{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-network-ai","slug":"network-ai","name":"network-ai","type":"framework","url":"https://network-ai.org","page_url":"https://unfragile.ai/network-ai","categories":["ai-agents","code-editors"],"tags":["ai-agents","agentic-ai","multi-agent","multi-agent-systems","agent-framework","llm","llm-agents","orchestration","swarm","swarm-intelligence","autonomous-agents","agentic-workflow","agentic-framework","generative-ai","mcp","model-context-protocol","langchain","autogen","crewai","openclaw"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-network-ai__cap_0","uri":"capability://tool.use.integration.multi.framework.agent.adapter.abstraction.layer","name":"multi-framework agent adapter abstraction layer","description":"Provides a unified TypeScript interface that abstracts over 27+ distinct AI agent frameworks (LangChain, AutoGen, CrewAI, OpenAI Assistants, LlamaIndex, Semantic Kernel, Haystack, DSPy, Agno, MCP, LangGraph, Anthropic Compute, etc.) through a common adapter pattern. Each framework gets a dedicated adapter that translates between the framework's native agent lifecycle (initialization, execution, tool binding, response handling) and Network-AI's standardized agent contract, enabling single-codebase orchestration across heterogeneous agent systems without rewriting business logic.","intents":["I want to build an agent that can switch between LangChain and CrewAI without rewriting core logic","I need to orchestrate agents from multiple frameworks in a single application","I want to avoid vendor lock-in by abstracting framework-specific APIs","I need to test agent behavior across different frameworks with the same test suite"],"best_for":["teams building multi-framework agent systems","enterprises migrating between agent frameworks","developers prototyping agents and want framework flexibility","organizations standardizing on a single agent orchestration layer"],"limitations":["Adapter coverage is framework-dependent — not all frameworks have feature parity in their adapters","Lowest-common-denominator abstraction may hide framework-specific optimizations or advanced features","Adapter maintenance burden grows linearly with number of supported frameworks","Performance overhead from translation layer adds latency per agent invocation (estimated 10-50ms per adapter call)"],"requires":["Node.js 18+","TypeScript 4.9+ (for type safety across adapters)","At least one target framework installed (LangChain, AutoGen, CrewAI, etc.)","MCP server running if using MCP adapter"],"input_types":["agent configuration objects","framework-specific agent instances","tool/function definitions","prompt templates","system messages"],"output_types":["standardized agent response objects","execution logs","tool call traces","framework-agnostic agent state"],"categories":["tool-use-integration","agent-orchestration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_1","uri":"capability://tool.use.integration.mcp.protocol.native.agent.binding","name":"mcp protocol-native agent binding","description":"Implements native Model Context Protocol (MCP) server integration allowing agents to discover, invoke, and compose tools exposed via MCP servers without manual schema translation. The framework handles MCP server lifecycle management (connection pooling, reconnection logic, capability discovery), marshals tool calls from agents into MCP-compliant requests, and unmarshals responses back into agent-consumable formats. Supports both stdio and SSE transport modes for MCP server communication.","intents":["I want my agent to use tools exposed by MCP servers without writing custom integration code","I need to connect agents to external MCP servers for database access, file operations, or domain-specific tools","I want tool discovery and capability negotiation to happen automatically at runtime","I need to support multiple concurrent MCP server connections with automatic failover"],"best_for":["teams adopting MCP as a standard tool protocol","developers building agent systems that integrate with Claude or other MCP-aware LLMs","organizations with existing MCP server infrastructure wanting agent access","builders creating tool ecosystems around MCP"],"limitations":["MCP server availability directly impacts agent reliability — no built-in circuit breaker or graceful degradation","Tool schema discovery happens at connection time; dynamic tool registration requires server restart","No caching of MCP tool schemas — each agent invocation may trigger capability re-negotiation","SSE transport mode may have higher latency than stdio for high-frequency tool calls"],"requires":["Node.js 18+","MCP server running and accessible (stdio or SSE endpoint)","MCP server implementation compatible with MCP 1.0+ spec"],"input_types":["MCP server connection configuration (host, port, transport type)","agent tool invocation requests","MCP protocol messages"],"output_types":["MCP tool schemas (JSON)","tool execution results","MCP protocol responses"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_10","uri":"capability://code.generation.editing.agent.testing.and.simulation.framework","name":"agent testing and simulation framework","description":"Provides testing utilities for agent behavior including mock LLM providers for deterministic testing, tool call simulation, and execution trace comparison. Implements property-based testing for agents (testing invariants across multiple executions) and scenario-based testing (testing agent behavior in specific situations). Supports snapshot testing of agent outputs and execution traces for regression detection.","intents":["I want to write unit tests for agents without calling real LLM APIs","I need to test agent behavior across multiple scenarios and verify consistency","I want to detect regressions when agent code changes using snapshot testing","I need to verify that agents handle errors and edge cases correctly"],"best_for":["teams building production agent systems","developers practicing test-driven development