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-# Observability
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-**Tools, models, datasets, and open workflows for understanding how AI systems behave in production.**
-
-Observability is an independent Hugging Face organization focused on **AI observability, LLM tracing, agent monitoring, evaluations, RAG diagnostics, model performance, reliability, cost analysis, and production debugging**.
-
-The goal is simple:
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-> **See what your AI system is doing, understand why it behaves that way, and improve it with evidence.**
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-Modern AI systems are increasingly complex. A single application may involve prompts, models, tools, retrieval, agents, external APIs, memory, structured outputs, and multiple decision steps.
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-Observability helps make those systems easier to inspect, measure, debug, and improve.
-
----
-
-## What Is AI Observability?
-
-AI observability is the practice of collecting and analyzing the signals needed to understand the behavior of AI systems.
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-Depending on the application, this may include:
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-- prompts
-- responses
-- traces
-- tool calls
-- agent steps
-- latency
-- token usage
-- model costs
-- retrieval quality
-- errors
-- retries
-- hallucinations
-- evaluation scores
-- model drift
-- user feedback
-- safety signals
-- structured outputs
-
-Traditional monitoring tells you **whether a system is running**.
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-AI observability should also help explain:
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-**what happened, why it happened, and where quality was lost.**
-
----
-
-## Focus Areas
-
-### 🔍 LLM Tracing
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-Tools for inspecting:
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-- prompts
-- model responses
-- chained calls
-- tool usage
-- retries
-- latency
-- token consumption
-- structured outputs
-- failure points
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-A useful trace should make a complex AI workflow understandable from end to end.
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-### 🤖 Agent Observability
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-AI agents introduce additional complexity.
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-Projects may focus on:
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-- agent trajectories
-- planning steps
-- tool selection
-- tool execution
-- memory usage
-- loops
-- failed actions
-- execution time
-- cost per task
-- completion quality
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-### 📚 RAG Observability
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-Retrieval-Augmented Generation systems require visibility into both retrieval and generation.
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-Possible metrics include:
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-- retrieved documents
-- ranking quality
-- context relevance
-- answer groundedness
-- citation coverage
-- retrieval latency
-- chunk quality
-- missing context
-- source usage
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-### 🧪 Evaluations
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-Evaluation should be part of production AI workflows.
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-Possible projects may support:
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-- response quality
-- task success
-- factuality
-- relevance
-- groundedness
-- safety
-- instruction following
-- structured-output validity
-- regression testing
-- human review
-
-### 📊 Model Monitoring
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-Observe model behavior over time using signals such as:
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-- latency
-- error rates
-- output distributions
-- response length
-- refusal rates
-- token usage
-- quality scores
-- model version
-- provider performance
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-### 💰 Cost Observability
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-AI costs can become difficult to understand across models and workflows.
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-Possible tools may track:
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-- input tokens
-- output tokens
-- model usage
-- cost per request
-- cost per user
-- cost per workflow
-- cost per successful task
-- provider comparison
-- caching impact
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-### ⚡ Performance & Reliability
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-Useful metrics may include:
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-- time to first token
-- total latency
-- throughput
-- error rate
-- timeout rate
-- retry rate
-- availability
-- provider failures
-- fallback behavior
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-### 🛡️ Safety Observability
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-Possible projects may help surface:
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-- unsafe outputs
-- policy violations
-- prompt injection
-- jailbreak attempts
-- sensitive-data exposure
-- anomalous tool calls
-- unexpected agent behavior
-- moderation events
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-### 🔄 Model & Prompt Changes
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-AI applications change frequently.
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-Observability can help compare:
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-- model versions
-- prompt versions
-- retrieval pipelines
-- system instructions
-- tool configurations
-- temperature settings
-- provider changes
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----
-
-## Possible Spaces
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-### 🔍 LLM Trace Viewer
-Visualize prompts, responses, tool calls, latency, and token usage for a single AI workflow.
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-### 🤖 Agent Trace Explorer
-Inspect agent trajectories and understand which actions led to success or failure.
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-### 📚 RAG Quality Checker
-Evaluate retrieval relevance, context coverage, and answer grounding.
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-### 🧪 Prompt Evaluation Dashboard
-Compare prompt versions using consistent test cases and metrics.
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-### 📊 Model Comparison
-Compare multiple models across quality, latency, token usage, and cost.
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-### 💰 Token Cost Calculator
-Estimate and compare LLM usage costs across models and workloads.
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-### ⚡ Latency Analyzer
-Analyze response times, bottlenecks, retries, and provider performance.
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-### 🧠 Hallucination Review Tool
-Structure manual or automated review of unsupported model claims.
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-### 🛡️ AI Safety Monitor
-Track safety-related events and suspicious interactions.
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-### 🔄 Regression Test Runner
-Run repeatable evaluation sets after model, prompt, or pipeline changes.
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-### 📈 Drift Explorer
-Compare output behavior across time periods, model versions, or datasets.
