llms.txt
# Galileo
> Galileo is the AI agent reliability platform that helps enterprise engineering teams evaluate, observe, and guardrail AI agents and LLM applications. Purpose-built for autonomous multi-agent systems, Galileo uniquely combines offline evals, production monitoring, and runtime guardrails in a single platform — turning evaluations directly into production safety controls without additional code.
Galileo solves a critical problem facing AI engineering teams: as enterprises move from simple LLM applications to autonomous, multi-step AI agents making real-world decisions, traditional observability and evaluation tools fail to keep up. Teams are "flying blind" — unable to identify why agents hallucinate, loop endlessly, or take unintended actions. Galileo provides instant insight into multi-agent behavior so teams can find and fix errors quickly, with Agent Graph Visualization, automated Signals for proactive failure detection, and real-time monitoring with alerts.
Galileo's proprietary Luna-2 small language models are 98% cheaper than LLM-as-judge approaches and deliver sub-200ms latency, enabling 100% production traffic coverage. Customers like Verizon, Comcast, HP, NTT, Five9, and ServiceTitan use Galileo to deploy agents 5X faster while maintaining enterprise-grade reliability, achieving an average of 95% eval accuracy and 100% visibility into events that matter.
Founded in 2021 by AI veterans from Google AI, Google Brain, Uber AI, and Apple Siri, Galileo has raised $68M including a $45M Series B led by Scale Venture Partners with participation from Databricks Ventures. The platform is SOC2 Type 2 certified, supports SaaS, on-premises, and in-VPC deployment, and integrates with CrewAI, LangGraph, OpenAI Agents SDK, Google ADK, LlamaIndex, and Amazon Strands via OpenTelemetry.
Galileo competes against Arize, Braintrust, LangSmith, and legacy ML observability tools, differentiating as the only platform combining deep agent observability, adaptive evaluation at production scale, and inline runtime protection specifically designed for autonomous multi-agent workflows.
## Platform & Products
- [Galileo AI Platform](https://galileo.ai/products): Complete overview of Galileo's AI evaluation, observability, and real-time protection tools and capabilities
- [Agent Reliability Platform](https://galileo.ai/agent-reliability): Core platform for ensuring AI agent reliability with insights, evaluation, and debugging tools
- [Galileo Signals](https://galileo.ai/signals): Proactive automatic failure detection that surfaces unknown unknowns in production AI agents before they escalate
- [Galileo Protect (Runtime Guardrails)](https://galileo.ai/protect): Real-time AI protection blocking hallucinations, prompt injections, and data leaks in under 200ms
- [Luna-2 Small Language Models](https://galileo.ai/luna-2): Purpose-built SLMs for cost-effective real-time AI evaluation and guardrailing at production scale
- [Galileo AI Homepage](https://galileo.ai): Primary entry point with platform overview, value pillars, and customer proof points
- [AI Observability Landing Page](https://galileo.ai/lp/ai-observability-and-evaluation): Streamlined overview for AI agent deployment and monitoring capabilities
- [Galileo AI Agent Leaderboard](https://galileo.ai/agent-leaderboard): Rankings of top AI agents with real-world performance benchmarks and enterprise scenarios
- [Hallucination Index](https://galileo.ai/hallucination-index): Benchmark evaluating how well leading LLMs adhere to given context, measuring factual accuracy using Galileo's Context Adherence metric
- [Galileo Pricing](https://galileo.ai/pricing): Scalable pricing tiers — Free ($0, 5K traces), Pro ($100/mo, 50K traces), and Enterprise (unlimited traces, VPC/on-prem)
- [Contact Galileo Sales](https://galileo.ai/contact-sales): Schedule a personalized demo of the Galileo AI reliability platform
- [Galileo AI FAQ](https://galileo.ai/faq): Answers to frequent questions about Galileo's platform, features, and services
