` ESC ` Platform [Guard\\ \\ Block AI hallucinations in real-time with guardrails](/content/platform/guard/index.html) [Evaluate\\ \\ Run comprehensive evaluations with 20+ metrics](/content/platform/evaluate/index.html) [Error Feed\\ \\ Sentry-style error tracking for AI agents](/content/platform/evaluate/error-feeds/index.html) [Simulations\\ \\ Simulate thousands of multi-turn conversations](/content/platform/simulate/index.html) [Scenarios\\ \\ Define branching conversation test scenarios](/content/platform/simulate/scenarios/index.html) [Synthetic Data\\ \\ Generate diverse, realistic test data](/content/platform/simulate/synthetic-data/index.html) [AI Optimization\\ \\ Continuous improvement with reinforcement learning](/content/platform/optimize/rl/index.html) [Tracing\\ \\ End-to-end request tracing for AI agents](/content/platform/monitor/tracing/index.html) [Dashboards\\ \\ Custom dashboards with drag-and-drop widgets](/content/platform/monitor/dashboards/index.html) [Alerting\\ \\ AI-powered alerts for anomalies and hallucination spikes](/content/platform/monitor/alerting/index.html) [Guardrails (Monitor)\\ \\ Real-time guardrail monitoring and block rate insights](/content/platform/monitor/guardrails/index.html) [Datasets\\ \\ Manage and version evaluation datasets](/content/platform/agents/datasets/index.html) [Experiments\\ \\ Structured experiments across models and prompts](/content/platform/agents/experiments/index.html) [Agent IDE\\ \\ Build & test AI agents visually](/content/platform/agents/ide/index.html) Pages [Home\\ \\ Future AGI - AI agent hallucination detection platform](/content/site-root.html) [Pricing\\ \\ Simple, transparent pricing. Start free, scale as you grow.](/content/pricing/index.html) [Enterprise\\ \\ Enterprise-grade AI safety at scale](/content/enterprise/index.html) [Startups\\ \\ $10K in free credits and 6 months Pro access](/content/startups/index.html) [Roadmap\\ \\ Public product roadmap - see what we're building next](/content/roadmap/index.html) [Blog\\ \\ Guides, engineering deep-dives, and product updates](/content/blog/index.html) [Research\\ \\ Papers on hallucination detection, evaluation, and guardrails](/content/research/index.html) [Customers\\ \\ Case studies from teams using Future AGI](/content/customers/index.html) [eBooks\\ \\ In-depth guides on AI agent evaluation and RAG](/content/ebooks/index.html) [Handbook\\ \\ The Flight Manual - how we work, what we believe](/content/handbook/index.html) Docs [Introduction\\ \\ Future AGI is an AI lifecycle platform designed to support enterprises throughout their AI journey. It combines rapid prototyping, rigorous evaluation, continuous observability, and reliable deployment to help build, monitor, optimize, and secure generative AI applications.](https://docs.futureagi.com/docs) [Self-Hosting\\ \\ Deploy the full Future AGI platform on your own infrastructure with Docker Compose or Kubernetes.](https://docs.futureagi.com/docs/self-hosting) [Quickstart\\ \\ Future AGI is an AI lifecycle platform designed to support enterprises throughout their AI journey. It combines rapid prototyping, rigorous evaluation, continuous observability, and reliable deployment to help build, monitor, optimize, and secure generative AI applications.](https://docs.futureagi.com/docs) [Setup Observability\\ \\ Set up Future AGI Observe for production monitoring. Configure auto-instrumented tracing for OpenAI, Anthropic, LangChain, and other LLM frameworks.](https://docs.futureagi.com/docs/quickstart/setup-observability) [Running Evals in Simulation\\ \\ Run evaluations in Future AGI simulations. Test AI agents against simulated customers and score interactions for quality, context retention, and escalation.](https://docs.futureagi.com/docs/quickstart/running-evals-in-simulation) [Generate Synthetic Data\\ \\ Generate synthetic datasets with Future AGI. Define schemas, column types, and constraints to create realistic data for training and evaluation.](https://docs.futureagi.com/docs/quickstart/generate-synthetic-data) [Create Prompts\\ \\ Create and manage AI prompts in Future AGI's Prompt Workbench. Design, test, version, and optimize prompts with built-in model selection and evaluation.](https://docs.futureagi.com/docs/quickstart/prompts) [Setup MCP Server\\ \\ Set up the Future AGI MCP Server to interact with the platform via natural language from Claude, Cursor, or VS Code using Model Context Protocol.](https://docs.futureagi.com/docs/quickstart/setup-mcp-server) [Annotations Quickstart\\ \\ Get started with annotations in 5 minutes -- create a label, set up a queue, add items, and start annotating.](https://docs.futureagi.com/docs/annotations/quickstart) [Prism AI Gateway Quickstart\\ \\ Make your first LLM request through Prism in under 5 minutes](https://docs.futureagi.com/docs/prism/quickstart) [Overview\\ \\ Add human feedback to your AI outputs with annotation labels, queues, and scores across traces, datasets, prototypes, and simulations.](https://docs.futureagi.com/docs/annotations) [Scores\\ \\ Understand the Score model -- the unified annotation primitive that stores labels, values, and metadata across all source types.](https://docs.futureagi.com/docs/annotations/concepts/scores) [Labels\\ \\ Create, configure, and manage annotation labels. Understand the five label types and when to use each.](https://docs.futureagi.com/docs/annotations/features/labels) [Queues\\ \\ Create and manage annotation queues: assignment strategies, multi-annotator support, review workflows, and queue lifecycle.](https://docs.futureagi.com/docs/annotations/features/queues) [Add Items to Queues\\ \\ Learn how to add traces, spans, sessions, dataset rows, prototypes, and simulation calls to annotation queues.](https://docs.futureagi.com/docs/annotations/features/add-items) [Annotate Items\\ \\ Complete guide to the annotation workspace -- label inputs, keyboard shortcuts, navigation, instructions, and completion workflow.](https://docs.futureagi.com/docs/annotations/features/annotate) [Inline Annotations\\ \\ Annotate traces, spans, sessions, and prototypes directly from their detail views without using queues.](https://docs.futureagi.com/docs/annotations/features/inline) [Analytics & Agreement\\ \\ Track annotation progress, annotator performance, label distribution, and inter-annotator agreement metrics.](https://docs.futureagi.com/docs/annotations/features/analytics) [Export Annotations\\ \\ Export completed annotations as datasets (JSON/CSV) for fine-tuning, evaluation, or analysis.](https://docs.futureagi.com/docs/annotations/features/export) [Automation Rules\\ \\ Set up rules to automatically add items to queues or pre-fill annotations based on conditions.](https://docs.futureagi.com/docs/annotations/features/automation) [Python SDK\\ \\ Annotate traces and manage annotation queues programmatically using the FutureAGI Python SDK.](https://docs.futureagi.com/docs/annotations/sdk/python) [JavaScript SDK\\ \\ Annotate traces and manage annotation queues programmatically using the FutureAGI JavaScript/TypeScript SDK.](https://docs.futureagi.com/docs/annotations/sdk/javascript) [Annotation Queue Using SDK\\ \\ Create and manage annotation queues programmatically using the Future AGI Python SDK.](https://docs.futureagi.com/docs/annotations/sdk/annotation-queue-using-sdk) [Overview\\ \\ Create, manage and analyze datasets for AI model development and evaluation](https://docs.futureagi.com/docs/dataset) [Understanding Datasets\\ \\ How datasets work in Future AGI: structure, column types, creation methods, and lifecycle.](https://docs.futureagi.com/docs/dataset/concept/understanding-dataset) [Static Columns\\ \\ Static columns store fixed values in a dataset that only change when manually updated.](https://docs.futureagi.com/docs/dataset/concept/static-column) [Dynamic Columns\\ \\ Columns that are generated automatically by running prompts, models, or code against your dataset rows.](https://docs.futureagi.com/docs/dataset/concept/dynamic-column) [Synthetic Data\\ \\ Generate realistic datasets from a schema without using real user data.](https://docs.futureagi.com/docs/dataset/concept/synthetic-data) [Create New Dataset\\ \\ Learn to create datasets to do experimentations on them](https://docs.futureagi.com/docs/dataset/features/create) [Add Rows to Dataset\\ \\ Learn how to add rows to your dataset](https://docs.futureagi.com/docs/dataset/features/add-rows) [Add Columns to Dataset\\ \\ Add static columns for fixed values or dynamic columns whose values are computed from other columns or external operations.](https://docs.futureagi.com/docs/dataset/features/add-columns) [Run Prompt in Dataset\\ \\ Learn how to execute prompts against your dataset and generate responses](https://docs.futureagi.com/docs/dataset/features/run-prompt) [Experiments in Dataset\\ \\ To test, validate, and compare different prompt configurations](https://docs.futureagi.com/docs/dataset/features/experiments) [Add Annotation\\ \\ Annotations are essential for refining datasets, evaluating model outputs, and improving the quality of AI-generated responses.](https://docs.futureagi.com/docs/dataset/features/annotate) [Overview\\ \\ Automatically detect, cluster, and fix errors in your AI agent traces with Error Feed.](https://docs.futureagi.com/docs/error-feed) [Error Taxonomy\\ \\ Categories, subcategories, and descriptions of all error types detected by Error Feed.](https://docs.futureagi.com/docs/error-feed/concepts/taxonomy) [Using Error Feed\\ \\ How to read scores, insights, clusters, and recommendations from Error Feed.](https://docs.futureagi.com/docs/error-feed/features/using-error-feed) [Overview\\ \\ Measure and compare quality of prompts and agents across datasets, simulations, and experiments.](https://docs.futureagi.com/docs/evaluation) [Understanding Evaluation\\ \\ How evaluation works in Future AGI: templates, judge models, results, and where evals run.](https://docs.futureagi.com/docs/evaluation/concepts/understanding-evaluation) [Eval Types\\ \\ The four evaluation methods in Future AGI: LLM as Judge, Deterministic, Statistical Metric, and LLM as Ranker, and how modality affects which ones apply.](https://docs.futureagi.com/docs/evaluation/concepts/eval-types) [Eval Templates\\ \\ What eval templates are, the difference between built-in and custom templates, and how output types work.](https://docs.futureagi.com/docs/evaluation/concepts/eval-templates) [Judge Models\\ \\ What a judge model is, how it scores responses, and how to choose the right one for your evaluation.](https://docs.futureagi.com/docs/evaluation/concepts/judge-models) [Eval Results\\ \\ What eval results contain, how to read them, and how results are stored and aggregated across runs.](https://docs.futureagi.com/docs/evaluation/concepts/eval-results) [Built-in Evals\\ \\ All built-in evaluation templates available on the platform.](https://docs.futureagi.com/docs/evaluation/builtin) [Evaluate via Platform & SDK\\ \\ Run evaluations via the Future AGI platform UI or the Python SDK.](https://docs.futureagi.com/docs/evaluation/features/evaluate) [Create Custom Evals\\ \\ Define custom evaluation criteria and rules for your use case beyond built-in templates.](https://docs.futureagi.com/docs/evaluation/features/custom) [Eval Groups\\ \\ Organize multiple evaluations into groups and run them together across datasets, simulations, and more.](https://docs.futureagi.com/docs/evaluation/features/groups) [Use Custom Models\\ \\ Use your own or third-party models for evaluations via supported providers or a custom API endpoint.](https://docs.futureagi.com/docs/evaluation/features/custom-models) [Future AGI Models\\ \\ Future AGI's proprietary models trained on a vast variety of datasets to perform evaluations.](https://docs.futureagi.com/docs/evaluation/features/futureagi-models) [Evaluate CI/CD Pipeline\\ \\ Run Future AGI evaluations in your CI/CD pipeline to assess model performance on every pull request and keep quality checks consistent before deployment.](https://docs.futureagi.com/docs/evaluation/features/cicd) [Overview\\ \\ Store your organization’s content to ground synthetic data generation and evaluations in real source material.](https://docs.futureagi.com/docs/knowledge-base) [Understanding Knowledge Base\\ \\ What a Knowledge Base is, what content types are supported, and how files are processed.](https://docs.futureagi.com/docs/knowledge-base/concepts/concept) [Create KB Using SDK\\ \\ Create and manage Knowledge Bases programmatically with the Future AGI Python SDK: create, update, add or remove files, and delete KBs from code or automation.](https://docs.futureagi.com/docs/knowledge-base/features/sdk) [Create KB Using UI\\ \\ Create and populate a Knowledge Base from the Future AGI platform: name it, upload documents, and wait for processing to finish.](https://docs.futureagi.com/docs/knowledge-base/features/ui) [Overview\\ \\ Monitor and evaluate LLM applications in production with real-time tracing, session analysis, and alerting.](https://docs.futureagi.com/docs/observe) [Understanding Observability\\ \\ Core concepts behind LLM observability: what gets captured, how data is structured, and why it matters.](https://docs.futureagi.com/docs/tracing/concepts) [What are Traces?