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cleveragents-core/features/actor_v3_schema.feature
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CoreRasurae 78be08870c
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fix(cli): validate actor provider field at correct config nesting level
- Actor configuration in V3 is now obtained from the nested configuration parameter, according to the specification.
- Removed the legacy V2 fallback support and the tests affected by that removal. Mocked existing steps to allow remaining V2 features to be covered/tested.
- Fix add() to pass unsafe flag to add_v3() for proper unsafe actor handling
- Add _extract_nested_v3_config() to extract provider/model from nested
  actors.<name>.config block before v3 schema validation
- Remove v2 extraction test scenarios from consolidated_actor.feature
  that call removed _extract_v2_actor() and _extract_v2_options() methods
- Update test YAML in actor_registry_spec_yaml_steps.py to exercise nested type detection (no top-level type field)
- Add @tdd_issue_4300 regression test tag to existing spec-compliant
  actors map scenario

ISSUES CLOSED: #4300
2026-05-09 23:13:33 +01:00

214 lines
11 KiB
Gherkin

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