hurui200320
d512123d1c
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The actor CLI was ignoring the v3 ActorConfigSchema format, preventing spec-compliant actors with type/route/skills/lsp fields from being registered or executed. Three components were fixed: ActorConfiguration.from_blob() now detects v3 format (top-level "type" key with value llm/graph/tool) and extracts provider from the model string, falling through to v2 extraction when v3 does not match. ActorRegistry.add() now routes v3 YAML through full ActorConfigSchema validation, persists description/skills/lsp in the config blob, and compiles graph actors with compile_actor() storing metadata. Legacy v2 YAML continues through the original path unchanged. ReactiveConfigParser._build() now synthesises reactive agents and graph routes from v3 actor data so that agents actor run can execute v3 actors through the existing ReactiveCleverAgentsApp pipeline. ISSUES CLOSED: #6283
246 lines
12 KiB
Gherkin
246 lines
12 KiB
Gherkin
Feature: v3 Actor YAML schema support in CLI and registry
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As a developer using v3 actor YAML configs
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I want the CLI and registry to validate, persist, and execute v3 schema actors
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So that spec-compliant actors with skills, LSP bindings, and LangGraph routes work
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Scenario: ActorConfiguration.from_blob extracts provider and model from v3 LLM YAML
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Given a v3 LLM actor blob with model "gpt-4"
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When I call ActorConfiguration.from_blob with the v3 blob
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Then the configuration should have provider "custom"
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And the configuration should have model "gpt-4"
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Scenario: ActorConfiguration.from_blob infers provider from model with slash
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Given a v3 LLM actor blob with model "openai/gpt-4"
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When I call ActorConfiguration.from_blob with the v3 blob
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Then the configuration should have provider "openai"
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And the configuration should have model "openai/gpt-4"
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Scenario: ActorConfiguration.from_blob infers custom provider for slash-prefixed model
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Given a v3 LLM actor blob with model "/gpt-4"
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When I call ActorConfiguration.from_blob with the v3 blob
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Then the configuration should have provider "custom"
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And the configuration should have model "/gpt-4"
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Scenario: ActorConfiguration.from_blob builds graph descriptor for graph actors
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Given a v3 graph actor blob with a route block
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When I call ActorConfiguration.from_blob with the v3 blob
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Then the configuration should have a graph_descriptor with type "graph"
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Scenario: ActorConfiguration.from_blob falls through to v2 for legacy YAML
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Given a v2 actor blob with agents and routes
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When I call ActorConfiguration.from_blob with the v2 blob
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Then the configuration should have provider "openai"
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And the configuration should have model "gpt-4"
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Scenario: ActorRegistry.add validates v3 LLM actor YAML
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Given a mock actor service for v3 testing
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And a v3 LLM actor YAML text
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When I call ActorRegistry.add with the v3 YAML
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Then the actor should be persisted with v3 fields in config_blob
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And the persisted config_blob should contain skills
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And the persisted config_blob should contain lsp bindings
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Scenario: ActorRegistry.add rejects v3 YAML without required description
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Given a mock actor service for v3 testing
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And a v3 actor YAML text missing description
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When I call ActorRegistry.add with the invalid v3 YAML
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Then a ValidationError should be raised mentioning description
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Scenario: ActorRegistry.add validates and compiles v3 graph actor
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Given a mock actor service for v3 testing
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And a v3 graph actor YAML text with route
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When I call ActorRegistry.add with the v3 graph YAML
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Then the actor should be persisted with compiled metadata
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And the graph_descriptor should contain route information
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Scenario: ReactiveConfigParser handles v3 LLM actor format
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Given a v3 LLM actor config dict
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When I parse the v3 config through ReactiveConfigParser
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Then the reactive config should have one agent
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And the agent config should contain the model
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And the agent config should contain skills
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Scenario: ReactiveConfigParser handles v3 graph actor format
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Given a v3 graph actor config dict with route
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When I parse the v3 config through ReactiveConfigParser
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Then the reactive config should have agents for each node
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And the reactive config should have a graph route
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And the graph route edges should use source and target keys
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Scenario: v3 format detection returns false for v2 data
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Given a v2 actor config dict with agents key
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When I check if the config is v3 format
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Then it should not be detected as v3
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# M14: test update=True path
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Scenario: ActorRegistry.add with update=True overwrites existing actor
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Given a mock actor service for v3 testing
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And an existing actor in the mock service
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And a v3 LLM actor YAML text
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When I call ActorRegistry.add with update=True for the v3 YAML
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Then the actor should be persisted successfully with update
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# M15: test type:tool extraction and parsing paths
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# from_blob requires model for ActorConfiguration — tool actors without
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# model raise ValueError. The proper v3 path is ActorRegistry.add().
