{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-sheet0--npi","slug":"sheet0--npi","name":"npi","type":"agent","url":"https://docs.npi.ai/docs","page_url":"https://unfragile.ai/sheet0--npi","categories":["ai-agents"],"tags":["agent","artificial-intelligence","autogpt","autonomous-agent","browser-automation","chatgpt","function-calling","gpt-4","intergration","large-language-models","llm","openai","prompt-engineering","workflow"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-sheet0--npi__cap_0","uri":"capability://tool.use.integration.function.calling.action.registry.with.llm.provider.abstraction","name":"function-calling action registry with llm provider abstraction","description":"Provides a standardized action library that abstracts function-calling across multiple LLM providers (OpenAI, Anthropic, etc.) through a unified schema-based registry. Developers define Python functions as actions, which are automatically converted to provider-specific function-calling schemas and routed to the appropriate LLM backend, enabling agents to invoke tools without provider-specific boilerplate.","intents":["I want my agent to call external APIs and Python functions without writing provider-specific code","I need to switch between OpenAI and Anthropic without rewriting my action definitions","I want to expose my Python functions as callable actions to an LLM agent"],"best_for":["autonomous agent developers building multi-provider LLM applications","teams migrating between LLM providers and needing provider-agnostic action definitions","Python developers building function-calling workflows without manual schema management"],"limitations":["Abstraction layer adds latency per function call due to schema translation and routing logic","Limited to Python function definitions — no native support for non-Python callables without wrapper adapters","Provider-specific function-calling features (e.g., parallel tool calls, streaming responses) may not be fully exposed through the abstraction"],"requires":["Python 3.8+","API key for at least one supported LLM provider (OpenAI, Anthropic, or compatible)","npi library installed via pip"],"input_types":["Python function definitions","Function signatures with type hints","Docstrings for action descriptions"],"output_types":["Provider-specific function schemas (OpenAI format, Anthropic format)","Function call results as structured data","Agent action execution logs"],"categories":["tool-use-integration","agent-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_1","uri":"capability://automation.workflow.browser.automation.action.suite.for.web.interaction","name":"browser automation action suite for web interaction","description":"Exposes a set of pre-built actions for browser automation (navigation, clicking, form filling, screenshot capture, text extraction) that agents can invoke to interact with web pages. These actions are wrapped as callable functions within the action registry, allowing LLM agents to autonomously browse and manipulate web content without direct Selenium/Playwright code.","intents":["I want my agent to navigate websites and extract information automatically","I need my agent to fill out web forms and submit them on my behalf","I want to capture screenshots and analyze page content as part of an agent workflow"],"best_for":["autonomous agents performing web scraping and data extraction tasks","RPA (Robotic Process Automation) workflows driven by LLM agents","developers building agents that interact with web-based SaaS tools"],"limitations":["Browser automation actions may timeout on slow or unresponsive pages, requiring retry logic","No built-in handling for JavaScript-heavy SPAs — may require explicit wait conditions for dynamic content","Screenshot and OCR capabilities depend on underlying browser driver performance and may not work reliably with complex CSS or overlays","Limited to actions exposed in the library — custom browser interactions require extending the action set"],"requires":["Python 3.8+","Browser driver (Chrome/Chromium for Puppeteer-based actions, or Firefox for Selenium-based)","npi library with browser automation extras installed","Sufficient system resources for headless browser instances"],"input_types":["URL strings","CSS/XPath selectors","Text input for form fields","Coordinate pairs for click actions"],"output_types":["Page HTML/DOM content","Screenshot images (PNG/JPEG)","Extracted text from page elements","Form submission confirmation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_2","uri":"capability://planning.reasoning.agent.task.decomposition.and.execution.planning","name":"agent task decomposition and execution planning","description":"Enables agents to break down high-level user requests into sequences of discrete actions by leveraging LLM reasoning to plan execution steps. The agent analyzes the user intent, determines which actions from the registry are needed, orders them logically, and executes them sequentially or conditionally based on intermediate results, implementing a form of chain-of-thought planning within the action execution loop.","intents":["I want my agent to automatically figure out what steps are needed to accomplish a complex task","I need my agent to handle multi-step workflows where later steps depend on earlier results","I want my agent to recover from action failures by replanning the remaining steps"],"best_for":["developers building autonomous agents for complex, multi-step workflows","teams implementing agentic AI for customer support or data processing pipelines","builders prototyping self-directed AI systems that adapt to task requirements"],"limitations":["Planning overhead adds latency proportional to task complexity — simple tasks may not benefit from decomposition","Agent may generate suboptimal or redundant action sequences if the LLM lacks sufficient context about action dependencies","No built-in backtracking or plan revision — if an action fails, the