\\ \\ In observability frameworks, a Trace is a comprehensive representation of the execution flow of a request within a system. It is composed of multiple spans, each capturing a specific operation or step in the process. Traces provide a holistic view of how different components interact and contribute to the overall behavior of the system.](https://docs.futureagi.com/docs/tracing/concepts/traces) [What are Spans?\\ \\ Understand spans in Future AGI tracing. Learn about span types including LLM, tool, chain, retriever, and embedding spans with their attributes.](https://docs.futureagi.com/docs/tracing/concepts/spans) [What is OpenTelemetry?\\ \\ Learn how Future AGI uses OpenTelemetry for vendor-neutral, high-performance tracing of AI applications with standardized telemetry collection.](https://docs.futureagi.com/docs/tracing/concepts/otel) [What is traceAI?\\ \\ Learn about traceAI, Future AGI's open-source package for standardized AI application tracing built on OpenTelemetry with framework-specific instrumentors.](https://docs.futureagi.com/docs/tracing/concepts/traceai) [Set Up Observability\\ \\ Instrument your application and send traces to an Observe project so you can monitor LLM calls, latency, and cost in one place.](https://docs.futureagi.com/docs/observe/features/quickstart) [Run Evals on Traces\\ \\ Run automated quality checks on your traced spans in Observe: filter spans, choose historic or continuous runs, set sampling and limits, and attach preset or custom evaluations.](https://docs.futureagi.com/docs/observe/features/evals) [Sessions\\ \\ Group traces into sessions so you can view and analyze multi-turn conversations, chatbot flows, and per-session metrics in Observe.](https://docs.futureagi.com/docs/observe/features/session) [Users\\ \\ View all traces, sessions, and metrics per end user in one place so you can debug, analyze behavior, and optimize at the user level.](https://docs.futureagi.com/docs/observe/features/users) [Alerts & Monitors\\ \\ Define monitors on Observe project metrics (system or evaluation) and get notified by email or Slack when values cross a threshold.](https://docs.futureagi.com/docs/observe/features/alerts) [Voice Observability\\ \\ Connect a voice provider (Vapi, Retell) and get call logs as traces in Observe without any SDK instrumentation.](https://docs.futureagi.com/docs/observe/features/voice) [Set Up Tracing\\ \\ Connect your application to Future AGI by registering a tracer provider and adding instrumentation with auto-instrumentors or manual OpenTelemetry spans.](https://docs.futureagi.com/docs/observe/features/manual-tracing/set-up-tracing) [Instrument with traceAI Helpers\\ \\ Future AGI's traceAI library offers convenient abstractions to streamline your manual instrumentation process.](https://docs.futureagi.com/docs/observe/features/manual-tracing/instrument-with-traceai-helpers) [Get Current Tracer and Span\\ \\ Access the active span or tracer at any point in your code to enrich it with additional attributes and context.](https://docs.futureagi.com/docs/observe/features/manual-tracing/get-current-span-context) [Enriching Spans with Attributes, Metadata, and Tags\\ \\ Capture additional context beyond what standard frameworks provide by enriching your traces with custom attributes, metadata, tags, session IDs, user IDs, and prompt templates.](https://docs.futureagi.com/docs/observe/features/manual-tracing/add-attributes-metadata-tags) [Logging Prompt Templates & Variables\\ \\ Attach prompt template data to spans so Future AGI can surface it in the prompt playground for testing changes without deploying.](https://docs.futureagi.com/docs/observe/features/manual-tracing/log-prompt-templates) [Events, Exceptions, and Status\\ \\ OpenTelemetry (OTEL) provides support for adding Events, Exceptions, and Status into spans.](https://docs.futureagi.com/docs/observe/features/manual-tracing/add-events-exceptions-status) [Set Session ID and User ID\\ \\ Adding SessionID and UserID as attributes to Spans for Tracing](https://docs.futureagi.com/docs/observe/features/manual-tracing/set-session-user-id) [Tool Spans Creation\\ \\ Manually trace tool functions alongside LLM calls by creating spans that capture inputs, outputs, and key events.](https://docs.futureagi.com/docs/observe/features/manual-tracing/create-tool-spans) [Mask Span Attributes\\ \\ Redact sensitive inputs, outputs, images, and embeddings from spans before they are exported:using environment variables or TraceConfig in code.](https://docs.futureagi.com/docs/observe/features/manual-tracing/mask-span-attributes) [Advanced Tracing (OTEL)\\ \\ Explore manual context propagation, custom decorators, and sampling techniques for real-world async, multi-service, and high-volume tracing scenarios.](https://docs.futureagi.com/docs/observe/features/manual-tracing/advanced-tracing-examples) [FI Semantic Conventions\\ \\ Use standardized attribute keys for spans to ensure consistent, queryable trace data across LLM models, frameworks, and vendors.](https://docs.futureagi.com/docs/observe/features/manual-tracing/semantic-conventions) [In-line Evaluations\\ \\ Run evaluations directly inside a traced span so results are automatically attached to that span in the Future AGI dashboard.](https://docs.futureagi.com/docs/observe/features/manual-tracing/in-line-evals) [Adding Annotations to your Spans\\ \\ Label spans with custom tags, human feedback, and notes using the bulk-annotation API.](https://docs.futureagi.com/docs/observe/features/manual-tracing/annotating-using-api) [Langfuse Integration\\ \\ Integrate Future AGI evaluations with Langfuse to attach evaluation results directly to your Langfuse traces.](https://docs.futureagi.com/docs/observe/features/manual-tracing/langfuse-integration) [Overview\\ \\ Auto-instrumentation for LLM applications across Python, JavaScript, and Java.](https://docs.futureagi.com/docs/tracing/auto) [OpenAI\\ \\ Set up auto-instrumentation for OpenAI with Future AGI tracing. Install traceAI-openai to capture chat completion, embedding, and tool call spans.](https://docs.futureagi.com/docs/tracing/auto/openai) [Anthropic\\ \\ Set up auto-instrumentation for Anthropic Claude with Future AGI tracing. Install traceAI-anthropic to capture LLM spans, inputs, and outputs.](https://docs.futureagi.com/docs/tracing/auto/anthropic) [AWS Bedrock\\ \\ Set up auto-instrumentation for AWS Bedrock with Future AGI tracing. Install traceAI-bedrock to capture model invocation spans and metadata.](https://docs.futureagi.com/docs/tracing/auto/bedrock) [Vertex AI\\ \\ Set up auto-instrumentation for Vertex AI with Future AGI tracing. Install traceAI-vertexai to capture Gemini model invocation and response spans.](https://docs.futureagi.com/docs/tracing/auto/vertexai) [Google GenAI\\ \\ Set up auto-instrumentation for Google GenAI with Future AGI tracing. Install traceAI-google-genai to capture Gemini model interaction spans.](https://docs.futureagi.com/docs/tracing/auto/google_genai) [Google ADK\\ \\ Set up auto-instrumentation for Google ADK with Future AGI tracing. Install traceai-google-adk to capture agent and tool execution spans.](https://docs.futureagi.com/docs/tracing/auto/google_adk) [Groq\\ \\ Set up auto-instrumentation for Groq with Future AGI tracing. Install traceAI-groq to capture high-speed inference spans and performance data.](https://docs.futureagi.com/docs/tracing/auto/groq) [MistralAI\\ \\ Set up auto-instrumentation for Mistral AI with Future AGI tracing. Install traceAI-mistralai to capture model inference spans and metadata.](https://docs.futureagi.com/docs/tracing/auto/mistralai) [Together AI\\ \\ Set up auto-instrumentation for Together AI with Future AGI tracing. Use traceAI-openai to capture inference spans from Together AI models.](https://docs.futureagi.com/docs/tracing/auto/togetherai) [Ollama\\ \\ Set up auto-instrumentation for Ollama with Future AGI tracing. Use traceAI-openai to capture spans from Ollama's OpenAI-compatible local LLM API.](https://docs.futureagi.com/docs/tracing/auto/ollama) [Portkey\\ \\ Set up auto-instrumentation for Portkey with Future AGI tracing. Install traceAI-portkey to capture routed LLM call spans and gateway metrics.](https://docs.futureagi.com/docs/tracing/auto/portkey) [LangChain\\ \\ Set up auto-instrumentation for LangChain with Future AGI tracing. Install traceAI-langchain to capture chain, tool, and LLM call spans.](https://docs.futureagi.com/docs/tracing/auto/langchain) [LangGraph\\ \\ Set up auto-instrumentation for LangGraph with Future AGI tracing. Capture agent graph execution and state transition spans via LangChain instrumentor.](https://docs.futureagi.com/docs/tracing/auto/langgraph) [LlamaIndex\\ \\ Set up auto-instrumentation for LlamaIndex with Future AGI tracing. Install traceAI-llamaindex to capture query, retrieval, and response spans.](https://docs.futureagi.com/docs/tracing/auto/llamaindex) [LlamaIndex Workflows\\ \\ Set up auto-instrumentation for LlamaIndex Workflows with Future AGI tracing. Trace workflow agent execution via the LlamaIndex instrumentor.](https://docs.futureagi.com/docs/tracing/auto/llamaindex-workflows) [LiteLLM\\ \\ Set up auto-instrumentation for LiteLLM with Future AGI tracing. Install traceAI-litellm to capture spans across multiple LLM provider calls.](https://docs.futureagi.com/docs/tracing/auto/litellm) [CrewAI\\ \\ Set up auto-instrumentation for CrewAI with Future AGI tracing. Install traceAI-crewai to capture crew task execution and agent interaction spans.](https://docs.futureagi.com/docs/tracing/auto/crewai) [AutoGen\\ \\ Set up auto-instrumentation for Autogen with Future AGI tracing. Install traceAI-autogen to capture multi-agent conversation spans automatically.](https://docs.futureagi.com/docs/tracing/auto/autogen) [Haystack\\ \\ Set up auto-instrumentation for Haystack with Future AGI tracing. Install traceAI-haystack to capture document pipeline and retrieval spans.](https://docs.futureagi.com/docs/tracing/auto/haystack) [DSPy\\ \\ Set up auto-instrumentation for DSPy with Future AGI tracing. Install traceAI-DSPy to capture program compilation and prediction spans automatically.](https://docs.futureagi.com/docs/tracing/auto/dspy) [OpenAI Agents\\ \\ Set up auto-instrumentation for OpenAI Agents SDK with Future AGI tracing. Install traceAI-openai-agents to capture agent workflow spans.](https://docs.futureagi.com/docs/tracing/auto/openai_agents) [Smol Agents\\ \\ Set up auto-instrumentation for Smol Agents with Future AGI tracing. Install traceAI-smolagents to capture lightweight agent execution spans.](https://docs.futureagi.com/docs/tracing/auto/smol_agents) [Instructor\\ \\ Set up auto-instrumentation for Instructor with Future AGI tracing. Install traceAI-instructor to capture structured output extraction spans.](https://docs.futureagi.com/docs/tracing/auto/instructor) [PromptFlow\\ \\ Set up auto-instrumentation for Prompt Flow with Future AGI tracing. Use traceAI-openai to capture prompt flow execution and LLM call spans.](https://docs.futureagi.com/docs/tracing/auto/promptflow) [Guardrails\\ \\ Set up auto-instrumentation for Guardrails AI with Future AGI tracing. Install traceAI-guardrails to trace validation and LLM interaction spans.](https://docs.futureagi.com/docs/tracing/auto/guardrails) [MCP\\ \\ Set up auto-instrumentation for MCP with Future AGI tracing. Install traceAI-mcp to capture Model Context Protocol server and tool call spans.](https://docs.futureagi.com/docs/tracing/auto/mcp) [Mastra\\ \\ Set up auto-instrumentation for Mastra with Future AGI tracing. Configure @traceai/mastra to export TypeScript agent spans to Future AGI.](https://docs.futureagi.com/docs/tracing/auto/mastra) [Vercel AI SDK\\ \\ Set up auto-instrumentation for Vercel AI SDK with Future AGI tracing. Install @traceai/vercel to capture AI function call spans in Next.js apps.](https://docs.futureagi.com/docs/tracing/auto/vercel) [LiveKit\\ \\ Integrate LiveKit with Future AGI for voice agent observability. Trace real-time voice interactions and monitor agent performance with traceAI-livekit.](https://docs.futureagi.com/docs/tracing/auto/livekit) [Pipecat\\ \\ Set up auto-instrumentation for Pipecat voice apps with Future AGI tracing. Install traceAI-pipecat to capture voice pipeline and processing spans.](https://docs.futureagi.com/docs/tracing/auto/pipecat) [Overview\\ \\ Set up TraceAI for Java applications. Initialize the tracer, configure credentials, and instrument your LLM clients, vector databases, and frameworks.](https://docs.futureagi.com/docs/tracing/auto/java) [Spring Boot\\ \\ Add tracing to Spring Boot apps with Spring AI. Configure application.yml, wrap your ChatModel and