` 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 ## How LLM Agents Are Evolving from Reactive Models into Autonomous Intelligent Systems In intelligent automation, many of the new-age AI systems are powered by large [language models](/content/blogs/exploring-how-multimodal-large-language-models-work/index.html) (LLMs). These systems are more than just models that react to random prompts; they are changing into independent agents capable of performing complex tasks. To know how these agents work, we need to look at the [LLM agents](/content/blogs/build-llm-agents/index.html) framework that powers the agents. This blog will examine the fundamental building blocks of these types of architectures, how they are designed, how they work, and how they can be optimized. ## What Is an LLM Agents Framework: How Modular Architecture Enables Reasoning, Planning, Memory, and Interaction Image 1: Applications of an LLM agent At its core, an **LLM agents framework** refers to the structural and functional blueprint that governs [how LLM agents](/content/blogs/build-llm-agents/index.html) are designed, deployed, and integrated. It is a combination of modular components that allow for reasoning, planning, memory, and interaction. This framework allows the agent to do a lot of things by itself. It can interact with the user, retrieve, compute, and respond on its own. Instead of being limited to single-turn systems like earlier NLPs, today’s LLM agents rely on a smart architecture that retains context, uses tools, and can self-reflect. Due to this, LLM agents framework allows these models to behave more like humans when it comes to reasoning and making decisions. ## Why Frameworks Matter in AI Agent Design: How Architecture Enables Tool Use, Memory, Planning, and Feedback Designing intelligent agents is more than just plugging an LLM into a user interface. It requires a holistic **AI agents design** process that includes environment perception, action selection, task planning, and execution. The **LLM agent architecture** acts as the backbone of this design, ensuring that each component works seamlessly with the others. A well-structured **LLM model framework** allows developers to: - Incorporate tools and APIs for external data retrieval - Enable memory modules for stateful conversations - Integrate planning algorithms for complex task execution - Support real-time updates and feedback loops Without a proper **framework for LLM applications**, deploying scalable and reliable agents becomes almost impossible. ## Core Components of an LLM Agents Framework: Six Building Blocks That Power Intelligent AI Agents ### Language Model Core: How GPT, LLaMA, and Claude Provide the Foundation for Agent-Like Behavior The language model infrastructure is the foundation. It is made up of GPT, LLaMA and Claude pretrained LLMs. These models are fine-tuned or prompted to support agent-like behavior. Key attributes of the language model core include: - Context window length - Instruction-following capability - Multi-turn coherence - Domain specialization ### Memory Modules: How Short-Term and Long-Term Memory Enable Stateful Contextual Conversations in LLM Agents Agents must remember prior interactions to maintain context. Memory can be: - Short-term memory stores the data of the current session. - Long-term memory is a memory that saves past information. The function of this module is essential to the efficiency of LLM agents framework for conducting ongoing and contextual conversations. ### Tool Use and Plugins: How Web Search APIs, Code Execution, and Database Queries Extend LLM Agent Capabilities Modern agents rely on tools other than themselves to operate. Examples include. - Web search APIs - Code execution environments - Database query engines Using a tool brings about diverse intelligent agent algorithms which help the model break tasks into smaller subtasks, call a relevant tool, and take the result to make a coherent final response. ### Planning and Reasoning Layer: How Task Decomposition, Chain-of-Thought, and Deliberative Planning Work in LLM Agents This layer helps in thinking of multiple things at once and helps complete complex tasks. It may involve: - Task decomposition strategies - Chain-of-thought prompting - Reactive or deliberative planning The cognitive architecture for LLM takes care of hypothetical reasoning, decision-making on the go, and so on. ### Orchestration Engine: How State Managers, Task Schedulers, and Monitoring Systems Route Agent Inputs and Outputs The orchestration engine is the control center that routes the inputs and outputs of components. It may include: - State managers - Task schedulers - Logging and monitoring systems It is crucial to implement a scalable LLM system design to address enterprise use cases. ### Feedback and Learning Loop: How Automated Evaluators and Human-in-the-Loop Mechanisms Improve Agent Responses Over Time Finally, feedback is key for continuous improvement. The agent’s replies get better over time due to automated evaluators and human-in-the-loop mechanisms. Incorporating feedback mechanisms also supports responsible AI practices, which are essential for building trustworthy **AI development platforms**. ## Types of LLM Agent Frameworks: Reactive, Planning-Based, and Tool-Augmented Architectures Explained There are many ways to design an LLM agents framework depending on the use case. Below are some common architectural patterns: ### Reactive Agent Architecture: How Lightweight Fast-Response Agents Handle FAQ and Customer Support Use Cases These agents