:
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:9102/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 40s
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restart: unless-stopped
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```
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**Actions:**
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- [ ] Create systemd service files for all components
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- [ ] Test auto-restart (kill -9 process)
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- [ ] Document restart behavior
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- [ ] Set up alerts for restart events
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---
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## 🌐 Stage 3: Ecosystem Integration (Week 3-4)
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### 3.1 Open Core Model
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**Objective**: Define what's open-source vs proprietary
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**Open Source (MIT License):**
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- ✅ Router core (`routing_engine.py`, `config_loader.py`)
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- ✅ Provider interfaces (`providers/base_provider.py`)
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- ✅ Base LLM providers (Ollama, OpenAI, DeepSeek)
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- ✅ DevTools backend (file ops, test execution)
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- ✅ RBAC service (role resolution)
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- ✅ Gateway bot (Telegram/Discord webhooks)
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- ✅ Utils (logging, validation)
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- ✅ Documentation (all `.md` files)
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- ✅ Test suites (`smoke.sh`, E2E tests)
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**Proprietary/Private (Optional):**
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- ⚠️ Custom CrewAI workflows (microDAO-specific)
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- ⚠️ Advanced RBAC policies (DAO-specific rules)
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- ⚠️ Custom LLM fine-tuning data
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- ⚠️ Enterprise features (SSO, audit logs)
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**Actions:**
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- [ ] Create `docs/open-core-model.md`
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- [ ] Add LICENSE file (MIT)
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- [ ] Update README with licensing info
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- [ ] Add CONTRIBUTING.md guide
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**Deliverable:** `docs/open-core-model.md`
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---
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### 3.2 Dify Integration
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**Objective**: Use DAGI Router as LLM backend for Dify
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**Architecture:**
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```
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Dify UI → Dify Backend → DAGI Router (:9102) → LLM/DevTools/CrewAI
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```
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**Integration Steps:**
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1. **Router as LLM Provider**
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```python
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# Dify custom LLM provider
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{
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"provider": "dagi-router",
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"base_url": "http://localhost:9102",
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"model": "dagi-stack",
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"api_key": "optional"
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}
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```
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2. **Adapter Endpoint**
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```python
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# router_app.py - Add Dify-compatible endpoint
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@app.post("/v1/chat/completions")
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async def dify_compatible(request: DifyRequest):
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# Convert Dify format → DAGI format
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dagi_request = convert_from_dify(request)
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result = await router.handle(dagi_request)
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# Convert DAGI format → Dify format
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return convert_to_dify(result)
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```
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3. **Tools Integration**
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```yaml
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# Dify tools.yaml
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tools:
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- name: devtools_read
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type: api
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url: http://localhost:9102/route
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method: POST
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params:
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mode: devtools
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metadata:
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tool: fs_read
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```
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**Actions:**
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- [ ] Create `/v1/chat/completions` endpoint
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- [ ] Add Dify format converters
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- [ ] Test with Dify UI
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- [ ] Document integration in `docs/dify-integration.md`
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**Deliverable:** `docs/dify-integration.md`