EmbeddingModel, and traces are collected automatically.](https://docs.futureagi.com/docs/tracing/auto/spring-boot) [OpenAI\\ \\ Trace OpenAI chat completions, embeddings, and streaming responses in Java with TracedOpenAIClient.](https://docs.futureagi.com/docs/tracing/auto/java/openai) [Anthropic\\ \\ Trace Anthropic Messages API calls in Java with TracedAnthropicClient. Uses reflection for cross-version compatibility.](https://docs.futureagi.com/docs/tracing/auto/java/anthropic) [AWS Bedrock\\ \\ Trace AWS Bedrock model invocations in Java with TracedBedrockRuntimeClient. Supports both InvokeModel (raw JSON) and Converse (typed API).](https://docs.futureagi.com/docs/tracing/auto/java/bedrock) [Cohere\\ \\ Trace Cohere chat, embedding, and reranking operations in Java with TracedCohereClient.](https://docs.futureagi.com/docs/tracing/auto/java/cohere) [Pinecone\\ \\ Trace Pinecone vector operations in Java with TracedPineconeIndex. Query, upsert, delete, and fetch with full span instrumentation.](https://docs.futureagi.com/docs/tracing/auto/java/pinecone) [LLM Providers\\ \\ Trace Google GenAI, Vertex AI, Azure OpenAI, Ollama, and Watsonx in Java. All use the same Traced wrapper pattern.](https://docs.futureagi.com/docs/tracing/auto/java/llm-providers) [Vector Databases\\ \\ Trace vector database operations in Java. Qdrant, Milvus, ChromaDB, Weaviate, MongoDB, Redis, pgvector, Azure AI Search, and Elasticsearch.](https://docs.futureagi.com/docs/tracing/auto/java/vector-databases) [Frameworks\\ \\ Trace LangChain4j and Semantic Kernel operations in Java. Framework-level wrappers that instrument chains, agents, and prompt invocations.](https://docs.futureagi.com/docs/tracing/auto/java/frameworks) [n8n\\ \\ With this integration, you can dynamically retrieve prompts from your Future AGI account, select specific versions, and compile prompts with variables - all within the familiar n8n interface.](https://docs.futureagi.com/docs/integrations/traceai/n8n) [Overview\\ \\ Iteratively improve prompts using evaluation-driven feedback and optimization algorithms for higher-quality, more consistent AI responses.](https://docs.futureagi.com/docs/optimization) [Understanding Optimization\\ \\ How prompt optimization works: the feedback loop, key components, algorithms, and how to choose the right one.](https://docs.futureagi.com/docs/optimization/concepts/concept) [Bayesian Search\\ \\ Use Bayesian optimization for few-shot prompt tuning: learns from trials to pick better example sets and configurations.](https://docs.futureagi.com/docs/optimization/optimizers/bayesian-search) [Meta-Prompt\\ \\ A guide to the Meta-Prompt optimizer, which uses a teacher LLM for deep reasoning-based prompt refinement through systematic failure analysis and rewriting.](https://docs.futureagi.com/docs/optimization/optimizers/meta-prompt) [ProTeGi\\ \\ A guide to ProTeGi (Prompt optimization with Textual Gradients), which systematically improves prompts by identifying failures, generating critiques, and applying targeted fixes.](https://docs.futureagi.com/docs/optimization/optimizers/protegi) [PromptWizard\\ \\ Learn about PromptWizard, a multi-stage feedback-driven optimizer that improves prompts through a cycle of mutation, critique, and refinement.](https://docs.futureagi.com/docs/optimization/optimizers/promptwizard) [GEPA\\ \\ Discover GEPA (Genetic Pareto), a powerful evolutionary algorithm that evolves prompts over generations using reflection and mutation for complex, high-stakes optimization.](https://docs.futureagi.com/docs/optimization/optimizers/gepa) [Random Search\\ \\ Understand the Random Search optimizer, a simple and effective gradient-free method for establishing a baseline in prompt optimization by exploring random variations.](https://docs.futureagi.com/docs/optimization/optimizers/random-search) [Using Python SDK\\ \\ Run prompt optimization from code with the agent-opt Python library.](https://docs.futureagi.com/docs/optimization/features/using-python-sdk) [Using Platform\\ \\ Run prompt optimization from the Future AGI UI: pick a dataset and column, configure prompt and evals, run optimization, and apply the best prompt.](https://docs.futureagi.com/docs/optimization/features/using-platform) [Overview\\ \\ A unified API gateway for 100+ LLM providers with built-in guardrails, intelligent routing, caching, cost controls, and full observability.](https://docs.futureagi.com/docs/prism) [Core Concepts\\ \\ Understand the key building blocks of Prism: gateways, virtual API keys, organizations, providers, and configurations.](https://docs.futureagi.com/docs/prism/concepts/core) [API Reference\\ \\ Endpoints, request headers, and response headers for the Prism AI Gateway.](https://docs.futureagi.com/docs/prism/concepts/api-reference) [Configuration\\ \\ How organization configuration works in Prism: sections, hierarchy, and real-time updates.](https://docs.futureagi.com/docs/prism/concepts/configuration) [Platform Integration\\ \\ How Prism AI Gateway connects to the broader Future AGI platform — observability, evaluation, protection, and experimentation.](https://docs.futureagi.com/docs/prism/concepts/platform-integration) [Manage Providers\\ \\ Add, configure, and manage LLM providers in Prism.](https://docs.futureagi.com/docs/prism/features/providers) [Routing & Reliability\\ \\ Configure load balancing, failover, retries, and circuit breaking across LLM providers.](https://docs.futureagi.com/docs/prism/features/routing) [Guardrails\\ \\ Set up safety guardrails to protect your LLM traffic with PII detection, prompt injection prevention, content moderation, and more.](https://docs.futureagi.com/docs/prism/features/guardrails) [Caching\\ \\ Reduce costs and latency with Prism's exact match and semantic caching.](https://docs.futureagi.com/docs/prism/features/caching) [Cost Tracking & Budgets\\ \\ Track LLM costs per request, set budget limits, and configure spend alerts.](https://docs.futureagi.com/docs/prism/features/cost-tracking) [Streaming\\ \\ Use Server-Sent Events (SSE) streaming with Prism for real-time LLM responses.](https://docs.futureagi.com/docs/prism/features/streaming) [Shadow Experiments\\ \\ Mirror a percentage of production LLM traffic to alternative models for zero-risk evaluation.](https://docs.futureagi.com/docs/prism/features/shadow-experiments) [Rate Limiting\\ \\ Control request throughput to the Prism AI Gateway with configurable rate limits.](https://docs.futureagi.com/docs/prism/features/rate-limiting) [MCP & A2A\\ \\ Connect AI agents to Prism using the Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol.](https://docs.futureagi.com/docs/prism/features/mcp-a2a) [Self-Hosted\\ \\ Deploy Prism AI Gateway on your own infrastructure using Docker or a Go binary.](https://docs.futureagi.com/docs/prism/deployment/self-hosted) [Overview\\ \\ Create, manage, and optimize AI prompts for reliable and consistent language model outputs.](https://docs.futureagi.com/docs/prompt) [Prompt Engineering\\ \\ What prompt engineering is, how to think about crafting effective prompts, and how the Prompt Workbench supports the iteration process.](https://docs.futureagi.com/docs/prompt/concepts/prompt-engineering) [Understanding Prompts\\ \\ What a prompt is, how it is structured, how variables work, and how prompts connect to models in the Prompt Workbench.](https://docs.futureagi.com/docs/prompt/concepts/understanding-prompts) [Versions and Labels\\ \\ How prompt versioning and deployment labels work in the Prompt Workbench.](https://docs.futureagi.com/docs/prompt/concepts/versions-and-labels) [Create Prompt from Scratch\\ \\ Build a new prompt manually in the Prompt Workbench with full control over structure, model, parameters, and variables.](https://docs.futureagi.com/docs/prompt/features/create-from-scratch) [Create from Existing Template\\ \\ Start from a pre-built prompt template in the Prompt Workbench and customize it for your use case.](https://docs.futureagi.com/docs/prompt/features/create-from-template) [Create with AI\\ \\ Generate a new prompt from a plain-language description using the Generate with AI feature in the Prompt Workbench.](https://docs.futureagi.com/docs/prompt/features/create-with-ai) [Prompt Workbench Using SDK\\ \\ Create, version, and run prompt templates programmatically using the Future AGI SDK (TypeScript/JavaScript or Python).](https://docs.futureagi.com/docs/prompt/features/sdk) [Linked Traces\\ \\ Associate prompts with production traces to monitor latency, token usage, and cost per prompt version in the Prompt Workbench.](https://docs.futureagi.com/docs/prompt/features/linked-traces) [Manage Folders\\ \\ Organize prompt templates into folders in the Prompt Workbench to keep your workspace navigable as your library grows.](https://docs.futureagi.com/docs/prompt/features/folders) [Overview\\ \\ Future AGI's Protect module brings real-time safety and policy enforcement directly into your GenAI application flow.](https://docs.futureagi.com/docs/protect) [Use Cases\\ \\ Future AGI's Protect acts as a vital guardrail for AI applications, ensuring security, reliability, and ethical compliance during real-time interactions across text, image, and audio modalities.](https://docs.futureagi.com/docs/protect/concepts/concept) [Run Protect via SDK\\ \\ Set up and configure Protect to apply real-time safety checks to your AI application's inputs and outputs.](https://docs.futureagi.com/docs/protect/features/run-protect) [Overview\\ \\ Build, test, and run multi-step AI workflows visually, no code required. Connect prompts, models, and agents on a drag-and-drop canvas.](https://docs.futureagi.com/docs/agent-playground) [Understanding Agent Playground\\ \\ Learn the core building blocks of Agent Playground: graphs, nodes, ports, edges, and node templates.](https://docs.futureagi.com/docs/agent-playground/concepts/understanding-agent-playground) [Versions & Execution\\ \\ Understand the version lifecycle, execution model, data routing, and batch execution in Agent Playground.](https://docs.futureagi.com/docs/agent-playground/concepts/versions-and-execution) [Create a Graph\\ \\ Create your first agent graph, manage metadata, and work with versions in Agent Playground.](https://docs.futureagi.com/docs/agent-playground/features/create-graph) [Build a Workflow\\ \\ Add nodes, configure them, and connect them into an AI agent pipeline using the visual graph editor.](https://docs.futureagi.com/docs/agent-playground/features/build-workflow) [Run & Monitor\\ \\ Execute agent workflows, view real-time results per node, and inspect execution history.](https://docs.futureagi.com/docs/agent-playground/features/run-and-monitor) [Overview\\ \\ Test and compare LLM configurations, prompts, and parameters before deploying to production.](https://docs.futureagi.com/docs/prototype) [Understanding Prototype\\ \\ What Prototype is, the problem it solves, and how versions, traces, and evals work together before you ship.](https://docs.futureagi.com/docs/prototype/concepts/understanding-prototype) [Versions and Runs\\ \\ What a version is in Prototype, how runs get tagged to a version, and how the dashboard uses versions to compare configurations.](https://docs.futureagi.com/docs/prototype/concepts/versions-and-runs) [Set Up Prototype\\ \\ Configure your environment, register your prototype project, and instrument your app so traces and evals appear in the Prototype dashboard.](https://docs.futureagi.com/docs/prototype/features/set-up-prototype) [Evals\\ \\ Define which evaluations run on your prototype outputs using EvalTags, mapping, and optional custom evals.](https://docs.futureagi.com/docs/prototype/features/evals) [Choose Winner\\ \\ Rank prototype versions by evaluation scores, cost, and latency, then select and promote the best-performing version to production.](https://docs.futureagi.com/docs/prototype/features/choose-winner) [Admin & Settings\\ \\ Learn how to access and manage your Future AGI API keys and secret keys from the developer dashboard for authentication.](https://docs.futureagi.com/docs/admin-settings) [API Keys\\ \\ Create and manage API keys for authenticating with Future AGI SDKs and APIs.](https://docs.futureagi.com/docs/admin-settings/api-keys) [Profile & Security\\ \\ Manage your profile information, password, two-factor authentication, and passkeys.](https://docs.futureagi.com/docs/admin-settings/profile-security) [Organization Settings\\ \\ Configure your organization name and security policies.](https://docs.futureagi.com/docs/admin-settings/organization-settings) [User Management\\ \\ Invite users, assign roles, and manage team members across your organization.](https://docs.futureagi.com/docs/admin-settings/user-management) [Workspace Management\\ \\ Create and configure workspaces to organize projects, teams, and resources.](https://docs.futureagi.com/docs/admin-settings/workspace-management) [AI Providers\\ \\ Configure LLM providers and custom models for evaluations, optimization, and