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-### 🧰 Observability Playground
-Experiment with traces, metrics, evaluations, and monitoring workflows in one place.
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----
-
-## Why Observability Matters
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-AI systems can fail in ways that are difficult to detect with traditional application monitoring.
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-A request may return successfully while the actual result is:
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-- irrelevant
-- incomplete
-- hallucinated
-- poorly grounded
-- too expensive
-- too slow
-- based on the wrong document
-- produced by an unintended tool sequence
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-That means operational health alone is not enough.
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-Teams need visibility into:
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-**quality + behavior + performance + cost + reliability**
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----
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-## Metrics We Care About
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-### Quality
-- task success
-- relevance
-- factuality
-- groundedness
-- completeness
-- instruction adherence
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-### Retrieval
-- context relevance
-- source coverage
-- ranking quality
-- retrieval success
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-### Performance
-- latency
-- throughput
-- time to first token
-- timeout rate
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-### Reliability
-- errors
-- retries
-- fallback usage
-- failed tool calls
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-### Cost
-- tokens
-- request cost
-- workflow cost
-- cost per successful task
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-### Safety
-- policy events
-- injection attempts
-- unsafe outputs
-- sensitive information exposure
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-### User Experience
-- ratings
-- corrections
-- retries
-- abandonment
-- feedback
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----
-
-## AI Evaluation Philosophy
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-A single score rarely tells the whole story.
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-Good evaluation should combine multiple signals:
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-**automated metrics + model-based evaluation + deterministic checks + human review**
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-Different tasks require different evaluation methods.
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-For example:
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-- JSON output → schema validation
-- retrieval → relevance evaluation
-- summarization → completeness and factuality
-- agents → task completion and action quality
-- classification → precision and recall
-- customer support → resolution quality and user feedback
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----
-
-## OpenTelemetry & Open Standards
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-Where useful, projects may explore interoperability with open standards and common telemetry formats.
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-Potential areas include:
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-- traces
-- spans
-- metrics
-- logs
-- OpenTelemetry
-- distributed tracing
-- structured events
-- trace context
-- model metadata
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-The goal is to make AI observability easier to integrate into existing engineering workflows.
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----
-
-## Technology
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-Projects may use:
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-- Hugging Face Transformers
-- Hugging Face Datasets
-- Hugging Face Spaces
-- open-weight models
-- OpenTelemetry
-- tracing systems
-- vector databases
-- evaluation frameworks
-- structured logging
-- Python
-- JavaScript
-- browser-based visualization
-- SQL / analytics pipelines
-- dashboards
-- custom metrics
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-Not every observability problem requires AI.
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-Where deterministic checks or conventional telemetry are more reliable, they should be preferred.
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----
-
-## Principles
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-### 👁️ Make Behavior Visible
-AI systems should not operate as unexplained black boxes.
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-### 📏 Measure What Matters
-Collect metrics that reflect real application quality, not just infrastructure health.
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-### 🔗 Preserve Context
-Traces should connect prompts, retrieval, tools, models, and outputs.
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-### 🧪 Evaluate Continuously
-Evaluation should happen before deployment and continue in production.
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-### 💰 Track Cost Alongside Quality
-A more expensive model is not automatically a better production choice.
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-### 🔐 Protect Sensitive Data
-Observability data can contain prompts, user input, business data, or personal information.
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-Data collection should be minimized, protected, and documented.
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-### 🔎 Prefer Evidence Over Guessing
-Debugging should be based on traces, metrics, evaluations, and reproducible examples.
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----
-
-## Who Is Observability For?
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-This organization may be useful for:
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-- AI engineers
-- ML engineers
-- LLM application developers
-- agent developers
-- MLOps teams
-- platform engineers
-- reliability engineers
-- evaluation teams
-- AI safety teams
-- data scientists
-- startups
-- enterprise AI teams
-- researchers
-- developers building AI infrastructure
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----
-
-## Responsible Use
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-Observability systems may capture sensitive information.
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-Projects should consider:
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-- data minimization
-- access control
-- retention
-- redaction
-- encryption
-- prompt privacy
-- user consent
-- regulatory requirements
-- model-provider policies
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-Monitoring should improve reliability without creating unnecessary privacy risk.
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----
-
-## Important Notice
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-Tools, models, and datasets published here are provided for **research, development, testing, monitoring, and technical experimentation**.
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-Unless explicitly stated otherwise, they do not guarantee:
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-- model correctness
-- production reliability
-- security
-- regulatory compliance
-- privacy compliance
-- complete detection of failures
-- complete detection of hallucinations
-- complete detection of unsafe behavior
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-AI observability should support qualified engineering and operational review.
-
----
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-## Independent Organization
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-**Observability is an independent Hugging Face community organization.**
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-It is not an official Hugging Face organization, standards body, monitoring vendor, or certification authority.
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-The name **Observability** describes the thematic focus of the organization and its projects.
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----
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-# Observability
-
-**Trace behavior. Measure quality. Improve AI systems.**