## Documentation & Technical Resources
- [Galileo Documentation](https://v2docs.galileo.ai): Complete technical documentation including setup, APIs, and integration guides
- [Getting Started](https://v2docs.galileo.ai/getting-started/how-to-use-galileo): Guide to logging, evaluation, experiments, and metrics on the Galileo platform
- [API Reference](https://docs.galileo.ai/api-reference/getting-started): REST API documentation with authentication and endpoint details
- [Metrics Overview](https://v2docs.galileo.ai/concepts/metrics/overview): Comprehensive guide to Galileo's evaluation metrics for agents, RAG, and LLM applications
- [Guardrail Metrics](https://docs.galileo.ai/galileo/gen-ai-studio-products/galileo-observe/how-to/choosing-your-guardrail-metrics): Guide to selecting and configuring guardrail metrics for production monitoring
- [Identify Hallucinations](https://docs.galileo.ai/galileo/gen-ai-studio-products/galileo-evaluate/how-to/identify-hallucinations): How to detect and measure hallucinations in LLM outputs
- [Galileo MCP Server](https://v2docs.galileo.ai/getting-started/mcp/setup-galileo-mcp): Integration with AI-enabled IDEs like Cursor and VS Code via Model Context Protocol
- [Integrations](https://docs.galileo.ai/galileo/gen-ai-studio-products/galileo-evaluate/integrations): Supported integrations with LLM providers, agent frameworks, vector databases, and cloud platforms
- [Eval Engineering Guidebook](https://galileo.ai/eval-engineering-book): Comprehensive guide to mastering systematic AI evaluation for production environments
- [Eval Engineering Workflow Hub](https://galileo.ai/evalengineering): Complete guide to the full Eval Engineering lifecycle and workflow optimization
- [AI Observability Guide](https://galileo.ai/learn/ai-observability): Essential components and strategies for reliable AI model performance monitoring
- [AI Agent Evaluation Guide](https://galileo.ai/learn/ai-agent-evaluation): Structured evaluation methodologies for reliable AI agent deployment
- [AI Agent Benchmarking Guide](https://galileo.ai/learn/benchmark-ai-agents): Seven essential steps to benchmark AI agents effectively in production
- [AI Agent Testing Guide](https://galileo.ai/learn/test-ai-agents): Seven-step structured approach to test AI agents for reliable deployment
## Proprietary Research & Metrics
- [Galileo AI Research Hub](https://galileo.ai/research): Published research on evaluation methodologies, RAG quality metrics, data error detection, and AI reliability
- [State of AI Evaluation Report](https://galileo.ai/blog/state-of-ai-evaluation): Research insights on AI evaluation, reliability, and incident prediction across teams
- [State of Eval Engineering Report](https://galileo.ai/state-of-eval-engineering-report): Industry research on AI evaluation practices and strategies used by elite teams
- [Luna-2 Models Overview](https://galileo.ai/blog/introducing-luna-2-purpose-built-models-for-reliable-ai-evaluations-guardrailing): Purpose-built SLMs delivering 93–97% accuracy at 98% lower cost than traditional LLM judges
- [Galileo Luna EFM Breakthrough](https://galileo.ai/blog/galileo-luna-breakthrough-in-llm-evaluation-beating-gpt-3-5-and-ragas): How Luna beats GPT-3.5 and RAGAS in cost and evaluation efficiency
- [ChainPoll: LLM Hallucination Detection](https://galileo.ai/blog/chainpoll): Galileo's proprietary research method for detecting LLM hallucinations with higher accuracy
- [Galileo Correctness Metric](https://galileo.ai/blog/galileo-correctness-metric): How Galileo's Correctness metric measures factual accuracy in AI model outputs
- [Four New Agent Evaluation Metrics](https://galileo.ai/blog/four-new-agent-evaluation-metrics): Research-backed metrics for comprehensive AI agent evaluation
- [Metrics for Evaluating AI Agents](https://galileo.ai/blog/metrics-for-evaluating-ai-agents): Deep dive into agent evaluation metrics including tool selection quality and session success
