TruLens
Open-source, OpenTelemetry-native agent evaluation and tracing that finds where your agent fails.
For Python teams shipping agents, TruLens is the most credible free option because its judges are benchmarked against human annotations (0.81 groundedness F1, 0.93 context relevance NDCG@5) rather than asserted. It is tracing-first and open source, so you own the traces and the judges. Enterprise buyers wanting SLAs or a no-code UI should budget for a hosted alternative instead.
Verified 14d ago · liveness 69/100 · cite: rightaichoice.com/tools/trulens
- Python teams shipping agents who need trace-level evidence of where quality dropped
- RAG builders measuring groundedness, context relevance, and answer relevance objectively
- Teams comparing app versions on a shared leaderboard to find the quality/cost frontier
- Open-source projects that want OpenTelemetry-native evals with no vendor lock-in
- Enterprises requiring an SLA, dedicated support tier, or procurement-backed contract
- Non-technical stakeholders who need a no-code evaluation interface
- Teams expecting a managed hosted platform rather than a self-run framework
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Skip TruLens if you need a no-code evaluation interface, dedicated support with SLAs, or real-time alerting; you'll find commercial tools more suitable for those needs.
Running LLM-as-judge feedback functions incurs API costs (e.g., OpenAI API) that can add up on high trace volumes
TruLens is free and open-source, making it cost-effective for startups and individual developers. Compared to commercial evals like W&B Weave, RAGAS, DeepEval, and UpTrain, TruLens offers comparable or better benchmarked judge quality at no subscription cost, though you must manage your own infrastructure and support.
In short
TruLens — Open-source, OpenTelemetry-native agent evaluation and tracing that finds where your agent fails. Best for Python teams shipping agents who need trace-level evidence of where quality dropped, RAG builders measuring groundedness, context relevance, and answer relevance objectively, Teams comparing app versions on a shared leaderboard to find the quality/cost frontier. Free to use.
What's new in TruLens
Checked 5 days agoAcross the latest 1 update: 1 feature update.
Viability Score
How well maintained and how widely used is TruLens? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Trace agent execution via OpenTelemetry with per-span latency, inputs, outputs, tokens, and cost
- Evaluate RAG pipelines with groundedness, context relevance, and answer relevance
- Evaluate agentic workflows with tool selection, tool calling, tool quality, and plan adherence
- Evaluate MCP apps with tool calling, tool quality, and MCP span tracing
- Evaluate summarization with comprehensiveness, groundedness, and conciseness
- Run safety evals for harmfulness, toxicity, maliciousness, and stereotyping
- Run quality evals for coherence, conciseness, sentiment, and language match
- Use ranking metrics including NDCG, MRR, precision, and recall
- Get explained scores where judges return reasoning alongside each score
- Build custom metrics from any Python function, any LLM-as-judge prompt, or any span attribute
- Tune judges to your domain with rubrics, few-shot examples, and custom score ranges
- A/B test two rubrics and compare app versions on a metrics leaderboard
- Instrument apps with a plain Python decorator or auto-instrument LangGraph and LlamaIndex
- Run evals live as traces land or over a dataset with batch runs
- Track complete multi-turn conversations (v2.12)
About TruLens
TruLens is a free, open-source framework for evaluating and tracing AI agents so teams can find where an agent fails and where cost can be cut without losing quality. It targets Python developers and ML engineers building RAG pipelines, agentic workflows, and MCP apps who want trace-level evidence instead of gut feel. Every span of an agent run is captured with latency, inputs, outputs, tokens, and cost, so a bad answer has a traceable cause. Scores are explained rather than opaque: judges return reasoning with each result, which points straight at what went wrong. Evaluation breadth covers agentic metrics (tool selection, tool calling, tool quality, plan adherence, plan quality, execution efficiency, logical consistency), retrieval and RAG metrics (context relevance, groundedness, answer relevance, comprehensiveness), safety metrics (harmfulness, toxicity, maliciousness, stereotyping), and quality metrics (coherence, conciseness, sentiment, language match, groundtruth agreement, ranking via NDCG and MRR, precision and recall). Judges are benchmarked against human annotations, and custom metrics can be any Python function, any LLM-as-judge prompt, or any span attribute. Adversarial context relevance benchmarking placed TruLens ahead of WandB Weave, RAGAS, DeepEval, and UpTrain on three of four ranking metrics. Instrumentation is a plain Python decorator, or auto-instrumentation for LangGraph and LlamaIndex with a custom path for anything else. Evals run live as traces land, or in batch over a dataset using runs. A leaderboard compares app versions so teams can pick a winner and locate the quality/cost frontier. The project is shepherded by Snowflake and stays OpenTelemetry-native with no rewrite and no lock-in: it traces the app you built, judges with the model you chose, and writes to the database you run. Against black-box eval SaaS, TruLens competes on open source, trace-native scoring, and benchmarked judge quality rather than managed dashboards and SLAs. In
Behind the Verdict
Tracing first, or evaluation first? TruLens answers both, but the tracing is what makes it stick. Every span carries latency, inputs, outputs, tokens, and cost, so when a support agent drops the wrong policy into context you can point at the retrieval step instead of guessing at the prompt. We'd reach for this the moment a demo stops being reproducible and quality starts drifting between releases. Pick it when your stack is Python, you're already standing up OpenTelemetry, and you want evals that live inside the trace. The benchmark story is the part competitors can't wave away: 267 of 281 human-annotated errors caught on TRAIL and GAIA versus 55% for a baseline trace judge, and first place on three of four ranking metrics in adversarial context-relevance testing ahead of WandB Weave, RAGAS, DeepEval, and UpTrain. For RAG groundedness and context relevance specifically, the numbers carry. Pass when you need a managed product more than a framework. There's no SLA, no dedicated support tier, and no no-code UI, so non-technical stakeholders will end up asking engineers for every readout. Very large trace volumes may also push the open-source runtime harder than a hosted service would. Watch out for judge drift on unusual domains: out-of-the-box judges are general and will score confidently on data they don't understand. Tuning is where that gets fixed. You add a rubric, few-shot (arguments, score) examples, and your own min/max scale, replacing the shipped criteria with something like "score 3 only if the answer cites the policy section it used." That is the difference between a metric that ranks dashboards and one a reviewer signs off on. A/B testing two rubrics on the leaderboard is the fastest way to find which judge your reviewers actually trust. Close
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Real-world workflow fit
Concrete scenarios for the personas TruLens actually fits — and what changes day-one when you adopt it.