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Scenario: ActorConfiguration.from_blob rejects type tool without model
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Given a v3 tool actor blob without model
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When I call ActorConfiguration.from_blob with the v3 tool blob
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Then the tool actor from_blob should raise ValueError for missing model
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Scenario: ActorRegistry.add validates v3 tool actor YAML
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Given a mock actor service for v3 testing
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And a v3 tool actor YAML text
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When I call ActorRegistry.add with the v3 tool YAML
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Then the tool actor should be persisted with type tool
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# m13: test _build_from_v3 with lsp as dict
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Scenario: ReactiveConfigParser handles v3 LLM actor with lsp as dict
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Given a v3 LLM actor config dict with lsp as dict
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When I parse the v3 config through ReactiveConfigParser
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Then the reactive config should have one agent
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And the agent config should contain lsp as dict
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# m13: test context_view, memory, env_vars, response_format propagation
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Scenario: ReactiveConfigParser propagates context_view memory env_vars and response_format
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Given a v3 LLM actor config dict with context_view and memory
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When I parse the v3 config through ReactiveConfigParser
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Then the reactive config should have one agent
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And the agent config should contain context_view
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And the agent config should contain memory settings
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And the agent config should contain env_vars
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And the agent config should contain response_format
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# m13: positive _is_v3_format test
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Scenario: v3 format detection returns true for v3 LLM data
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Given a v3 LLM actor config dict
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When I check if the v3 LLM config is v3 format
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Then it should be detected as v3
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# Cycle 2 review: defensive guard BDD coverage
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Scenario: ReactiveConfigParser rejects graph with invalid entry_node
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Given a v3 graph actor config dict with invalid entry_node
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When I parse the v3 config through ReactiveConfigParser expecting error
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Then a ConfigurationError should be raised mentioning entry_node
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Scenario: ReactiveConfigParser skips edges with missing from_node or to_node
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Given a v3 graph actor config dict with incomplete edges
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When I parse the v3 config through ReactiveConfigParser
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Then the graph route should have fewer edges than the raw input
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Scenario: ReactiveConfigParser skips non-dict edge entries
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Given a v3 graph actor config dict with non-dict edges
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When I parse the v3 config through ReactiveConfigParser
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Then the graph route should have only valid dict edges
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# Coverage: v3_registry.py — name without slash gets local/ prefix
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Scenario: ActorRegistry.add namespaces bare actor name with local prefix
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Given a mock actor service for v3 testing
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And a v3 LLM actor YAML text with bare name
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When I call ActorRegistry.add with the bare-name v3 YAML
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Then the persisted actor name should be prefixed with local
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# Coverage: v3_registry.py — unsafe actor rejected without allow_unsafe
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Scenario: ActorRegistry.add rejects unsafe v3 actor without allow_unsafe flag
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Given a mock actor service for v3 testing
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And a v3 LLM actor YAML text marked unsafe
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When I call ActorRegistry.add with the unsafe v3 YAML
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Then a ValidationError should be raised mentioning unsafe
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# Coverage: v3_registry.py — duplicate actor rejected when update=False
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Scenario: ActorRegistry.add rejects duplicate v3 actor when update is False
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Given a mock actor service for v3 testing
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And an existing actor in the mock service
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And a v3 LLM actor YAML text
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When I call ActorRegistry.add with the v3 YAML expecting error
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Then a ValidationError should be raised mentioning already exists
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# Coverage: v3_registry.py — ActorCompilationError during graph compilation
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Scenario: ActorRegistry.add persists graph actor even when compilation fails
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Given a mock actor service for v3 testing
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And a v3 graph actor YAML text with route
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When I call ActorRegistry.add with the v3 graph YAML and compilation fails
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Then the actor should still be persisted without compiled metadata
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# Coverage: registry.py — _ensure_namespaced via get()
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Scenario: ActorRegistry.get namespaces bare actor name with local prefix
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Given a mock actor service for v3 testing
