{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"agentops","slug":"agentops","name":"AgentOps","type":"agent","url":"https://www.agentops.ai","page_url":"https://unfragile.ai/agentops","categories":["observability","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"agentops__cap_0","uri":"capability://automation.workflow.session.replay.with.point.in.time.debugging","name":"session-replay-with-point-in-time-debugging","description":"Records complete agent execution traces including LLM calls, tool invocations, and multi-agent interactions, enabling developers to rewind and replay agent runs with point-in-time precision. The platform captures full event sequences and renders them in a visual timeline interface, allowing inspection of intermediate states, prompts, and responses at any execution point without re-running the agent.","intents":["Debug why an agent made a specific decision at step 5 of a 20-step execution","Understand the exact sequence of tool calls and LLM responses that led to a failure","Inspect what prompts were sent to the LLM and what responses were received in production","Replay a user-reported issue without needing to reproduce it manually"],"best_for":["AI agent developers debugging production failures","Teams managing multi-agent systems with complex interaction patterns","Enterprise users requiring audit trails for compliance"],"limitations":["Requires agent to be instrumented with AgentOps SDK — cannot replay agents not using the platform","Replay is read-only visualization; cannot modify execution state and re-run from arbitrary points","Data retention policies vary by tier (defaults unknown); older sessions may be archived or deleted","Latency/performance impact of event capture overhead not documented"],"requires":["Python 3.8+ with agentops SDK installed","Agent framework with AgentOps integration (specific frameworks not listed in documentation)","Active AgentOps account (free tier supports basic replay)"],"input_types":["agent execution traces (captured automatically via SDK instrumentation)","LLM call logs","tool invocation records","error logs and stack traces"],"output_types":["interactive timeline visualization","structured event sequence (JSON/structured format implied)","audit logs with timestamps and metadata"],"categories":["automation-workflow","debugging-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_1","uri":"capability://data.processing.analysis.multi.provider.llm.cost.tracking.and.monitoring","name":"multi-provider-llm-cost-tracking-and-monitoring","description":"Tracks token consumption and spending across 400+ LLM providers and models by intercepting LLM API calls through the AgentOps SDK, maintaining up-to-date pricing data for each model, and aggregating costs across multiple agents and sessions. The platform provides real-time cost visualization, token counting for every LLM interaction, and cost-per-session breakdowns to identify expensive agent behaviors.","intents":["Monitor total LLM spending across all agents to stay within budget","Identify which agents or sessions are consuming the most tokens and incurring highest costs","Compare cost efficiency of different LLM models for the same task","Track token usage patterns to optimize prompt engineering and reduce API spend"],"best_for":["Teams deploying multiple agents with different LLM backends","Cost-conscious builders optimizing LLM spend in production","Enterprise users requiring detailed cost allocation and chargeback"],"limitations":["Pricing data must be kept current; outdated pricing tables will produce inaccurate cost estimates","Does not provide cost optimization recommendations — only tracking and visualization","Cost tracking overhead (SDK instrumentation) not quantified; may add latency to LLM calls","Supports 400+ LLMs but specific list and pricing update frequency unknown","Cannot retroactively track costs for agents not instrumented with AgentOps"],"requires":["Python 3.8+ with agentops SDK","LLM API keys configured in agent (OpenAI, Anthropic, or other supported providers)","AgentOps account with cost tracking enabled (available on free tier)"],"input_types":["LLM API calls (intercepted via SDK)","token counts from LLM responses","model identifiers and pricing data"],"output_types":["cost aggregations (per session, per agent, per model)","token usage reports","cost trend visualizations","structured cost data (JSON implied)"],"categories":["data-processing-analysis","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_10","uri":"capability://data.processing.analysis.dashboard.and.visualization.interface","name":"dashboard-and-visualization-interface","description":"Provides a web-based dashboard for visualizing agent metrics, session replays, cost trends, and error logs with interactive charts, timelines, and drill-down capabilities. The dashboard enables non-technical stakeholders to understand agent behavior and performance without accessing raw logs or code.","intents":["View agent performance metrics and trends in a centralized dashboard","Drill down from high-level metrics