* feat(agents): implement category-based agent organization system - Organize agents into domain categories (core, development, content, data, product, learning) - Move 10 agents to category subdirectories with proper git rename tracking - Update 13 subagents with category and type metadata in frontmatter - Add category metadata files (0-category.json) documenting common patterns - Implement local registry fallback in install script for offline development - Add comprehensive validation suite with 15 automated tests (100% pass rate) - Enhance registry validation with duplicate ID and consistency checks - Update eval framework with intelligent path resolution (backward compatible) - Archive legacy eval structure to _archive/ for reference - Update all documentation to reflect category-based structure - Bump version to 0.5.0 with accurate CHANGELOG Technical Details: - 23 agents organized (10 category agents, 13 subagents) - 6 category directories created - Path resolution supports both agent IDs and category paths - Registry schema updated to v2.0.0 - 159 files changed, 872 insertions(+), 10738 deletions(-) BREAKING CHANGE: Agent file paths now use category structure. Update references from .opencode/agent/openagent.md to .opencode/agent/core/openagent.md. Eval framework maintains backward compatibility via path resolution. * refactor(evals): consolidate documentation and enhance test infrastructure - Remove temporary project tracking files (PHASE_5_COMPLETE, PROJECT_COMPLETE, etc.) - Consolidate evaluation framework docs into main README - Enhance test execution with improved logging and multi-prompt support - Move system-builder from core to meta category - Add comprehensive test suites for openagent with organized structure - Create evaluation test structure for all subagents - Clean up archived workflows and redundant documentation - Update registry to reflect new agent organization - Add shared test templates and golden test patterns * feat(ci): add manual trigger support for bot-created PRs in validate-registry workflow - Add pr_number input to workflow_dispatch for manually triggering validation - Fetch PR details dynamically when triggered manually - Support both automatic (PR event) and manual (workflow_dispatch) triggers - Enable validation of bot-created PRs like automated version bumps - Update branch detection and push logic to handle both trigger types - Add documentation explaining how to manually trigger for bot PRs * feat(evals): add explicit context file validation to test framework - Add expectedContextFiles field to test YAML schema for explicit context file specification - Enhance context-loading-evaluator to support both auto-detect and explicit validation modes - Update documentation with comprehensive guide and examples - Clean up archived legacy test structure (90+ old test files) - Add new example tests demonstrating explicit context validation - Backward compatible with existing tests * feat(evals): add multi-agent logging system and performance optimizations Implement comprehensive multi-agent logging and performance improvements for the eval framework. Task 01: Multi-Agent Logging System - Add complete hierarchical logging module (evals/framework/src/logging/) - SessionTracker: tracks parent-child delegation hierarchies - MultiAgentLogger: pretty-prints logs with visual indentation - Formatters: box characters and emoji formatting - 37 passing unit tests (session-tracker, logger, integration) - Integrate with SDK event stream handler - Hook into session.created, message.updated, message.part.updated events - Real-time child session detection via timestamp heuristics - Message deduplication for cleaner output - Enable in debug mode only (<1% performance overhead) - Add demo script and comprehensive documentation Task 02: Performance Optimizations - Reduce grace period from 5s to 2s (67% reduction, 10-20% faster tests) - Add PerformanceMetricsEvaluator for bottleneck identification - Collects tool latencies, inference time, idle time - Provides full performance visibility - Update smoke test to validate delegation (core feature) - Replace simple read test with multi-agent delegation test - Tests both parent and child agent functionality - Validates multi-agent logging system Results: - 37 unit tests passing - Smoke test passing (95/100 score) - Clean hierarchical logging output - 10-20% faster test execution - Full multi-agent visibility Files changed: 16 files, +2678 lines Time saved: ~6-8 days (completed in 6 hours vs 3-5 day estimate) * feat(evals): show child agent execution in non-debug mode - Enable MultiAgentLogger in both debug and non-debug modes - Add verbose parameter to control output level - Non-verbose mode shows concise child session lifecycle: - Child agent started message - Child agent completed with duration - Verbose mode (--debug) shows full delegation hierarchy - Update documentation to reflect new behavior This provides visibility into delegation without overwhelming output, giving confidence that child agents are actually running. * feat(evals): improve delegation testing and behavior validation Major improvements to eval framework: Test Suite: - Add 00-smoke-test.yaml (basic read operation) - Rename 01-smoke-test.yaml → 02-delegation-test.yaml (delegation test) - Add simple-responder test agent for delegation testing - Add debug test: simple-subagent-call.yaml Evaluators: - Add agent-model-evaluator.ts for agent/model validation - Enhance behavior-evaluator.ts with detailed task tool output - Improve delegation-evaluator.ts with better evidence tracking Schema & Execution: - Add expectedAgent and expectedModel to test schema - Improve test executor with better logging and error handling Documentation: - Update evals/README.md with new features and performance improvements - Remove outdated MULTI_AGENT_LOGGING_COMPLETE.md Registry: - Add simple-responder test agent to registry Fixes: - Fix dashboard.sh path resolution These changes provide better visibility into delegation, improved test coverage, and clearer validation of agent behavior. * chore: add GitHub Actions workflow and plugin documentation - Add .github/workflows/evals/run-evaluations.yml for automated eval testing - Add dev/ai-tools/opencode/plugins/Plugin-inspiration.md for plugin development reference |
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Prompt Library System
Multi-model prompt variants with integrated evaluation framework for testing and validation.