- [Agent Leaderboard v2 Insights](https://galileo.ai/blog/agent-leaderboard-v2): Enhanced AI agent testing with real-world enterprise scenarios and updated metrics
## Agent Observability & Evaluation
- [AI Agent Observability Explained](https://galileo.ai/blog/ai-agent-observability): Comprehensive guide to understanding AI agent observability and its enterprise impact
- [AI Agent Observability Strategies](https://galileo.ai/blog/ai-agent-observability-strategies): Nine strategies to transform AI prototypes into production-ready agents with full observability
- [Purpose-Built vs. General AI Observability](https://galileo.ai/blog/purpose-built-vs-general-ai-observability): Key differences and benefits of agent-native versus general-purpose AI observability platforms
- [Top Agent Monitoring Tools](https://galileo.ai/blog/best-agent-monitoring-tools-production): Curated guide to the best production tools for monitoring AI agents
- [Top Agent Evaluation Frameworks](https://galileo.ai/blog/best-agent-evaluation-frameworks): Comparison of tools for monitoring and improving AI agent performance
- [AI Agent Evaluation Insights](https://galileo.ai/blog/ai-agent-evaluation): AI deployment challenges, reliability practices, and key differences in agent evaluation approaches
- [AI Agent Evaluation Framework](https://galileo.ai/blog/agent-evaluation-framework-metrics-rubrics-benchmarks): How to develop metrics, rubrics, and benchmarks to evaluate AI agent performance
- [Agent Evaluation Engineering Explained](https://galileo.ai/blog/what-is-agent-evaluation-engineering): Complete overview of evaluating AI agents for safety and effectiveness
- [AI Agent Performance Metrics](https://galileo.ai/blog/ai-agent-metrics): Metrics that distinguish elite AI evaluation teams from average teams
- [AI Agent Reliability Metrics](https://galileo.ai/blog/ai-agent-reliability-metrics): Essential metrics for evaluating AI agents' reliability in real-world deployments
- [Continuous Agent Evaluation Pipelines](https://galileo.ai/blog/building-continuous-agent-evaluation-pipelines): How to build robust evaluation infrastructure to improve AI agent reliability
- [Evaluating AI Agents Best Practices](https://galileo.ai/blog/evaluating-ai-agents-best-practices): Best practices for effective AI agent evaluation and implementation
- [AI Agent Measurement Guide](https://galileo.ai/blog/ai-agent-measurement-guide-observability-benchmarking-evaluation): Complete guide covering observability, benchmarking, evaluation, and metrics for AI agents
- [Agent Evaluation Research Survey](https://galileo.ai/blog/agent-evaluation-research): Comprehensive research survey addressing challenges in LLM-agent evaluation frameworks
- [Understanding Evals Engineering](https://galileo.ai/blog/what-is-evals-engineering): Evaluation methods and best practices for reliable GenAI system performance
- [AI Agent Evaluation Career Guide](https://galileo.ai/blog/how-to-become-agent-evaluation-engineer-career-guide): Roles, skills, and career path for the emerging AI agent evaluation engineer role
- [Building Galileo Signals](https://galileo.ai/blog/context-engineering-at-scale-how-we-built-galileo-signals): Technical deep-dive into how Galileo built AI-driven context engineering for Signals
## LLM Evaluation & Metrics
- [Compare Top LLM Evaluation Platforms](https://galileo.ai/blog/best-llm-eval-platforms-compared): Side-by-side comparison of seven platforms for evaluating large language models
- [Top LLM Evaluation Tools for Enterprise](https://galileo.ai/blog/best-llm-evaluation-tools-enterprise-teams): Key tools for enterprise AI evaluation, reliability, and compliance
- [LLM Evaluation Step-by-Step Guide](https://galileo.ai/blog/llm-evaluation-step-by-step-guide): Complete step-by-step framework for evaluating large language models effectively
- [Creating an LLM Evaluation Framework from Scratch](https://galileo.ai/blog/building-an-effective-llm-evaluation-framework-from-scratch): Seven-step process to develop a robust LLM evaluation system