other platform features.](https://docs.futureagi.com/docs/admin-settings/ai-providers) [Integrations\\ \\ Connect Future AGI to external tools for observability, alerting, analytics, and log archival.](https://docs.futureagi.com/docs/admin-settings/integrations) [Usage Summary\\ \\ Track API calls, token usage, and evaluation runs across your organization and workspaces.](https://docs.futureagi.com/docs/admin-settings/usage-summary) [Billing & Pricing\\ \\ Manage your subscription, add funds, configure auto-reload, and view invoices.](https://docs.futureagi.com/docs/admin-settings/billing-pricing) [Roles & Permissions\\ \\ Resources](https://docs.futureagi.com/docs/roles-and-permissions) [Installation\\ \\ Install the Future AGI SDK and configure it for your project.](https://docs.futureagi.com/docs/installation) [FAQ\\ \\ Find answers to common questions about Future AGI products.](https://docs.futureagi.com/docs/faq) [Release Notes\\ \\ Latest Future AGI release notes covering new features, improvements, and bug fixes across datasets, evaluations, simulation, and observability products.](https://docs.futureagi.com/docs/release-notes) [Overview\\ \\ Test AI agents and prompts through controlled simulations before deploying to production.](https://docs.futureagi.com/docs/simulation) [Agent Definition\\ \\ An agent definition is a configuration that specifies how your AI agent behaves during voice or chat conversations](https://docs.futureagi.com/docs/simulation/concepts/agent-definition) [Scenarios\\ \\ Scenarios defines the test cases, customer profiles, and conversation flows that your AI agent will encounter during simulations.](https://docs.futureagi.com/docs/simulation/concepts/scenarios) [Personas\\ \\ Create personas that represent the customers or users in your simulation tests for more realistic scenarios.](https://docs.futureagi.com/docs/simulation/concepts/personas) [Run Voice Simulation\\ \\ Create and run voice simulation tests from the platform to test your agent against scenarios.](https://docs.futureagi.com/docs/simulation/features/run-simulation) [Chat Simulation Using SDK\\ \\ Run Future AGI chat simulations from Python by providing an agent callback and executing an existing Run Test.](https://docs.futureagi.com/docs/simulation/features/simulation-using-sdk) [Replay\\ \\ Replay real production sessions in a dev environment using chat simulation to debug, iterate, and improve your agent.](https://docs.futureagi.com/docs/simulation/features/observe-to-simulate) [Prompt Simulation\\ \\ Test your prompts in realistic multi-turn conversations directly from the Prompt Workbench — no agent deployment or SDK required.](https://docs.futureagi.com/docs/simulation/features/prompt-simulation) [Evaluate Tool Calling\\ \\ Evaluate the tool-calling capabilities of your agent in simulation runs.](https://docs.futureagi.com/docs/simulation/features/evaluate-tool-calling) [View Results\\ \\ Read simulation results: transcripts, evaluation scores, performance analytics, and call logs.](https://docs.futureagi.com/docs/simulation/features/view-results) [Fix My Agent\\ \\ In-depth diagnostics and targeted fixes for your agent's performance issues based on simulation results](https://docs.futureagi.com/docs/simulation/features/fix-my-agent) [Overview\\ \\ Connect Future AGI with your existing AI frameworks, LLM providers, and tools.](https://docs.futureagi.com/docs/integrations) [OpenAI\\ \\ Integrate OpenAI with Future AGI for auto-instrumented tracing. Capture chat completions, embeddings, and tool calls with traceAI-openai.](https://docs.futureagi.com/docs/integrations/traceai/openai) [Anthropic\\ \\ Integrate Anthropic Claude with Future AGI for auto-instrumented tracing. Install traceAI-anthropic and capture LLM calls with full observability.](https://docs.futureagi.com/docs/integrations/traceai/anthropic) [AWS Bedrock\\ \\ Integrate AWS Bedrock with Future AGI for auto-instrumented tracing. Capture model invocations and monitor performance with traceAI-bedrock.](https://docs.futureagi.com/docs/integrations/traceai/bedrock) [Vertex AI\\ \\ Integrate Vertex AI (Gemini) with Future AGI observability. Trace model calls and monitor performance using traceAI-vertexai instrumentation.](https://docs.futureagi.com/docs/integrations/traceai/vertexai) [Google GenAI\\ \\ Integrate Google GenAI with Future AGI observability. Set up traceAI-google-genai to capture model calls and monitor performance automatically.](https://docs.futureagi.com/docs/integrations/traceai/google_genai) [Google ADK\\ \\ Integrate Google ADK with Future AGI for auto-instrumented tracing. Monitor Google AI agent calls and tool usage with traceAI-google-adk.](https://docs.futureagi.com/docs/integrations/traceai/google_adk) [Groq\\ \\ Integrate Groq with Future AGI observability. Set up traceAI-groq to automatically trace high-speed inference calls and monitor LLM performance.](https://docs.futureagi.com/docs/integrations/traceai/groq) [MistralAI\\ \\ Integrate Mistral AI with Future AGI observability. Set up traceAI-mistralai to capture model calls and monitor inference performance automatically.](https://docs.futureagi.com/docs/integrations/traceai/mistralai) [Together AI\\ \\ Integrate Together AI with Future AGI observability. Trace inference calls to Together AI models using the traceAI-openai compatible package.](https://docs.futureagi.com/docs/integrations/traceai/togetherai) [Ollama\\ \\ Integrate Ollama with Future AGI observability. Trace locally-hosted LLM calls using the traceAI-openai package with Ollama's OpenAI-compatible API.](https://docs.futureagi.com/docs/integrations/traceai/ollama) [Portkey\\ \\ Integrate Portkey AI gateway with Future AGI observability. Trace routed LLM calls and monitor performance with traceAI-portkey instrumentation.](https://docs.futureagi.com/docs/integrations/traceai/portkey) [LangChain\\ \\ Integrate LangChain with Future AGI for auto-instrumented tracing. Capture chain executions, tool calls, and LLM interactions with traceAI-langchain.](https://docs.futureagi.com/docs/integrations/traceai/langchain) [LangGraph\\ \\ Integrate LangGraph with Future AGI observability. Trace agent graph execution, tool usage, and state transitions using the LangChain instrumentor.](https://docs.futureagi.com/docs/integrations/traceai/langgraph) [LlamaIndex\\ \\ Integrate LlamaIndex with Future AGI observability. Set up traceAI-llamaindex to trace queries, retrieval, and response generation automatically.](https://docs.futureagi.com/docs/integrations/traceai/llamaindex) [LlamaIndex Workflows\\ \\ Integrate LlamaIndex Workflows with Future AGI. Trace workflow-based agent execution and data processing using the LlamaIndex instrumentor.](https://docs.futureagi.com/docs/integrations/traceai/llamaindex-workflows) [LiteLLM\\ \\ Integrate LiteLLM with Future AGI observability. Set up traceAI-litellm to trace calls across multiple LLM providers through a unified interface.](https://docs.futureagi.com/docs/integrations/traceai/litellm) [CrewAI\\ \\ Integrate CrewAI with Future AGI observability. Set up traceAI-crewai to trace multi-agent crew task execution and tool usage automatically.](https://docs.futureagi.com/docs/integrations/traceai/crewai) [AutoGen\\ \\ Integrate Autogen with Future AGI observability. Set up traceAI-autogen for automatic tracing of multi-agent conversations and workflows.](https://docs.futureagi.com/docs/integrations/traceai/autogen) [Haystack\\ \\ Integrate Haystack with Future AGI observability. Set up traceAI-haystack to trace document processing pipelines and LLM calls automatically.](https://docs.futureagi.com/docs/integrations/traceai/haystack) [DSPy\\ \\ Integrate DSPy with Future AGI observability. Set up traceAI-DSPy to automatically trace DSPy program compilation and inference pipelines.](https://docs.futureagi.com/docs/integrations/traceai/dspy) [OpenAI Agents\\ \\ Integrate OpenAI Agents SDK with Future AGI. Trace agent tool calls, handoffs, and reasoning steps automatically with traceAI-openai-agents.](https://docs.futureagi.com/docs/integrations/traceai/openai_agents) [Smol Agents\\ \\ Integrate Smol Agents with Future AGI observability. Set up traceAI-smolagents to trace lightweight agent tool calls and reasoning automatically.](https://docs.futureagi.com/docs/integrations/traceai/smol_agents) [Instructor\\ \\ Integrate Instructor with Future AGI observability. Trace structured LLM output extraction and validation automatically using traceAI-instructor.](https://docs.futureagi.com/docs/integrations/traceai/instructor) [PromptFlow\\ \\ Integrate Prompt Flow with Future AGI observability. Trace prompt flow executions and LLM calls automatically using the traceAI-openai package.](https://docs.futureagi.com/docs/integrations/traceai/promptflow) [Guardrails\\ \\ Integrate Guardrails AI with Future AGI observability. Trace guardrail validations and LLM interactions automatically using traceAI-guardrails.](https://docs.futureagi.com/docs/integrations/traceai/guardrails) [MCP\\ \\ Integrate Model Context Protocol (MCP) with Future AGI. Trace MCP server interactions and tool calls with traceAI-mcp auto-instrumentation.](https://docs.futureagi.com/docs/integrations/traceai/mcp) [Mastra\\ \\ Integrate Mastra with Future AGI for TypeScript agent observability. Configure trace export using the @traceai/mastra package for LLM monitoring.](https://docs.futureagi.com/docs/integrations/traceai/mastra) [Vercel AI SDK\\ \\ Integrate Vercel AI SDK with Future AGI. Set up @traceai/vercel for automatic tracing of AI-powered Next.js and Vercel applications.](https://docs.futureagi.com/docs/integrations/traceai/vercel) [LiveKit\\ \\ Integrations](https://docs.futureagi.com/docs/integrations/traceai/livekit) [Pipecat\\ \\ Integrate Pipecat with Future AGI for voice application observability. Trace and monitor voice pipelines with OpenTelemetry-based traceAI-pipecat.](https://docs.futureagi.com/docs/integrations/traceai/pipecat) [Overview\\ \\ Set up TraceAI for Java applications. Initialize the tracer, configure credentials, and instrument your LLM clients, vector databases, and frameworks.](https://docs.futureagi.com/docs/integrations/traceai/java) [Spring Boot\\ \\ Add tracing to Spring Boot apps with Spring AI. Configure application.yml, wrap your ChatModel and EmbeddingModel, and traces are collected automatically.](https://docs.futureagi.com/docs/integrations/traceai/spring-boot) [OpenAI\\ \\ Trace OpenAI chat completions, embeddings, and streaming responses in Java with TracedOpenAIClient.](https://docs.futureagi.com/docs/integrations/traceai/java/openai) [Anthropic\\ \\ Trace Anthropic Messages API calls in Java with TracedAnthropicClient. Uses reflection for cross-version compatibility.](https://docs.futureagi.com/docs/integrations/traceai/java/anthropic) [AWS Bedrock\\ \\ Trace AWS Bedrock model invocations in Java with TracedBedrockRuntimeClient. Supports both InvokeModel (raw JSON) and Converse (typed API).](https://docs.futureagi.com/docs/integrations/traceai/java/bedrock) [Cohere\\ \\ Trace Cohere chat, embedding, and reranking operations in Java with TracedCohereClient.](https://docs.futureagi.com/docs/integrations/traceai/java/cohere) [Pinecone\\ \\ Trace Pinecone vector operations in Java with TracedPineconeIndex. Query, upsert, delete, and fetch with full span instrumentation.](https://docs.futureagi.com/docs/integrations/traceai/java/pinecone) [LLM Providers\\ \\ Trace Google GenAI, Vertex AI, Azure OpenAI, Ollama, and Watsonx in Java. All use the same Traced wrapper pattern.](https://docs.futureagi.com/docs/integrations/traceai/java/llm-providers) [Vector Databases\\ \\ Trace vector database operations in Java. Qdrant, Milvus, ChromaDB, Weaviate, MongoDB, Redis, pgvector, Azure AI Search, and Elasticsearch.](https://docs.futureagi.com/docs/integrations/traceai/java/vector-databases) [Frameworks\\ \\ Trace LangChain4j and Semantic Kernel operations in Java. Framework-level wrappers that instrument chains, agents, and prompt invocations.](https://docs.futureagi.com/docs/integrations/traceai/java/frameworks) [n8n\\ \\ With this integration, you can dynamically retrieve prompts from your Future AGI account, select specific versions, and compile prompts with variables - all within the familiar n8n interface.](https://docs.futureagi.com/docs/integrations/traceai/n8n) [Langfuse\\ \\ Pull your existing Langfuse traces, spans, and scores into Future AGI automatically.](https://docs.futureagi.com/docs/integrations/import/langfuse) [Datadog\\ \\ Forward Prism Gateway logs and metrics from Future AGI to Datadog automatically.](https://docs.futureagi.com/docs/integrations/export/datadog) [PostHog\\ \\ Send LLM usage events from Future AGI's Prism Gateway to PostHog for product analytics.](https://docs.futureagi.com/docs/integrations/export/posthog) [Mixpanel\\ \\ Send LLM usage events from Future AGI's Prism Gateway to Mixpanel for product analytics.](https://docs.futureagi.com/docs/integrations/export/mixpanel) [PagerDuty\\ \\ Route Future AGI alerts to PagerDuty so your on-call team gets paged when something breaks.](https://docs.futureagi.com/docs/integrations/export/pagerduty) [Cloud Storage\\ \\ Archive Prism Gateway logs to S3, Azure Blob Storage, or Google Cloud Storage as compressed JSONL files.](https://docs.futureagi.com/docs/integrations/export/cloud-storage) [Message Queues\\ \\ Stream Prism Gateway logs to Amazon SQS or Google Pub/Sub for real-time processing.](https://docs.futureagi.com/docs/integrations/export/message-queues) [Overview\\ \\ Practical guides and tutorials for using Future AGI products effectively](https://docs.futureagi.com/docs/cookbook) [Running Your First Eval\\ \\ Score LLM outputs for hallucination, toxicity, and custom quality criteria — from local metrics to LLM-as-Judge.](https://docs.futureagi.com/docs/cookbook/quickstart/first-eval) [Custom Eval Metrics: Write Your Own Evaluation Criteria\\ \\ Define quality criteria in plain English and run them as reusable eval metrics from the dashboard or SDK on any dataset or production trace.](https://docs.futureagi.com/docs/cookbook/quickstart/custom-eval-metrics) [Hallucination Detection with Faithfulness & Groundedness\\ \\ Score RAG outputs for faithfulness and groundedness to catch hallucinations before they reach users.](https://docs.futureagi.com/docs/cookbook/quickstart/hallucination-detection) [RAG Pipeline Evaluation: Debug Retrieval vs Generation\\ \\ Score retrieval quality and generation quality independently to pinpoint whether your RAG pipeline is failing at retrieval or generation.](https://docs.futureagi.com/docs/cookbook/quickstart/rag-evaluation) [Multimodal Evaluation: Images, Audio, and PDF\\ \\ Score image captions, detect AI-generated images, evaluate audio quality and TTS accuracy, and verify OCR output against source PDFs using built-in eval metrics.](https://docs.futureagi.com/docs/cookbook/quickstart/multimodal-eval) [Tone, Toxicity, and Bias Detection Evals\\ \\ Evaluate LLM outputs for professional tone, harmful content, and demographic bias using the evaluate() function in a customer service scenario.](https://docs.futureagi.com/docs/cookbook/quickstart/tone-toxicity-bias-eval) [Evaluate Customer Agent Conversations\\ \\ Score multi-turn conversations for quality, context retention, query handling, loop detection, escalation, and prompt conformance using built-in Turing metrics.](https://docs.futureagi.com/docs/cookbook/quickstart/conversation-eval) [Dataset SDK: Upload, Evaluate, and Download Results\\ \\ Upload a CSV, run batch evaluations across every row, and download scored results: all from the SDK.](https://docs.futureagi.com/docs/cookbook/quickstart/batch-eval) [Async Evaluations for Large-Scale Testing\\ \\ Fire-and-forget async evaluations, poll for results, and run parallel evals across hundreds of items using the Evaluator SDK.](https://docs.futureagi.com/docs/cookbook/quickstart/async-batch-eval) [Text-to-SQL Evaluation\\ \\ Evaluate LLM-generated SQL queries using the built-in text\_to\_sql Turing metric, local string comparison, and execution-based validation against a live database.](https://docs.futureagi.com/docs/cookbook/quickstart/text-to-sql-eval) [Chat Simulation: Run Multi-Persona Conversations via SDK\\ \\ Use FutureAGI's Chat Simulation feature to define personas, generate scenarios, execute multi-turn conversations via the SDK, and diagnose failures with Fix My Agent.](https://docs.futureagi.com/docs/cookbook/quickstart/chat-simulation-personas) [Voice Simulation: Define Agents, Personas, and Run Call Tests\\ \\ Use Voice Simulation to define voice agents with provider credentials, build caller personas with accent and speed controls, generate call scenarios, run parallel call tests with evaluations, and diagnose failures with Fix My Agent.](https://docs.futureagi.com/docs/cookbook/quickstart/voice-simulation) [Tool-Calling Agent Simulation with Tracing\\ \\ Run a tool-calling agent through simulated scenarios, trace every tool invocation as child spans, and inspect results in the Tracing dashboard.](https://docs.futureagi.com/docs/cookbook/quickstart/tool-calling-simulation) [Simulate from the Prompt Workbench\\ \\ Run a simulation against your prompt directly from the FutureAGI Prompts page — no SDK, no code required.](https://docs.futureagi.com/docs/cookbook/quickstart/prompt-workbench-simulation) [Create and Manage Datasets from the Dashboard\\ \\ Create a dataset, add columns, enter rows manually, import from CSV, run evaluations, and export — all from the FutureAGI dashboard, no code required.](https://docs.futureagi.com/docs/cookbook/quickstart/dataset-management) [Synthetic Data Generation: Create Test Datasets from a Schema\\ \\ Use FutureAGI's Synthetic Data Generation feature to define column schemas, set categorical distributions, and generate structured test datasets — no code required.](https://docs.futureagi.com/docs/cookbook/quickstart/synthetic-data-generation) [Annotate Datasets with Human-in-the-Loop Workflows\\ \\ Create annotation views, define labels, assign annotators, and log annotations programmatically via the SDK.](https://docs.futureagi.com/docs/cookbook/quickstart/dataset-annotation) [Import Datasets from Hugging Face\\ \\ Pull any public Hugging Face dataset into FutureAGI with a single SDK call and run evaluations on it.](https://docs.futureagi.com/docs/cookbook/quickstart/huggingface-dataset-import) [Dynamic Dataset Columns: Enrich Rows with AI-Generated Data\\ \\ Use Dynamic Columns to add AI-generated summaries, sentiment labels, extracted entities, vector-retrieved context, parsed JSON fields, and conditional routing to any dataset — no code required.](https://docs.futureagi.com/docs/cookbook/quickstart/dynamic-dataset-columns) [Prompt Versioning: Create, Label, and Serve Prompt Versions\\ \\ Use FutureAGI's Prompt Versioning feature to create prompt templates, commit numbered versions, assign labels like production, and serve the right version at runtime via SDK.](https://docs.futureagi.com/docs/cookbook/quickstart/prompt-versioning) [Prototype and Iterate on LLM Applications\\ \\ Register a prototype project, auto-evaluate spans with EvalTags, iterate with versioned prompts, compare versions, and choose the winner before deploying to production.](https://docs.futureagi.com/docs/cookbook/quickstart/prototype-llm-app) [Manual Tracing: Add Custom Spans to Any Application\\ \\ Instrument any Python application with custom spans, user context, and metadata - and see every call visualized in the FutureAGI Tracing dashboard.](https://docs.futureagi.com/docs/cookbook/quickstart/manual-tracing) [Session-Based Observability for Multi-Turn Conversations\\ \\ Group every span from a multi-turn chatbot by session and user ID so conversations appear as a single, filterable unit in the FutureAGI Tracing dashboard.](https://docs.futureagi.com/docs/cookbook/quickstart/session-observability) [Monitoring & Alerts: Track LLM Performance and Set Quality Thresholds\\ \\ Generate rich trace data from a multi-step RAG agent, analyze historical performance trends in the Charts tab, and configure alerts with thresholds and notifications.](https://docs.futureagi.com/docs/cookbook/quickstart/monitoring-alerts) [Inline Evals in Tracing: Score Every Response as It's Generated\\ \\ Attach quality scores directly to production traces so you can see faithfulness, toxicity, and custom evals alongside every LLM call in FutureAGI Tracing.](https://docs.futureagi.com/docs/cookbook/quickstart/inline-evals-tracing) [Distributed Tracing: Connect Spans Across Services\\ \\ Propagate OpenTelemetry trace context across microservices so every span - from your API gateway to your LLM backend - shows up in a single trace.](https://docs.futureagi.com/docs/cookbook/quickstart/distributed-tracing) [Prompt Optimization: Improve a Prompt Automatically\\ \\ Use the agent-opt SDK to take a weak baseline prompt, run automated optimization, and deploy the best-performing variant - no manual prompt engineering required.](https://docs.futureagi.com/docs/cookbook/quickstart/prompt-optimization) [Compare Optimization Strategies: ProTeGi, GEPA, and PromptWizard\\ \\ Run three optimization algorithms on the same task with different evaluation metrics and compare results to pick the best strategy for your use case.](https://docs.futureagi.com/docs/cookbook/quickstart/compare-optimizers) [Dataset Optimization: Improve Prompts Directly in Your Dataset\\ \\ Use the dashboard Optimization tab to run automated prompt improvement on any Run Prompt column: no SDK code required.](https://docs.futureagi.com/docs/cookbook/quickstart/dataset-optimization) [Protect: Add Safety Guardrails to LLM Outputs\\ \\ Use FutureAGI Protect to screen text for prompt injection, PII, toxicity, and bias with a single API call — stack multiple safety rules and switch to Protect Flash for high-volume pipelines.](https://docs.futureagi.com/docs/cookbook/quickstart/protect-guardrails) [Knowledge Base: Upload Documents and Query with the SDK\\ \\ Upload documents to a Knowledge Base, manage files programmatically with the SDK, and use Knowledge Bases for grounded evaluations and synthetic data generation.](https://docs.futureagi.com/docs/cookbook/quickstart/knowledge-base) [Experimentation: Compare Prompts and Models on a Dataset\\ \\ Use the Experimentation feature to run multiple prompt variants across different models on the same dataset, evaluate outputs, and pick the winning configuration.](https://docs.futureagi.com/docs/cookbook/quickstart/experimentation-compare-prompts) [Evaluation-Driven Development: Score Every Prompt Change Before Shipping\\ \\ Build a local eval loop that scores prompts against a test suite, compare before-and-after results, and gate promotion on quality thresholds.](https://docs.futureagi.com/docs/cookbook/quickstart/eval-driven-dev) [CI/CD Eval Pipeline: Automate Quality Gates in GitHub Actions\\ \\ Set up FutureAGI's CI/CD Eval Pipeline to run automated quality gates on every pull request, failing builds when eval scores drop below your configured thresholds.](https://docs.futureagi.com/docs/cookbook/quickstart/cicd-eval-pipeline) [Agent Compass: Surface Agent Failures Automatically\\ \\ Instrument your AI agent with tracing, let Agent Compass analyze traces for errors, and review clustered failure patterns with actionable recommendations in the Feed dashboard.](https://docs.futureagi.com/docs/cookbook/quickstart/agent-compass-debug) [Using FutureAGI Evals\\ \\ Use FutureAGI Evals to evaluate your AI models](https://docs.futureagi.com/docs/cookbook/using-futureagi-evals) [Using FutureAGI Protect\\ \\ Use FutureAGI Protect to protect your data](https://docs.futureagi.com/docs/cookbook/using-futureagi-protect) [Using FutureAGI Dataset\\ \\ Use FutureAGI Dataset to create and manage your datasets](https://docs.futureagi.com/docs/cookbook/using-futureagi-dataset) [Using FutureAGI KB\\ \\ Use FutureAGI Knowledge Base to create and manage your knowledge base](https://docs.futureagi.com/docs/cookbook/using-futureagi-kb) [Portkey Integration\\ \\ Combine Portkey and Future AGI for end-to-end LLM observability. Benchmark multiple models on response quality, latency, and cost.](https://docs.futureagi.com/docs/cookbook/portkey-integration) [LangChain/LangGraph\\ \\ Add observability and evaluation to LangChain and LangGraph agents using Future AGI's tracing SDK for completeness, groundedness, and hallucination detection.](https://docs.futureagi.com/docs/cookbook/langchain-langgraph) [LlamaIndex PDF RAG\\ \\ Build a production-ready LlamaIndex PDF RAG chatbot with Future AGI observability, tracing, and real-time evaluation of retrieval quality.](https://docs.futureagi.com/docs/cookbook/llamaindex-pdf-rag) [CrewAI Research Team\\ \\ Learn how to build a multi-agent research system using CrewAI with integrated observability and in-line evaluations from FutureAGI for real-time quality monitoring.](https://docs.futureagi.com/docs/cookbook/crewai-research-team) [MongoDB\\ \\ Learn how to build production-grade PDF RAG chatbots using MongoDB Atlas for vector search and Future AGI to trace, evaluate, and real-time performance monitoring of LLM pipelines](https://docs.futureagi.com/docs/cookbook/mongodb) [Meeting Summarization\\ \\ Evaluate meeting summarization quality using Future AGI. Score AI-generated summaries from transcripts for accuracy and completeness.](https://docs.futureagi.com/docs/cookbook/meeting-summarization) [AI SDR Evaluation\\ \\ Evaluate AI-generated sales outreach messages using Future AGI. Score SDR openers for relevance, personalization, and value proposition alignment.](https://docs.futureagi.com/docs/cookbook/ai-sdr) [AI Agents Evaluation\\ \\ Evaluate AI agent function-calling and response quality using Future AGI's evaluation SDK with metrics like tool use accuracy and safety.](https://docs.futureagi.com/docs/cookbook/ai-agents) [Image Evaluation\\ \\ Evaluate AI-generated