respond directly to user inputs without memory or long-term planning. They are fast, lightweight, and easy to implement. For instance, a customer support chatbot that answers FAQs like “What is your return policy?” operates effectively as a reactive agent. ### Planning-Based Architecture: How Reasoning-First Agents Execute Multi-Step Tasks Like Research Summarization Unlike the responsive agents who react to the current situation, planning based agents use reasoning. They compute a sequence of actions to achieve the desired goals. It is useful for applications which require structured decisions. For example, the agent undertaking the research summary might first plan to collect funding papers first, extract key insights and finally organize the findings by topic. ### Tool-Augmented Agent Architecture: How API-Calling Agents Handle Complex Developer and Workflow Automation Tasks Finally, agents increased by a tool are able to invoke external APIs, use plugins, or run code. They can adapt to complex workflows, making them very flexible. A developer assistant, for instance, could get documentation, generate code, and test it, all in a single interaction. Flexible programming structures for AI will allow a designer to make agents for specific applications. ## Use Cases of LLM Agent Frameworks Across Industries: Customer Support, Healthcare, Legal, Education, and Software LLM agents can be used in a variety of industries due to their versatility. Below are some compelling use cases: ### Customer Support: How LLM Agents Handle Nuanced Queries, Troubleshoot Problems, and Detect Emotional Tone - LLM agents are changing the way businesses communicate with their customers. Companies are no longer just deploying scripted chatbots. Instead, they have intelligent agents that can understand a more nuanced query, troubleshoot technical problems, and even detect emotional tone to escalate a conversation. - **For example,** an e-commerce platform can use an LLM-powered support agent to help customers with order tracking, process returns and respond empathetically to complaints based on detected sentiment. This leads to faster resolution times and improved customer satisfaction. ### Healthcare Assistants: How LLM Agents Retrieve Patient History, Suggest Diagnostics, and Reduce Admin Burden - In the healthcare sector, [LLM agents](/content/blogs/build-llm-agents/index.html) may improve clinical workflows and patient care.  When the EHR system is integrated with these agents, they can retrieve the patient history for physicians, suggest diagnostic alternatives, schedule follow-up visits, etc. - **For instance**, a hospital might use an LLM assistant that automatically compiles relevant patient data and recommends next steps based on recent lab results, thereby reducing the administrative burden on medical staff. ### Legal Research: How LLM Agents Scan Documents, Extract Cases, and Generate Legal Summaries for Law Firms - Lawyers and other legal professionals are likely to benefit from agents capable of processing and analyzing legal text. A LLM agent can simply scan a document, extract cases, and generate a brief summary instead of having to scour through long court judgments or outdated precedent cases. - **For example,** a law firm might use an agent to prepare a quick overview of similar intellectual property cases before a client meeting, saving hours of manual review. ### Education: How LLM Agents Act as Intelligent Tutors That Adapt to Individual Student Learning Styles - Educational platforms use LLM agents for personalized and adaptive learning experiences for students. These agents can serve as intelligent tutors;  they can help a student understand difficult concepts; giving them practice problems according to their learning style and answering their queries in real-time. - **For instance**, a math learning app might employ an LLM-based agent that explains algebraic equations in multiple ways until the student demonstrates understanding, thereby increasing engagement and retention. ### Software Development: How LLM Agents Write Code, Detect Bugs, and Deploy to GitHub Through Natural Language - LLM agents are slowly revolutionizing the software engineering development lifecycle. When you integrate it with DevOps tools, these agents can write code snippets, detect bugs, suggest improvements, and even deploy. - **For example,** a development team could use a coding agent to generate boilerplate code for a REST API, test the endpoints, and push the code to a GitHub repository, all through natural language commands. This streamlines development and accelerates product delivery. This app makes heavy use of the underlying language model infrastructure and other components to perform strongly. ## Challenges in Building LLM Agent Architectures: Latency, Cost, Security, and Alignment While large language model agents are impressive, generating scalable and robust llm models isn’t an easy task.  