API Reference

Browse the full osmosis-ai Python SDK surface below. The SDK is organized around two main capabilities:

Reward & Evaluation

Decorators and functions for scoring LLM outputs using local rules or LLM-based rubrics

Remote Rollout

Build custom agent loops for training infrastructure integration

Reward API

Decorators

@osmosis_reward

Decorator for local reward functions that compute scores without API calls. Use this for deterministic evaluation logic (exact match, regex, keyword matching). Signature:

@osmosis_reward
def function_name(
    solution_str: str,
    ground_truth: str,
    extra_info: dict = None,
    **kwargs
) -> float

Parameters:

solution_str (str, required) - Text to evaluate
ground_truth (str, required) - Reference answer
extra_info (dict, optional) - Additional context
**kwargs (required) - Future compatibility (see warning below)

Returns: float - Score value Example:

from osmosis_ai import osmosis_reward

@osmosis_reward
def exact_match(solution_str: str, ground_truth: str, extra_info: dict = None, **kwargs) -> float:
    return 1.0 if solution_str.strip() == ground_truth.strip() else 0.0

@osmosis_rubric

Decorator for LLM-based evaluation functions. Use this for subjective evaluation that requires semantic understanding (helpfulness, tone, quality). Signature:

@osmosis_rubric
def function_name(
    solution_str: str,
    ground_truth: str | None,
    extra_info: dict,
    **kwargs
) -> float

Parameters:

solution_str (str, required) - Text to evaluate
ground_truth (str | None, required) - Reference answer (can be None)
extra_info (dict, required) - Configuration and context
**kwargs (required) - Future compatibility (see warning below)

Returns: float - Score value Example:

from osmosis_ai import osmosis_rubric, evaluate_rubric

@osmosis_rubric
def quality_check(solution_str: str, ground_truth: str | None, extra_info: dict, **kwargs) -> float:
    return evaluate_rubric(
        rubric="Evaluate response quality",
        solution_str=solution_str,
        model_info={"provider": "openai", "model": "gpt-5.2"},
        ground_truth=ground_truth
    )

Rubric Evaluation

evaluate_rubric()

Evaluate text using an LLM-based rubric. This is the core function for LLM-powered evaluation. Signature:

def evaluate_rubric(
    rubric: str,
    solution_str: str,
    model_info: dict,
    ground_truth: str | None = None,
    original_input: str | None = None,
    metadata: dict | None = None,
    score_min: float = 0.0,
    score_max: float = 1.0,
    timeout: int | None = None,
    return_details: bool = False
) -> float | dict

Parameters:

Parameter	Type	Required	Description
`rubric`	str	Yes	Natural language evaluation criteria
`solution_str`	str	Yes	Text to evaluate
`model_info`	dict	Yes	LLM provider configuration
`ground_truth`	str	No	Reference answer
`original_input`	str	No	Original user query
`metadata`	dict	No	Additional context
`score_min`	float	No	Minimum score (default: 0.0)
`score_max`	float	No	Maximum score (default: 1.0)
`timeout`	int	No	Request timeout in seconds
`return_details`	bool	No	Return full response (default: False)

model_info Structure:

{
    "provider": "openai",           # Required
    "model": "gpt-5.2",         # Required
    "api_key": "sk-...",            # Optional
    "api_key_env": "OPENAI_API_KEY", # Optional
    "timeout": 30                   # Optional
}

Returns:

float - Score (when return_details=False)
dict - Full response with score, explanation, raw payload (when return_details=True)

Example:

from osmosis_ai import evaluate_rubric

score = evaluate_rubric(
    rubric="Evaluate how helpful the response is.",
    solution_str="Click 'Forgot Password' to reset.",
    model_info={"provider": "openai", "model": "gpt-5.2"}
)

Exceptions

MissingAPIKeyError

Raised when an API key is not found for a provider.

from osmosis_ai import MissingAPIKeyError

try:
    score = evaluate_rubric(...)
except MissingAPIKeyError as e:
    print(f"API key not found: {e}")

ProviderRequestError

Raised when a provider request fails.

from osmosis_ai import ProviderRequestError

try:
    score = evaluate_rubric(...)
except ProviderRequestError as e:
    print(f"Provider error: {e}")

ModelNotFoundError

Raised when a specified model is not available (subclass of ProviderRequestError).

from osmosis_ai import ModelNotFoundError

try:
    score = evaluate_rubric(...)
except ModelNotFoundError as e:
    print(f"Model not found: {e}")

