{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-agentops","slug":"pypi-agentops","name":"agentops","type":"agent","url":"https://pypi.org/project/agentops/","page_url":"https://unfragile.ai/pypi-agentops","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-agentops__cap_0","uri":"capability://automation.workflow.agent.execution.tracing.with.session.recording","name":"agent execution tracing with session recording","description":"Records complete execution traces of AI agent runs including LLM calls, tool invocations, and state transitions. Implements automatic instrumentation via Python decorators and context managers that capture function calls, arguments, return values, and timing metadata without requiring manual logging code. Stores traces in a session-based structure enabling replay and debugging of multi-step agent workflows.","intents":["I need to see exactly what my agent did during a failed run, including every LLM call and tool invocation","I want to record agent sessions for debugging and understanding unexpected behavior","I need to capture the full execution timeline of an agent to identify performance bottlenecks"],"best_for":["AI agent developers building multi-step autonomous systems","Teams debugging complex LLM-based workflows in production","Researchers analyzing agent behavior patterns across multiple runs"],"limitations":["Tracing overhead scales with agent complexity — deeply nested tool calls may add 50-200ms per step","Session storage requires external backend (cloud or local) — no built-in persistence","Decorator-based instrumentation requires code modification; cannot retroactively trace unmodified libraries"],"requires":["Python 3.8+","AgentOps API key for cloud session storage","Integration with supported LLM providers (OpenAI, Anthropic, etc.)"],"input_types":["Python function calls","LLM API requests","Tool invocation parameters"],"output_types":["JSON session traces","Structured execution logs","Timeline metadata"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_1","uri":"capability://data.processing.analysis.llm.call.monitoring.and.cost.tracking","name":"llm call monitoring and cost tracking","description":"Automatically intercepts and logs all LLM API calls (prompts, completions, token counts, latency) across multiple providers. Implements provider-agnostic instrumentation that wraps OpenAI, Anthropic, Cohere, and other client libraries to capture request/response metadata. Aggregates usage metrics and calculates per-call and per-session costs based on published pricing models.","intents":["I need to track how much my agent is spending on LLM API calls","I want to see which prompts are most expensive and optimize them","I need to monitor token usage patterns to understand model behavior"],"best_for":["Developers managing LLM costs in production agents","Teams optimizing prompt efficiency and token usage","Organizations requiring cost attribution per agent or workflow"],"limitations":["Cost calculations depend on accurate pricing data — may lag behind provider price changes","Cannot track costs for self-hosted or fine-tuned models without custom configuration","Latency measurements include network overhead and cannot isolate model inference time"],"requires":["Python 3.8+","LLM provider API keys (OpenAI, Anthropic, etc.)","AgentOps SDK initialized before LLM client instantiation"],"input_types":["LLM API requests","Token counts","Provider pricing data"],"output_types":["Cost metrics (USD per call/session)","Token usage statistics","Latency measurements"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_10","uri":"capability://safety.moderation.compliance.and.audit.logging","name":"compliance and audit logging","description":"Records all agent actions in an immutable audit log suitable for compliance and regulatory requirements. Implements tamper-evident logging with checksums and timestamps. Provides filtering and export capabilities for compliance reporting (HIPAA, SOC2, etc.) and enables retention policies based on data sensitivity.","intents":["I need to maintain audit logs of all agent actions for compliance","I want to prove that my agent behaved correctly in a specific scenario","I need to export logs for regulatory audits or investigations"],"best_for":["Organizations in regulated industries (healthcare, finance, legal)","Teams requiring compliance documentation for AI systems","Enterprises needing audit trails for governance"],"limitations":["Immutable logging adds storage overhead — may require dedicated infrastructure","Compliance requirements vary by jurisdiction — generic implementation may not meet all needs","Sensitive data in logs requires encryption and access controls — adds complexity"],"requires":["Python 3.8+","Secure storage backend (encrypted database or cloud service)","Compliance framework knowledge (HIPAA, SOC2, etc.)"],"input_types":["Agent actions and decisions","LLM calls and responses","Tool invocations"],"output_types":["Immutable