These problem areas require consideration and thought when designing financial products. ### Latency: How Multi-Step Tasks Like Chain-of-Thought Reasoning Introduce Response Delays in LLM Agents - One common challenge is latency. LLM agents often perform multi-step tasks such as document summarization, reasoning from the chain of thought, or data extraction from external APIs. - For example, a customer support agent that queries multiple knowledge bases to generate accurate answers may introduce noticeable delays in response times. This latency can be especially problematic in high-speed applications like financial trading bots or real-time chat interfaces, where every millisecond counts. ### Cost: How Compute-Heavy GPU Requirements Make Scalable LLM Agent Deployment Expensive for Startups - Another critical issue is operational cost. LLMs (large language models) of billions of parameters must run on high-performance GPUs with large memory. - For instance, an AI research team deploying agents for scientific literature review may face escalating cloud bills due to frequent, compute-heavy queries. This makes scalability a challenge, particularly for startups and academic teams with limited budgets. ### Security: How Memory Leaks and Prompt Injection Attacks Create Vulnerabilities in LLM Agent Systems - Many designers of LLM agents are concerned with security issues. Without proper safeguards, agents may leak high-value information or become a target of attacks. - For example, if a legal assistant agent is prompted cleverly, it might unintentionally reveal sensitive client information stored in its memory. Similarly, attackers can craft malicious inputs to trigger unintended behaviors, such as executing harmful tool calls or bypassing filters. ### Alignment: How Ensuring Ethical and Goal-Aligned Behavior Prevents Harmful or Misleading Agent Outputs - Ensuring ethical and goal-aligned behavior is another major hurdle. LLM agents must follow specific instructions and avoid generating harmful or misleading content. - For instance, a healthcare chatbot should avoid giving medical advice that contradicts clinical guidelines. However, without careful tuning, an agent may still generate incorrect or biased responses, leading to real-world consequences like misinformation or reputational damage. Because of these challenges, one must focus on thoughtful design on the part of the AI agent, thorough testing, and regular evaluation. This helps the developer in building agents which are intelligent and efficient while being secure, cost-effective and in line with user expectations. ## Best Practices for Developing a Reliable LLM Agents Framework: Modularity, Safety, Feedback, and Performance To build a functional and scalable **framework for LLM applications**, consider these best practices: 1. **Start Modular**: Design each component independently for flexibility and reusability. 2. **Implement Feedback Loops**: Allow for error correction and user feedback. 3. **Prioritize Safety**: Apply guardrails and ethical constraints from the outset. 4. **Use Open Standards**: Leverage open-source libraries and interoperable APIs. 5. **Optimize for Performance**: Balance quality and speed by tuning prompts and caching responses. When developers follow these principles, they can expect their agents to work across different AI development platforms. ## Tools and Platforms Supporting LLM Agent Frameworks: LangChain, OpenAI, AutoGPT, Haystack, and RAG Pipelines A variety of tools has been created to help design modern LLM systems. Some of the most notable ones include: 1. **LangChain**: Offers a modular way to build LLM agents using prompts, memory, tools, and chains. 2. **OpenAI Functions + Agents**: Provides built-in support for tool usage and multi-step planning. 3. **AutoGPT and BabyAGI**: Experimental platforms that explore autonomous AI agents capable of completing objectives without supervision. 4. **Haystack and RAG Pipelines**: Support retrieval-augmented generation for document-based applications. Together, these tools enable a strong cognitive architecture for LLM, designed for enterprise and research-grade applications. ## Future of LLM Agent Architectures: On-Device Agents, Federated Learning, Explainability, and Multi-Agent Collaboration Expect the LLM agents framework to further evolve as the development of AI continues. Future developments may include: ### Fully On-Device Agents: How Privacy-Critical Applications Will Run Complex LLM Models Without Cloud Transmission - While on-device LLM agents already exist in limited forms, next-generation agents will likely run much more complex models entirely on smartphones, wearables and edge devices with low latency. - For highly privacy-sensitive applications like a personal health assistant that analyzes his biometric data without ever transmitting it to the cloud this will be vital. On the one hand, this ensures confidentiality. ### Federated and Continual Learning: How Decentralized Adaptive Models Will Improve Across Distributed Networks - With a rise in the popularity of federated learning, we will also witness sophisticated decentralized training approaches where LLM agents will learn continuously from the data of diverse users across their devices without violating privacy. - This will allow for large-scale, real-time improvements across distributed networks of agents, such as smart home systems that work together to learn a user’s preferences and conditions. ### Explainable and Trustworthy Agents: How Future LLM Agents Will Justify Decisions and Quantify Uncertainty - AI used in critical sectors like healthcare, law and finance is becoming a necessity to explain. - LLM agents in the future will not only give reasons behind their decisions in easily understandable human terms but will also further expound on their reasoning and quantify uncertainty for the users so that the latter may trust and effectively supervise autonomous decisions. ### Dynamic Multi-Agent Collaboration: How Specialized LLM Agents Will Cooperate in Real Time Across Industries - It is already well-explored by the research surrounding multi-agent collaboration, but likely architectures will