aditya
e476d2de0e
Extend actor YAML schema to support hierarchical graphs with explicit node types (agent, tool, conditional, subgraph), per-node LSP bindings (lsp_binding with server, languages, auto, capabilities), and tool-source references (skills, mcp_servers, agent_skills). Add schema validation for namespaced actor references, duplicate node IDs, edge target existence, and graph reachability — all nodes must be reachable from entry_node via explicit edges or conditional node routing targets. Update loader to report YAML parse errors with precise line/column positions and schema validation errors with dotted field paths and remediation hints pointing to docs/reference/actor_config.md. Add docs/reference/actor_config.md as the practical configuration reference covering hierarchical graph examples, node type table, topology rules, and common error cases with fix guidance. Refresh examples/actors/graph_workflow.yaml to replace deprecated actor_path with actor_ref. Add benchmarks/actor_yaml_bench.py for schema load overhead. Tests: 95 Behave scenarios, 10 Robot smoke tests (including hierarchical loader smoke test), security scan clean, coverage 99% (threshold 97%). ISSUES CLOSED: #157
215 lines
5.7 KiB
YAML
215 lines
5.7 KiB
YAML
# Graph Actor - Test-Driven Development Workflow
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# Demonstrates a multi-node graph with conditional routing and subgraphs
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name: workflows/test_driven_dev
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type: graph
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description: Test-driven development workflow with automated testing and feedback loops
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version: "1.0"
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# LLM model for agent nodes
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model: gpt-4
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# Graph topology
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route:
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# Define all nodes in the workflow
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nodes:
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# Entry point: planning agent
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- id: planner
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type: agent
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name: Test Planner
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description: Plans test cases based on requirements
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config:
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model: gpt-4
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prompt: |
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You are a test planning expert. Analyze the requirements and create
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a comprehensive test plan covering:
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- Unit tests for individual functions
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- Integration tests for component interactions
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- Edge cases and error conditions
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tools:
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- files/read_file
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- files/list_directory
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# Write tests first
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- id: test_writer
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type: agent
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name: Test Writer
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description: Writes test cases based on the plan
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config:
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model: gpt-4
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prompt: |
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You are a test writing expert. Write pytest tests based on the plan.
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Follow best practices:
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- Use descriptive test names
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- Include docstrings
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- Use fixtures appropriately
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- Test one thing per test
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tools:
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- files/write_file
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- files/read_file
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# Run the tests (should fail initially)
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- id: run_tests
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type: tool
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name: Test Runner
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description: Executes pytest test suite
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config:
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tool_name: testing/run_pytest
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parameters:
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verbose: true
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coverage: true
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# Check test results
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- id: check_results
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type: conditional
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name: Test Result Checker
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description: Routes based on test pass/fail status
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config:
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conditions:
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- check: "state.get('tests_passed') == True"
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route_to: code_review
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- check: "state.get('tests_passed') == False"
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route_to: implementation_writer
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# Write implementation to make tests pass
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- id: implementation_writer
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type: agent
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name: Implementation Writer
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description: Writes code to make the tests pass
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config:
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model: gpt-4
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prompt: |
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You are an implementation expert. Write clean, well-documented code
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that makes the failing tests pass. Follow SOLID principles and
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write maintainable code.
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tools:
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- files/write_file
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- files/read_file
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# Run tests again after implementation
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- id: rerun_tests
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type: tool
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name: Test Rerunner
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description: Re-executes tests after implementation
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config:
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tool_name: testing/run_pytest
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parameters:
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verbose: true
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coverage: true
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# Check if tests pass now
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- id: verify_tests
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type: conditional
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name: Test Verification
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description: Verify tests pass after implementation
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config:
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conditions:
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- check: "state.get('tests_passed') == True"
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route_to: code_review
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- check: "state.get('tests_passed') == False and state.get('retry_count', 0) < 3"
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route_to: debug_failures
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- check: "state.get('tests_passed') == False and state.get('retry_count', 0) >= 3"
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route_to: escalate
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# Debug test failures
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- id: debug_failures
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type: agent
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name: Debugger
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description: Analyzes and fixes test failures
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config:
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model: gpt-4
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prompt: |
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You are a debugging expert. Analyze the test failures and fix the
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implementation. Look for:
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- Logic errors
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- Edge cases
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- Type mismatches
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- Missing error handling
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tools:
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- files/read_file
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- files/write_file
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# Code review (subgraph)
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- id: code_review
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type: subgraph
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name: Code Reviewer
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description: Runs code review workflow
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actor_ref: local/code-reviewer
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# Escalate if tests keep failing
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- id: escalate
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type: tool
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name: Escalation Handler
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description: Escalates persistent failures to human
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config:
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tool_name: notifications/send_alert
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parameters:
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channel: engineering
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priority: high
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# Define edges (workflow transitions)
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edges:
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# Linear flow from planner to test writer
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- from_node: planner
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to_node: test_writer
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# Run tests after writing them
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- from_node: test_writer
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to_node: run_tests
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# Check results after running tests
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- from_node: run_tests
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to_node: check_results
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# Conditional routing from check_results
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# (handled by the conditional node itself)
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# Write implementation if tests fail
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- from_node: implementation_writer
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to_node: rerun_tests
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# Verify tests after rerunning
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- from_node: rerun_tests
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to_node: verify_tests
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# Debug if tests still fail
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- from_node: debug_failures
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to_node: rerun_tests
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# All paths eventually lead to code review or escalation
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# (handled by conditional nodes)
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# Entry and exit points
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entry_node: planner
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exit_nodes:
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- code_review
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- escalate
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# Context settings
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context_view: strategist
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memory:
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enabled: true
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max_messages: 100
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max_tokens: 16000
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summarize_old: true
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context:
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include_files:
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- "README.md"
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- "requirements.txt"
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- "pyproject.toml"
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include_dirs:
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- "src/"
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- "tests/"
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exclude_patterns:
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- "**/__pycache__/**"
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- "*.pyc"
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- "**/.pytest_cache/**"
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- "**/htmlcov/**"
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max_context_tokens: 32000
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# Environment variables
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env_vars:
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PYTEST_ARGS: --verbose --cov --cov-report=html
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MAX_RETRIES: "3"
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