agent may not automatically replan unless explicitly instructed","Requires clear action descriptions and docstrings; poorly documented actions lead to incorrect planning decisions"],"requires":["Python 3.8+","LLM provider with sufficient reasoning capability (GPT-4 or equivalent recommended)","Well-documented action registry with clear descriptions of each action's purpose and parameters","npi library with planning/reasoning components"],"input_types":["Natural language task descriptions","User intent statements","Contextual information about available actions"],"output_types":["Execution plan (ordered list of actions)","Action parameters and arguments","Execution trace with intermediate results","Final task completion status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_3","uri":"capability://memory.knowledge.multi.turn.agent.conversation.with.context.persistence","name":"multi-turn agent conversation with context persistence","description":"Maintains conversation history and context across multiple agent-user interactions, allowing agents to reference previous messages, build on prior decisions, and maintain state throughout a session. The agent uses this persistent context to inform action selection and planning, enabling coherent multi-turn workflows where each turn builds on the accumulated conversation history.","intents":["I want my agent to remember what we discussed in previous turns and use that context for new requests","I need my agent to maintain task state across multiple user interactions","I want my agent to ask clarifying questions and use the answers in subsequent actions"],"best_for":["conversational AI agents handling customer support or data analysis workflows","interactive autonomous systems where users guide agents through multi-step processes","developers building stateful agent applications with persistent user sessions"],"limitations":["Context window size is limited by the underlying LLM — long conversations may require summarization or context pruning","No built-in persistence to disk or database — conversation history is lost on agent restart unless explicitly saved","Context management adds memory overhead proportional to conversation length; very long sessions may degrade performance","No automatic context relevance filtering — irrelevant historical context may confuse the agent's planning decisions"],"requires":["Python 3.8+","LLM provider with sufficient context window (8k+ tokens recommended)","npi library with conversation management components","Optional: external storage (database, file system) for conversation persistence"],"input_types":["User messages (text)","Agent responses and action results","Contextual metadata (timestamps, user IDs, session identifiers)"],"output_types":["Conversation history (structured message list)","Agent responses with action summaries","Context summaries for long conversations","Session state snapshots"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_4","uri":"capability://automation.workflow.action.result.validation.and.error.handling.with.retry.logic","name":"action result validation and error handling with retry logic","description":"Automatically validates action execution results against expected output types and schemas, detects failures or unexpected responses, and implements configurable retry strategies (exponential backoff, circuit breakers) to recover from transient errors. Failed actions are logged with context, and agents can inspect error details to decide whether to retry, skip, or replan the remaining workflow.","intents":["I want my agent to automatically retry failed actions instead of giving up immediately","I need to know when actions fail and why, so I can debug agent workflows","I want my agent to handle transient errors (network timeouts, rate limits) gracefully"],"best_for":["production autonomous agents that need reliability and fault tolerance","developers building agents that interact with unreliable external APIs","teams implementing observability and debugging for agent workflows"],"limitations":["Retry logic adds latency and may not help with permanent failures (e.g., invalid credentials, 404 errors)","No built-in circuit breaker across multiple actions — a single flaky API can cause cascading retries","Retry strategies are generic; domain-specific error handling requires custom logic","Error logging and inspection require integration with external monitoring/logging systems for production use"],"requires":["Python 3.8+","npi library with error handling and retry components","Optional: logging framework (Python logging, structlog) for error inspection","Optional: monitoring/alerting system for production error tracking"],"input_types":["Action execution results","Expected output schemas","Error responses from external services","Retry configuration (max attempts, backoff strategy)"],"output_types":["Validation status (success/failure)","Error details and stack traces","Retry attempt logs","Final result or failure notification"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_5","uri":"capability://tool.use.integration.dynamic.action.registry.extension.and.custom.action.definition","name":"dynamic action registry extension and custom action definition","description":"Allows developers to define custom actions by decorating Python functions with action metadata (name, description, parameters), which are automatically registered and made available to the agent. The registry is dynamic — new actions can be added at runtime without restarting the agent, and actions can be conditionally enabled/disabled based on agent state or user permissions.","intents":["I want to add custom actions specific to my domain without modifying the npi library","I need to enable/disable certain actions based on user permissions or agent state","I want to dynamically load actions from plugins or external modules at runtime"],"best_for":["developers building domain-specific agents with custom