to specific sessions or events","Share agent performance reports with non-technical stakeholders","Monitor real-time agent activity and cost spending"],"best_for":["Teams with non-technical stakeholders (product managers, executives) needing visibility","Organizations requiring centralized agent monitoring across multiple teams","Developers preferring visual debugging over log analysis"],"limitations":["Dashboard performance with large datasets (1000s of agents, millions of events) not documented","Customization options for dashboards not detailed","Export capabilities (PDF, CSV) not mentioned","Mobile or responsive design not mentioned; likely desktop-focused"],"requires":["Web browser with JavaScript support","AgentOps account with dashboard access"],"input_types":["agent metrics and events from SDK"],"output_types":["interactive visualizations (charts, timelines, tables)","drill-down reports","exported data (format unknown)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_11","uri":"capability://automation.workflow.self.hosted.and.on.premise.deployment.options","name":"self-hosted-and-on-premise-deployment-options","description":"Offers self-hosted deployment on AWS, GCP, or Azure, and on-premise deployment for organizations with data residency or security requirements. The platform provides containerized deployment options and infrastructure-as-code templates, enabling organizations to run AgentOps in their own cloud or on-premise environments while maintaining data sovereignty.","intents":["Deploy AgentOps in a private cloud or on-premise environment for data residency","Maintain full control over agent observability data without sending it to SaaS platform","Integrate AgentOps with existing on-premise infrastructure and security policies"],"best_for":["Enterprise organizations with data residency requirements (GDPR, HIPAA, etc.)","Teams with strict security policies prohibiting cloud SaaS","Organizations with existing on-premise infrastructure and DevOps expertise"],"limitations":["Self-hosting and on-premise deployment only available at Enterprise tier (not free/Pro)","Deployment and infrastructure requirements not documented","Support for self-hosted deployments (SLAs, patches) not detailed","Licensing model for self-hosted deployments not specified","Requires DevOps expertise to deploy and maintain"],"requires":["Enterprise tier AgentOps subscription","AWS, GCP, or Azure account (for cloud self-hosting)","Kubernetes or Docker expertise (implied)","Network connectivity between agents and self-hosted platform"],"input_types":["deployment configuration (cloud provider, region, etc.)"],"output_types":["self-hosted AgentOps instance","infrastructure-as-code templates (implied)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_2","uri":"capability://data.processing.analysis.fine.tuning.cost.optimization.via.completion.caching","name":"fine-tuning-cost-optimization-via-completion-caching","description":"Analyzes saved LLM completions from agent runs and identifies opportunities to fine-tune specialized models on frequently-repeated completion patterns, claiming to reduce inference costs by up to 25x. The platform presumably identifies common prompt-completion pairs and recommends fine-tuning targets, though the exact mechanism for cost calculation and fine-tuning workflow is not documented.","intents":["Reduce LLM API costs for agents with repetitive tasks or patterns","Identify which agent behaviors are most expensive and could benefit from fine-tuning","Estimate cost savings from fine-tuning before committing to training"],"best_for":["High-volume agent deployments with repetitive tasks","Teams with budget constraints looking to reduce per-inference costs","Organizations willing to invest in fine-tuning infrastructure"],"limitations":["Claims '25x cheaper' but baseline cost and actual savings methodology not documented","Requires sufficient volume of saved completions to identify fine-tuning opportunities","Fine-tuning workflow integration with external services (OpenAI, Anthropic) not detailed","No information on fine-tuning latency, training time, or minimum viable dataset size","Cost of fine-tuning itself (training fees) not addressed in marketing materials"],"requires":["Python 3.8+ with agentops SDK","Sufficient historical agent runs to identify patterns (minimum threshold unknown)","Access to fine-tuning APIs (OpenAI, Anthropic, or other supported providers)","AgentOps Pro or Enterprise tier (free tier support unknown)"],"input_types":["saved LLM completions from agent runs","prompt-completion pairs","token usage and cost data"],"output_types":["fine-tuning recommendations","cost savings estimates","fine-tuned model identifiers (implied)"],"categories":["data-processing-analysis","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_3","uri":"capability://safety.moderation.compliance.and.security.audit.logging","name":"compliance-and-security-audit-logging","description":"Captures and logs all agent actions (LLM calls, tool invocations, errors, prompt injections) in an immutable audit trail