images for description alignment, artistic requirements, and replacement quality using the Future AGI SDK.](https://docs.futureagi.com/docs/cookbook/image-evaluation) [Implement Observability\\ \\ Master AI observability with FutureAGI. Track LLM performance, monitor metrics, and optimize Python apps. Step-by-step guide with examples.](https://docs.futureagi.com/docs/cookbook/observability) [Text-to-SQL Evaluation\\ \\ Build and evaluate a Text-to-SQL agent with Future AGI. Test natural language to SQL conversion accuracy using automated evaluation metrics.](https://docs.futureagi.com/docs/cookbook/text-to-sql) [RAG with LangChain\\ \\ Experiment with LangChain RAG configurations using Future AGI. Build and evaluate a retrieval-augmented generation app with OpenAI embeddings.](https://docs.futureagi.com/docs/cookbook/rag-langchain) [Evaluate RAG Apps\\ \\ Evaluate RAG applications with Future AGI using context adherence, retrieval quality, answer correctness, and other retrieval-augmented generation metrics.](https://docs.futureagi.com/docs/cookbook/evaluate-rag) [Trustworthy RAG Chatbots\\ \\ Evaluate RAG chatbot trustworthiness across retrieval accuracy, prompt injection resilience, privacy compliance, and tone adaptation with Future AGI.](https://docs.futureagi.com/docs/cookbook/trustworthy-rag) [Decrease RAG Hallucination\\ \\ Reduce hallucinations in RAG pipelines by benchmarking chunking, retrieval, and chain strategies with Future AGI's evaluation suite.](https://docs.futureagi.com/docs/cookbook/decrease-hallucination) [End-to-End Prompt Optimization\\ \\ Optimize prompts end-to-end with Future AGI. Learn evaluation-driven prompt refinement using automated scoring and version tracking.](https://docs.futureagi.com/docs/cookbook/end-to-end-optimization) [Basic Prompt Optimization\\ \\ A hands-on guide to optimizing your first prompt using the agent-opt Python library with a simple Random Search strategy.](https://docs.futureagi.com/docs/cookbook/basic-optimization) [GEPA Optimization\\ \\ A guide to using GEPA, a powerful evolutionary algorithm for state-of-the-art prompt optimization in complex, high-stakes scenarios.](https://docs.futureagi.com/docs/cookbook/gepa-optimization) [Eval Metrics for Optimization\\ \\ Learn how to use the FutureAGI platform, local LLM-as-a-judge, and local heuristic metrics to guide your prompt optimization.](https://docs.futureagi.com/docs/cookbook/eval-metrics-optimization) [Compare Strategies\\ \\ A practical guide to selecting the best optimization strategy (Bayesian Search, Meta-Prompt, GEPA, etc.) based on your specific task and goals.](https://docs.futureagi.com/docs/cookbook/compare-optimization) [Import Datasets\\ \\ Learn how to prepare and integrate datasets from various sources (in-memory, CSV, JSON, JSONL) for effective prompt optimization.](https://docs.futureagi.com/docs/cookbook/import-datasets) [Chat Simulation with Fix My Agent\\ \\ Simulate AI chat agents at scale and get instant AI-powered diagnostics to improve performance](https://docs.futureagi.com/docs/cookbook/chat-simulation-fix-agent) [Simulate SDK Demo\\ \\ This cookbook demonstrates how to use the agent-simulate SDK to test a conversational voice AI agent.](https://docs.futureagi.com/docs/cookbook/simulate-sdk) [Error Feed with Google ADK\\ \\ Set up a multi-agent system using Google ADK, send traces to Future AGI, and analyze agent errors with Error Feed.](https://docs.futureagi.com/docs/cookbook/error-feed/google-adk-multi-agent) [SDK Overview\\ \\ Evaluate LLM outputs, trace AI calls, optimize prompts, and test voice agents. Python, TypeScript, Java, and C# supported.](https://docs.futureagi.com/docs/sdk) [Overview\\ \\ Evaluate LLM outputs with 76+ local metrics, cloud Turing models, or custom LLM-as-Judge criteria. Part of the ai-evaluation Python package.](https://docs.futureagi.com/docs/sdk/evals) [Running Evaluations\\ \\ Run evaluations with the evaluate() function — local heuristics, cloud Turing, or LLM-as-Judge, auto-routed based on your inputs.](https://docs.futureagi.com/docs/sdk/evals/evaluate) [Distributed Evaluator\\ \\ Run evaluations at scale with blocking, async, or distributed execution. Backends for ThreadPool, Celery, Ray, Temporal, and Kubernetes. Built-in resilience.](https://docs.futureagi.com/docs/sdk/evals/distributed) [AutoEval\\ \\ Auto-generate evaluation pipelines from app descriptions. Pre-built templates for customer support, RAG, code assistants, healthcare, and more.](https://docs.futureagi.com/docs/sdk/evals/autoeval) [Guardrails\\ \\ Screen AI inputs and outputs with model-based safety checks and fast local scanners. 14 guard models, 14 scanners, async and batch support.](https://docs.futureagi.com/docs/sdk/evals/guardrails-module) [Local & Hybrid\\ \\ Run evaluations locally with zero API calls. Auto-route between local and cloud metrics. Use Ollama for offline LLM-based scoring.](https://docs.futureagi.com/docs/sdk/evals/local) [OpenTelemetry\\ \\ Built-in OpenTelemetry for the AI evaluation SDK. Auto-instrument LLM calls, track costs, enrich spans with scores, and export to any backend.](https://docs.futureagi.com/docs/sdk/evals/otel) [Code Security\\ \\ AST-based vulnerability detection for AI-generated code. 15 detectors, 4 evaluation modes, multi-language support, built-in benchmarks, and dual-judge scoring.](https://docs.futureagi.com/docs/sdk/evals/code-security) [Overview\\ \\ Browse all 76+ local evaluation metrics by category. String checks, JSON validation, similarity, hallucination, RAG, agents, structured output, and guardrails.](https://docs.futureagi.com/docs/sdk/evals/metrics) [String & Similarity\\ \\ 23 local metrics for keyword matching, regex, length checks, BLEU, ROUGE, Levenshtein, and embedding similarity.](https://docs.futureagi.com/docs/sdk/evals/metrics/string) [JSON & Structured\\ \\ 14 metrics for validating JSON correctness, schema compliance, type checking, and structured output quality.](https://docs.futureagi.com/docs/sdk/evals/metrics/json) [Hallucination\\ \\ Detect hallucinations, unsupported claims, and contradictions in LLM outputs. 5 context-grounded metrics with optional NLI and LLM augmentation.](https://docs.futureagi.com/docs/sdk/evals/metrics/hallucination) [RAG\\ \\ 19 local metrics for evaluating RAG pipelines — retrieval quality, generation faithfulness, advanced reasoning, and composite scores.](https://docs.futureagi.com/docs/sdk/evals/metrics/rag) [Agents & Functions\\ \\ 11 metrics for evaluating agent trajectories, tool use, reasoning quality, and function call correctness. All run locally via evaluate().](https://docs.futureagi.com/docs/sdk/evals/metrics/agents) [Guardrails\\ \\ Security-focused scanner metrics that detect prompt injection, PII, secrets, and SQL injection in under 10ms.](https://docs.futureagi.com/docs/sdk/evals/metrics/guardrails) [Cloud Evals\\ \\ Run pre-built evaluation templates on Future AGI's Turing cloud models. 100+ templates covering safety, RAG, hallucination, conversation quality, and more.](https://docs.futureagi.com/docs/sdk/evals/cloud-evals) [LLM-as-Judge\\ \\ Define custom grading criteria and run them with any LLM — GPT-4o, Gemini, Claude, Ollama, or any LiteLLM-supported model.](https://docs.futureagi.com/docs/sdk/evals/llm-judge) [Streaming\\ \\ Check LLM output token-by-token as it streams. Detect toxic content, PII, or quality drops mid-generation and stop early.](https://docs.futureagi.com/docs/sdk/evals/streaming) [Feedback Loops\\ \\ Submit corrections to scoring results, calibrate thresholds over time, and store feedback in ChromaDB for continuous improvement.](https://docs.futureagi.com/docs/sdk/evals/feedback) [Datasets\\ \\ Create, populate, and manage datasets for evaluation. Upload CSV/JSON files, import from HuggingFace, add LLM-generated columns, and run evaluations at scale.](https://docs.futureagi.com/docs/sdk/datasets) [Tracing\\ \\ Set up OpenTelemetry tracing across Python, TypeScript, Java, and C#. Auto-instrument 45+ frameworks or create custom spans with FITracer.](https://docs.futureagi.com/docs/sdk/tracing) [Protect\\ \\ Guard AI inputs and outputs in real-time. Check for content moderation, bias, security threats, and data privacy violations.](https://docs.futureagi.com/docs/sdk/protect) [Knowledge Base\\ \\ Upload documents to build knowledge bases for RAG evaluation and context injection. Create, update, and manage files.](https://docs.futureagi.com/docs/sdk/knowledgebase) [Annotation Queues\\ \\ Reference for the AnnotationQueue class in the Future AGI Python SDK.](https://docs.futureagi.com/docs/sdk/annotation-queues) [Prompt Optimization\\ \\ Automatically improve your prompts with 6 SOTA algorithms. Random Search, Bayesian, ProTeGi, Meta-Prompt, PromptWizard, and GEPA.](https://docs.futureagi.com/docs/sdk/optimization) [Simulation Testing\\ \\ Test voice AI agents at scale with simulated customer personas. Run conversations, capture audio, and score performance.](https://docs.futureagi.com/docs/sdk/simulate) [Introduction\\ \\ Complete REST API reference for the Future AGI platform.](https://docs.futureagi.com/docs/api) [Health Check\\ \\ Returns 200 status when server is up and running. No authentication required.](https://docs.futureagi.com/docs/api/health/healthcheck) [Get Evals List\\ \\ Retrieves a list of evaluations for a given dataset, with options for filtering and ordering.](https://docs.futureagi.com/docs/api/evals-list/getevalslist) [Create Eval Group\\ \\ Creates a new evaluation group within the user's workspace.](https://docs.futureagi.com/docs/api/eval-groups/createevalgroup) [List Eval Groups\\ \\ Retrieves a paginated list of evaluation groups for the user's workspace, including sample groups.](https://docs.futureagi.com/docs/api/eval-groups/listevalgroups) [Retrieve Eval Group\\ \\ Retrieves detailed information about a specific evaluation group, including its members.](https://docs.futureagi.com/docs/api/eval-groups/retrieveevalgroup) [Update Eval Group\\ \\ Updates an entire evaluation group's details.](https://docs.futureagi.com/docs/api/eval-groups/updateevalgroup) [Delete Eval Group\\ \\ Soft deletes an evaluation group and removes all its associated evaluation templates.](https://docs.futureagi.com/docs/api/eval-groups/deleteevalgroup) [Apply Eval Group\\ \\ Applies an evaluation group to a set of data, creating user evaluation metrics.](https://docs.futureagi.com/docs/api/eval-groups/applyevalgroup) [Edit Eval List\\ \\ Adds or removes evaluation templates from an evaluation group.](https://docs.futureagi.com/docs/api/eval-groups/editevallist) [Get Eval Log Details\\ \\ Retrieves detailed logs for a specific evaluation template, with support for advanced filtering, sorting, and pagination. This endpoint uses a GET req...](https://docs.futureagi.com/docs/api/eval-logs-metrics/getevallogdetails) [Create Scenario\\ \\ Creates a new scenario from a dataset, a script, or a generated/provided graph. The creation is processed in the background.](https://docs.futureagi.com/docs/api/scenarios/createscenario) [Edit Scenario\\ \\ Updates the properties of a specific scenario, such as its name, description, associated graph, or the simulator agent's prompt.](https://docs.futureagi.com/docs/api/scenarios/editscenario) [Add Empty Rows\\ \\ Adds a specified number of empty rows to an existing scenario. This is useful for populating a scenario with placeholders for future data entry.](https://docs.futureagi.com/docs/api/scenarios/addemptyrowstodataset) [Add Rows with AI\\ \\ Initiates an asynchronous task to generate and add a specified number of new rows to a scenario's dataset using AI. A description can be provided to g...](https://docs.futureagi.com/docs/api/scenarios/addscenariorowswithai) [Create Agent Definition\\ \\ Create a new agent definition and its first version.](https://docs.futureagi.com/docs/api/agent-definitions/createagentdefinition) [Create Agent Version\\ \\ Create a new version of an existing agent definition by providing updated agent properties and a commit message.](https://docs.futureagi.com/docs/api/agent-versions/createagentversion) [Create Run Test\\ \\ Creates and configures a new test run, associating it with scenarios, an agent definition, and detailed evaluation configurations.](https://docs.futureagi.com/docs/api/run-tests/createruntest) [Execute Run Test\\ \\ Triggers the execution of a specified test run. The execution can be customized to include or exclude specific scenarios.](https://docs.futureagi.com/docs/api/run-tests/executeruntest) [Create Dataset\\ \\ Create a new dataset with rows and columns in your organization.](https://docs.futureagi.com/docs/api/datasets/create-dataset) [Upload Dataset from File\\ \\ Create a new dataset by uploading a local file.](https://docs.futureagi.com/docs/api/datasets/upload-dataset) [Create Score\\ \\ Create a single annotation score on a source.](https://docs.futureagi.com/docs/api/annotations/scores/create-score) [Bulk Create Scores\\ \\ Create multiple