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---
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### 3.3 MCP Server (Model Context Protocol)
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**Objective**: Expose DAGI Stack as MCP-compatible server
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**MCP Tools:**
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```json
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{
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"tools": [
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{
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"name": "router_call",
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"description": "Route request to LLM/agent",
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"parameters": {
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"prompt": "string",
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"mode": "chat|crew|devtools",
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"metadata": "object"
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}
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},
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{
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"name": "devtools_task",
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"description": "Execute DevTools task",
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"parameters": {
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"tool": "fs_read|fs_write|run_tests",
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"params": "object"
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}
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},
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{
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"name": "workflow_run",
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"description": "Run CrewAI workflow",
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"parameters": {
|
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"workflow": "string",
|
||||
"inputs": "object"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "microdao_query",
|
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"description": "Query microDAO RBAC/metadata",
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"parameters": {
|
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"dao_id": "string",
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"query_type": "roles|members|proposals"
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||||
}
|
||||
}
|
||||
]
|
||||
}
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||||
```
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**Implementation:**
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```python
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# mcp-server/main.py
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from mcp import Server, Tool
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server = Server("dagi-stack")
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||||
@server.tool("router_call")
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||||
async def router_call(prompt: str, mode: str, metadata: dict):
|
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# Call DAGI Router
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pass
|
||||
|
||||
@server.tool("devtools_task")
|
||||
async def devtools_task(tool: str, params: dict):
|
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# Call DevTools
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||||
pass
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||||
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||||
# ... more tools
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||||
|
||||
if __name__ == "__main__":
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||||
server.run(port=9400)
|
||||
```
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||||
|
||||
**Actions:**
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||||
- [ ] Create `mcp-server/` directory
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||||
- [ ] Implement MCP server (Python)
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||||
- [ ] Define 4-5 core tools
|
||||
- [ ] Test with Claude Desktop / Cursor
|
||||
- [ ] Document in `docs/mcp-integration.md`
|
||||
|
||||
**Deliverable:** `mcp-server/main.py`, `docs/mcp-integration.md`
|
||||
|
||||
---
|
||||
|
||||
## 📈 Success Metrics
|
||||
|
||||
| Metric | Target | Current | Status |
|
||||
|--------|--------|---------|--------|
|
||||
| Uptime | 99%+ | TBD | 🟡 |
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||||
| Response time (P95) | < 4s | TBD | 🟡 |
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||||
| Error rate | < 0.5% | TBD | 🟡 |
|
||||
| Real dialogs processed | 50+ | 0 | 🔴 |
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||||
| Dify integration | Working | Not started | 🔴 |
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||||
| MCP server | Beta | Not started | 🔴 |
|
||||
|
||||
---
|
||||
|
||||
## 🗂️ Deliverables
|
||||
|
||||
### Week 1
|
||||
- [ ] Production deployment successful
|
||||
- [ ] 5-10 real dialogs documented
|
||||
- [ ] `docs/analysis/real-world-feedback-week1.md`
|
||||
- [ ] Updated `SCENARIOS.md` with real-world examples
|
||||
|
||||
### Week 2
|
||||
- [ ] LLM performance optimized (token limits, retry, queue)
|
||||
- [ ] `config/profiles/prod.yml` created
|
||||
- [ ] Systemd services configured
|
||||
- [ ] Auto-restart tested
|
||||
|
||||
### Week 3
|
||||
- [ ] `docs/open-core-model.md` published
|
||||
- [ ] LICENSE file added (MIT)
|
||||
- [ ] CONTRIBUTING.md created
|
||||
|
||||
### Week 4
|
||||
- [ ] `docs/dify-integration.md` published
|
||||
- [ ] `/v1/chat/completions` endpoint implemented
|
||||
- [ ] Dify integration tested
|
||||
- [ ] `mcp-server/` skeleton created
|
||||
- [ ] `docs/mcp-integration.md` published
|
||||
|
||||
---
|
||||
|
||||
## 🔄 Phase 4 → Phase 5 Transition
|
||||
|
||||
**Phase 5: Scale & Ecosystem Growth**
|
||||
|
||||
After Phase 4 completion:
|
||||
1. Horizontal scaling (load balancer + multiple Router instances)
|
||||
2. Distributed tracing (Jaeger/Zipkin)
|
||||
3. On-chain governance integration (proposals, voting)
|
||||
4. Public open-source release (GitHub, docs site)
|
||||
5. Community growth (Discord, contributor onboarding)
|
||||
|
||||
---
|
||||
|
||||
**Phase 4 Start Date**: TBD
|
||||
**Phase 4 Target Completion**: 4 weeks after first deploy
|
||||
**Owner**: DAARION Core Team
|
||||
**Version**: 0.3.0 (planned)
|
||||
Reference in New Issue
Block a user