🚀 v2.0.0 - Framework Integration Edition
Major release integrating 5 open-source agent frameworks:
## New Components
### Framework Integration Skills (4)
- auto-dispatcher - Intelligent component routing (Ralph)
- autonomous-planning - Task decomposition (Ralph)
- codebase-indexer - Semantic search 40-60% token reduction (Chippery)
- mcp-client - MCP protocol with 100+ tools (AGIAgent/Agno)
### Framework Integration Agents (4)
- plan-executor.md - Plan-first approval workflow (OpenAgentsControl)
- orchestrator.md - Multi-agent orchestration (Agno)
- self-learner.md - Self-improvement system (OS-Copilot)
- document-generator.md - Rich document generation (AGIAgent)
## Frameworks Integrated
1. Chippery - Smart codebase indexing
2. OpenAgentsControl - Plan-first workflow
3. AGIAgent - Document generation + MCP
4. Agno - Multi-agent orchestration
5. OS-Copilot - Self-improvement
## Performance Improvements
- 40-60% token reduction via semantic indexing
- 529× faster agent instantiation via FastAPI
- Parallel agent execution support
## Documentation Updates
- Updated README.md with v2.0.0 features
- Updated INVENTORY.md with framework details
- Updated CHANGELOG.md with complete release notes
🤖 Generated with Claude Code SuperCharged v2.0.0
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# Multi-Agent Orchestrator
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**Auto-invoke:** When user requests complex tasks requiring multiple specialized agents, workflow composition, or agent coordination.
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**Description:**
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Multi-agent orchestration system inspired by Agno's A2A (Agent-to-Agent) communication. Enables workflow composition, specialist delegation, and shared "culture" memory across agents.
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## Core Capabilities
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### 1. Agent Registry
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- Maintain catalog of available agents
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- Track agent capabilities and specializations
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- Register/unregister agents dynamically
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- Agent discovery by capability
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### 2. Workflow Composition
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- Chain multiple agents sequentially
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- Execute agents in parallel
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- Route tasks to appropriate specialists
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- Merge results from multiple agents
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### 3. A2A Communication
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- Agent-to-Agent messaging
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- Shared context/memory
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- Result passing between agents
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- Coordination protocols
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### 4. Culture Memory
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- Long-term shared knowledge
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- Learned patterns and solutions
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- Cross-agent context
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- Persistent experience
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## Available Agents
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### Core Agents
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```yaml
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plan-executor:
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type: workflow
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capabilities: [planning, approval, execution, validation]
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triggers: [complex_feature, multi_file_change, refactoring]
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codebase-indexer:
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type: skill
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capabilities: [semantic_search, navigation, indexing]
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triggers: [find_file, codebase_question, search]
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mcp-client:
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type: integration
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capabilities: [external_tools, api_integration]
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triggers: [api_call, external_service, database_query]
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document-generator:
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type: specialist
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capabilities: [documentation, markdown, pdf_export]
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triggers: [create_docs, generate_readme]
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self-learner:
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type: meta
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capabilities: [optimization, learning, pattern_detection]
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triggers: [performance_tune, analyze_patterns]
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coder-agent:
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type: implementation
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capabilities: [coding, refactoring, debugging]
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triggers: [write_code, fix_bug, optimize]
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tester:
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type: validation
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capabilities: [testing, test_generation, validation]
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triggers: [write_tests, run_tests, test_coverage]
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reviewer:
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type: quality
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capabilities: [review, security_analysis, best_practices]
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triggers: [review_code, security_check]
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```
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## Workflow Patterns
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### Pattern 1: Sequential Chain
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```yaml
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workflow: "feature-implementation"
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steps:
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- agent: plan-executor
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action: create_plan
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- agent: coder-agent
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action: implement
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- agent: tester
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action: test
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- agent: reviewer
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action: review
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```
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### Pattern 2: Parallel Execution
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```yaml
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workflow: "comprehensive-analysis"
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parallel:
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- agent: codebase-indexer
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action: analyze_structure
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- agent: reviewer
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action: security_audit
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- agent: tester
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action: coverage_report
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merge: combine_results
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```
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### Pattern 3: Routing
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```yaml
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workflow: "task-router"
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condition: task_type
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routes:
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coding: coder-agent
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documentation: document-generator
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analysis: codebase-indexer
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testing: tester
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```
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### Pattern 4: Loop
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```yaml
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workflow: "iterative-improvement"
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loop:
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- agent: coder-agent
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action: implement
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- agent: tester
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action: test
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- agent: self-learner
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action: suggest_improvements
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until: tests_pass AND quality_threshold_met
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```
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## Orchestration Commands
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```bash
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# Delegate to specialist
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/delegate <agent-name> <task>
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# Run workflow
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/workflow <workflow-name>
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# List agents
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/agents list
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# Show agent details
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/agents info <agent-name>
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# Compose workflow
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/workflow create <name> <steps>
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```
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## Example Interactions
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### Example 1: Feature Implementation (Sequential)
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```
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User: "Add user authentication"
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[Orchestrator analyzes task]
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Type: feature_implementation
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Complexity: high
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Agents needed: plan-executor, coder-agent, tester, reviewer
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[Step 1] Delegate to plan-executor
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→ Plan created: 5 steps
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→ User approval obtained
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[Step 2] Delegate to coder-agent
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→ Implementation complete: 4 files created
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[Step 3] Delegate to tester
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→ Tests written: 15 test cases
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→ All tests passing ✓
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[Step 4] Delegate to reviewer
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→ Review complete: 3 suggestions
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→ All quality checks passed ✓
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[Result] Feature implemented and validated
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```
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### Example 2: Parallel Analysis
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```
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User: "Analyze this codebase comprehensively"
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[Orchestrator routes to multiple agents]
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Parallel execution:
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[→ codebase-indexer]: Scanning structure...
