Platform Comparison
Humanloop Alternatives for Physical AI Data
Humanloop was an LLM evaluation and prompt-management platform that shut down on September 8, 2025 after its team joined Anthropic. If you need a like-for-like replacement for LLM evals, the closest options are Weights & Biases, LangSmith, and Braintrust. If you build robots or embodied agents, Humanloop was never the right tool: physical AI needs real-world capture, multi-sensor enrichment, and robotics-ready annotation, not prompt evals. Claru is a physical AI data marketplace that delivers teleoperation and multi-sensor datasets in RLDS, LeRobot, and MCAP for manipulation, navigation, and vision-language-action models.
Quick facts
- Topic
- Humanloop
- Audience
- Procurement leads, ML ops, robotics engineers
- Deliverable
- Buyer-facing reference + procurement guidance
What Humanloop Was Built For
Humanloop was an LLM evaluation and prompt-management platform for enterprise product teams: version prompts, A/B-test model outputs, and watch production metrics across OpenAI and Anthropic models. In 2025 its team joined Anthropic, and the platform shut down on September 8, 2025[1], leaving customers to export prompt histories and migrate.
If you are here to keep running LLM evals, the near-equivalents are Weights & Biases, LangSmith, and Braintrust. If you landed here because you build robots or embodied agents, the honest answer is that Humanloop never solved your problem. It had no capture tooling, no sensor fusion, and no robotics annotation primitives. Text-generation quality and gripper trajectories are different data problems, and no prompt versioner bridges them. Claru sits on the second side of that split: a physical AI data marketplace where buyers post a spec and vetted capture partners return sample packets, then scale to training-ready teleoperation and multi-sensor datasets.
What Robots Need That LLM Data Never Does
LLM training starts from a reservoir that already exists: scraped web text, licensed corpora, synthetic generations. Physical AI has no such reservoir. Every manipulation demonstration is captured once, by hand, on real hardware. That is why a serious community effort like DROID still needed a distributed fleet of Franka robots to gather 76,000 trajectories across 564 scenes and 86 tasks[2]. You cannot download your way past this. Capture is the bottleneck, and capture quality sets the model's ceiling.
Then the raw stream has to be enriched into something a policy can learn from. A ten-second clip can hold 300 RGB frames, matching depth maps, thousands of joint-angle readings, and tactile samples, none of it labeled. Policies need affordance labels, 6-DOF grasp poses, contact and slip events, and failure modes layered on top. RT-1 trained on 130,000 demonstrations carrying language instructions and success labels[3]; EPIC-KITCHENS-100 took 100 hours of egocentric video and added 90,000 action segments plus 20 million bounding boxes[4]. Humanloop's text metrics (BLEU, ROUGE, semantic similarity) mean nothing against a grasp pose.
Why Robotics Formats and Labels Are Structurally Different
LLM evaluation scores text against a reference or a human preference. Robotics evaluation demands geometric precision and temporal consistency: millimeter-accurate placement, smooth execution, real-time replanning under perturbation. The labels are spatial, not lexical.
Format is where most teams get surprised. Open X-Embodiment pooled robot demonstrations from 22 embodiments across 21 institutions, and nearly every contributing dataset used a different schema: some logged end-effector pose in world coordinates, others in the robot base frame; some sampled at 10 Hz, others at 30 Hz[5]. Making them trainable together meant coordinate-frame transforms, temporal resampling, and action-space normalization. That work is why the field standardized on robotics-native containers, RLDS for observation-action-reward trajectories[6], MCAP for time-aligned multi-sensor streams[7], and HDF5 for hierarchical sensor arrays[8]. Humanloop exports prompt logs as JSON or CSV. Neither carries a camera intrinsic, a robot URDF, or a synchronized timestamp, so neither loads into a training pipeline without a rewrite.
Humanloop vs Claru at a Glance
Both call themselves a data platform. They do unrelated jobs. The split, dimension by dimension:
| Dimension | Humanloop | Claru |
|---|---|---|
| Core job | LLM prompt eval and monitoring | Physical AI capture, enrichment, delivery |
| Where data comes from | Already exists (API logs, synthetic text) | Captured on demand by vetted partners |
| Labels | Text metrics (semantic similarity, safety) | 6-DOF poses, affordance masks, contact and failure events |
| Output format | JSON / CSV log dumps | RLDS, LeRobot, MCAP, custom schemas to S3/GCS/Azure |
| Rights | Not applicable | Consent artifacts, location releases, per-trajectory provenance |
| Status | Shut down September 8, 2025 | Live marketplace, ~10,000 collectors across 100 countries |
How Claru Delivers a Physical AI Dataset
Claru runs a spec-first pipeline, so you never pay for footage that misses your acceptance rubric. Enrichment uses CVAT plus custom 6-DOF pose tooling, delivery carries per-trajectory provenance records[9], and the output drops straight into LeRobot training scripts[10]. The five stages:
- 01
Scope the spec
Define tasks, environments, sensor modalities (RGB-D, LiDAR, force-torque, proprioception), success criteria, and delivery format. Suppliers respond only when their rigs meet it.