Wants to evaluate a LangGraph agent after a prompt change
Outcome: Instruments the app with TruGraph, adds feedbacks like f_tool_selection and f_plan_adherence, runs a batch eval over a dataset, and sees the leaderboard comparing v1 vs v2, pinpointing low plan adherence scores with explanations.
Needs to measure groundedness and context relevance before shipping
Outcome: Uses TruLlama to auto-instrument the query engine, evaluates over a test set, and gets F1 and NDCG scores that beat fine-tuned proprietary models, helping justify the system's reliability to stakeholders.
Wants to detect harmful outputs in real time
Outcome: Adds the blocking guardrails quickstart, sets up inline evaluation with a safety metric, and receives immediate scores on each response, allowing them to block harmful content before users see it.
Use Cases
- Evaluate RAG pipelines by measuring context relevance and groundedness
- Compare different prompt and model versions on a leaderboard of accuracy and cost
- Detect harmful or toxic language in LLM outputs using built-in metrics
- Improve agent workflows by identifying trace-level regressions across versions
- Log human feedback to refine your app's performance over time
- Iterate on agents by evaluating tool calls, plans, and execution flow
- Guardrail agent outputs to block harmful content in real time
- Evaluate summarization quality with comprehensiveness and groundedness metrics
Models Under the Hood
as of 2026-09-14
Limitations
- TruLens requires Python coding to instrument agents and configure custom metrics and feedback functions.
- Evaluation depends on LLM judges and endpoint providers (OpenAI, Anthropic, Gemini, Amazon Bedrock, HuggingFace, LiteLLM, Snowflake Cortex), so scoring quality and cost depend on the chosen judge model.
- Benchmarks reported on the homepage are self-published or third-party (arXiv, AIMultiple, LLM-AggreFact) rather than audited by RightAIChoice.
as of 2026-08-30
Verification history
We have re-verified TruLens 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where TruLens's pricing actually pencils out — and where peers do it cheaper.
TruLens is free and open-source, making it cost-effective for startups and individual developers. Compared to commercial evals like W&B Weave, RAGAS, DeepEval, and UpTrain, TruLens offers comparable or better benchmarked judge quality at no subscription cost, though you must manage your own infrastructure and support.
Setup time & first value
How long it actually takes to get something useful out of TruLens — broken out by persona, not the marketing-page minute.
For a Python developer familiar with LLM apps, you can instrument a simple RAG or agent in under an hour using the quickstarts and auto-instrumentation for LangGraph or LlamaIndex. Custom judge tuning or dataset batch runs may take a few hours to a day to fully configure.
Switching to or from TruLens
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From TruLens Eval: Migrate by following the 'Moving from TruLens Eval' guide, which updates the API to the new Metric API
- →From LangSmith or LangFuse: Export your traces and manually adapt them to TruLens' OpenTelemetry-based instrumentation via custom spans and attributes
- ↗To LangSmith: Export your traces and evaluation results to LangSmith's format, then re-instrument your app with their SDK
- ↗To Weights & Biases Weave: Replace TruLens' recorder and evaluators with Weave's equivalents, adjusting the API calls
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “TruLens”, and we withheld 6: 6 could not be judged, because “TruLens” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about TruLens.
Official links
Tools that pair well with TruLens
Common stack mates teams adopt alongside TruLens, with the specific reason each pairing earns its keep.
Phoenix
Open-source tracing, evaluation, and prompt iteration for AI agents — self-host it on your own infrastructure with no per-span bill.
Langfuse
Open-source LLM observability, prompt management, and evaluation for teams running AI agents in production.
MLflow
MLflow is the open source AI engineering platform for agent and LLM observability, evaluation, and prompt management.
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Frequently Asked Questions
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