with agents","organizations requiring agent behavior validation","teams optimizing agent reliability"],"limitations":["Mock LLM providers may not capture real LLM behavior (hallucinations, inconsistencies)","Property-based testing requires explicit property definitions — no automatic property inference","Snapshot testing is brittle — minor output changes require snapshot updates","Simulation may not capture real-world tool latency or failure modes"],"requires":["Node.js 18+","Testing framework (Jest, Mocha, Vitest, etc.)","Test scenario definitions"],"input_types":["agent configurations","mock LLM responses","test scenarios","property definitions"],"output_types":["test results","execution traces","snapshot diffs","property violation reports"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_11","uri":"capability://automation.workflow.agent.configuration.management.and.deployment","name":"agent configuration management and deployment","description":"Provides configuration management for agents including environment-specific configurations (dev, staging, production), secrets management (API keys, credentials), and deployment orchestration. Supports configuration validation against schemas, hot-reloading of agent configurations without restart, and configuration versioning with rollback capabilities. Integrates with infrastructure-as-code tools and CI/CD pipelines for automated agent deployment.","intents":["I want to manage different agent configurations for dev, staging, and production environments","I need to securely manage API keys and credentials for agents","I want to deploy agent configuration changes without restarting the application","I need to integrate agent deployment with my CI/CD pipeline"],"best_for":["teams operating agents in multiple environments","organizations with security/compliance requirements for secrets management","developers practicing infrastructure-as-code","teams requiring frequent agent configuration updates"],"limitations":["Hot-reloading may cause inconsistencies if agents are mid-execution during reload","Configuration validation is schema-based — requires explicit schema definition","Secrets management requires integration with external secret stores (Vault, AWS Secrets Manager)","Configuration versioning adds storage overhead"],"requires":["Node.js 18+","Configuration file format (JSON, YAML, TOML)","Optional: secrets management system (Vault, AWS Secrets Manager, etc.)"],"input_types":["configuration files","environment variables","secrets","deployment specifications"],"output_types":["validated agent configurations","deployment manifests","configuration audit logs","rollback instructions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_12","uri":"capability://automation.workflow.agent.performance.profiling.and.optimization","name":"agent performance profiling and optimization","description":"Provides profiling tools to identify performance bottlenecks in agent execution including LLM call latency, tool invocation overhead, and decision-making latency. Implements automatic performance recommendations (e.g., 'caching tool results would save 500ms per execution') and supports performance regression detection. Tracks performance metrics over time and correlates performance changes with code/configuration changes.","intents":["I want to identify why my agent is slow and where to optimize","I need to track agent performance over time and detect regressions","I want automatic recommendations for performance improvements","I need to profile multi-agent systems to find bottlenecks in agent coordination"],"best_for":["teams optimizing agent performance for production","developers building latency-sensitive agent systems","organizations monitoring agent performance SLAs","teams investigating performance regressions"],"limitations":["Profiling overhead adds latency to agent execution (estimated 5-15%)","Performance recommendations are heuristic-based — may not apply to all use cases","Regression detection requires baseline performance data — not useful for new agents","Multi-agent profiling may be complex due to concurrent execution and inter-agent communication"],"requires":["Node.js 18+","Performance metrics collection infrastructure"],"input_types":["agent execution traces","performance metrics","baseline performance data"],"output_types":["performance profiles","bottleneck identification","optimization recommendations","regression reports"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_13","uri":"capability://safety.moderation.agent.security.and.input.validation","name":"agent security and input validation","description":"Implements input validation and sanitization for agent prompts, tool parameters, and outputs to prevent prompt injection, tool misuse, and data exfiltration. Supports configurable validation rules (regex patterns, schema validation, semantic validation) and automatic detection of suspicious patterns (e.g., attempts to override system prompts). Integrates with security scanning tools and provides audit logs for security events.","intents":["I want to prevent prompt injection attacks on my agents","I need to validate tool parameters to prevent misuse (e.g., SQL injection through tool calls)","I want to detect and block suspicious agent behavior (e.g., attempts to access unauthorized resources)","I need audit logs of all security events for compliance purposes"],"best_for":["teams deploying agents in security-sensitive environments","organizations with compliance/regulatory requirements","developers building user-facing agent systems","teams concerned about LLM security vulnerabilities"],"limitations":["Input validation may block legitimate use cases — requires careful rule tuning","Semantic validation (detecting prompt injection) is heuristic-based and may have false positives/negatives","Security scanning adds latency per agent step (estimated 10-50ms)","No protection against sophisticated attacks that evade validation rules"],"requires":["Node.js 18+","Validation rule definitions","Optional: security scanning service"],"input_types":["agent prompts","tool parameters","agent outputs","validation rules"],"output_types":["validation results","security alerts","audit logs","blocked requests"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_2","uri":"capability://data.processing.analysis.cross.framework.tool.schema.normalization","name":"cross-framework tool schema normalization","description":"Translates tool/function definitions between incompatible schema formats used by different frameworks (OpenAI function calling format, Anthropic tool_use format, LangChain StructuredTool, CrewAI Tool, etc.) into a canonical internal representation and back. Handles parameter validation, type coercion, and error mapping so a single tool definition can be used across frameworks without duplication. Supports JSON Schema, TypeScript interfaces, and Zod schema inputs for tool definition.","intents":["I want to define a tool once and use it across LangChain, CrewAI, and OpenAI Assistants","I need to convert between OpenAI function calling format and Anthropic tool_use format","I want type-safe tool definitions that work across multiple frameworks","I need to validate tool parameters consistently regardless of which framework calls the tool"],"best_for":["multi-framework agent builders","teams standardizing tool definitions across agent systems","developers building tool libraries that work with multiple frameworks","organizations migrating tools between frameworks"],"limitations":["Some framework-specific tool features (e.g., OpenAI's 'strict' mode) may not translate to other frameworks","Complex nested schemas may lose fidelity during translation between formats","No automatic handling of framework-specific tool metadata or execution hints","Type coercion may fail silently for edge cases — requires explicit error handling"],"requires":["Node.js 18+","TypeScript 4.9+ for type safety","Zod or JSON Schema for schema definition (optional but recommended)"],"input_types":["JSON Schema objects","TypeScript type definitions","Zod schemas","OpenAI function calling format","Anthropic tool_use format","LangChain StructuredTool definitions"],"output_types":["normalized tool schema objects","framework-specific tool definitions","parameter validation results","type-safe tool invocation interfaces"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_3","uri":"capability://planning.reasoning.agent.execution.orchestration.with.multi.provider.llm.routing","name":"agent execution orchestration with multi-provider llm routing","description":"Orchestrates agent execution across multiple LLM providers (OpenAI, Anthropic, Ollama, local models, etc.) with dynamic routing based on cost, latency, or capability requirements. Handles agent lifecycle management (initialization, step execution, tool invocation, termination), maintains execution context across provider boundaries, and implements fallback logic if a provider fails. Supports both synchronous and asynchronous execution modes with configurable timeout and retry policies.","intents":["I want to run the same agent with different LLM providers and compare results","I need to route agent requests to the cheapest provider that meets capability requirements","I want automatic fallback to a backup LLM provider if the primary provider fails","I need to execute multiple agents in parallel with different LLM backends"],"best_for":["cost-conscious teams optimizing LLM spend across providers","organizations with multi-provider LLM contracts","developers building resilient agent systems with provider redundancy","teams experimenting with different LLM backends for agent behavior"],"limitations":["Provider routing decisions are static or rule-based — no learned routing based on historical performance","Context switching between providers may cause inconsistent agent behavior due to model differences","No built-in cost tracking or billing integration — requires external accounting","Fallback logic adds latency (retry overhead) during provider failures"],"requires":["Node.js 18+","API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Network connectivity to LLM provider endpoints"],"input_types":["agent configuration with provider preferences","routing rules (cost threshold, latency SLA, capability requirements)","LLM provider credentials","agent execution requests"],"output_types":["agent execution results","provider routing decisions","execution metrics (latency, cost, token usage)","fallback/retry logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_4","uri":"capability://memory.knowledge.agent.state.persistence.and.resumption","name":"agent state persistence and resumption","description":"Provides serialization and deserialization of agent execution state (current step, tool call history, context window, intermediate results) to enable agent resumption after interruption or failure. Supports multiple persistence backends (in-memory, file system, Redis, database) through a pluggable storage interface. Handles state versioning and migration to support agent code updates without losing execution history. Captures full execution traces for debugging and audit purposes.","intents":["I want to pause an agent mid-execution and resume it later without losing progress","I need to persist agent execution history for audit and compliance purposes","I want to recover from agent failures by resuming from the last successful step","I need to debug agent behavior by replaying execution traces"],"best_for":["long-running agent workflows (hours/days)","regulated industries requiring execution audit trails","teams building fault-tolerant agent systems","developers debugging complex multi-step agent behavior"],"limitations":["State serialization overhead adds latency per agent step (estimated 5-20ms depending on backend)","Large context windows or long execution histories may exceed storage limits","State versioning requires manual migration logic when agent code changes","No built-in conflict resolution for concurrent agent executions with shared