High-stakes support and RAG
Flag answers that may be unsupported, based on the wrong retrieved context, or outside the knowledge base before they reach a customer.
Independent tool overview
TLM Playground at a glance
Cleanlab TLM Playground has evolved into Cleanlab Detect, a reliability layer that scores LLM and agent outputs in real time so applications can flag uncertain answers and route them to a fallback or human review.
Visit the official TLM Playground site ↗
- Current product
- Cleanlab Detect, built around TLM trust scoring
- Best for
- Production LLM, RAG, support, and agent reliability
- Output
- 0–1 trustworthiness score with optional explanation
- Works with
- Cleanlab-generated or existing model responses
- Pricing
- Free API tokens, then usage-based; enterprise by quote
- Ownership
- Acquired by Handshake AI in January 2026
Overview
What TLM Playground is
The original TLM Playground is now part of Cleanlab's broader Detect product. The underlying Trustworthy Language Model API remains documented, while the current product page presents Detect as a production reliability layer for hallucinations, wrong context, and knowledge gaps.
TLM can work in two modes. It can generate an answer and a trustworthiness score in one request, or it can score an existing prompt-and-response pair produced by another model. The second approach lets a team keep its current LLM stack while adding a common evaluation signal.
The score runs from 0 to 1 and represents model uncertainty about whether an output may be incorrect or otherwise flawed. It is an operational signal, not proof that an answer is true. A high score can still accompany a wrong answer, and a low score can accompany a correct one.
Cleanlab documents support for natural-language answers, classifications, structured output, and tool calls. Integrations and examples cover RAG systems, OpenAI APIs and agents, LangChain, LangGraph, streaming, multi-turn conversations, and batch evaluation.
Handshake AI acquired Cleanlab on January 28, 2026. Cleanlab's current site still offers Detect, documentation, API onboarding, and enterprise demos, but prospective enterprise customers should confirm current packaging, support, data handling, and roadmap in their agreement.
Use cases
Who TLM Playground is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Agent tool-call gating
Add a reliability signal before an agent takes an action, while retaining explicit approval for consequential operations.
Evaluation and review prioritization
Rank large response datasets so evaluators can focus on likely failures, uncertain examples, and threshold edge cases.
Multi-model applications
Apply one uncertainty layer across responses from different model providers without replacing every generation call.
Capabilities
Core TLM Playground features
Response trustworthiness scores
Assigns a 0–1 score intended to identify outputs that deserve more scrutiny.
Generate-and-score mode
The TLM prompt method returns a generated response together with its trustworthiness score.
Score-existing-output mode
The API can evaluate a response from another LLM or a human-written prompt-and-response dataset.
Explanations and quality presets
Optional logs can explain low scores, while five quality presets trade off evaluation quality, latency, and cost.
Structured output and tool-call scoring
Documentation covers classifications, structured responses, and proposed agent actions in addition to ordinary text.
RAG and agent integrations
Examples cover retrieval systems, OpenAI Responses and Agents, LangChain, LangGraph, multi-turn chat, streaming, and file search.
Batch, async, and private deployment options
TLM supports larger evaluation jobs and asynchronous calls; Cleanlab also advertises private VPC deployment for latency-sensitive enterprise use.
Process
How the TLM Playground workflow works
Step 1
Build a representative evaluation set
Collect normal cases, known failures, ambiguous questions, adversarial inputs, long contexts, and tool-call examples from the actual product.
Step 2
Send the complete context
When scoring an existing response, give TLM the same system instructions, retrieved material, conversation history, and user request that the original model received.
Step 3
Log the response and score
Store the model, prompt version, retrieved context, response, trust score, explanation, latency, and eventual user or reviewer outcome.
Step 4
Calibrate thresholds by risk
Compare scores with human labels and choose different thresholds for harmless copy suggestions, customer answers, and consequential agent actions.
Step 5
Define explicit fallback behavior
Map low scores to abstention, a clarifying question, a safer model, a retrieval retry, or human review instead of merely showing the score.
Step 6
Launch in shadow mode
Measure missed errors, false alarms, added latency, and cost before allowing the score to alter production responses.
Step 7
Monitor drift and exceptions
Re-evaluate thresholds when prompts, models, knowledge bases, languages, tool schemas, or user behavior change.
Cost
TLM Playground pricing and free plan
Cleanlab advertises free TLM API tokens for getting started. After those are used, the API moves to pay-per-token billing shown inside the Cleanlab Account under Usage & Billing. Public pages do not state the current free allowance or token rates. Cleanlab Detect enterprise deployments use request-a-demo pricing.
TLM API trial
Free tokens
A Cleanlab account includes an initial allocation for testing the TLM API.
