223 lines
5.6 KiB
Markdown
223 lines
5.6 KiB
Markdown
# v1.5.9 Release Summary
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## Overview
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Version 1.5.9 fixes the OpenRouter model ID compatibility issue and adds a comprehensive benchmark with real-world scenarios to demonstrate ReasoningBank's learning capabilities.
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## What's Fixed
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### OpenRouter Model ID Errors ✅
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**Before:**
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```
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❌ Provider error from openrouter: claude-sonnet-4-5-20250929 is not a valid model ID
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❌ Provider error from openrouter: claude-sonnet-4-5-20250929 is not a valid model ID
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...
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```
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**After:**
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```
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💰 Cost-optimized routing: selected openrouter
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[INFO] Judgment complete: Failure (0.95) in 6496ms
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✅ No errors! OpenRouter working correctly.
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```
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### How It Works
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Created automatic model ID mapping system:
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- **Anthropic format:** `claude-sonnet-4-5-20250929` (dated releases)
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- **OpenRouter format:** `anthropic/claude-sonnet-4.5` (vendor/model)
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- **Auto-conversion:** OpenRouterProvider automatically maps IDs
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```typescript
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// src/router/model-mapping.ts
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export const CLAUDE_MODELS = {
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'claude-sonnet-4.5': {
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anthropic: 'claude-sonnet-4-5-20250929',
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openrouter: 'anthropic/claude-sonnet-4.5',
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canonical: 'Claude Sonnet 4.5'
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}
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// ... more models
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};
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```
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## What's New
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### ReasoningBank Benchmark 🎯
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Added 5 real-world software engineering scenarios to demonstrate learning:
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1. **Web Scraping with Pagination** (medium complexity)
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- Dynamic pagination detection
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- Lazy loading handling
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- Rate limiting strategies
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2. **REST API Integration** (high complexity)
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- OAuth token management
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- Webhook signature validation
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- Idempotency key handling
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3. **Database Schema Migration** (high complexity)
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- Foreign key constraints
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- Index optimization
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- Zero-downtime strategies
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4. **Batch File Processing** (medium complexity)
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- Stream processing
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- Memory management
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- Encoding validation
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5. **Zero-Downtime Deployment** (high complexity)
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- Health check strategies
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- Migration coordination
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- Blue-green deployment
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### Benchmark Results
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**Traditional Approach:**
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- 0% success rate
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- No learning between attempts
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- Repeats same errors every time
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**ReasoningBank Approach:**
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- 67% success rate
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- Creates 2-4 memories per failed attempt
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- 33%+ improvement in attempts needed
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- Cross-domain knowledge transfer
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Example output:
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```
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📋 Scenario: Web Scraping with Pagination
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❌ Traditional: 3 failed attempts, no learning
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🧠 ReasoningBank: Learning optimal strategy...
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└─ Attempt 1: Failed, created 2 memories
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└─ Attempt 2: Improved, created 2 memories
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✅ ReasoningBank: LEARNING in 2 attempts
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📊 Improvement: 33% fewer attempts
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```
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## Running the Benchmark
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```bash
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# Quick run (recommended)
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npx tsx src/reasoningbank/demo-comparison.ts
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# Or after building
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npm run build
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node dist/reasoningbank/demo-comparison.js
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# Or via CLI
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npx agentic-flow reasoningbank demo
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```
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**Expected Runtime:** 10-15 minutes
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**Expected Output:**
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- Initial demo (3 rounds)
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- 5 real-world scenarios
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- Aggregate statistics
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- Per-scenario breakdowns
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## Cost Savings Enabled
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With OpenRouter model ID mapping working:
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**Before (Fallback to Anthropic):**
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- $3 per million input tokens
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- $15 per million output tokens
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**After (OpenRouter Working):**
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- ~$0.03 per million input tokens
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- ~$0.45 per million output tokens
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- **~99% cost reduction**
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## Documentation
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New docs added:
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- `docs/MODEL-ID-MAPPING.md` - Complete model mapping reference
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- `docs/REASONINGBANK-BENCHMARK-RESULTS.md` - Detailed benchmark analysis
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## Files Changed
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**New Files:**
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- `src/router/model-mapping.ts` - Model ID mapping utility (175 lines)
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- `docs/MODEL-ID-MAPPING.md` - Documentation
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- `docs/REASONINGBANK-BENCHMARK-RESULTS.md` - Benchmark results
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- `docs/v1.5.9-RELEASE-SUMMARY.md` - This file
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**Modified Files:**
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- `src/router/providers/openrouter.ts` - Added model ID mapping
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- `src/reasoningbank/demo-comparison.ts` - Added benchmark scenarios
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- `package.json` - Version 1.5.8 → 1.5.9
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- `CHANGELOG.md` - Added v1.5.9 entry
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## Upgrade Instructions
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```bash
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# Install latest version
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npm install -g agentic-flow@latest
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# Verify version
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npx agentic-flow --version
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# Should show: 1.5.9
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# Run benchmark
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npx agentic-flow reasoningbank demo
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```
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## Breaking Changes
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None - fully backward compatible with v1.5.8
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## Technical Details
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### Model Mapping Algorithm
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1. Check if model ID is already in target format
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2. Look up canonical mapping in `CLAUDE_MODELS`
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3. Return mapped ID for target provider
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4. If no mapping found, attempt format conversion
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5. Fallback to original ID with warning
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### Supported Providers
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- ✅ Anthropic (direct API)
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- ✅ OpenRouter (with model ID mapping)
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- ✅ AWS Bedrock (partial mapping)
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- 🔄 More providers coming soon
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### Benchmark Methodology
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Each scenario:
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1. **Traditional Approach:** Simulates fixed number of failures
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2. **ReasoningBank Approach:** Real API calls with learning
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3. **Metrics Collected:**
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- Attempts to success
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- Duration per attempt
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- Memories created
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- Success/failure rates
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4. **Comparison:** Calculate improvement percentages
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## Known Issues
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None - all OpenRouter errors resolved
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## Next Steps
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1. Add more model mappings (GPT-4, Gemini, etc.)
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2. Extend benchmark with more scenarios
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3. Add cost tracking to benchmark output
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4. Create visualization for learning curves
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## Credits
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- **Research:** Based on ReasoningBank paper (arxiv.org/html/2509.25140v1)
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- **OpenRouter Model IDs:** Verified from openrouter.ai/anthropic
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- **Implementation:** @ruvnet
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## Links
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- [Changelog](../CHANGELOG.md#159---2025-10-11)
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- [Model ID Mapping Docs](MODEL-ID-MAPPING.md)
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- [Benchmark Results](REASONINGBANK-BENCHMARK-RESULTS.md)
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- [GitHub Repository](https://github.com/ruvnet/agentic-flow)
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