floeval 2.0.0

A multi-backend evaluation framework for LLM, RAG, and agentic systems.

Floeval

Multi-backend evaluation framework for LLM, RAG, prompt, and agent systems.

Overview

Floeval supports five evaluation types:

Eval type	What you are scoring	Key dataset fields
LLM	Direct question-answer quality without retrieval	user_input, llm_response
RAG	Answer quality and retrieval performance with context	user_input, llm_response, contexts
Prompt	One or more system prompts against the same dataset	Partial dataset + prompts_file (with or without RAG)
Agent	Single-agent trace quality, tool use, and goal achievement	AgentDataset (full or partial)
Agentic Workflow	Multi-agent DAG pipelines scored end-to-end	AgentDataset + DAG config

Floeval supports the following workflows:

• evaluating full datasets that already contain llm_response
• generating responses from partial datasets and scoring them in the same run
• expanding partial datasets across prompt variants with prompt_ids and prompts_file
• routing metrics across ragas, deepeval, builtin, and custom
• evaluating single-agent traces (pre-captured, Python callable, or FloTorch runner)
• evaluating multi-agent DAG workflows with WorkflowRunner
• capturing traces from Python callables, LangChain-style agents, or optional FloTorch runners

Features

• CLI and Python API: run evaluations from config files or integrate directly into code
• Five eval types: LLM, RAG, Prompt (with and without RAG), Agent, and Agentic Workflow
• Multi-provider metrics: mix ragas, deepeval, builtin, and custom metrics in one evaluation
• Prompt-aware generation: compare system-prompt variants at scale with prompt_ids and prompts_file
• Agent evaluation: score pre-captured traces or collect traces at runtime
• Agentic workflow evaluation: evaluate multi-agent DAG pipelines with WorkflowRunner
• Custom metrics: define function-based metrics or LLM-as-judge criteria
• Dataset format flexibility: accepts {"samples": [...]}, JSON array, or JSONL; field aliases question/answer supported

Installation

Version 0.2.0b1 is a pre-release. Installing from PyPI may require --pre:

pip install --pre floeval

Optional FloTorch support for agent Mode 4 and agentic workflow evaluation:

pip install "floeval[flotorch]"

Development install:

pip install -e .
pip install -e .[dev]

Quick Start

Python API — LLM / RAG evaluation

from floeval import Evaluation, DatasetLoader
from floeval.config.schemas.io.llm import OpenAIProviderConfig

llm_config = OpenAIProviderConfig(
    base_url="https://api.openai.com/v1",
    api_key="your-api-key",
    chat_model="gpt-4o-mini",
    embedding_model="text-embedding-3-small",
)

dataset = DatasetLoader.from_samples(
    [
        {
            "user_input": "What is RAG?",
            "llm_response": "RAG stands for Retrieval-Augmented Generation.",
            "contexts": ["RAG combines retrieval with generation."],
        }
    ],
    partial_dataset=False,
)

evaluation = Evaluation(
    dataset=dataset,
    llm_config=llm_config,
    metrics=["answer_relevancy", "faithfulness"],
    default_provider="ragas",
)

results = evaluation.run()
print(results.aggregate_scores)

Python API — Prompt evaluation (multi-prompt)

from floeval import Evaluation, DatasetLoader
from floeval.config.schemas.io.llm import OpenAIProviderConfig

llm_config = OpenAIProviderConfig(
    base_url="https://api.openai.com/v1",
    api_key="your-api-key",
    chat_model="gpt-4o-mini",
    embedding_model="text-embedding-3-small",
)

partial_dataset = DatasetLoader.from_samples(
    [
        {
            "user_input": "Summarize this customer ticket.",
            "prompt_ids": ["concise", "detailed"]
        }
    ],
    partial_dataset=True,
)

evaluation = Evaluation(
    dataset=partial_dataset,
    llm_config=llm_config,
    metrics=["answer_relevancy"],
    default_provider="ragas",
    dataset_generator_model="gpt-4o-mini",
    prompts_file="prompts.yaml",
)

results = evaluation.run()
for row in results.sample_results:
    print(row["prompt_id"], row["metrics"]["answer_relevancy"]["score"])

