Confident AI
DeepEval-powered LLM evaluation platform to test, benchmark, and safeguard apps
TruLens
Systematically evaluate, track, and improve your LLM applications
TruLens provides fine‑grained, stack‑agnostic instrumentation and comprehensive evaluations for LLM apps, helping you identify failure modes, compare versions, and iterate confidently.
Overview
Highlights
Stack‑agnostic instrumentation for prompts, models, retrievers, and knowledge sources
Customizable feedback functions covering honesty, harmlessness, helpfulness, and RAG metrics
Interactive UI for comparing experiment runs and visualizing evaluation results
Single‑command installation and Python API for seamless integration
Pros
- Enables systematic failure‑mode analysis
- Works with any LLM provider or custom model
- Extensible feedback definitions in pure Python
- MIT‑licensed open‑source encourages community contributions
Considerations
- Requires a Python environment; not language‑agnostic
- Learning curve for creating custom feedback functions
- UI needs a local server or compatible hosting
- Primary focus on RAG; non‑RAG use cases may need extra setup
Managed products teams compare with
When teams consider TruLens, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Teams building RAG pipelines that need rigorous evaluation
- Researchers comparing LLM model variants across experiments
- DevOps engineers adding observability to AI services
- Organizations tracking model performance over time
Not ideal when
- Projects limited to non‑Python tech stacks
- Simple prototypes where manual testing suffices
- Environments unable to run a web‑based UI
- Use cases requiring real‑time low‑latency monitoring
How teams use it
RAG pipeline benchmarking
Identify which retriever‑model combination yields highest relevance and factuality scores
Prompt iteration analysis
Quantify how changes in prompt wording affect helpfulness and hallucination rates across model versions
Continuous model drift monitoring
Track evaluation metrics over deployments to detect performance degradation early
Agent behavior auditing
Evaluate AI agents against honesty and safety criteria, exposing unsafe decision paths
Tech snapshot
Python85%
Jupyter Notebook9%
TypeScript6%
Makefile1%
Shell1%
JavaScript1%
Frequently asked questions
How do I install TruLens?
Run `pip install trulens` in your Python environment.
Can TruLens work with any LLM provider?
Yes, its instrumentation is stack‑agnostic and works with OpenAI, Anthropic, Hugging Face, and others.
Do I need to write code to use the UI?
After instrumenting your app with TruLens, the UI can be launched with a single command to explore runs.
Is there support for custom evaluation metrics?
You can define your own feedback functions in Python and plug them into the evaluation pipeline.
What license is TruLens released under?
TruLens is released under the MIT license.
Project at a glance
Active- Stars
- 3,043
- Watchers
- 3,043
- Forks
- 243
LicenseMIT
Repo age5 years old
Last commityesterday
Primary languagePython
Last synced 3 hours ago