Netra
Netra is an AI observability, evaluation, and simulation platform for engineering teams working with AI agents. It is described as a way to evaluate, trace, and monitor every decision agents make, with an emphasis on making those agents reliable. The site also calls it a red teaming framework for security testing agents.
Its main functions are grouped into observability, evaluation, simulation, Agent Insights, and Agent OPFOR. Observability is described as watching agents reason in real time from intent to response. Evaluation scores every trace automatically, using built-in or custom evaluators. Simulation dry-runs agents against tools, edge cases, and personas before they reach production. Agent Insights is used to surface failure patterns, cost spikes, and silent regressions before users do. Agent OPFOR is intended for security testing of agents.
The platform is presented for engineering teams and AI application workflows rather than for a general audience. The page’s supporting text says it provides full observability into AI applications and mentions tracing every LLM call, evaluating agent quality, and monitoring AI costs in production. It also names auto-instrumentation for OpenAI, LangChain, CrewAI, and 30+ integrations.
Netra is available through its website, with sign-in, docs, blogs, contact, and a book-a-demo flow shown on the page. The site also includes an open source launch link and GitHub reference, and it lists a Pricing page and an About us page among the top-level links. The product is explicitly described as an AI observability, evaluation, and simulation platform.
Overview
12 featuresIn the LLM eval & observability space, Netra takes a focused approach. It focuses on ensuring reliability, traceability, and quality of AI agents and LLM-powered applications in production environments. It is built as an open-source project for AI engineers and developers building and deploying agent-based or LLM-powered applications. Netra is open source under the Open Source license. It ships for the web and the command line.
Netra first shipped in 2026. The project is developed in the open on GitHub with 557 stars and 394 commits in the last 90 days. Among its 12 catalogued features are agent observability, automated evaluation, and simulation scenarios. It exposes integrations via a public API.
Summary written by a language model from the project’s public pages.
- Agent observability
- Automated evaluation
- Simulation scenarios
- Agent insights
- Security testing
- Cost monitoring
- Failure pattern detection
- Integration with OpenAI
- Integration with LangChain
- Integration with CrewAI
- Custom evaluators
- Real-time tracing
Built with & integrations
- AWS
- x-amz-cf-id header · x-amz-cf-pop header · via header
- multiple
- bLangChain in the HTML · bLlamaIndex in the HTML
- local_oss
- bollama in the HTML
Trust & compliance
Indexing history
1What PulseGate has recorded for this listing
Frequently asked questions about Netra
- What is Netra?
- Netra focuses on ensuring reliability, traceability, and quality of AI agents and LLM-powered applications in production environments. It is catalogued under LLM eval & observability on PulseGate.
- Who should use Netra?
- Netra is an open-source project built for AI engineers and developers building and deploying agent-based or LLM-powered applications.
- Is Netra free?
- Yes — Netra is open source under the Open Source license and free to use.
- What platforms does Netra run on?
- Netra runs on the web and the command line.
- Is Netra still active?
- The GitHub repository shows 394 commits in the last 90 days.
- What are alternatives to Netra?
- Similar projects tracked by PulseGate include Red Teamer Intuition Quotient, agentharness-verifier, and llm-canary.Red Teamer Intuition Quotientagentharness-verifierllm-canary
- How long has Netra been around?
- Netra first shipped in 2026.
- Is Netra open source?
- Yes — Netra is open source under the Open Source license, developed on GitHub.
Also in LLM eval & observability
Same category — not a similarity match