- API key required
- Current token allowance is shown after account creation
- Suitable for proof-of-concept and evaluation work
TLM API usage
Pay per token
Continued API use is billed by token, with rates varying by base model and quality configuration.
- Current rates appear in the Cleanlab Account
- Higher quality presets generally cost more and take longer
- Using an external generation model can add that provider's charges
Cleanlab Detect enterprise
Custom
Production deployment, private infrastructure, support, and commercial terms are handled through Cleanlab sales.
- Request a demo
- Confirm volume, latency, retention, deployment, and support terms
- Confirm the current roadmap following the Handshake AI acquisition
Pricing checked . Check current pricing at the source ↗
Assessment
TLM Playground strengths and limitations
Where it stands out
- Can score an existing LLM response without forcing a full model migration
- Supports text, structured output, classification, RAG, and tool-call workflows
- Turns uncertainty into an actionable fallback or escalation signal
- Quality presets let teams trade evaluation quality against latency and cost
- Official examples cover several common agent and application frameworks
- Can be used for both real-time production checks and offline dataset evaluation
What to consider
- A trustworthiness score is not a guarantee of factual correctness or safety
- High-scoring responses can still be wrong, and correct responses can receive low scores
- Thresholds do not transfer cleanly across domains, languages, models, prompts, and risk levels
- Open-ended, subjective, or creative outputs are harder to judge than answers with clear correctness criteria
- Scoring an existing response requires the same complete prompt and context supplied to the original model
- Checking every response adds latency and cost; Cleanlab's advertised response time as low as 300 milliseconds is a best-case vendor figure
- A scorer that shares model families or data sources with the generator can share some of the same blind spots
- The public privacy policy says information transmitted through the service may be processed, so teams handling sensitive data should confirm retention, subprocessors, model-provider routing, and private-deployment terms
- Public self-serve token rates and allowances are not documented outside the customer account
- The acquisition and product-name transition make it important to confirm current packaging, support, and roadmap before a long-term commitment
Compare
TLM Playground alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Helicone
Consider Helicone when request logging, cost tracking, caching, and LLM observability are the primary need rather than a dedicated trustworthiness score.
Explore Helicone →Coding
AgentOps
Consider AgentOps when the priority is tracing, testing, and debugging multi-step agent behavior.
Explore AgentOps →Coding
LangChain
Consider LangChain when you want an application framework for building custom retrieval, evaluation, routing, and fallback logic rather than a standalone scoring service.
Explore LangChain →Questions
TLM Playground FAQs
Is TLM Playground still active?
The original TLM Playground has been folded into Cleanlab's current Detect product. The TLM API, Python client, tutorials, and account-based onboarding remain available.
What does the trustworthiness score mean?
It is a 0–1 uncertainty signal intended to estimate whether a response may be incorrect or flawed. It should guide review and fallback logic, not be treated as proof that an answer is true.
Can TLM score responses from any LLM?
Yes. Cleanlab documents a method for scoring an existing prompt-and-response pair from another model, as well as a mode where TLM generates and scores the response itself.
What score threshold should I use?
Cleanlab's documentation describes scores below 0.7 as typically low and scores above 0.9 as typically high, but production thresholds should be calibrated against labeled examples from the specific domain and risk level.
Can Cleanlab Detect replace human review?
No. It can prioritize review and trigger a safer path, but consequential decisions still need appropriate validation, policy controls, and human approval.
How much does Cleanlab TLM cost?
Cleanlab offers free API tokens, then pay-per-token billing. Current token allowances and rates appear inside the Cleanlab Account rather than on a public pricing table. Enterprise Detect pricing is custom.
Can it evaluate agent tool calls?
Yes. Cleanlab documents scoring proposed tool calls, but a score should not be the only authorization for payments, account changes, data deletion, or other consequential actions.
Who owns Cleanlab?
Handshake AI acquired Cleanlab on January 28, 2026. Cleanlab's current site continues to present Detect, Remediate, documentation, and enterprise contact options.
Does Cleanlab process prompts and responses?
Cleanlab's public privacy policy says it processes information uploaded or transmitted through its services. Organizations with sensitive data should confirm current retention, subprocessors, regional hosting, training-use restrictions, and private-deployment options in writing.
Bottom line
Our TLM Playground verdict
Cleanlab TLM and Detect are compelling when a production team needs a model-independent uncertainty signal that can drive retries, abstention, or escalation. The value comes from careful calibration and fallback design, not from placing blind trust in a single score. Run it against labeled domain failures, measure added latency and cost, and confirm current commercial and data-handling terms after the Handshake AI acquisition.
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