- 02
Capture in the real world
Vetted partners run wearable rigs, teleoperation interfaces, and mobile robots with synchronized, timestamped sensors across diverse sites for scene variation.
- 03
Enrich every clip
Annotators add object masks, grasp affordances, contact points, trajectory waypoints, and failure labels using CVAT and custom 6-DOF pose tools.
- 04
Validate against physics
Reviewers reject motion blur, sensor desync, and physically implausible trajectories or contact forces before anything ships.
- 05
Deliver training-ready
Datasets ship as RLDS, MCAP, or LeRobot files with camera intrinsics, coordinate frames, and provenance records, loadable straight into a training loop.
When to Choose an Eval Tool vs a Capture Marketplace
Choose an LLM eval platform if your pipeline is prompt to text: chatbots, content tools, semantic search, code assistants. You are iterating on instruction templates and watching latency, token cost, and regressions, and Weights & Biases, LangSmith, or Braintrust cover that after Humanloop's migration cutoff[11].
Choose a capture marketplace like Claru if your model consumes multi-sensor inputs and emits motor commands: manipulation policies, navigation stacks, and vision-language-action models like OpenVLA[12] or world models like NVIDIA Cosmos[13]. When you vet any physical-AI data partner, pressure-test four things: can they capture in your target environments and modalities; do they deliver robotics-native formats with complete metadata (camera calibration, robot URDF, sensor specs); do they ship rights-cleared data with consent and provenance; and can they scope a paid sample before you commit to scale. Claru answers those through LeRobot-ready delivery and a sample-packet-first workflow.
Other Alternatives Worth Knowing
For LLM evals, the drop-in options are Weights & Biases (experiment tracking), LangSmith (LangChain-native), Braintrust, and PromptLayer. All handle prompt iteration, output eval, and production monitoring.
For physical AI data, the field splits into managed services and open repositories. Scale AI's Physical AI division runs proprietary collection facilities; Appen and Sama offer crowdsourced annotation with limited robotics primitives. Managed vendors carry longer lead times and higher minimums. On the open side, Open X-Embodiment pools demonstrations from 22 robot embodiments across 21 institutions, free[14], and Hugging Face hosts 200+ robotics datasets, but you inherit the format-harmonization and rights-clearance work. The tradeoff never changes: open data is free but unshaped to your embodiment, while a marketplace prices fresh capture against your exact spec.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- Humanloop joins Anthropic
Humanloop team joining Anthropic and the platform sunsetting on September 8, 2025
humanloop.com ↩ - DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
DROID dataset scale: 76,000 trajectories across 564 scenes and 86 tasks
arXiv ↩ - RT-1: Robotics Transformer for Real-World Control at Scale
RT-1 training data: 130,000 demonstrations with language instructions and success labels
arXiv ↩ - Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100
EPIC-KITCHENS-100 annotation volume: 90,000 action segments, 20M bounding boxes
arXiv ↩ - Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment scale: 22 robot embodiments across 21 institutions with heterogeneous schemas
arXiv ↩ - RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
RLDS ecosystem for dataset generation, sharing, and consumption
arXiv ↩ - MCAP specification
MCAP specification for robotics data interchange
MCAP ↩ - Introduction to HDF5
HDF5 technical capabilities for scientific data management
The HDF Group ↩ - truelabel data provenance glossary
Truelabel's provenance tracking for physical AI datasets
truelabel.ai ↩ - LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch
LeRobot technical architecture and dataset requirements
arXiv ↩ - Humanloop is sunsetting. Migrate to Weights & Biases as an alternative
Migration path off the sunset Humanloop platform after its September 8, 2025 shutdown
wandb.ai ↩ - OpenVLA: An Open-Source Vision-Language-Action Model
OpenVLA's RLDS format requirements for trajectory data
arXiv ↩ - NVIDIA GR00T N1 technical report
Cosmos technical requirements for synchronized MCAP sensor data
arXiv ↩ - Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment dataset heterogeneity and harmonization challenges
arXiv ↩ - scale.com physical ai
Scale AI's Physical AI division as managed data collection alternative
scale.com - Scale AI: Expanding Our Data Engine for Physical AI
Scale's infrastructure model and capital requirements for data facilities
scale.com - Project site
BridgeData V2 as example of scene diversity impact on model performance
rail-berkeley.github.io - BridgeData V2: A Dataset for Robot Learning at Scale
BridgeData V2 improvements: 30% success rate gain from environmental variation
arXiv - RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
RT-2 as example of web-scale vision-language pretraining for robotics
robotics-transformer2.github.io - RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
RT-2's cross-modal annotation requirements for internet image-text alignment
arXiv - RLDS with TensorFlow Datasets
RLDS format specification for TensorFlow Datasets
TensorFlow - MCAP guides
MCAP format guides for robotics applications
MCAP - h5py groups
