Humanloop

Overview

Humanloop is an LLM operations platform that supports the lifecycle of large language model applications, from initial experimentation to production deployment and ongoing optimization. The platform is designed for developers and technical buyers involved in building and maintaining systems powered by LLMs. Its core functionality centers on facilitating prompt engineering, model evaluation, and data management within LLM workflows. This includes tools for versioning prompts, conducting A/B tests on different model responses, and collecting human feedback for data labeling.

The platform addresses challenges associated with the iterative nature of LLM development, where prompt variations, model updates, and data drift can impact application performance. Humanloop provides a centralized environment for teams to collaborate on prompt design, track changes, and compare the outputs of various LLM configurations. This is particularly relevant for use cases requiring consistent and reliable LLM responses, such as customer support chatbots, content generation systems, or internal knowledge retrieval tools.

Humanloop's capabilities extend to monitoring and improving LLM applications in production. It enables developers to capture and log LLM inputs and outputs, which can then be used for analysis and fine-tuning. The data labeling features allow for human review of model responses, generating high-quality datasets for supervised fine-tuning or reinforcement learning from human feedback (RLHF). This integrated approach aims to reduce the manual effort involved in improving LLM performance and ensuring alignment with desired outcomes. The platform supports Python and TypeScript SDKs for integration into existing development pipelines, offering programmatic access to its features for managing LLM experiments and deployments.

For organizations prioritizing data governance and security, Humanloop offers compliance with standards such as SOC 2 Type II and GDPR, which can be a consideration for enterprise deployments handling sensitive information. The platform is suitable for teams that require structured processes for experimenting with LLMs, evaluating their effectiveness against specific criteria, and maintaining performance in live applications. For instance, in applications where the quality of generated text directly impacts user experience or business outcomes, tools for systematic evaluation and improvement become necessary. Humanloop positions itself as a system of record for LLM interactions, providing visibility into how models are performing and where improvements can be made across different prompts and models, a critical aspect of MLOps for large language models, as discussed by Google AI in their considerations for responsible AI development responsible AI practices.

Key features

• LLM Experimentation: Tools for rapid iteration and testing of different prompts, models, and parameters to optimize LLM outputs.
• Prompt Management: Centralized system for versioning, storing, and organizing prompts, allowing for collaboration and tracking of prompt history.
• Data Labeling: Capabilities to collect human feedback on LLM responses, enabling the creation of high-quality datasets for model fine-tuning and evaluation.
• Model Evaluation: Frameworks for assessing the performance of LLMs based on custom metrics and human annotations, facilitating objective comparison between different models or prompt variations.
• A/B Testing: Functionality to deploy multiple versions of an LLM application or prompt in production and compare their performance with real user traffic.
• Production Workflows: Tools for logging, monitoring, and managing LLM interactions in live applications, helping to identify and address performance issues.

Pricing

Humanloop offers a tiered pricing structure, including a free tier for initial use and paid plans for expanded capabilities. As of June 2026, the pricing is as follows:

For detailed and up-to-date pricing information, refer to the Humanloop pricing page.

Common integrations

• LLM Providers: Integrates with various large language model APIs, allowing users to connect to models from providers like OpenAI, Anthropic, and others.
• Data Storage: Connects with data storage solutions to ingest and export data for labeling and model training.
• Version Control Systems: Supports integration with Git-based systems for managing prompt templates and codebases.
• Monitoring Tools: Can integrate with existing monitoring and observability platforms for a unified view of application performance.
• Python Applications: Provides a Python SDK for direct integration into Python-based LLM applications and workflows.
• TypeScript/JavaScript Applications: Offers a TypeScript SDK for integration into web and server-side JavaScript/TypeScript environments.

Alternatives

• LangChain: An open-source framework for developing applications powered by language models, focusing on composition and chaining of LLM components.
• Weights & Biases: A platform for MLOps, including experiment tracking, model versioning, and dataset management, applicable to LLM development.
• Arize AI: An ML observability platform that helps monitor and troubleshoot ML models in production, including LLMs, for performance drift and data quality issues.

Getting started

To get started with Humanloop, you can install the Python SDK and begin by setting up a basic prompt experiment. The following example demonstrates how to initialize the Humanloop client and make a request with a prompt:

import humanloop

# Replace with your actual Humanloop API key
humanloop.api_key = "YOUR_HUMANLOOP_API_KEY"

# Define your prompt and model parameters
project_name = "My First LLM Project"
model_name = "gpt-4o"  # Example model, specify your desired model

def generate_response(query: str):
    try:
        response = humanloop.chat(
            project=project_name,
            model=model_name,
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": query}
            ],
            # Optional: Add a tag to categorize your requests
            tags=["initial_test"]
        )
        return response.choices[0].message.content
    except humanloop.HumanloopAPIError as e:
        print(f"An API error occurred: {e}")
        return None

# Example usage
user_query = "Explain the concept of large language models in one sentence."
llm_response = generate_response(user_query)

if llm_response:
    print(f"LLM Response: {llm_response}")

# You can also log feedback or evaluation data later
# For example, to log a specific interaction for review:
# humanloop.log(
#     project=project_name,
#     message_id=response.id, # Use the ID from the chat response
#     output=llm_response,
#     feedback={"rating": 5, "comment": "Excellent explanation!"}
# )

This Python code snippet illustrates a foundational interaction with the Humanloop platform. After setting your API key, you define a project and model, then use the humanloop.chat function to send a prompt and receive a response. The platform automatically logs these interactions, which can then be viewed and analyzed in the Humanloop UI for performance monitoring and iterative refinement. Further details on advanced features like prompt templating, A/B testing, and data labeling are available in the Humanloop documentation.