with timestamps and metadata, supporting compliance frameworks including SOC-2, HIPAA, and NIST AI RMF at the Enterprise tier. The platform provides role-based access control, custom SSO integration, and Slack Connect for audit notifications, enabling organizations to demonstrate compliance with regulatory requirements.","intents":["Maintain an immutable audit trail of all agent actions for regulatory compliance","Detect and log potential prompt injection attacks or security incidents","Restrict access to sensitive agent data based on user roles and permissions","Integrate compliance monitoring with existing security infrastructure (SSO, Slack)"],"best_for":["Enterprise organizations subject to HIPAA, SOC-2, or NIST AI RMF compliance requirements","Teams handling sensitive data (healthcare, finance, legal) requiring audit trails","Multi-user organizations needing role-based access control"],"limitations":["Compliance certifications (SOC-2, HIPAA, NIST AI RMF) only available at Enterprise tier, not free/Pro","Self-hosting and on-premise deployment only available at Enterprise tier","Specific compliance controls and audit log retention policies not documented","Prompt injection detection mechanism not detailed; unclear what constitutes a detected injection","Custom SSO and Slack Connect require Enterprise tier; basic authentication only on lower tiers"],"requires":["Python 3.8+ with agentops SDK","AgentOps Enterprise tier for compliance certifications","LDAP/SAML provider for custom SSO (Enterprise)","Slack workspace for Slack Connect integration (Enterprise)"],"input_types":["all agent execution events (LLM calls, tool invocations, errors)","user access logs","security events (prompt injections, unauthorized access attempts)"],"output_types":["immutable audit logs with timestamps","compliance reports (format unknown)","access control policies","security incident notifications"],"categories":["safety-moderation","compliance-monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_4","uri":"capability://data.processing.analysis.agent.performance.benchmarking.and.comparison","name":"agent-performance-benchmarking-and-comparison","description":"Provides tools to benchmark and compare agent performance across multiple dimensions (cost, latency, success rate, token efficiency) by aggregating metrics from multiple agent runs and sessions. The platform claims to have tested 400+ agents and provides guidance on agent selection, though specific benchmarking methodology and available metrics are not detailed in documentation.","intents":["Compare performance of different agent implementations on the same task","Identify performance regressions after code changes or model updates","Benchmark agent efficiency (cost per successful completion, latency, token usage)","Select optimal agent architecture based on production performance data"],"best_for":["Teams evaluating multiple agent frameworks or implementations","Developers optimizing agent performance in production","Organizations comparing LLM models or agent configurations"],"limitations":["Specific benchmarking metrics and methodology not documented","Comparison requires agents to be instrumented with AgentOps SDK","Benchmarking data only available for agents using the platform (not external agents)","No information on statistical significance testing or confidence intervals","Reference benchmarks for '400+ tested agents' not publicly available"],"requires":["Python 3.8+ with agentops SDK","Multiple agent runs or sessions to establish baseline metrics","AgentOps account (available on free tier)"],"input_types":["agent execution metrics (cost, latency, success rate, token usage)","multiple agent runs for statistical aggregation"],"output_types":["performance comparison reports","metric aggregations (mean, median, percentiles implied)","performance trend visualizations","agent selection recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_5","uri":"capability://tool.use.integration.framework.agnostic.sdk.instrumentation","name":"framework-agnostic-sdk-instrumentation","description":"Provides a single Python SDK (`pip install agentops`) that integrates with multiple agent frameworks through a plugin/hook architecture, capturing events from any framework without requiring framework-specific code changes. The platform claims 'one SDK, many integrations' and supports native integrations with 'top agent frameworks' (specific frameworks not listed), enabling developers to add observability to existing agents with minimal code modifications.","intents":["Add observability to an existing agent without rewriting or refactoring code","Switch between agent frameworks while maintaining consistent observability","Integrate AgentOps into a multi-framework agent ecosystem"],"best_for":["Teams using multiple agent frameworks and wanting unified observability","Developers adding observability to existing agents with minimal changes","Organizations evaluating agent frameworks without committing to observability infrastructure"],"limitations":["Specific supported frameworks not documented; 