HDF5 Python interface for hierarchical data access
h5py - Apache Arrow Parquet files
Apache Parquet for columnar robotics dataset storage
Apache Arrow - Apache Parquet file format
Parquet file format specification and compression capabilities
Apache Parquet - CVAT polygon annotation manual
CVAT polygon annotation workflows for object segmentation
docs.cvat.ai - RLDS with TensorFlow Datasets
RLDS delivery format with observation-action-reward structure
TensorFlow - Foxglove MCAP documentation
MCAP delivery format for multi-sensor robotics streams
Foxglove - Diffusion Policy training example
LeRobot training script examples for policy learning
GitHub - LeRobot documentation
LeRobot documentation for dataset loading and model training
Hugging Face - Scale AI: Expanding Our Data Engine for Physical AI
Scale's physical AI data collection infrastructure and approach
scale.com - appen.com data collection
Appen's data collection capabilities and sensor support
appen.com - sama.com computer vision
Sama's computer vision annotation services and limitations
sama.com - Project site
Open X-Embodiment dataset repository and access
robotics-transformer-x.github.io - Dataset cards are not yet standardized for physical AI procurement
Hugging Face dataset card standards and metadata completeness gaps
Hugging Face - truelabel data provenance glossary
Provenance documentation requirements for compliance and reproducibility
truelabel.ai - truelabel data provenance glossary
Truelabel's provenance tracking implementation for physical AI data
truelabel.ai
FAQ
What is Humanloop and why is it sunsetting?
Humanloop was an LLM evaluation platform offering prompt management, observability, and systematic evaluation workflows for AI product teams. In 2025, the company announced that its team had joined Anthropic and the platform would sunset on September 8, 2025. The acquisition reflected Anthropic's investment in evaluation infrastructure for frontier models. Existing customers received migration timelines and data export instructions to transition to alternative LLM evaluation platforms before the cutoff date.
How is Claru different from Humanloop?
Humanloop focused on LLM application development—prompt iteration, text output evaluation, and production monitoring for chatbots and content generators. Claru focuses on physical AI training data—real-world capture, multi-sensor enrichment, and robotics-ready delivery for manipulation policies, navigation systems, and embodied agents. Humanloop assumed training data already existed; Claru creates physical data through a collector marketplace deploying wearable rigs, teleoperation interfaces, and mobile robots to capture demonstrations across diverse environments.
What formats does Claru deliver for robotics training?
Claru delivers datasets in RLDS (observation-action-reward trajectories), MCAP (time-aligned multi-sensor streams), LeRobot datasets, and custom schemas, pushed to your S3, GCS, or Azure bucket. Every dataset carries metadata (camera intrinsics, robot URDF, sensor calibration) and per-trajectory provenance records. Because the output is already a robotics-native container, it loads into LeRobot training scripts or a custom policy loader without a conversion step.
How long does Claru take to deliver a physical AI dataset?
Delivery time depends on dataset scope: the number of sensor modalities, hours of capture, and environment diversity all factor in. The marketplace model runs vetted partners in parallel across regions, so capture is not gated by a single facility's schedule the way centralized collection is. Every engagement starts with a scoped sample packet, so you see representative footage and QA evidence before committing to full scale.
What alternatives exist for LLM evaluation after Humanloop sunsets?
Alternative LLM evaluation platforms include Weights & Biases (experiment tracking with LLM-specific features), LangSmith (LangChain-native prompt management and evals), Braintrust (collaborative prompt engineering with team workflows), and PromptLayer (prompt versioning and observability dashboards). These platforms address similar use cases: iterating on prompts, comparing model outputs, tracking production performance, and optimizing cost-quality tradeoffs for text-generation applications. Teams should evaluate migration paths based on existing integrations, evaluation methodology preferences, and collaboration requirements.
Can Claru support custom sensor configurations for robotics data?
Yes, Claru's marketplace supports custom sensor configurations including RGB-D cameras, LiDAR (mechanical and solid-state), force-torque sensors, tactile arrays, proprioceptive feedback (joint encoders, IMUs), thermal cameras, and audio capture. Teams specify sensor requirements in bounty definitions, and Claru provisions hardware or coordinates with collectors using compatible equipment. The platform handles sensor calibration, timestamp synchronization, and coordinate frame alignment. Delivered datasets include full sensor specifications, calibration parameters, and metadata schemas for reproducible model training.
Looking for humanloop alternatives?
Specify modality, task, environment, requested rights posture, and delivery format. Truelabel routes the request to candidate capture partners and helps scope consent/provenance artifacts and commercial licensing requirements for buyer review before delivery.
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