audit logs","Compliance reports","Exported log archives"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_2","uri":"capability://tool.use.integration.tool.call.instrumentation.and.validation","name":"tool call instrumentation and validation","description":"Captures all tool/function invocations made by agents including function name, arguments, return values, and execution time. Implements automatic wrapping of tool registries and function definitions to log calls without modifying tool implementations. Validates tool schemas and can enforce constraints like argument types, return value formats, and execution timeouts.","intents":["I need to see what tools my agent called and with what arguments","I want to validate that tools are being called correctly before execution","I need to track which tools are most frequently used and their success rates"],"best_for":["Developers building agents with complex tool ecosystems","Teams debugging tool integration issues and argument mismatches","Organizations monitoring tool usage patterns for optimization"],"limitations":["Schema validation only works with explicitly registered tools — dynamically generated tools may bypass validation","Timeout enforcement requires async/await patterns; synchronous tools cannot be interrupted mid-execution","Argument logging may expose sensitive data (API keys, credentials) — requires careful filtering configuration"],"requires":["Python 3.8+","Tool definitions with type hints or JSON schemas","AgentOps SDK initialized before tool registration"],"input_types":["Function signatures","Tool schemas (JSON Schema or Pydantic models)","Function arguments and return values"],"output_types":["Tool call logs with arguments and results","Validation errors and constraint violations","Execution metrics (latency, success/failure)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_3","uri":"capability://memory.knowledge.agent.state.and.memory.snapshots","name":"agent state and memory snapshots","description":"Captures periodic snapshots of agent internal state including memory, context windows, and decision variables throughout execution. Implements state serialization that preserves complex Python objects (lists, dicts, custom classes) and stores them alongside execution traces. Enables comparison of state across execution steps to identify where agent behavior diverged from expected paths.","intents":["I need to see what information my agent had available when it made a decision","I want to compare agent state at different points in execution to debug logic errors","I need to understand how agent memory evolved during a multi-step task"],"best_for":["Developers debugging agent decision-making and reasoning","Teams analyzing agent memory management and context window usage","Researchers studying how agent state influences behavior"],"limitations":["Serialization of complex objects may fail for non-JSON-serializable types — requires custom encoders","Frequent snapshots increase storage overhead — sampling strategies may miss critical state changes","State comparison tools are basic — no built-in diff visualization or anomaly detection"],"requires":["Python 3.8+","JSON-serializable or custom-encoder-compatible state objects","AgentOps backend with sufficient storage for state snapshots"],"input_types":["Agent memory objects","Context dictionaries","Custom state classes"],"output_types":["JSON state snapshots","State change logs","Serialized object representations"],"categories":["memory-knowledge","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_4","uri":"capability://automation.workflow.web.dashboard.for.session.visualization.and.replay","name":"web dashboard for session visualization and replay","description":"Provides a web-based UI for viewing recorded agent sessions with interactive timeline visualization, LLM call details, tool invocation logs, and cost breakdowns. Implements client-side rendering of execution traces with filtering and search capabilities. Supports session replay mode that reconstructs agent execution step-by-step with state snapshots and decision points highlighted.","intents":["I want to visually explore what happened during an agent run without parsing logs","I need to share agent execution details with non-technical team members","I want to replay a session step-by-step to understand agent decision-making"],"best_for":["Product managers and stakeholders reviewing agent behavior","Developers debugging complex multi-step workflows visually","Teams conducting post-mortems on agent failures"],"limitations":["Dashboard performance degrades with very large sessions (>10k steps) — may require pagination or sampling","Replay mode is read-only — cannot modify state and re-execute","Visualization is optimized for typical agent patterns — highly custom workflows may not render clearly"],"requires":["AgentOps cloud account with dashboard access","Modern web browser (Chrome, Firefox, Safari, Edge)","Internet connectivity