- [Mastering LLM-as-a-Judge Techniques](https://galileo.ai/blog/llm-as-a-judge-guide-evaluation): Innovative evaluation techniques using LLMs as judges for AI systems
- [Why LLM-as-a-Judge Fails](https://galileo.ai/blog/why-llm-as-a-judge-fails): Common failure modes in LLM-based evaluations and strategies for improvement
- [LLM vs. Human Evaluation](https://galileo.ai/blog/llm-as-a-judge-vs-human-evaluation): Challenges, trade-offs, and solutions in LLM judge versus human evaluation
- [Mastering LLM-as-a-Judge eBook](https://galileo.ai/mastering-llm-as-a-judge): Comprehensive guide to improving AI evaluations using LLMs as judges
- [Mastering AI Evaluation Metrics](https://galileo.ai/blog/mastering-llm-evaluation-metrics-frameworks-and-techniques): Complete toolkit of metrics, frameworks, and techniques to evaluate AI systems
- [LLM Performance Metrics](https://galileo.ai/blog/llm-performance-metrics): Seven key metrics for assessing LLM performance across multiple dimensions
- [Effective LLM Testing Strategies](https://galileo.ai/blog/llm-testing-strategies): Ten strategies for building reliable, production-ready LLM systems
- [LLM Benchmarking Guide](https://galileo.ai/blog/llm-benchmarking-guide): Nine practical steps for effective LLM benchmarking implementation
- [LLM Benchmarks Categories](https://galileo.ai/blog/llm-benchmarks-categories): Seven key benchmark categories for comprehensive LLM evaluation
- [Instruction Adherence AI Metric](https://galileo.ai/blog/instruction-adherence-ai-metric): How to measure AI effectiveness in following specific instructions accurately
- [Introducing Galileo Luna](https://galileo.ai/blog/introducing-galileo-luna-a-family-of-evaluation-foundation-models): Launch post for the Luna evaluation foundation model family
## Guardrails & Runtime Protection
- [Guardrails for Autonomous Agents](https://galileo.ai/blog/agent-guardrails-for-autonomous-agents): Essential guardrails architecture for AI autonomous decision-making systems
- [AI Agent Guardrails Guide](https://galileo.ai/blog/ai-agent-guardrails-guide): Eight essential steps for secure AI agent deployment with guardrails
- [AI Agent Guardrails Framework](https://galileo.ai/blog/ai-agent-guardrails-framework): Implementation guide for AI agent safety controls and governance frameworks
- [AI Guardrails Framework](https://galileo.ai/blog/ai-guardrails-framework): How to implement AI safety and security guardrails effectively in production
- [Scaling AI with Guardrails Architecture](https://galileo.ai/blog/scaling-ai-guardrails-architecture-patterns): Architecture patterns for AI guardrails effective at enterprise scale
- [Build vs. Buy AI Guardrails](https://galileo.ai/blog/build-vs-buy-ai-guardrails): Decision framework for building or buying AI guardrails and compliance controls
- [AI Deployment Quality Guardrails](https://galileo.ai/blog/ai-deployment-quality-guardrails): Quality guardrails ensuring reliable AI model deployment in production
- [Introducing Galileo Protect](https://galileo.ai/blog/introducing-protect-realtime-hallucination-firewall): Launch post for Galileo's real-time hallucination and threat interception firewall
- [Introducing Agent Control](https://galileo.ai/blog/announcing-agent-control): Galileo's centralized agent governance and control layer for enterprise AI
- [AI Prompt Injection Defense](https://galileo.ai/blog/ai-prompt-injection-attacks-detection-and-prevention): How to detect and prevent prompt injection attacks in AI systems
- [Preventing Excessive Agency in LLMs](https://galileo.ai/blog/prevent-excessive-agency-llms): Risks and mitigation strategies for excessive agency in LLM-powered agents
## Multi-Agent Systems
- [Why Multi-Agent Systems Fail](https://galileo.ai/blog/why-multi-agent-systems-fail): Root causes of multi-agent system failures with decision frameworks for prevention