scores on a single source in one request.](https://docs.futureagi.com/docs/api/annotations/scores/bulk-create-scores) [Get Scores for Source\\ \\ Retrieve all scores for a specific source.](https://docs.futureagi.com/docs/api/annotations/scores/get-scores-for-source) [List Scores\\ \\ List scores with optional filters.](https://docs.futureagi.com/docs/api/annotations/scores/list-scores) [Delete Score\\ \\ Soft-delete a score. Only the creator or org admin can delete.](https://docs.futureagi.com/docs/api/annotations/scores/delete-score) [Create Label\\ \\ Create a new annotation label.](https://docs.futureagi.com/docs/api/annotations/labels/create-label) [List Labels\\ \\ List annotation labels with optional filters.](https://docs.futureagi.com/docs/api/annotations/labels/list-labels) [Get Label\\ \\ Retrieve a specific annotation label by ID.](https://docs.futureagi.com/docs/api/annotations/labels/get-label) [Update Label\\ \\ Update an existing annotation label.](https://docs.futureagi.com/docs/api/annotations/labels/update-label) [Delete Label\\ \\ Soft-delete an annotation label.](https://docs.futureagi.com/docs/api/annotations/labels/delete-label) [Restore Label\\ \\ Restore a previously deleted annotation label.](https://docs.futureagi.com/docs/api/annotations/labels/restore-label) [Create Queue\\ \\ Create a new annotation queue with assignment strategy and configuration.](https://docs.futureagi.com/docs/api/annotations/queues/create-queue) [List Queues\\ \\ List annotation queues with optional filtering and pagination.](https://docs.futureagi.com/docs/api/annotations/queues/list-queues) [Get Queue\\ \\ Retrieve details of a specific annotation queue.](https://docs.futureagi.com/docs/api/annotations/queues/get-queue) [Update Queue\\ \\ Update an existing annotation queue's configuration.](https://docs.futureagi.com/docs/api/annotations/queues/update-queue) [Delete Queue\\ \\ Soft-delete an annotation queue.](https://docs.futureagi.com/docs/api/annotations/queues/delete-queue) [Update Status\\ \\ Transition an annotation queue to a new status.](https://docs.futureagi.com/docs/api/annotations/queues/update-status) [Get Progress\\ \\ Retrieve progress statistics for an annotation queue.](https://docs.futureagi.com/docs/api/annotations/queues/get-progress) [Get Analytics\\ \\ Retrieve detailed analytics for an annotation queue.](https://docs.futureagi.com/docs/api/annotations/queues/get-analytics) [Get Agreement\\ \\ Retrieve inter-annotator agreement metrics for a queue.](https://docs.futureagi.com/docs/api/annotations/queues/get-agreement) [Export\\ \\ Export annotation queue items and their annotations as JSON or CSV.](https://docs.futureagi.com/docs/api/annotations/queues/export) [Export to Dataset\\ \\ Export completed annotations from a queue into a FutureAGI dataset.](https://docs.futureagi.com/docs/api/annotations/queues/export-to-dataset) [Add Label to Queue\\ \\ Attach an annotation label to a queue.](https://docs.futureagi.com/docs/api/annotations/queues/add-label) [Remove Label\\ \\ Detach an annotation label from a queue.](https://docs.futureagi.com/docs/api/annotations/queues/remove-label) [Get or Create Default\\ \\ Get the default annotation queue for a project, dataset, or agent, creating one if it doesn't exist.](https://docs.futureagi.com/docs/api/annotations/queues/get-or-create-default) [Find Queues for Source\\ \\ Find annotation queues that contain a specific source item.](https://docs.futureagi.com/docs/api/annotations/queues/find-queues-for-source) [List Items\\ \\ List items in an annotation queue with optional filtering and pagination.](https://docs.futureagi.com/docs/api/annotations/items/list-items) [Add Items\\ \\ Add source items to an annotation queue in bulk.](https://docs.futureagi.com/docs/api/annotations/items/add-items) [Bulk Remove Items\\ \\ Remove multiple items from an annotation queue at once.](https://docs.futureagi.com/docs/api/annotations/items/bulk-remove-items) [Get Annotate Detail\\ \\ Retrieve a queue item with full source data for the annotation UI.](https://docs.futureagi.com/docs/api/annotations/items/get-annotate-detail) [Get Next Item\\ \\ Retrieve the next available item for the current user to annotate.](https://docs.futureagi.com/docs/api/annotations/items/get-next-item) [Submit Annotations\\ \\ Submit annotations and notes for a queue item.](https://docs.futureagi.com/docs/api/annotations/items/submit-annotations) [Complete Item\\ \\ Mark a queue item as completed and optionally receive the next item.](https://docs.futureagi.com/docs/api/annotations/items/complete-item) [Skip Item\\ \\ Skip a queue item, marking it as skipped by the current user.](https://docs.futureagi.com/docs/api/annotations/items/skip-item) [Get Item Annotations\\ \\ Retrieve all annotations submitted for a specific queue item.](https://docs.futureagi.com/docs/api/annotations/items/get-item-annotations) [Assign Items\\ \\ Assign queue items to a specific annotator.](https://docs.futureagi.com/docs/api/annotations/items/assign-items) [Release Item\\ \\ Release a reserved queue item so it can be assigned to another annotator.](https://docs.futureagi.com/docs/api/annotations/items/release-item) [Bulk Annotate Spans\\ \\ Submit annotations and notes for multiple observation spans in a single request.](https://docs.futureagi.com/docs/api/annotations/bulk/bulk-annotate-spans) No results found `↑` `↓` Navigate`↵` Open Table of Contents ## Why Guardrail Metrics Are Essential for Responsible AI Governance As artificial intelligence (AI) becomes more common in our lives and businesses, we must use it responsibly. AI systems can assist in decision-making processes for critical matters like job recruitment, criminal justice, and more. However, if these systems are not monitored properly then they can give biased undesirable misleading results. This is where AI guardrail metrics come into play. Guardrail metrics for making sure AI models are ethical, reliable and safe. AI governance refers to norms and standards guiding responsible AI development, deployment and use. Global demand for AI governance is on the rise. How do we define, implement and refine these guardrails? Let’s explore: ## What Are Guardrail Metrics in AI? Definition, Purpose, and How They Work Guardrail metrics, therefore, are a set of predefined performance and ethical standards that help ensure an AI model remains accountable, fair, and transparent. Although these metrics do not eliminate bias, inaccuracy, or unpredictability, they nevertheless help monitor, measure, and mitigate these risks, thereby ensuring that AI systems operate within acceptable boundaries ## Types of AI Guardrail Metrics: Accuracy, Bias, Safety, and Explainability ### Accuracy Metrics Accuracy metrics measure how factually correct AI-generated outputs are. In other words, this involves assessing the accuracy and consistency of the answers, comparing them to the output and training data in the model. Furthermore, these metrics help ensure that the AI’s responses are reliable and aligned with the intended information. In addition, accuracy metrics assess whether a cosmetic lipstick shade recommender AI provides the correct lipstick shade in the right context. High accuracy ensures that users receive reliable and trustworthy information, reducing misinformation risks. ### Bias and Fairness Metrics [Bias and fairness](/content/blog/fairness-in-ai-how-to-detect-and-mitigate-bias-in-llm-outputs-using-future-agi-metrics/index.html) metrics in AI decision-making are important to measure. They are useful in uncovering and reducing bias. In simpler terms, these metrics indicate how much the AI is biased against certain social groups. An AI-driven recruiting tool, therefore, should not prefer a male applicant who is equally qualified to a female applicant. As a result, fairness metrics are essential to ensure non-discrimination, build trust, and ultimately help deploy the AI ethically and optimally. ### Safety Metrics Safety metrics, therefore, help assess whether AI-generated content is free from harmful, offensive, or misleading responses. Specifically, this includes detecting hate speech, harmful advice, or dangerous suggestions. For instance, AIs should not only discourage but also actively avoid providing any illegal solutions. Therefore, by using safety checks, organizations can both protect users and, at the same time, safeguard their own reputations. ### Explainability and Transparency Metrics Metrics of explainability and transparency express how easily AI decisions can be understood and justified. These metrics help engineers and end-users easily comprehend how an AI system arrives at its decisions. For instance, when an AI model denies a loan application, it must also explain what factors-such as credit score and income-were responsible. Furthermore, when AI systems are simple and clear, they are easier to understand and troubleshoot. By integrating these guardrail metrics, organizations can enhance their AI governance, improve model performance, and address ethical concerns more effectively. ## Why Guardrail Metrics Are Essential for AI Accountability: Four Critical Reasons ### Preventing Harmful Outputs Without strict guardrail metrics, AI models can generate misleading, offensive, or even dangerous content. For example, a search engine that utilizes AI might show frauds on a health treatment, which could be damaging through the search results, or a chatbot might express hate speech. That’s where guardrail metrics come in, they watch out for and restrict these things by flagging and filtering before it gets to the user to help the AI be safe. ### Ensuring Fairness Moreover, discrimination is often further reinforced by the bias present in AI-led decisions, especially in critical areas such as hiring, credit, and healthcare. Like, if an AI model trained on biased historical data favors certain groups in loan approvals, it could deny fair opportunities to others. “Guardrail metrics require any AI project in the enterprise to release a score that maps bias and takes corrective action against the bias. They detect inherent bias and modify model behavior through a fairness adjustment. To achieve this, techniques that are often used include data balancing, bias audits, and demographic parity testing. ### Improving Trust and Adoption AI adoption depends on trust. When companies as well as users view that AI operates in the ethical framework, they tend to accept it. If a bank employs artificial intelligence to approve loans, the customer will want to ensure that the bank’s AI is not being biased. Metrics for ethical AI are those whose decision (and behaviour) can be explained and are consistent. This builds confidence in the AI system among businesses and users and leads to more widespread AI adoption. People start to trust the system due to its data utility. ### Regulatory Compliance With regulations like the GDPR and the AI Act being enacted or proposed, organizations can manage the legislative risk related to AI models, by putting in place proper oversight. For instance, information-rich AI-based decisions that are relied on or have an impact on the user should comply with the GDPR. Guardrail metrics help companies meet this requirement by documenting how decisions are made to ensure conformity of AI-generated outputs to legal requirements. As a result, this lowers the chance of punishments, legal actions, and damage to reputation, while simultaneously increasing innovation. ## Key Challenges in Implementing AI Guardrail Metrics ### Defining Universal Standards What constitutes “fair” and “safe” AI? However, an overall set of guardrail metrics will be challenging to set because of diverging views across industries and geographies. For instance, the healthcare space will most likely care more about patient privacy and accuracy, while finance will focus more on bias mitigation. Different countries, like countries in the EU, are setting rules on AI like the EU AI Act, which sets standards that all AI systems should follow. Yet, because of differences in laws and cultures, the definition of “fair” can differ based on your country. Developing a universal accountability framework for AI is decidedly not easy. ### Balancing Performance vs Control Overly strict guardrails may lower AI’s productivity, while loosened guardrails would increase risks. Finding the balance of performance and accountability is always a hard challenge. An AI system won’t be able to say creative or useful things if it is hampered with too many guardrails preventing it from saying harmful things. But they can create false or harmful content without sufficient restriction as well. Companies often have to adjust thresholds and monitoring strategies to strike the right balance between user safety and system capability. ### Dynamic Nature of AI Behavior AI models evolve continuously, requiring real-time updates to ethical AI metrics. Static guardrails become outdated quickly, so we