to AgentOps servers"],"input_types":["Session traces (JSON)","Execution metadata"],"output_types":["Interactive HTML/JavaScript UI","Filtered/searched trace subsets","Step-by-step replay visualization"],"categories":["automation-workflow","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_5","uri":"capability://automation.workflow.multi.agent.coordination.tracking","name":"multi-agent coordination tracking","description":"Tracks interactions between multiple agents in a system including message passing, shared state updates, and coordination events. Implements correlation of traces across agent instances using unique session IDs and parent-child relationships. Visualizes agent communication patterns and identifies bottlenecks or deadlocks in multi-agent workflows.","intents":["I need to understand how multiple agents are coordinating and communicating","I want to debug issues that only occur when agents interact","I need to see the full execution graph across all agents in a system"],"best_for":["Teams building multi-agent systems with complex coordination","Developers debugging inter-agent communication issues","Organizations analyzing agent collaboration patterns"],"limitations":["Requires explicit correlation IDs — agents must pass context through message headers","Visualization becomes complex with >5 concurrent agents — may require filtering or hierarchical views","Deadlock detection is not automatic — requires custom rules or manual analysis"],"requires":["Python 3.8+","Multiple agent instances with AgentOps instrumentation","Shared session context or correlation ID mechanism"],"input_types":["Agent traces with correlation IDs","Inter-agent messages","Coordination events"],"output_types":["Multi-agent execution graphs","Message flow diagrams","Coordination event logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_6","uri":"capability://data.processing.analysis.automated.performance.profiling.and.bottleneck.detection","name":"automated performance profiling and bottleneck detection","description":"Analyzes execution traces to identify performance bottlenecks including slow LLM calls, expensive tool invocations, and inefficient agent loops. Implements statistical analysis of timing data to flag outliers and suggests optimization opportunities. Compares performance across multiple sessions to identify regressions or improvements.","intents":["I want to know which parts of my agent are slowest","I need to identify if a recent change made my agent slower","I want suggestions for optimizing my agent's performance"],"best_for":["Developers optimizing agent latency and throughput","Teams monitoring agent performance in production","Organizations analyzing performance trends over time"],"limitations":["Bottleneck detection is heuristic-based — may produce false positives for legitimate slow operations","Optimization suggestions are generic — require domain knowledge to implement effectively","Performance analysis requires multiple sessions for statistical significance — single-run analysis is limited"],"requires":["Python 3.8+","Multiple recorded sessions for comparative analysis","AgentOps backend with analytics capabilities"],"input_types":["Execution traces with timing data","Multiple session records"],"output_types":["Performance reports with bottleneck identification","Optimization suggestions","Performance trend graphs"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_7","uri":"capability://safety.moderation.error.tracking.and.failure.analysis","name":"error tracking and failure analysis","description":"Captures exceptions, API errors, and agent failures with full context including the execution state at failure time. Implements error grouping that clusters similar failures across sessions to identify recurring issues. Provides root cause analysis by correlating errors with preceding LLM calls, tool invocations, and state changes.","intents":["I need to understand why my agent failed and what state it was in","I want to group similar failures to identify patterns","I need to know if a failure is a one-off or a recurring issue"],"best_for":["Developers debugging agent failures in production","Teams identifying and prioritizing bug fixes","Organizations tracking error trends and reliability metrics"],"limitations":["Error grouping is based on error message similarity — may incorrectly group unrelated failures","Root cause analysis is correlative, not causal — requires manual investigation to confirm","Sensitive data in error messages (API responses, user input) may be logged — requires filtering"],"requires":["Python 3.8+","AgentOps SDK initialized to catch exceptions","Multiple failure instances for pattern detection"],"input_types":["Exception objects","Error messages","Execution context at failure time"],"output_types":["Error logs with full context","Error grouping and clustering","Root cause analysis