'top agent frameworks' is vague","SDK integration requires agent code to import and initialize AgentOps (not zero-touch)","Instrumentation overhead (latency impact) not quantified","Framework-specific hooks may not capture all events; coverage varies by framework","No information on SDK version compatibility with specific framework versions"],"requires":["Python 3.8+","pip or equivalent package manager","Agent framework with AgentOps integration (specific frameworks unknown)","API key or credentials for LLM providers used by agent"],"input_types":["agent framework code","LLM API calls","tool invocations"],"output_types":["instrumented agent (no code changes to agent logic)","event streams sent to AgentOps platform"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_6","uri":"capability://automation.workflow.real.time.cost.alerts.and.budget.management","name":"real-time-cost-alerts-and-budget-management","description":"Monitors LLM spending in real-time and triggers alerts when costs exceed configured thresholds, enabling developers to detect runaway spending or unexpected cost spikes. The platform provides budget tracking and visualization, though specific alert mechanisms (email, Slack, webhooks) and budget enforcement capabilities are not detailed in documentation.","intents":["Receive alerts when an agent's LLM spending exceeds a daily or monthly budget","Detect cost anomalies or runaway agents consuming excessive tokens","Set spending limits to prevent unexpected bills from LLM API providers"],"best_for":["Teams with strict LLM budgets or cost constraints","Developers deploying agents to production without cost guardrails","Organizations needing real-time cost visibility and alerts"],"limitations":["Alert mechanisms (email, Slack, webhooks) not documented","Budget enforcement (hard limits vs soft alerts) not specified","Alert latency not documented; real-time may have delays","No information on alert configuration granularity (per-agent, per-model, global)","Cannot prevent agent execution if budget exceeded; alerts are notifications only"],"requires":["Python 3.8+ with agentops SDK","AgentOps account with cost tracking enabled","Budget thresholds configured in AgentOps dashboard"],"input_types":["real-time LLM cost data","configured budget thresholds"],"output_types":["cost alerts (email, Slack, or webhook implied)","budget status dashboards"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_7","uri":"capability://automation.workflow.multi.agent.interaction.tracing","name":"multi-agent-interaction-tracing","description":"Captures and visualizes interactions between multiple agents in a coordinated system, including message passing, tool sharing, and sequential or parallel execution patterns. The platform traces the full execution graph of multi-agent systems, enabling developers to understand how agents coordinate and where bottlenecks or failures occur in complex agent networks.","intents":["Debug interactions between multiple agents in a coordinated system","Understand message flow and data passing between agents","Identify bottlenecks or failures in multi-agent execution pipelines","Visualize the execution graph of complex agent networks"],"best_for":["Teams building multi-agent systems with complex coordination patterns","Developers debugging agent-to-agent communication failures","Organizations deploying hierarchical or swarm-based agent architectures"],"limitations":["Requires all agents in the system to be instrumented with AgentOps SDK","Visualization of large agent networks (100+ agents) may be complex or slow","Message passing format and protocol not specified; assumes standard event model","No information on support for asynchronous or event-driven agent communication","Tracing overhead for multi-agent systems not quantified"],"requires":["Python 3.8+ with agentops SDK","All agents in the system instrumented with AgentOps","Agents must emit events for inter-agent communication"],"input_types":["agent execution events","inter-agent messages or function calls","tool invocations shared between agents"],"output_types":["multi-agent execution graph visualization","message flow diagrams","interaction sequence logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_8","uri":"capability://data.processing.analysis.event.based.pricing.and.usage.tracking","name":"event-based-pricing-and-usage-tracking","description":"Implements a freemium pricing model based on event volume (free tier: 5,000 events/month; Pro: unlimited events at $40+/month), where each LLM call, tool invocation, or agent action counts as an event. The platform tracks event consumption in real-time and enforces tier limits, enabling developers to understand observability costs and scale usage as needed.","intents":["Understand how many events an agent generates and what tier is required","Monitor event consumption to avoid unexpected billing","Estimate observability costs for scaling agents to production"],"best_for":["Developers evaluating AgentOps with low-volume agents (free tier)","Teams scaling agents to production and needing