Results for: debug

...On the deployment side, it operates a globally distributed inference network to ensure low-latency (sub-50 ms) execution and exposes metrics like latency, cost, and usage in real time. It aims to simplify shipping resilient AI systems by combining debugging, evaluation, safety, and deployment into one stack, so you're not stitching together observability, infrastructure, and orchestration.

Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? ...

...It provides developers with visual, interactive insights, including searchable workflow replays, step-by-step video, and graph-based replays of agent decisions, decision tree visualizations, and side‑by‑side simulation comparisons, that enable you to observe exactly how your agent reasons and why it succeeds or fails. The tool dramatically reduces iteration time from weeks or days to mere minutes by streamlining debugging and optimization through instant feedback loops, real‑time “time‑travel” editing, mass simulations, trajectory clustering, customizable evaluation rubrics, and prompt versioning. Lucidic AI integrates seamlessly with major LLMs and frameworks and offers advanced QA/QC mechanisms like alerts, workflow sandboxing, and more.

...Foxglove supports real-time connections to systems like ROS and ROS 2 via bridges and web sockets, enables cross-platform workflows (desktop app for Linux, Windows, and macOS), and facilitates rapid analysis, debugging, and performance optimization by synchronizing diverse data sources in time and space.

...Its Luna models distill expensive LLM-as-judge evaluators into lower-cost, low-latency guardrails that can monitor production traffic. Built for enterprises and developers, Galileo helps teams debug failures, improve agent behavior, enforce AI policies, and ship AI applications with more confidence.

fixa is an open source platform designed to help monitor, debug, and improve AI-driven voice agents. It offers comprehensive tools to track key performance metrics, such as latency, interruptions, and correctness in voice interactions. Users can measure response times, track latency metrics like TTFW and p50/p90/p95, and flag instances where the voice agent interrupts the user. Additionally, fixa allows for custom evaluations to ensure the voice agent provides accurate responses, and it offers custom Slack alerts to notify teams when issues arise. ...

...Accurate cost attribution, agent health monitoring, a unified agent registry, and EU AI Act evidence generation make it suitable for enterprise deployments. With a lightweight open-source SDK and managed platform, Traccia enables teams to build, debug, monitor, and govern AI agents at scale without vendor lock-in, using standard OpenTelemetry instrumentation.

...It offers compliance enforcement by automating adherence to regulatory standards through custom policy rules, real-time content moderation to block harmful, toxic, or biased outputs from AI, debugging AI outputs by testing and monitoring responses against custom safety policies, policy-driven AI governance by applying centralized safety rules across all AI interactions, and trust-building for AI by guaranteeing safe, accurate, and brand-compliant outputs. The platform caters to various industries, including healthcare, finance, legal technology, customer support, education technology, and ecommerce & retail, providing tailored solutions to ensure AI responses align with industry-specific regulations and standards. ...

Aquarium's embedding technology surfaces the biggest problems in your model performance and finds the right data to solve them. Unlock the power of neural network embeddings without worrying about maintaining infrastructure or debugging embedding models. Automatically find the most critical patterns of model failures in your dataset. Understand the long tail of edge cases and triage which issues to solve first. Trawl through massive unlabeled datasets to find edge-case scenarios. Bootstrap new classes with a handful of examples using few-shot learning technology. ...

...Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.

telemetry.dev is an OpenTelemetry-native observability platform for AI agents and LLM applications. Developers can trace model calls, tool steps, retrievals, logs, and metrics; inspect failures, latency, token usage, and estimated cost; and compare activity by model, provider, and environment. It supports first-party TypeScript and Python SDKs, provider and framework integrations, and standard OTLP/HTTP ingest. Environment-level capture controls, SDK masking, and server-side redaction help...

...Features: Agent Simulation Agent Evaluation Prompt Playground Logging/Tracing Workflows Custom Evaluators- AI, Programmatic and Statistical Dataset Curation Human-in-the-loop Use Case: Simulate and test AI agents Evals for agentic workflows: pre and post-release Tracing and debugging multi-agent workflows Real-time alerts on performance and quality Creating robust datasets for evals and fine-tuning Human-in-the-loop workflows

...Roll out GenAI features to specific user groups with guardrails, data privacy safeguards, and advanced RAG pipelines. Visualize all events triggered by agents for fast debugging. Get granular control on cost, latency, and performance. Connect to your favorite AI models, or bring your own. Speed up your workflow with out-of-the-box components built for agentic AI systems. Manage core stages of the LLM app lifecycle in one central platform. Self-hosted or hybrid deployment with SOC 2 and GDPR compliance for enterprise security.