reports"],"categories":["safety-moderation","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_8","uri":"capability://tool.use.integration.integration.with.llm.provider.sdks","name":"integration with llm provider sdks","description":"Provides automatic instrumentation for OpenAI, Anthropic, Cohere, and other LLM provider Python SDKs through monkey-patching or wrapper classes. Implements provider-specific request/response parsing to extract prompts, completions, and metadata without modifying user code. Maintains compatibility with provider SDK updates through version detection and conditional instrumentation.","intents":["I want to track LLM calls without modifying my existing code","I need to work with multiple LLM providers and see unified metrics","I want automatic cost tracking across different LLM APIs"],"best_for":["Developers using standard LLM provider SDKs","Teams using multiple LLM providers in the same agent","Organizations requiring minimal code changes for observability"],"limitations":["Monkey-patching approach may conflict with other instrumentation libraries","Custom LLM provider implementations or local models require manual instrumentation","Provider SDK updates may break instrumentation — requires maintenance"],"requires":["Python 3.8+","Supported LLM provider SDK (openai, anthropic, cohere, etc.)","AgentOps SDK imported before LLM client instantiation"],"input_types":["LLM provider SDK calls"],"output_types":["Instrumented LLM API requests and responses","Extracted prompts and completions","Provider-specific metadata"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-agentops__cap_9","uri":"capability://automation.workflow.structured.logging.with.context.propagation","name":"structured logging with context propagation","description":"Provides structured logging API that automatically includes execution context (session ID, agent ID, step number) in all log messages. Implements context managers and decorators that propagate context through function calls and async operations. Integrates with Python's logging module to enable filtering and routing based on context.","intents":["I want all my agent logs to include session and step context automatically","I need to filter logs by agent or session without manual tagging","I want to correlate my custom logs with AgentOps traces"],"best_for":["Developers adding custom logging to instrumented agents","Teams correlating application logs with agent traces","Organizations using centralized logging systems (ELK, Datadog, etc.)"],"limitations":["Context propagation requires async-aware implementation — may not work with all async libraries","Custom log handlers must be configured to extract and use context","Context is thread-local or async-local — may not propagate across process boundaries"],"requires":["Python 3.8+","Python logging module","AgentOps SDK initialized before logging"],"input_types":["Log messages (strings)","Log levels (DEBUG, INFO, WARNING, ERROR)"],"output_types":["Structured log records with context","Filtered log streams"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","AgentOps API key for cloud session storage","Integration with supported LLM providers (OpenAI, Anthropic, etc.)","LLM provider API keys (OpenAI, Anthropic, etc.)","AgentOps SDK initialized before LLM client instantiation","Secure storage backend (encrypted database or cloud service)","Compliance framework knowledge (HIPAA, SOC2, etc.)","Tool definitions with type hints or JSON schemas","AgentOps SDK initialized before tool registration","JSON-serializable or custom-encoder-compatible state objects"],"failure_modes":["Tracing overhead scales with agent complexity — deeply nested tool calls may add 50-200ms per step","Session storage requires external backend (cloud or local) — no built-in persistence","Decorator-based instrumentation requires code modification; cannot retroactively trace unmodified libraries","Cost calculations depend on accurate pricing data — may lag behind provider price changes","Cannot track costs for self-hosted or fine-tuned models without custom configuration","Latency measurements include network overhead and cannot isolate model inference time","Immutable logging adds storage overhead — may require dedicated infrastructure","Compliance requirements vary by jurisdiction — generic implementation may not meet all needs","Sensitive data in logs requires encryption and access controls — adds complexity","Schema validation only works with explicitly registered tools — dynamically generated tools may bypass validation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:21.281Z","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=pypi-agentops","compare_url":"https://unfragile.ai/compare?artifact=pypi-agentops"}},"signature":"qe7YLwoEwJZXeO2RGeuK2fwBpiYQGXEPdmcxCflKd32Xi1BKJiG2Sa7Bg7ULDgUTCmF/5S7QlbI2m+ueM2AeAg==","signedAt":"2026-06-22T18:14:50.810Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-agentops","artifact":"https://unfragile.ai/pypi-agentops","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}