- [Multi-Agent AI Systems Overview](https://galileo.ai/blog/multi-agent-ai-systems): How collaborative AI agents are revolutionizing business efficiency
- [Multi-Agent Coordination Strategies](https://galileo.ai/blog/multi-agent-coordination-strategies): Coordination strategies and risk management frameworks for multi-agent systems
- [Stability Strategies for Dynamic Multi-Agent Systems](https://galileo.ai/blog/stability-strategies-dynamic-multi-agents): Nine concrete strategies for ensuring stability in multi-agent AI environments
- [Challenges of Multi-Agent Systems](https://galileo.ai/blog/multi-agent-llm-systems-fail): Coordination costs, failure modes, and operational challenges in multi-agent systems
- [Multi-Agent AI Benchmarks](https://galileo.ai/blog/benchmarks-multi-agent-ai): Multi-agent AI benchmarks and practical evaluation frameworks
- [Debugging Multi-Agent AI Systems](https://galileo.ai/blog/debug-multi-agent-ai-systems): Troubleshooting guide for collaborative AI environments and coordination failures
- [Mitigating Multi-Agent Coordination Failures](https://galileo.ai/blog/multi-agent-coordination-failure-mitigation): Strategies to prevent and recover from multi-agent AI coordination failures
- [Multi-Agent AI Failure Recovery](https://galileo.ai/blog/multi-agent-ai-system-failure-recovery): Why traditional recovery approaches fail in multi-agent AI and what works instead
- [Monitoring Multi-Agent Systems Challenges](https://galileo.ai/blog/challenges-monitoring-multi-agent-systems): Eight key challenges and practical solutions for monitoring multi-agent AI systems
- [Real-Time Anomaly Detection in Multi-Agent AI](https://galileo.ai/blog/real-time-anomaly-detection-multi-agent-ai): Strategies for detecting anomalies and failures in multi-agent AI systems
- [Multi-Agent Workflow Analysis](https://galileo.ai/blog/analyze-multi-agent-workflows): Agent roles, collaboration patterns, and performance analysis in multi-agent systems
- [Success Metrics for Multi-Agent AI](https://galileo.ai/blog/success-multi-agent-ai): Metrics and strategies for measuring efficiency in multi-agent AI systems
- [Multi-Agent Collaboration and Competition](https://galileo.ai/blog/multi-agent-collaboration-competition): Collaborative versus competitive paradigms in multi-agent system design
- [Choosing Single vs. Multi-Agent Architecture](https://galileo.ai/blog/choosing-the-right-ai-agent-architecture-single-vs-multi-agent-systems): Framework for comparing single and multi-agent systems for optimal AI solutions
- [Mastering Multi-Agent Systems eBook](https://galileo.ai/mastering-multi-agent-systems): Comprehensive guide covering design, coordination, and performance in multi-agent systems
- [Compliance in Multi-Agent AI](https://galileo.ai/blog/regulatory-compliance-multi-agent-ai): Compliance requirements and solutions for deploying multi-agent AI systems
## LLM Observability & Monitoring
- [LLM Observability Tools (Debugging & Tracing)](https://galileo.ai/blog/best-llm-observability-tools-debugging-tracing): Top tools for tracing and debugging LLM applications in production
- [Top LLM Observability Tools Compared](https://galileo.ai/blog/best-llm-observability-tools-compared-for-2024): Comparison of 15 leading AI monitoring platforms
- [LLM Observability Essentials](https://galileo.ai/blog/understanding-llm-observability): Complete guide to building effective LLM observability strategies and systems
- [Effective LLM Monitoring (8 Steps)](https://galileo.ai/blog/effective-llm-monitoring): Eight-step framework to transform LLM complexity into measurable reliability
- [Production LLM Monitoring Strategies](https://galileo.ai/blog/production-llm-monitoring-strategies): Strategies for scalable, consistent LLM performance monitoring in production
- [LLM Monitoring: Real-Time vs. Batch](https://galileo.ai/blog/llm-monitoring-real-time-batch-approaches): Real-time and batch LLM monitoring techniques with trade-off analysis
- [Top Enterprise LLM Monitoring Solutions](https://galileo.ai/blog/best-