must adapt. A language model using live data may encounter new slang, evolving social norms, or emerging threats it wasn’t trained on. The model will generate harmful and inappropriate comments without real-time updates. AI must be updated until it is the same as the human brain that will enhance growth. ### Handling Edge Cases Unexpected scenarios-such as adversarial attacks or ethical dilemmas, can break even the most robust AI model oversight frameworks. Addressing these outliers requires continuous monitoring and improvement. For example, a model designed to detect harmful content might fail, especially when an adversary cleverly hides their harmful intent under seemingly harmless-looking language. Consequently, this can make it more challenging for the model to identify and flag the harmful content accurately. There will be better supervision through robust anomaly detection systems which will mitigate the risk and improve AI’s resilience. ## Strategies for Effective AI Guardrail Metric Implementation ### Human-in-the-Loop Monitoring Incorporating human oversight ensures AI outputs align with ethical standards and real-world expectations. Humans can figure out edge cases and subtle cases which may be misjudged by an AI model, thereby preventing harmful outputs. For instance, in content moderation, human reviewers can evaluate context and intent more effectively than AI alone, ultimately reducing false positives. Feedback from humans who watch over the continuous learning process helps develop a tool’s memory. ### Automated Feedback Loops AI should not operate in isolation. When self-learning systems receive proper feedback from the user, they accordingly try to make the required changes in their system and, as a result, implement self-learning. Self-learning systems enhance their system to avoid human feedback. Let us say, AI model is given queries like customer support but it misclassifies, and in this case, feedback loop can help adjust the model’s parameters for better accuracy. Feedback loops should be designed to capture user interactions, model errors, and external environmental changes, ensuring continuous optimization of guardrail metrics. ### Multi-Layered Evaluation No one measure can guarantee AI accountability since many aspects affect AI performance. To govern AI systems holistically, we need to make them accurate (the predictions should be correct as often as possible), fair (the machine that makes the predictions should not discriminate against any demographic), safe (it should not generate harmful or dangerous outputs), and transparent (the modelling decision can be explained). For instance, an AI model used in healthcare should not only provide accurate diagnoses but also ensure that recommendations are unbiased across different patient demographics. Layering multiple metrics helps identify hidden risks and improve overall model reliability. ### Transparency in Reporting Organizations, therefore, should be more transparent about how they manage their AI models - for example, by clearly explaining how they set the guardrails, how they continuously monitor them, and, when necessary, how they make changes. Publishing performance reports and sharing failure cases (along with corrective actions) builds public trust and helps meet regulatory requirements. For example, an organization must prepare a report about any identified bias (disproportionate rejection of applicants) against a loan approval system powered by AI. It also ensures of fixing the bias within the system. Being transparent means we take responsibility for our actions. ### Guardrail Metrics A/B Testing Guardrail Metrics A/B Testing is nothing but a technique that can be used to assess the impact of different guardrails. When organizations observe differences in user satisfaction, safety, accuracy, and fairness by comparing two versions of a model, it uses one version with existing guardrail metrics and the second with new guardrail metrics. For example, one might test a chatbot with two different safety filters, to see which one reduces harm better while maintaining helpfulness. This process ensures thresholds are fine-tuned and their performance is validated before full deployment of the management system. At [Future AGI (FAGI)](/content/site-root.html), we not only provide cutting-edge guardrail metrics, but also ensure AI safety, accuracy, and fairness. In doing so, we help create trustworthy and responsible AI systems. Start integrating our guardrail solutions today to enhance your AI governance. [Learn more here.](https://docs.futureagi.com/docs/sdk/evals/metrics/guardrails/) ## Real-World Case Studies: How Google, OpenAI, and Healthcare AI Use Guardrail Metrics ### Google AI Guardrails Google employs stringent guardrail metrics in its search algorithms and recommendation systems, ensuring responsible AI outputs. For example, Google Search’s algorithm ranks content based on relevance, quality, and trustworthiness, using signals like E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) ( [Google Search Central, 2022](https://developers.google.com/search/docs/fundamentals/creating-helpful-content?utm_source=chatgpt.com)) . In YouTube recommendations, Google applies guardrails to prevent the spread of harmful or misleading content by reducing the visibility of conspiracy theories and extremist material ( [Google Blog, 2019](https://en.wikipedia.org/wiki/YouTube_moderation?utm_source=chatgpt.com)) . As a result, the guardrails not only helped us deliver more accurate and trustworthy information to users, but also effectively shielded them against algorithmic abuse. ### OpenAI ChatGPT ChatGPT incorporates AI model oversight through moderation layers and human-in-the-loop feedback, refining outputs for accuracy and ethical alignment. For example, OpenAI implemented reinforcement learning from human feedback (RLHF) to fine-tune ChatGPT’s responses ( [OpenAI, 2022](https://openai.com/research/instruction-following)). If a user asks about sensitive topics or harmful content, the moderation system filters the output or redirects the conversation to safer ground. For example, real-world applications include ChatGPT’s ability to deny requests for dangerous advice (e.g., how to make explosives). At the same time, it provides helpful, fact-based answers for general queries. The guardrails ensure that the AI remains helpful without crossing ethical or legal boundaries. ### Healthcare AI Models In medical applications, ethical AI metrics play a crucial role. Specifically, they ensure AI-driven diagnostics remain unbiased and clinically sound. As a result, they help reduce risks associated with misdiagnosis. For example, IBM Watson was used in cancer diagnosis and treatment planning. To ensure safety, guardrails were put in place so that its recommendations relied only on peer-reviewed medical research and clinical guidelines (IBM, 2021). Similarly, Google’s DeepMind developed an AI system for diagnosing eye diseases. In this case, the model was trained on diverse datasets to help avoid racial or demographic biases. ( [Nature, 2018](https://www.nature.com/articles/s41591-018-0177-5)). Guardrails in healthcare AI help prevent models from overfitting to patient groups, ensuring more equitable and accurate medical advice across different populations. ### Guardrail Metrics A/B Testing in Practice A lot of AI developers use Guardrail Metrics A/B Testing in the deployment pipeline as a matter of routine. AI firms, for example, run controlled A/B tests to see how different fairness or safety thresholds impact users. For instance, one version could use stricter bias detection filters, while another might use looser ones. Through these experiments, teams can then assess which guardrail setups work best in real-world scenarios. In doing so, they ensure that the AI remains ethical and, at the same time, not unduly limited. ## The Future of Guardrail Metrics in AI Accountability: Regulations, Self-Regulation, and Collaboration ### Evolving Regulatory Landscape Global governments are crafting policies to determine future guardrails metrics. The EU AI Act is one such framework which mandates transparency, risk impact assessments, and accountability in AI-produced content and algorithms. Similarly, the U.S. The AI Bill of Rights provides for consumer protection for algorithmic bias and discrimination. Companies must comply to worldwide law and rule surrounding the AI. For example, if a company is developing generative AI tools, then it should clearly implement user consent and data privacy. As a result, it can avoid potential fines. ### Self-Regulating AI Models The next horizon in AI governance is for models to monitor themselves and modify their ethical AI metrics without relying on people. Futuristic AI systems will have continuous monitoring and review processes that will help in detecting and correcting biases, inaccuracies, and errors without any human intervention. A chatbot may adjust its tone and language in real-time to not be biased or insulting on user feedback. Also, AI models used in finance could stop looking at transaction patterns to avoid discrimination. All in all, businesses will pay more attention to become more scalable and cut down on overall operational costs – something that becomes increasingly possible with self-regulation. ### Cross-Industry Collaboration We can’t let AI accountability end up in isolated hands. Having accountable AI is the responsibility of us all. The tech companies, regulators, and researchers should work together to develop common oversight standards that impact AI models. For instance, Google, Microsoft, and Meta joined forces in the Partnership on AI to develop good practices for transparency and fairness in artificial intelligence (AI). Joining forces with others across diverse industries can help to tackle issue(s) in a sector, for instance improving explanations for AI models for health or bias in hiring algorithms. Creating guidelines will help improve our aim for global AI reliability, consistency, and trustworthiness. ## Why Guardrail Metrics Are Central to Fair, Trustworthy, and Compliant AI in 2026 As AI transforms industries, guardrail metrics are key to ensuring ethical and accountable use. They offer a clear way to monitor AI for fairness, accuracy, and transparency. Of course, challenges remain-like setting universal standards and managing AI’s shifting behavior. However, solutions such as human-in-the-loop systems, automated feedback, and multi-dimensional evaluations are helping. Meanwhile, evolving regulations and growing focus on AI governance mean businesses must act now. Strong oversight builds trust, ensures compliance, and supports responsible use. Ultimately, the future of AI depends on accountability. Guardrail metrics help detect and reduce bias, making them central to fair and trustworthy AI. Want to explore how we can assess and improve AI fairness? Visit our blog -it explains it in a simpler and practical manner - [Fairness in AI: How to Detect and Mitigate Bias in LLM Outputs Using Future AGI Metrics](/content/blog/fairness-in-ai-how-to-detect-and-mitigate-bias-in-llm-outputs-using-future-agi-metrics/index.html) ## Frequently Asked Questions About AI Guardrail Metrics and Accountability ### What are guardrail metrics in AI and how do they work? Guardrail metrics are guidelines for AI set by the researchers to monitor if it is behaving ethically and legally. These metrics guarantee that the artificial intelligence operates within a reasonable level of risk and does not produce something harmful and also reduces bias while increasing accountability. It is important to monitor and enhance the deployment of responsible AI in all industries. ### Why are guardrail metrics essential for AI accountability? AI systems need guardrail metrics to ensure they don’t stray outside their useful envelopes. They help in being accountable by revealing performance gaps, minimizing risks of bias or misinformation, and ensuring trustworthy outputs. These metrics are safety metrics and help promote responsible AI usage and innovation, preventing unintended consequences. ### What are the top AI accountability issues guardrail metrics address? There are many accountability issues related to AI such as input bias, lack of transparency, data risk etc. Another concern relates to the diversion of AI systems and poor human monitoring. By focusing on strong guardrail metrics, we can use AI Technologies that are on the right path. ### Why is explainability important in AI accountability? Explainability in AI refers to how users can understand how and why a model works. This transparency helps to secure trust and helps organizations spot errors, biases or misuse. When AI outcomes are audited and interpreted by stakeholders, this not only helps in strengthening accountability but also ensures compliance with ethical standards and regulatory requirements. 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