Types

ModelInfo (TypedDict)

from osmosis_ai.rubric_types import ModelInfo

model_info: ModelInfo = {
    "provider": "openai",
    "model": "gpt-5.2",
    "api_key_env": "OPENAI_API_KEY",
    "timeout": 30
}

Field	Type	Required	Description
`provider`	str	Yes	Provider name (see Supported Providers)
`model`	str	Yes	Model identifier
`api_key`	str	No	API key value directly
`api_key_env`	str	No	Environment variable name for API key
`timeout`	float	No	Request timeout in seconds
`score_min`	float	No	Minimum score override
`score_max`	float	No	Maximum score override
`system_prompt`	str \| None	No	Custom system prompt for the evaluator
`original_input`	str \| None	No	Original user query for context
`reasoning_effort`	str \| None	No	Reasoning effort level (provider-specific)

RewardRubricRunResult (TypedDict)

Returned when return_details=True:

from osmosis_ai.rubric_types import RewardRubricRunResult

result: RewardRubricRunResult = {
    "score": 0.85,              # float
    "explanation": "...",       # str
    "raw": {...}                # Any - raw LLM response
}

Complete Reward Example

from osmosis_ai import osmosis_reward, osmosis_rubric, evaluate_rubric
from dotenv import load_dotenv

load_dotenv()

# Local reward function
@osmosis_reward
def exact_match(solution_str: str, ground_truth: str, extra_info: dict = None, **kwargs) -> float:
    return 1.0 if solution_str.strip() == ground_truth.strip() else 0.0

# Remote rubric evaluator
@osmosis_rubric
def semantic_eval(solution_str: str, ground_truth: str | None, extra_info: dict, **kwargs) -> float:
    return evaluate_rubric(
        rubric="Compare semantic similarity (0-1 scale)",
        solution_str=solution_str,
        ground_truth=ground_truth,
        model_info={"provider": "openai", "model": "gpt-5.2"}
    )

# Usage
solution = "The capital of France is Paris"
truth = "Paris is France's capital"

local_score = exact_match(solution, truth)
semantic_score = semantic_eval(solution, truth, {})

print(f"Exact match: {local_score}")      # 0.0
print(f"Semantic: {semantic_score}")      # ~1.0

Remote Rollout API

Build custom agent loops that integrate with Osmosis training infrastructure. Your agent runs as an HTTP server while the training cluster handles LLM inference and trajectory collection.

Quick Overview

Component	Description
`RolloutAgentLoop`	Base class for implementing agents
`RolloutContext`	Execution context with `chat()`, `complete()`, `error()` methods
`RolloutRequest`	Initial request with messages, parameters, and metadata
`create_app()`	Factory function to create FastAPI server

Remote Rollout Documentation

For complete Remote Rollout API documentation, guides, and examples, see the dedicated Remote Rollout section.

Python SDK

API Reference

API Reference

Reward & Evaluation

Remote Rollout

Reward API

Decorators

@osmosis_reward

@osmosis_rubric

Rubric Evaluation

evaluate_rubric()

Exceptions

MissingAPIKeyError

ProviderRequestError

ModelNotFoundError

Types

ModelInfo (TypedDict)

RewardRubricRunResult (TypedDict)

Complete Reward Example

Remote Rollout API

Quick Overview

Remote Rollout Documentation

Next Steps

CLI Reference

Remote Rollout

Python SDK

​API Reference

Reward & Evaluation

Remote Rollout

​Reward API

​Decorators

​@osmosis_reward

​@osmosis_rubric

​Rubric Evaluation

​evaluate_rubric()

​Exceptions

​MissingAPIKeyError

​ProviderRequestError

​ModelNotFoundError

​Types

​ModelInfo (TypedDict)

​RewardRubricRunResult (TypedDict)

​Complete Reward Example

​Remote Rollout API

​Quick Overview

Remote Rollout Documentation

​Next Steps

CLI Reference

Remote Rollout

API Reference

Reward API

Decorators

@osmosis_reward

@osmosis_rubric

Rubric Evaluation

evaluate_rubric()

Exceptions

MissingAPIKeyError

ProviderRequestError

ModelNotFoundError

Types

ModelInfo (TypedDict)

RewardRubricRunResult (TypedDict)

Complete Reward Example

Remote Rollout API

Quick Overview

Next Steps