🎯 Quick Start
Testing a Prompt Variant
# Test with eval framework (recommended)
cd evals/framework
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=smoke-test
# Test with specific model
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/llama3.2 --suite=core-tests
# View results
open ../results/index.html
Using a Variant Permanently
# Switch to a variant
./scripts/prompts/use-prompt.sh --agent=openagent --variant=llama
# Restore default (canonical agent file)
./scripts/prompts/use-prompt.sh --agent=openagent --variant=default
📁 Structure
.opencode/
├── agent/ # Canonical agent prompts (defaults)
│ ├── openagent.md # OpenAgent default (Claude-optimized)
│ └── opencoder.md # OpenCoder default
└── prompts/ # Model-specific variants
├── README.md # This file
├── openagent/ # OpenAgent variants
│ ├── gpt.md # GPT-4 optimized
│ ├── gemini.md # Gemini optimized
│ ├── grok.md # Grok optimized
│ ├── llama.md # Llama/OSS optimized
│ ├── TEMPLATE.md # Template for new variants
│ ├── README.md # Variant documentation
│ └── results/ # Per-variant test results
│ ├── default-results.json # Default (agent file) results
│ ├── gpt-results.json
│ ├── gemini-results.json
│ └── llama-results.json
└── opencoder/ # OpenCoder variants
└── ...
Architecture:
- Agent files (
.opencode/agent/*.md) = Canonical defaults (source of truth) - Prompt variants (
.opencode/prompts/<agent>/<model>.md) = Model-specific optimizations - Results always saved to
.opencode/prompts/<agent>/results/(including default)
🧪 Evaluation Framework Integration
Running Tests with Variants
The eval framework automatically:
- ✅ Switches to the specified variant
- ✅ Runs your test suite
- ✅ Tracks results per variant
- ✅ Restores the default prompt after tests
# Smoke test (1 test, ~30s)
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=smoke-test
# Core suite (7 tests, ~5-8min)
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=core-tests
# Custom test pattern
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --pattern="01-critical-rules/**/*.yaml"
Auto-Model Detection
Variants specify recommended models in their YAML frontmatter:
---
model_family: llama
recommended_models:
- ollama/llama3.2
- ollama/qwen2.5
---
If you don't specify --model, the framework uses the first recommended model.
Results Tracking
Results are saved in two locations:
- Main results:
evals/results/latest.json(includesprompt_variantfield) - Per-variant:
.opencode/prompts/{agent}/results/{variant}-results.json
View in dashboard: evals/results/index.html (filter by variant)
📝 Creating a New Variant
Step 1: Copy Template
cp .opencode/prompts/openagent/TEMPLATE.md .opencode/prompts/openagent/my-variant.md
Step 2: Edit Metadata
---
model_family: oss
recommended_models:
- ollama/my-model
status: experimental
maintainer: your-name
description: Optimized for my specific use case
---
Step 3: Customize Prompt
Edit the prompt content below the frontmatter for your target model.
Step 4: Validate
# Validate the variant exists and metadata is correct
cd evals/framework
npm run eval:sdk -- --agent=openagent --prompt-variant=my-variant --suite=smoke-test
Step 5: Test Thoroughly
# Run core test suite
npm run eval:sdk -- --agent=openagent --prompt-variant=my-variant --suite=core-tests
# Check results
open ../results/index.html
Step 6: Document Results
Update .opencode/prompts/openagent/README.md with:
- Test results (pass rate, timing)
- Known issues or limitations
- Recommended use cases
🎯 Available Variants
OpenAgent
| Variant | Model Family | Status | Best For |
|---|---|---|---|
default |
Claude | ✅ Stable | Production use, Claude models |
gpt |
GPT | ✅ Stable | GPT-4, GPT-4o |
gemini |
Gemini | ✅ Stable | Gemini 2.0, Gemini Pro |
grok |
Grok | ✅ Stable | Grok models (free tier) |
llama |
Llama/OSS | ✅ Stable | Llama, Qwen, DeepSeek, other OSS |
See openagent/README.md for detailed test results.