to budget for observability","Cost-conscious organizations comparing observability platform pricing"],"limitations":["Definition of 'event' not precisely documented; unclear what counts (LLM calls only, or all SDK calls?)","Free tier limit (5,000 events/month) may be insufficient for high-volume agents","Pro tier pricing ($40+/month) is vague; exact pricing and overage charges unknown","No information on event retention or archival policies","Pricing does not account for data storage or API rate limits"],"requires":["AgentOps account (free tier available)","Python 3.8+ with agentops SDK"],"input_types":["agent execution events"],"output_types":["event usage reports","tier recommendations","billing statements"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__cap_9","uri":"capability://automation.workflow.error.and.failure.logging.with.context","name":"error-and-failure-logging-with-context","description":"Captures errors, exceptions, and agent failures with full execution context (preceding LLM calls, tool invocations, prompts, responses), enabling developers to understand root causes without manual log analysis. The platform logs stack traces, error messages, and the complete execution path leading to failure, providing rich debugging information for production issues.","intents":["Understand why an agent failed or returned an error in production","Inspect the full execution context (prompts, responses, tool calls) leading to a failure","Identify patterns in agent failures across multiple runs","Debug errors without needing to reproduce them manually"],"best_for":["Teams debugging production agent failures","Developers analyzing error patterns to improve agent robustness","Organizations requiring detailed error logs for compliance"],"limitations":["Error context capture depends on SDK instrumentation; errors outside SDK scope may not be logged","Stack traces and error messages may contain sensitive information (prompts, API keys)","No information on error deduplication or grouping by root cause","Error retention policies not documented; older errors may be archived"],"requires":["Python 3.8+ with agentops SDK","Agent framework with error event support"],"input_types":["exception objects and stack traces","error messages","execution context (preceding events)"],"output_types":["error logs with full context","error trend reports","root cause analysis (implied)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"agentops__headline","uri":"capability://data.processing.analysis.ai.agent.observability.platform","name":"ai agent observability platform","description":"AgentOps is an observability and evaluation platform designed for AI agents, offering tools for session replays, compliance monitoring, and performance optimization in production environments.","intents":["best AI agent observability tool","AI agent performance tracking for production","how to debug AI agents","compliance monitoring for AI agents","session replay tools for AI agents"],"best_for":["AI developers","AI operations teams"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ with agentops SDK installed","Agent framework with AgentOps integration (specific frameworks not listed in documentation)","Active AgentOps account (free tier supports basic replay)","Python 3.8+ with agentops SDK","LLM API keys configured in agent (OpenAI, Anthropic, or other supported providers)","AgentOps account with cost tracking enabled (available on free tier)","Web browser with JavaScript support","AgentOps account with dashboard access","Enterprise tier AgentOps subscription","AWS, GCP, or Azure account (for cloud self-hosting)"],"failure_modes":["Requires agent to be instrumented with AgentOps SDK — cannot replay agents not using the platform","Replay is read-only visualization; cannot modify execution state and re-run from arbitrary points","Data retention policies vary by tier (defaults unknown); older sessions may be archived or deleted","Latency/performance impact of event capture overhead not documented","Pricing data must be kept current; outdated pricing tables will produce inaccurate cost estimates","Does not provide cost optimization recommendations — only tracking and visualization","Cost tracking overhead (SDK instrumentation) not quantified; may add latency to LLM calls","Supports 400+ LLMs but specific list and pricing update frequency unknown","Cannot retroactively track costs for agents not instrumented with AgentOps","Dashboard performance with large datasets (1000s of agents, millions of events) not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:19.836Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=agentops","compare_url":"https://unfragile.ai/compare?artifact=agentops"}},"signature":"1tUyJw6YeEub5EL0s6CO6PJjPP7YhCt24u3PXeNzkvMSKZgUOTRhT53nSsK9npY1akPLE+7+KeE8x4zzk6CqCA==","signedAt":"2026-06-21T04:48:24.815Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/agentops","artifact":"https://unfragile.ai/agentops","verify":"https://unfragile.ai/api/v1/verify?slug=agentops","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}