Python API — Agent evaluation

from floeval.api.agent_evaluation import AgentEvaluation
from floeval.config.schemas.io.agent_dataset import AgentDataset, PartialAgentSample
from floeval.config.schemas.io.llm import OpenAIProviderConfig
from floeval.utils.agent_trace import capture_trace, log_turn

llm_config = OpenAIProviderConfig(
    base_url="https://api.openai.com/v1",
    api_key="your-api-key",
    chat_model="gpt-4o-mini",
)

@capture_trace
def my_agent(user_input: str) -> str:
    response = f"Handled: {user_input}"
    log_turn(response)
    return response

dataset = AgentDataset(
    samples=[
        PartialAgentSample(
            user_input="Reset my password",
            reference_outcome="Password reset instructions were provided.",
        )
    ]
)

evaluation = AgentEvaluation(
    dataset=dataset,
    agent=my_agent,
    llm_config=llm_config,
    metrics=["goal_achievement"],
)

results = evaluation.run()
print(results.summary)

Python API — Agentic Workflow evaluation

import json
from floeval.api.agent_evaluation import AgentEvaluation
from floeval.config.schemas.io.agent_dataset import AgentDataset, PartialAgentSample
from floeval.config.schemas.io.llm import OpenAIProviderConfig
from floeval.flotorch import WorkflowRunner  # requires floeval[flotorch]

llm_config = OpenAIProviderConfig(
    base_url="https://gateway.example/openai/v1",
    api_key="your-gateway-key",
    chat_model="gpt-4o-mini",
)

dag_config = json.loads(open("workflow_config.json").read())
runner = WorkflowRunner(dag_config=dag_config, llm_config=llm_config)

dataset = AgentDataset(
    samples=[
        PartialAgentSample(
            user_input="What is the status of order #12345?",
            reference_outcome="The order is shipped and arriving tomorrow.",
        )
    ]
)

evaluation = AgentEvaluation(
    dataset=dataset,
    agent_runner=runner,
    llm_config=llm_config,
    metrics=["goal_achievement", "ragas:agent_goal_accuracy"],
)

results = evaluation.run()
print(results.summary)

CLI

# Evaluate a full LLM/RAG dataset
floeval evaluate -c config.yaml -d dataset.json -o results.json

# Evaluate a partial dataset (generate + score in one run)
floeval evaluate -c config.yaml -d partial_dataset.json -o results.json

# Generate first, then evaluate later
floeval generate -c config.yaml -d partial_dataset.json -o complete.json
floeval evaluate -c config.yaml -d complete.json -o results.json

# Prompt evaluation with a prompts file
floeval evaluate -c prompt_config.yaml -d partial_dataset.json -o prompt_results.json

# Single-agent evaluation
floeval evaluate -c agent_config.yaml -d agent_dataset.json --agent -o agent_results.json

# Agentic workflow evaluation
floeval evaluate -c workflow_config.yaml -d agent_dataset.json --agent -o workflow_results.json

Project Structure

• api/ - public evaluation APIs and dataset loaders
• core/execution/ - response generation and execution internals
• metric_providers/ - provider-specific metric implementations
• config/schemas/ - config, dataset, and prompt schemas
• cli/ - command-line entry points
• utils/ - trace capture, loaders, and helper utilities
• flotorch/ - optional FloTorch integration (WorkflowRunner, FloTorchRunner)

Documentation

Detailed docs live in docs/:

• Setup & Prerequisites
• Examples
• Prompt Evaluation
• Agent Evaluation
• Agentic Workflow
• Agent Tracing
• Metrics
• Custom Metrics
• API Reference
• Troubleshooting

License

MIT