OpenCoder
Coming soon.
🔧 Advanced Usage
Custom Test Suites
Create custom test suites for your variant:
# Create suite
cp evals/agents/openagent/config/smoke-test.json \
evals/agents/openagent/config/my-suite.json
# Edit suite (add your tests)
# Validate suite
cd evals/framework && npm run validate:suites openagent
# Run with your variant
npm run eval:sdk -- --agent=openagent --prompt-variant=my-variant --suite=my-suite
See evals/TEST_SUITE_VALIDATION.md for details.
Comparing Models
Test the same variant with different models:
# Test Llama 3.2
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/llama3.2 --suite=core-tests
# Test Qwen 2.5
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/qwen2.5 --suite=core-tests
# Compare results in dashboard
open evals/results/index.html
📊 Understanding Results
Dashboard Features
The results dashboard (evals/results/index.html) shows:
- ✅ Filter by prompt variant
- ✅ Filter by model
- ✅ Pass/fail rates per variant
- ✅ Test execution times
- ✅ Detailed test results
Result Files
Main results (evals/results/latest.json):
{
"meta": {
"agent": "openagent",
"model": "ollama/llama3.2",
"prompt_variant": "llama",
"model_family": "llama"
},
"summary": {
"total": 7,
"passed": 7,
"failed": 0,
"pass_rate": 1
}
}
Per-variant results (.opencode/prompts/openagent/results/llama-results.json):
- Tracks all test runs for this variant
- Shows trends over time
- Helps identify regressions
🚀 For Contributors
Creating a Variant for PR
- Create your variant in
.opencode/prompts/<agent>/<model>.md - Test thoroughly with eval framework
- Document results in agent README
- Submit PR with variant file only
PR Requirements
- ✅ Variant has YAML frontmatter with metadata
- ✅ Variant passes core test suite (≥85% pass rate)
- ✅ Results documented in agent README
- ✅ Agent file unchanged (unless updating default)
- ✅ No
default.mdfiles in prompts directory - ✅ CI validation passes
Validation
# Validate your variant
cd evals/framework
npm run eval:sdk -- --agent=openagent --prompt-variant=your-variant --suite=core-tests
# Ensure PR uses default
./scripts/prompts/validate-pr.sh
🎓 Design Principles
1. Agent Files are Canonical Defaults
- Agent files (
.opencode/agent/*.md) are the source of truth - Tested and production-ready
- Optimized for Claude (primary model)
- Modified through normal PR process
2. Variants are Model-Specific Optimizations
- Stored in
.opencode/prompts/<agent>/<model>.md - Optimized for specific models/use cases
- May have different trade-offs
- Results documented transparently
3. Results are Tracked
- Every test run tracked per variant
- Dashboard shows variant performance
- Easy to compare variants
4. Easy to Test
- One command to test any variant
- Automatic model detection
- Results saved automatically
5. Safe to Experiment
- Variants don't affect default
- Easy to switch and restore
- Test before committing
📚 Related Documentation
- Eval Framework Guide - How to run tests
- Test Suite Validation - Creating test suites
- OpenAgent Variants - OpenAgent-specific docs
- Contributing Guide - Contribution guidelines
🆘 Troubleshooting
Variant Not Found
# List available variants
ls .opencode/prompts/openagent/*.md
# Check variant exists
npm run eval:sdk -- --agent=openagent --prompt-variant=your-variant --suite=smoke-test
Tests Failing
- Check variant metadata (YAML frontmatter)
- Verify recommended model is available
- Run with debug flag:
--debug - Check results in dashboard
Model Not Available
# Check available models
# For Ollama: ollama list
# For OpenRouter: check openrouter.ai/models
# Specify model explicitly
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/llama3.2
💡 Tips
- Start with smoke-test - Fast validation (1 test, ~30s)
- Use core-tests for thorough testing - 7 tests, ~5-8min
- Check dashboard regularly - Visual feedback on variant performance
- Document your findings - Help others by sharing results
- Test with multiple models - Same variant may perform differently
🔮 Future Enhancements
- Automated variant comparison reports
- Performance benchmarking across variants
- Variant recommendation based on model
- Historical trend analysis
- A/B testing framework
Questions? See openagent/README.md or open an issue.