business logic","teams implementing multi-tenant agent systems with per-user action permissions","builders creating extensible agent platforms with plugin architectures"],"limitations":["Custom actions must follow the npi action interface — incompatible functions require wrapper adapters","No built-in validation of custom action schemas — poorly defined actions may cause agent planning failures","Dynamic action registration adds complexity to agent state management and may introduce race conditions in concurrent scenarios","Limited tooling for discovering or documenting dynamically registered actions"],"requires":["Python 3.8+","Understanding of npi action interface and decorator syntax","npi library with action registration components","Optional: plugin loading framework (importlib, pkg_resources) for dynamic action discovery"],"input_types":["Python function definitions","Action metadata (name, description, parameter schemas)","Permission/capability definitions for conditional action enablement"],"output_types":["Registered action in the agent's action registry","Action schema in provider-specific format","Action availability status (enabled/disabled)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_6","uri":"capability://automation.workflow.agent.execution.tracing.and.debugging.with.step.by.step.logs","name":"agent execution tracing and debugging with step-by-step logs","description":"Records detailed execution traces for each agent step, including action invocations, parameters, results, and reasoning decisions. Developers can inspect these traces to understand why an agent made specific choices, debug planning failures, and optimize action sequences. Traces include timing information, error details, and intermediate state snapshots.","intents":["I want to understand why my agent chose a particular action or sequence of actions","I need to debug agent failures by seeing exactly what happened at each step","I want to optimize my agent's performance by analyzing execution traces"],"best_for":["developers debugging autonomous agent behavior and planning decisions","teams monitoring production agents and investigating unexpected behavior","builders optimizing agent performance and action selection strategies"],"limitations":["Detailed tracing adds overhead and increases memory usage proportional to trace verbosity","Traces can become very large for long-running agents, requiring external storage or sampling strategies","No built-in visualization tools — developers must parse trace data manually or integrate with external tools","Sensitive information (API keys, user data) may be captured in traces, requiring careful handling for compliance"],"requires":["Python 3.8+","npi library with tracing/debugging components","Optional: logging framework for structured trace output","Optional: trace visualization/analysis tools (custom dashboards, ELK stack, etc.)"],"input_types":["Agent execution events (action invocation, result, decision)","Trace configuration (verbosity level, sampling rate)","Filtering criteria (action type, time range, error status)"],"output_types":["Structured execution traces (JSON, structured logs)","Timing information per step","Error details and stack traces","Agent reasoning/planning decisions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_7","uri":"capability://planning.reasoning.conditional.action.execution.with.state.based.branching","name":"conditional action execution with state-based branching","description":"Allows agents to execute actions conditionally based on agent state, previous action results, or user-defined predicates. Agents can branch execution paths (if-then-else logic) based on intermediate results, enabling adaptive workflows that respond to changing conditions without requiring explicit replanning. Conditions are evaluated at runtime and can reference action outputs, context variables, and agent state.","intents":["I want my agent to take different actions based on the results of previous steps","I need my agent to handle conditional logic (if data exists, do X, else do Y)","I want my agent to adapt its workflow based on external conditions or user input"],"best_for":["developers building adaptive agents that respond to changing conditions","teams implementing complex workflows with multiple execution paths","builders creating agents that handle edge cases and error conditions gracefully"],"limitations":["Conditional logic must be defined explicitly — agents cannot automatically infer conditions from task descriptions","Complex branching logic can become difficult to manage and debug without proper visualization tools","No built-in support for loops or recursive execution — long-running workflows may require external orchestration","Condition evaluation adds latency per branch point, potentially slowing down execution"],"requires":["Python 3.8+","npi library with conditional execution components","Understanding of agent state and action result structures","Optional: workflow visualization tools for managing complex branching logic"],"input_types":["Predicates/conditions (boolean expressions)","Action results and state variables","User-defined branching rules"],"output_types":["Execution path (sequence of actions taken)","Conditional evaluation results","Final workflow output"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_8","uri":"capability://text.generation.language.agent.output.formatting.and.response.templating","name":"agent output formatting and response templating","description":"Provides templating and formatting capabilities to structure agent outputs according to user-defined schemas or templates. Agents can generate responses in specific formats (JSON, markdown, HTML, plain text) and validate outputs against expected schemas before returning them to users. This enables consistent, structured responses from agents regardless of the underlying LLM's output format.","intents":["I want my agent to always return responses