WrapperConfig Reference¶

WrapperConfig is the configuration object for TheseusWrapper. All fields have sensible defaults — most use cases only need 2–3 overrides.

from ct_toolkit import TheseusWrapper, WrapperConfig

config = WrapperConfig(
    template="finance",
    kernel_name="finance",
    divergence_l1_threshold=0.12,
    log_requests=True,
)

wrapper = TheseusWrapper(provider="openai", config=config)

All parameters¶

Identity¶

Parameter	Type	Default	Description
`template`	`str`	`"general"`	Identity template name. Defines the embedding reference vector.
`kernel_name`	`str`	`"default"`	Constitutional Kernel to load. Built-ins: `default`, `defense`, `finance`.
`kernel_path`	`str \\| Path \\| None`	`None`	Explicit path to a custom kernel YAML. Overrides `kernel_name`.
`project_root`	`str \\| Path \\| None`	`None`	Path to your project root. CT Toolkit looks for `config/*.yaml` here.
`parent_kernel`	`ConstitutionalKernel \\| None`	`None`	Propagated kernel from a parent agent. Its anchors become read-only axioms.

Divergence thresholds¶

Parameter	Type	Default	Description
`divergence_l1_threshold`	`float`	`0.15`	L1 ECS score above which an `l1_warning` is issued.
`divergence_l2_threshold`	`float`	`0.30`	L1 score above which the L2 LLM-as-judge path is triggered.
`divergence_l3_threshold`	`float`	`0.50`	L1 score above which the L3 ICM battery is triggered.

Stricter thresholds for critical systems

For medical, financial, or defense applications, use tighter thresholds: python config = WrapperConfig( divergence_l1_threshold=0.10, divergence_l2_threshold=0.20, divergence_l3_threshold=0.40, )

Judge and embedding clients¶

Parameter	Type	Default	Description
`judge_client`	`Any`	`None`	Separate LLM client for L2/L3 analysis.
`judge_provider`	`str`	`None`	Explicit provider for judge calls (for example, `"ollama"`).
`judge_model`	`str`	`None`	Explicit model for judge calls (shared by L2 and L3 judge paths).
`embedding_client`	`Any`	`None`	Client for real embedding API calls (L1). Falls back to keyword vectors if `None`.
`embedding_model`	`str`	`"text-embedding-3-small"`	Embedding model name.
`strict_embedding`	`bool`	`False`	If `True`, raises `RuntimeError` when embedding API fails instead of falling back.

Provenance & logging¶

Parameter	Type	Default	Description
`vault_type`	`str`	`"local"`	Storage backend. Currently only `"local"` (SQLite) is supported.
`vault_path`	`str`	`"./ct_provenance.db"`	Path to the SQLite provenance database.
`log_requests`	`bool`	`True`	Whether to write every interaction to the provenance log.
`auto_inject_kernel`	`bool`	`True`	Whether to inject kernel rules into the system prompt automatically.

Auto-correction loop¶

Parameter	Type	Default	Description
`auto_correction`	`bool`	`False`	Enable the L2→L1 self-correction loop.
`max_correction_retries`	`int`	`1`	Maximum retry attempts before returning the last response.

config = WrapperConfig(
    auto_correction=True,
    max_correction_retries=2,
    judge_client=openai.OpenAI(base_url="http://localhost:11434/v1", api_key="ollama"),
    judge_provider="ollama",
    judge_model="mistral",
)

Alerting¶

Parameter	Type	Default	Description
`drift_alert_callback`	`Callable[[dict], None] \\| None`	`None`	Called when a drift event or context compression failure occurs.

def my_alert(payload: dict):
    print(f"ALERT: drift={payload['similarity']:.2f}")
    # send to Slack, PagerDuty, etc.

config = WrapperConfig(drift_alert_callback=my_alert)

Elasticity scheduling¶

Parameter	Type	Default	Description
`elasticity_max_thresholds`	`tuple[float, float, float] \\| None`	`None`	Maximum `(L1, L2, L3)` thresholds an experienced agent can reach.
`elasticity_growth_rate`	`float \\| None`	`None`	Rate at which divergence tolerance increases with experience.
`risk_profile`	`RiskProfile \\| None`	`None`	Structural risk profile affecting elasticity growth rate.

