API - ARISEConfig

All configuration for an ARISE instance. Pass to ARISE(config=...) or use individual fields as defaults.

from arise import ARISEConfig

config = ARISEConfig(
    model="gpt-4o-mini",
    sandbox_backend="subprocess",
    failure_threshold=5,
    max_evolutions_per_hour=3,
    verbose=True,
)

arise = ARISE(agent_fn=my_agent, reward_fn=reward_fn, config=config)

Fields

Core

Field	Type	Default	Description
`model`	`str`	`"gpt-4o-mini"`	LLM model for tool synthesis (not your agent’s model)
`verbose`	`bool`	`True`	Print episode status and evolution progress

Sandbox

Field	Type	Default	Description
`sandbox_backend`	`str`	`"subprocess"`	`"subprocess"` or `"docker"`
`sandbox_timeout`	`int`	`30`	Seconds before sandbox kills the process

Evolution Triggers

Field	Type	Default	Description
`failure_threshold`	`int`	`5`	Consecutive failures before evolution triggers
`plateau_window`	`int`	`10`	Episodes to look back for plateau detection
`plateau_min_improvement`	`float`	`0.05`	Minimum success rate improvement to avoid plateau trigger
`max_evolutions_per_hour`	`int`	`3`	Rate limit for evolution cycles (cost control)
`max_refinement_attempts`	`int`	`3`	Max LLM retries to fix a failing skill
`max_synthesis_workers`	`int`	`3`	Max concurrent tool synthesis threads

Library

Field	Type	Default	Description
`max_library_size`	`int`	`50`	Max number of active skills before synthesis stops
`skill_store_path`	`str`	`"./arise_skills"`	Local SQLite skill library path
`trajectory_store_path`	`str`	`"./arise_trajectories"`	Local SQLite trajectory store path
`max_trajectories`	`int`	`1000`	Max trajectories to retain (older ones are pruned)

Security

Field	Type	Default	Description
`allowed_imports`	`list[str] \| None`	`None`	Whitelist of importable modules. `None` = no restriction. Always set in production.

Distributed Mode (S3 + SQS)

Field	Type	Default	Description
`s3_bucket`	`str \| None`	`None`	S3 bucket for distributed skill store
`s3_prefix`	`str`	`"arise"`	S3 key prefix
`sqs_queue_url`	`str \| None`	`None`	SQS queue URL for trajectory reporting
`aws_region`	`str`	`"us-east-1"`	AWS region
`skill_cache_ttl_seconds`	`int`	`30`	How often to refresh skills from S3

Skill Registry

Field	Type	Default	Description
`registry_bucket`	`str \| None`	`None`	S3 bucket for the skill registry
`registry_prefix`	`str`	`"arise-registry"`	S3 key prefix for the registry
`registry_check_before_synthesis`	`bool`	`True`	Check registry before calling the LLM to synthesize

Multi-Model Routing

Field	Type	Default	Description
`model_routes`	`dict[str, str] \| None`	`None`	Route specific synthesis tasks to different models
`auto_select_model`	`bool`	`False`	Auto-promote the model with the best synthesis track record

config = ARISEConfig(
    model_routes={
        "gap_detection": "gpt-4o-mini",    # cheap for analysis
        "synthesis": "claude-sonnet-4-5-20250929",  # better code quality
        "refinement": "gpt-4o-mini",
    },
    auto_select_model=True,
)

Telemetry

Field	Type	Default	Description
`enable_telemetry`	`bool`	`False`	Enable OpenTelemetry spans for evolution steps (requires `arise-ai[otel]`)

Examples

Development (minimal config):

config = ARISEConfig(
    failure_threshold=2,    # evolve quickly
    verbose=True,
)

Production (locked down):

config = ARISEConfig(
    model="gpt-4o-mini",
    sandbox_backend="docker",
    sandbox_timeout=30,
    failure_threshold=5,
    max_evolutions_per_hour=3,
    max_library_size=50,
    allowed_imports=["json", "re", "hashlib", "csv", "math", "base64", "datetime"],
    verbose=False,
)

Distributed with registry:

config = ARISEConfig(
    s3_bucket="arise-skills-prod",
    sqs_queue_url="https://sqs.us-west-2.amazonaws.com/.../arise-trajectories",
    aws_region="us-west-2",
    registry_bucket="arise-registry-prod",
    registry_check_before_synthesis=True,
    model="gpt-4o-mini",
    allowed_imports=["json", "re", "hashlib"],
)