in a specific format (JSON, markdown, etc.)","I need to validate agent outputs against a schema before returning them to users","I want to transform raw agent outputs into user-friendly formats"],"best_for":["developers building agents that need to return structured data to downstream systems","teams implementing agents with strict output format requirements","builders creating user-facing agents that need consistent, readable responses"],"limitations":["Template-based formatting may not work well with highly variable or unstructured outputs","Schema validation adds latency and may fail if the agent generates unexpected output formats","No built-in handling for partial or incomplete outputs — agents must generate complete responses matching the schema","Complex formatting logic may require custom template engines or post-processing"],"requires":["Python 3.8+","npi library with output formatting components","Optional: templating engine (Jinja2, etc.) for complex formatting","Optional: schema validation library (Pydantic, jsonschema) for output validation"],"input_types":["Agent output (text, structured data)","Output format specification (template, schema)","Formatting rules and transformations"],"output_types":["Formatted agent response (JSON, markdown, HTML, plain text)","Validation status (success/failure)","Structured data matching the specified schema"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sheet0--npi__cap_9","uri":"capability://automation.workflow.agent.performance.monitoring.and.metrics.collection","name":"agent performance monitoring and metrics collection","description":"Collects and aggregates metrics about agent performance, including action execution times, success/failure rates, token usage, and cost estimates. Metrics are tracked per action, per session, and globally, enabling developers to identify bottlenecks, optimize expensive operations, and monitor agent health in production. Metrics can be exported to external monitoring systems.","intents":["I want to understand which actions are slowest and optimize them","I need to track token usage and costs for my agent to manage expenses","I want to monitor agent success rates and identify reliability issues"],"best_for":["teams operating agents in production and needing performance visibility","developers optimizing agent efficiency and reducing operational costs","builders monitoring agent health and detecting degradation"],"limitations":["Metrics collection adds overhead and may impact agent performance, especially for high-frequency actions","No built-in alerting — requires integration with external monitoring systems for production alerts","Metrics are in-memory by default — require external storage for long-term retention and analysis","Cost estimation depends on accurate pricing data from LLM providers, which may change or be incomplete"],"requires":["Python 3.8+","npi library with metrics collection components","Optional: monitoring system (Prometheus, CloudWatch, Datadog) for metrics export","Optional: cost tracking data from LLM providers"],"input_types":["Action execution events","Token usage data from LLM providers","Pricing configuration"],"output_types":["Performance metrics (execution time, success rate, token usage)","Cost estimates and actual costs","Aggregated metrics per action/session/global","Metrics in exportable format (JSON, Prometheus format)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API key for at least one supported LLM provider (OpenAI, Anthropic, or compatible)","npi library installed via pip","Browser driver (Chrome/Chromium for Puppeteer-based actions, or Firefox for Selenium-based)","npi library with browser automation extras installed","Sufficient system resources for headless browser instances","LLM provider with sufficient reasoning capability (GPT-4 or equivalent recommended)","Well-documented action registry with clear descriptions of each action's purpose and parameters","npi library with planning/reasoning components","LLM provider with sufficient context window (8k+ tokens recommended)"],"failure_modes":["Abstraction layer adds latency per function call due to schema translation and routing logic","Limited to Python function definitions — no native support for non-Python callables without wrapper adapters","Provider-specific function-calling features (e.g., parallel tool calls, streaming responses) may not be fully exposed through the abstraction","Browser automation actions may timeout on slow or unresponsive pages, requiring retry logic","No built-in handling for JavaScript-heavy SPAs — may require explicit wait conditions for dynamic content","Screenshot and OCR capabilities depend on underlying browser driver performance and may not work reliably with complex CSS or overlays","Limited to actions exposed in the library — custom browser interactions require extending the action set","Planning overhead adds latency proportional to task complexity — simple tasks may not benefit from decomposition","Agent may generate suboptimal or redundant action sequences if the LLM lacks sufficient context about action dependencies","No built-in backtracking or plan revision — if an action fails, the agent may not automatically replan unless explicitly instructed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2466879237242846,"quality":0.3,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:09.058Z","last_commit":"2025-03-31T10:05:53Z"},"community":{"stars":228,"forks":11,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sheet0--npi","compare_url":"https://unfragile.ai/compare?artifact=sheet0--npi"}},"signature":"Ka/pcX26RcpzItQBxybFm0f78LJcditlsrwrpRMmxojXUJE0E7tlizraMKP96OAgqIewDaqQhmW3gdymFp0TDA==","signedAt":"2026-06-23T01:43:44.749Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sheet0--npi","artifact":"https://unfragile.ai/sheet0--npi","verify":"https://unfragile.ai/api/v1/verify?slug=sheet0--npi","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"}}