have real-time collaboration between specialized LLM agents. - In the supply chain example, systems ranging from logistics companies to market analysis bots to stock inventory will cooperate and create a marketplace for resources and services where they will self-best offer based on price and capability. Moreover, improvements in **natural language processing models** and **AI programming structures** will continue to drive innovation in this space. ## How Understanding LLM Agent Architecture Components Helps Developers Build More Effective AI Systems When we explore the [LLM agents](/content/blogs/build-llm-agents/index.html) framework, we understand how complicated and powerful they are. Every piece plays an essential part, from modular memory units to using outside tools and advanced planning and feedback systems. When all of this is done correctly, they can create a system that is strong, adaptable as well as smart, which can do many things. It is very important to fully understand the components of [LLM agents](/content/blogs/build-llm-agents/index.html) architecture if you are a developer, researcher, or business strategist. When you align your own goals against a robust LLM model framework, you can build more effective solutions, as well as leverage the full power of intelligent agents. Thus, it is possible to change the flow of not just one but also many workflows and industries via the agents. ### **Turn Ambition Into Action with AGI** [Future AGI](/content/site-root.html) assist forward-thinking teams in harnessing the power of artificial intelligence to produce real results. We are ready to help you to scale up with confidence, whether you’re optimizing workflows or building next-gen products. [Book a call](/content/contact-us/index.html) today. ## Frequently Asked Questions About LLM Agent Architectures and Frameworks ### What are the key differences between traditional chatbots and LLM-based agent frameworks? Traditional chatbots are rule-based systems that help follow a certain script normally. They are unable to adapt through user inputs for dynamic conversations. Conversely, LLM agents draw on cutting-edge language models organized with architectural flexibility, in-memory access, planning and reasoning capabilities, and tool use. This helps them with difficult tasks, retain context over multiple turns, and behave similarly to a human. ### How can businesses benefit from implementing an LLM model framework across departments? Companies can make use of the LLM model framework to deploy intelligent agents in all departments for maximum benefit. These agents can automate customer service, optimize legal research, personalize education, aid in healthcare decision-making, and speed up software development. Companies can save money, work faster, and offer tailored user experiences through smart AI agent design. ### What are the biggest challenges in building reliable and scalable LLM agent frameworks today? Several challenges such as latency, computation, security and alignment are too big to ignore. Developers must manage performance vs. scalability while using Ai responsibly. To make LLM agents trustworthy, we can use modular design, prompt tuning (to guide model behavior), secure (robust) architectures, and human-in-the loop feedback. ### How do memory modules and tool integration enhance LLM agent performance and contextual accuracy? By integrating memory and tools, LLM agents can comprehend the context and execute a relevant action. Memory modules are helpful in engaging agents that can remember facts through multiple dialogues with users. Web search APIs, code execution environments, or databases, for example, are tools through which agents can perform dynamic tasks with real-time data and accurate outputs. When working together, these parts make LLM agents smart tasks assistants instead of just responders. Related Articles [View all](/content/blog/index.html) \\ \\ Guide](/content/blog/voice-ai-evaluation-infrastructure-developers-guide/index.html) [Voice AI Evaluation Infrastructure: A Developer's Guide to Testing Voice Agents Before They Hit Production](/content/blog/voice-ai-evaluation-infrastructure-developers-guide/index.html) Build production-grade voice AI evaluation in 2026. Covers STT, LLM & TTS metrics, five evaluation layers, synthetic testing frameworks, and key pitfalls to avoid. Rishav Hada·Mar 24, 2026 5 min \\ \\ Guide](/content/blog/llm-benchmarking-compare-2025/index.html) [LLM Benchmarking: Compare Top AI Models for Your Specific Needs](/content/blog/llm-benchmarking-compare-2025/index.html) Compare top LLMs in 2026 including GPT-5, Grok-4, Claude 4, and Gemini 2.5 Pro. Covers reasoning, coding, context window, speed, cost benchmarks, and use-case. Sahil N·Sep 26, 2025 5 min \\ \\ Guide](/content/blog/top-5-ai-guardrailing-tools-2025/index.html) [Top 5 AI Guardrailing Tools in 2025](/content/blog/top-5-ai-guardrailing-tools-2025/index.html) Compare top 5 AI guardrailing tools in 2026: Future AGI Protect, Galileo, Arize, Robust Intelligence, and Bedrock. Covers coverage, latency, and fit. NVJK Kartik·Jul 23, 2025 5 min Stay updated on AI observability Get weekly insights on building reliable AI systems. No spam. Subscribe You're subscribed! AI AssistantBeta FutureAGI AI Assistant Ask me anything about the FutureAGI platform — I can search across all docs instantly. What is FutureAGI?What can FutureAGI do?How do I run my first evaluation?How do I set up tracing?How do I detect hallucinations? Built by FAGI with ❤️ Explain "Exploring the Core Componen…"