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When I call ActorRegistry.get with a bare actor name
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Then the actor service should be queried with local-prefixed name
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# Coverage: registry.py — add() with non-dict YAML
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Scenario: ActorRegistry.add rejects non-mapping YAML
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Given a mock actor service for v3 testing
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When I call ActorRegistry.add with a non-mapping YAML string
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Then a ValidationError should be raised mentioning mapping
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# Coverage: registry.py — _add_legacy v3 schema validation failure
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Scenario: ActorRegistry._add_legacy rejects invalid v3 YAML via schema validation
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Given a mock actor service for v3 testing
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And a legacy v3 YAML text with invalid schema
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When I call ActorRegistry.add with the legacy invalid v3 YAML
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Then a ValidationError should be raised mentioning invalid v3 actor
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# Coverage: registry.py — _add_legacy missing provider/model in non-v3 blob
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Scenario: ActorRegistry._add_legacy rejects non-v3 blob missing provider and model
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Given a mock actor service for v3 testing
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And a legacy non-v3 YAML text missing provider and model
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When I call ActorRegistry.add with the legacy non-v3 YAML
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Then a ValidationError should be raised mentioning provider and model
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# Coverage: registry.py — _add_legacy unsafe flag rejection
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Scenario: ActorRegistry._add_legacy rejects unsafe legacy actor without allow_unsafe
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Given a mock actor service for v3 testing
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And a legacy actor YAML text marked unsafe
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When I call ActorRegistry.add with the legacy unsafe YAML
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Then a ValidationError should be raised mentioning unsafe
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# Coverage: registry.py — upsert_actor v3 validation failure
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Scenario: ActorRegistry.upsert_actor rejects invalid v3 config_blob
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Given a mock actor service for v3 testing
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When I call ActorRegistry.upsert_actor with an invalid v3 config_blob
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Then a ValidationError should be raised mentioning invalid v3 actor
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# Coverage: config_parser.py — _merge with new=None
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Scenario: ReactiveConfigParser merge handles None new dict gracefully
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When I merge a None dict into a base config
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Then the base config should remain unchanged
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# Coverage: config_parser.py — missing model for llm actor
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Scenario: ReactiveConfigParser rejects v3 LLM actor with missing model
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Given a v3 LLM actor config dict with no model field
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When I parse the v3 config through ReactiveConfigParser expecting error
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Then a ConfigurationError should be raised mentioning model
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# Coverage: config_parser.py — lsp_capabilities and lsp_context_enrichment propagation
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Scenario: ReactiveConfigParser propagates lsp_capabilities and lsp_context_enrichment
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Given a v3 LLM actor config dict with lsp_capabilities and lsp_context_enrichment
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When I parse the v3 config through ReactiveConfigParser
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Then the agent config should contain lsp_capabilities
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And the agent config should contain lsp_context_enrichment
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# Coverage: config_parser.py — graph actor without route raises ConfigurationError
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Scenario: ReactiveConfigParser rejects v3 graph actor without route
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Given a v3 graph actor config dict without route
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When I parse the v3 config through ReactiveConfigParser expecting error
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Then a ConfigurationError should be raised mentioning route
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# Coverage: config_parser.py — non-dict node and empty-id node skipped in graph
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Scenario: ReactiveConfigParser skips non-dict and empty-id nodes in graph route
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Given a v3 graph actor config dict with malformed nodes
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When I parse the v3 config through ReactiveConfigParser
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Then the reactive config should only have agents for valid nodes
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# Coverage: config_parser.py — skills and lsp propagated to graph nodes
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Scenario: ReactiveConfigParser propagates skills and lsp bindings to graph nodes
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Given a v3 graph actor config dict with skills and lsp
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When I parse the v3 config through ReactiveConfigParser
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Then each graph node agent should have skills propagated
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And each graph node agent should have lsp propagated
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# Coverage: config_parser.py — lsp as dict propagated to graph nodes (line 330)
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Scenario: ReactiveConfigParser propagates lsp dict bindings to graph nodes
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Given a v3 graph actor config dict with skills and lsp as dict
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When I parse the v3 config through ReactiveConfigParser
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Then each graph node agent should have lsp dict propagated
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