Agents Never Sleep vs Trulens: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agents Never Sleep and Trulens — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agents Never Sleep
Agents Never Sleep
A tiny $4.99 Mac menu bar app that keeps long-running AI agents alive with the laptop lid closed, using one three-level slider.
Key features
- Closed-Lid Operation: Keeps agents and background jobs running with the MacBook lid shut, which the built-in caffeinate command cannot do.
- Three-Level Slider: One control with Sleepy, Awake, and Never sleeps settings, so changing sleep behavior takes a single drag rather than a terminal command.
- Menu Bar Only: Lives entirely in the menu bar with no window, dock icon, or configuration screens to manage.
- Native and Tiny: A small native macOS binary rather than a wrapped web app, minimizing overhead on a machine already running agents.
- Zero Data Collection: No telemetry, analytics, or tracking; what runs on the machine stays on the machine.
- Safe Revert: Toggling back down restores normal sleep behavior, avoiding the common mistake of leaving sudo pmset -a disablesleep 1 permanently enabled.
- No-Questions Refund: A 14-day money-back guarantee with no form to fill in and no reason required.
Best for
- Overnight Agent Runs: Letting a coding agent work through a long task while the laptop is closed and put away.
- Long Builds and Tests: Preventing a multi-hour build, test suite, or data job from being interrupted mid-run by sleep.
- Mobile Working: Carrying a MacBook between rooms or offices without propping the lid open to keep a job alive.
- Model Downloads and Training: Keeping large model downloads or local fine-tuning runs going unattended.
- Avoiding pmset Mistakes: Replacing manual sudo pmset toggles that are easy to enable and easy to forget to undo.
- Remote Sessions: Keeping a Mac reachable and working for a remote session or background service while it sits closed on a desk.
Trulens
TruEra
Open-source toolkit to instrument, evaluate, and track LLM applications with feedback functions and dashboard-driven comparisons.
Key features
- Fine-Grained Instrumentation: Records calls across prompt, model, retriever, and knowledge-source boundaries to capture full context for each LLM interaction and enable detailed post-hoc analysis.
- Feedback Functions Framework: Pluggable evaluators (feedback functions) that run automatically alongside app executions to check for metrics like groundedness, helpfulness, and safety and flag failing responses.
- RAG-Focused Tooling: Built-in patterns and examples for Retrieval-Augmented Generation workflows (the RAG Triad) to evaluate retriever effectiveness and end-to-end grounding of responses.
- Dashboard & Leaderboards: A web UI to view runs, compare app versions, surface failure modes, and maintain leaderboards for experiments and evaluation metrics.
- Provider & Stack Agnostic Integrations: Support for multiple model providers and orchestration layers (examples and issue threads reference OpenAI, Ollama, Gemini, LangChain adapters), allowing reuse across different stacks.
- Virtual Records & Simulation: Utilities like TruVirtual and VirtualApp to create virtualized records for offline testing and deterministic evaluation of feedback functions.
- Observability & OTEL Plans: Design docs and a PRD for OpenTelemetry integration to standardize spans and make instrumentation more debuggable and extensible.
- Package Distribution & Quickstart: Installable Python package (pip install trulens) with quick usage examples to instrument a prototype and start collecting evaluations rapidly.
- Fine-grained, stack-agnostic instrumentation to capture app records and interactions with LLMs and retrievers
- Configurable feedback functions for automated evaluation (e.g., groundedness, correctness, custom metrics)
- Support for virtual apps and virtual records to simulate and evaluate pipelines
- Integrations/providers for multiple LLM endpoints (OpenAI, Azure OpenAI, LiteLLM, Ollama, Gemini, TruLlama) and retriever backends
- Dashboard/UI for visualizing runs, leaderboards, token usage and cost metrics
- Experiment tracking and run comparison across app versions and configurations
- Python package available on PyPI (pip install trulens) and hosted source/issue tracker on GitHub
- Provider-specific feedback provider classes (e.g., trulens_eval.feedback.provider.openai.AzureOpenAI)
- Support for popular stacks like LangChain and vector stores (examples include Pinecone integration)
- Extensible feedback/provider architecture to add custom evaluators and endpoints
Best for
- Instrumenting LLM Apps: Add TruLens instrumentation to a RAG or chat app to automatically record prompts, model outputs, retriever calls, and metadata for later analysis.
- Automated Feedback Evaluation: Run feedback functions on each recorded run to detect hallucinations, grounding failures, or policy/safety violations during CI or experimentation.
- Model and Prompt Comparison: Use the dashboard and leaderboards to compare different model families, prompt templates, or retriever configurations side-by-side using consistent metrics.
- Offline Testing with Virtual Records: Create VirtualApp/VirtualRecord datasets to reproduce and test failure modes offline and validate feedback function fixes before deployment.
- Observability Integration: Integrate TruLens traces with OpenTelemetry (or other observability tooling) to align LLM evaluations with standard telemetry and tracing pipelines.
- Cost & Token Monitoring: Track token usage and cost metrics across different providers and model configurations to optimize for budget and performance.
- Debugging Provider Integrations: Use recorded traces and feedback outputs to diagnose provider-specific issues (e.g., adapter errors for OpenAI, LangChain, Ollama) and iterate on provider configs.
- Instrumenting and evaluating RAG systems end-to-end during development
- Running automated feedback-based evaluations of LLM outputs (groundedness, helpfulness, safety checks)
- Tracking experiments and comparing different model/prompt/knowledge-source configurations
- Monitoring token usage and cost metrics per provider and run
- Debugging provider integrations and feedback functions during development
- Creating virtualized test runs to validate evaluation logic without live calls