humanloop
**Humanloop** is a **collaborative LLMOps platform for developing, evaluating, and managing production LLM applications** — providing a shared workspace where engineers and domain experts can iterate on prompts, run systematic evaluations against test datasets, collect user feedback, and fine-tune models based on production performance data.
**What Is Humanloop?**
- **Definition**: A commercial LLMOps platform (SaaS, founded 2021 in London) that acts as the development environment for LLM-powered features — combining a collaborative prompt IDE, evaluation framework, feedback collection, and model fine-tuning in a single platform with SDK integration for production logging.
- **Prompt Playground**: A spreadsheet-like interface where teams define input variables, try different prompt templates, run them against multiple test cases simultaneously, and compare outputs side-by-side — turning prompt iteration from individual developer work into a collaborative team activity.
- **Model Configuration**: Prompts, model parameters (temperature, max_tokens, stop sequences), and model selection are stored as versioned "Model Configs" — changes to prompts are decoupled from code deployments, enabling rapid iteration.
- **Evaluation Pipelines**: Define test cases (input → expected output pairs), run them against any prompt version, score outputs using human raters or LLM judges, and see quality scores change as prompts evolve.
- **Feedback Collection**: Collect end-user feedback (thumbs up/down, ratings, corrections) in production via the SDK, automatically linking feedback to the prompt version and model config that generated the response.
**Why Humanloop Matters**
- **Cross-Functional Iteration**: Domain experts (doctors, lawyers, financial analysts) who understand correct outputs can directly edit and test prompts in the Humanloop UI — removing the engineering bottleneck where every prompt change requires a code commit.
- **Quality Guardrails**: Before deploying a new prompt version, test it against a regression suite — Humanloop blocks deployment if the new version scores worse than the current version on your quality metrics.
- **Data Flywheel**: User feedback collected in production creates labeled datasets automatically — the same data that identifies problems can be used to fine-tune future models.
- **Systematic Evaluation**: Ad-hoc "vibes-based" prompt testing is replaced by quantitative evaluation — track Accuracy, Faithfulness, Helpfulness, or custom metrics over time as prompts evolve.
- **Team Alignment**: Shared visibility into what prompts are deployed in production, what their quality scores are, and what user feedback says — eliminates the "what prompt is running in production?" confusion common in fast-moving AI teams.
**Core Humanloop Features**
**Prompt IDE**:
- Multi-turn conversation design with system, user, and assistant message templates.
- Variable interpolation — `{{customer_name}}`, `{{issue_description}}` — with live test inputs.
- Side-by-side comparison of different model configs on the same test inputs.
- One-click deployment from playground to production.
**SDK Integration (Production Logging)**:
```python
from humanloop import Humanloop
hl = Humanloop(api_key="hl-...")
response = hl.chat(
project="customer-support",
model_config={"model": "gpt-4o", "temperature": 0.3},
messages=[{"role": "user", "content": "I need help with my bill."}],
inputs={"customer_name": "Alice"}
)
print(response.data[0].output)
# Log user feedback
hl.feedback(data_id=response.data[0].id, type="rating", value="positive")
```
**Evaluation Workflow**:
```python
# Create test dataset
dataset = hl.evaluations.create_dataset(
project="customer-support",
name="billing-test-cases",
datapoints=[
{"inputs": {"customer_name": "Alice"}, "target": {"response": "billing explanation"}}
]
)
# Run evaluation
evaluation = hl.evaluations.run(
project="customer-support",
dataset_id=dataset.id,
config_id="current-production-config"
)
```
**Fine-Tuning Pipeline**:
- Collect production logs with user feedback → filter for positive examples → create fine-tuning dataset → trigger fine-tuning job → evaluate fine-tuned model against regression suite → deploy if improvement confirmed.
**Humanloop vs Alternatives**
| Feature | Humanloop | PromptLayer | Langfuse | LangSmith |
|---------|----------|------------|---------|----------|
| Collaborative IDE | Excellent | Good | Limited | Good |
| Non-technical users | Excellent | Limited | Limited | Limited |
| Evaluation system | Strong | Moderate | Strong | Strong |
| Fine-tuning support | Yes | No | No | No |
| Feedback collection | Excellent | Basic | Good | Good |
| Open source | No | No | Yes | No |
**Use Cases**
- **Customer Support Bots**: Iteratively improve response quality with domain expert input and real user satisfaction signals.
- **Document Analysis**: Fine-tune extraction prompts on domain-specific examples collected from production corrections.
- **Code Assistants**: Systematic evaluation of code generation quality across programming languages and task types.
- **Content Generation**: A/B test prompt variants for marketing copy with engagement metrics as quality signals.
Humanloop is **the platform that enables AI product teams to develop LLM features collaboratively, evaluate them systematically, and improve them continuously based on real user feedback** — by closing the loop between production behavior and prompt iteration, Humanloop transforms LLM feature development from an art into an engineering discipline.