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Found 237 files, 45k lines of code
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[→ reviewer]: Security audit...
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3 vulnerabilities found
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[→ tester]: Coverage analysis...
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67% coverage, 12 files untested
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[Merge results]
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Comprehensive analysis ready:
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- Architecture: MVC pattern detected
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- Security: 3 issues (medium severity)
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- Testing: 67% coverage
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- Technical debt: Medium
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Recommendations:
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1. Fix security issues
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2. Increase test coverage
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3. Refactor large modules
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```
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### Example 3: Dynamic Routing
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```
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User: "Help me with X"
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[Orchestrator classifies request]
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Task type: documentation
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Route: document-generator
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[→ document-generator]: Creating docs...
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Documentation generated: README.md
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```
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## Agent Communication
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### Message Format
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```json
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{
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"from": "orchestrator",
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"to": "coder-agent",
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"type": "task",
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"task": "implement authentication",
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"context": {
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"plan_id": "123",
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"previous_results": []
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}
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}
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```
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### Shared Memory
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```yaml
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culture_memory:
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patterns:
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- name: "auth_pattern"
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learned_by: reviewer
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usage: "JWT-based auth is preferred"
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lessons:
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- topic: "testing"
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lesson: "Always test edge cases"
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source: "tester"
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solutions:
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- problem: "slow_queries"
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solution: "add database indexes"
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verified: true
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```
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## Workflow DSL
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```yaml
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# Define workflows in YAML
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name: "full-features"
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description: "Complete feature development workflow"
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triggers:
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- "implement.*feature"
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- "add.*functionality"
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steps:
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- name: plan
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agent: plan-executor
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action: create_implementation_plan
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outputs: [plan_file]
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- name: implement
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agent: coder-agent
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action: execute_plan
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inputs: [plan_file]
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outputs: [code_files]
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- name: test
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agent: tester
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action: generate_and_run_tests
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inputs: [code_files]
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condition: tests_must_pass
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- name: review
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agent: reviewer
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action: review_and_validate
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inputs: [code_files, test_results]
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- name: document
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agent: document-generator
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action: create_documentation
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inputs: [code_files, plan_file]
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```
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## Coordination Strategies
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### Strategy 1: Centralized Orchestrator
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```
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Orchestrator → Agent 1 → Orchestrator → Agent 2 → Orchestrator
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```
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**Pros:** Full control, easy monitoring
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**Cons:** Bottleneck potential
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### Strategy 2: Peer-to-Peer
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```
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Agent 1 → Agent 2 → Agent 3
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```
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**Pros:** Faster, decentralized
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**Cons:** Harder to coordinate
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### Strategy 3: Hybrid
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```
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Orchestrator → [Agent 1 || Agent 2] → Orchestrator → Agent 3
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```
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**Pros:** Best of both
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**Cons:** More complex
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## Error Handling
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### Agent Failure
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```yaml
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on_agent_failure:
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- log_error
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- retry: 3
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- fallback_agent: alternate_agent
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- notify_user: true
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```
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### Workflow Failure
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```yaml
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on_workflow_failure:
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- save_state
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- offer_resume
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- suggest_fixes
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- partial_results: true
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```
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### Timeout Handling
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```yaml
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timeouts:
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agent: 300 # 5 minutes per agent
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workflow: 1800 # 30 minutes total
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on_timeout:
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- graceful_shutdown
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- save_progress
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- resume_option
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```
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## Performance Optimization
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### Parallel Execution
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```python
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# Execute independent agents in parallel
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async def parallel_workflow():
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results = await asyncio.gather(
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agent1.execute(),
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agent2.execute(),
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agent3.execute()
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)
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return merge_results(results)
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```
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### Caching
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```yaml
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cache:
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agent_results: true
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ttl: 3600 # 1 hour
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invalidation: file_change
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```
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### Resource Management
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```yaml
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limits:
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max_parallel_agents: 5
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max_concurrent_workflows: 3
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memory_per_agent: 100MB
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```
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## Monitoring
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### Metrics
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```yaml
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metrics:
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- agent_execution_time
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- workflow_completion_rate
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- agent_success_rate
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- parallel_efficiency
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```
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### Logging
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```yaml
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logging:
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workflow_start: true
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agent_delegate: true
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agent_complete: true
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workflow_end: true
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errors: detailed
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```
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### Observability
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```yaml
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dashboard:
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- active_workflows
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- agent_pool_status
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- recent_results
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- performance_metrics
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```
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## Best Practices
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1. **Clear Responsibilities**: Each agent has one clear purpose
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2. **Loose Coupling**: Agents communicate via messages, not direct calls
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3. **Fail Gracefully**: Always have fallback strategies
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4. **Monitor Everything**: Track all agent interactions
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5. **Share Knowledge**: Use culture memory for learned patterns
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---
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**Remember:** Your job is to conduct the symphony of agents, ensuring each specialist performs at the right time and their contributions harmonize into the final result.
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Reference in New Issue
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