from ct_toolkit.divergence.scheduler import RiskProfile

config = WrapperConfig(
    elasticity_max_thresholds=(0.25, 0.45, 0.70),
    elasticity_growth_rate=0.001,
    risk_profile=RiskProfile(has_tool_calling=True, mcp_server_count=2),
)

Context Compression (v0.3.6+)¶

Parameter	Type	Default	Description
`compression_passive_detection`	`bool`	`True`	Automatically detect silent provider-side history compression via shrinkage heuristics.
`compression_threshold`	`float`	`0.80`	Similarity score floor (0.0 to 1.0) for summarization drift analysis.

config = WrapperConfig(
    compression_passive_detection=True,
    compression_threshold=0.88,
    drift_alert_callback=my_handler  # Triggered on drift
)

Staged Approval (Cooldown) (v0.3.8+)¶

Parameter	Type	Default	Description
`endorsement_cooldown_base`	`int`	`300`	Minimum cooldown duration in seconds (5 min).
`endorsement_cooldown_max`	`int`	`3600`	Maximum cooldown duration (1 hour).
`endorsement_no_probe_penalty`	`int`	`600`	Penalty added if no probes are found (+10 min).

config = WrapperConfig(
    endorsement_cooldown_base=600, # 10 min
    endorsement_no_probe_penalty=1200, # +20 min
)

Capability Handshake & Policy Control (v0.3.20+)¶

Parameter	Type	Default	Description
`capability_cache_file`	`str`	`"config/llm_capability.yaml"`	Path to startup capability cache and policy registry YAML.
`capability_refresh_interval_s`	`int`	`3600`	Refresh interval for provider/model capability refresh.
`capability_enable_active_handshake`	`bool`	`False`	If enabled, attempts lightweight active capability checks via provider metadata.
`capability_force_refresh`	`bool`	`False`	Forces capability refresh on access instead of waiting for interval expiry.
`policy_role`	`str`	`"main"`	Role selector used by policy resolution (`main`, `sub`, `judge`, `l3`).
`policy_environment`	`str`	`"prod"`	Environment selector for policy overrides (`dev`, `test`, `prod`).

config = WrapperConfig(
    capability_cache_file="config/llm_capability.yaml",
    policy_role="sub",
    policy_environment="test",
)

Rigorous Analysis Mode¶

Enables simultaneous L1, L2, and L3 scanning to ensure maximum identity alignment:

rigorous_mode is the preferred flag. enterprise_mode is still accepted for backward compatibility but is deprecated and may be removed in a future release.

config = WrapperConfig(
    rigorous_mode=True,
    judge_provider="openai",
    judge_model="gpt-4o-mini",
)

In enterprise mode, a combined risk score is calculated:

risk = l1_score
     + (l2_confidence × 0.4  if misaligned)
     + ((1 - l3_health) × 0.4  if L3 ran)

action_required = risk >= l3_threshold OR L3 not healthy

Full example¶

import openai
from ct_toolkit import TheseusWrapper, WrapperConfig
from ct_toolkit.divergence.scheduler import RiskProfile

judge = openai.OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")  # Can be a different provider/endpoint

config = WrapperConfig(
    # Identity
    template="medical",
    kernel_name="medical",

    # Strict thresholds for clinical use
    divergence_l1_threshold=0.10,
    divergence_l2_threshold=0.20,
    divergence_l3_threshold=0.40,

    # Use a separate judge model
    judge_client=judge,
    judge_provider="ollama",
    judge_model="mistral",

    # Vault
    vault_path="./clinical_provenance.db",
    log_requests=True,

    # Auto-correction
    auto_correction=True,
    max_correction_retries=1,

    # Drift alerting
    drift_alert_callback=lambda p: print(f"CLINICAL ALERT: {p}"),

    # Risk-adjusted elasticity
    elasticity_max_thresholds=(0.20, 0.40, 0.60),
    elasticity_growth_rate=0.0005,
    risk_profile=RiskProfile(has_tool_calling=True),
)

wrapper = TheseusWrapper(provider="openai", config=config)