state"],"requires":["Node.js 18+","Persistence backend (in-memory, file system, Redis, database)","Sufficient storage capacity for execution traces"],"input_types":["agent execution state objects","persistence backend configuration","state version metadata"],"output_types":["serialized agent state","execution traces","state recovery checkpoints","audit logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_5","uri":"capability://planning.reasoning.agent.composition.and.hierarchical.task.decomposition","name":"agent composition and hierarchical task decomposition","description":"Enables composition of multiple agents into hierarchical structures where parent agents can spawn child agents, delegate tasks, and aggregate results. Implements task decomposition patterns (breaking complex goals into subtasks) with automatic dependency resolution and parallel execution where possible. Handles inter-agent communication through a message queue abstraction and manages resource allocation (token budgets, concurrent execution limits) across the agent hierarchy.","intents":["I want to build a multi-agent system where a manager agent delegates tasks to specialist agents","I need to decompose a complex task into subtasks and execute them in parallel","I want to implement hierarchical agent structures (teams of agents managing teams of agents)","I need to coordinate agent execution with dependency constraints (task A must complete before task B)"],"best_for":["teams building complex multi-agent systems","organizations implementing hierarchical agent architectures","developers building agent swarms with task coordination","builders creating agent frameworks on top of Network-AI"],"limitations":["Hierarchical depth may impact latency — each level of delegation adds communication overhead","No automatic load balancing across agents — requires manual resource allocation configuration","Task decomposition is manual — no built-in task planning or goal decomposition","Inter-agent communication through message queues adds complexity for debugging"],"requires":["Node.js 18+","Message queue or in-memory queue implementation","Parent and child agent instances configured and initialized"],"input_types":["parent agent configuration","child agent definitions","task decomposition rules","dependency constraints","resource allocation policies"],"output_types":["aggregated results from child agents","execution dependency graphs","resource utilization metrics","inter-agent communication logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_6","uri":"capability://safety.moderation.agent.monitoring.logging.and.observability","name":"agent monitoring, logging, and observability","description":"Captures detailed execution metrics, logs, and traces for all agent operations (LLM calls, tool invocations, decision points, errors) with structured logging and optional integration with observability platforms (OpenTelemetry, Datadog, New Relic, etc.). Provides real-time dashboards for agent health, token usage, latency, and error rates. Implements distributed tracing across multi-agent systems to track request flows through hierarchical agent structures.","intents":["I want to monitor agent performance and identify bottlenecks in multi-agent systems","I need detailed logs of every LLM call and tool invocation for debugging","I want to track token usage and costs across agents and providers","I need to integrate agent metrics with my existing observability stack (Datadog, New Relic)"],"best_for":["teams operating production agent systems","organizations with observability/monitoring requirements","developers debugging complex multi-agent behavior","teams optimizing agent performance and cost"],"limitations":["Logging overhead adds latency per agent step (estimated 2-10ms depending on log destination)","Distributed tracing requires explicit trace ID propagation across agent boundaries","No built-in alerting — requires external monitoring platform for alerts","Sensitive data in logs (prompts, tool results) may require redaction logic"],"requires":["Node.js 18+","Logging backend (console, file, cloud logging service)","Optional: OpenTelemetry collector or observability platform"],"input_types":["agent execution events","LLM call details","tool invocation records","error/exception data"],"output_types":["structured logs","execution traces","performance metrics","observability platform events"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_7","uri":"capability://text.generation.language.agent.prompt.template.management.and.versioning","name":"agent prompt template management and versioning","description":"Provides a centralized system for managing, versioning, and deploying agent prompt templates across frameworks. Supports template inheritance, variable substitution, and conditional prompt sections based on agent context or capabilities. Implements A/B testing infrastructure for comparing prompt variants and their impact on agent behavior. Tracks prompt versions with git-like history and enables rollback to previous versions.","intents":["I want to manage prompt templates for multiple agents in a centralized location","I need to version prompts and roll back to previous versions if a new prompt degrades performance","I want to A/B test different prompts and measure their impact on agent behavior","I need to reuse prompt components across multiple agents without duplication"],"best_for":["teams managing multiple agents with complex prompts","organizations optimizing agent behavior through prompt engineering","developers building prompt libraries for agent systems","teams requiring prompt audit trails for compliance"],"limitations":["No automatic prompt optimization — requires manual A/B testing and iteration","Template variable substitution is manual — no built-in context inference","A/B testing requires external metrics collection to measure impact","Prompt versioning adds storage overhead for large prompt libraries"],"requires":["Node.js 18+","Storage backend for prompt templates (file system, database, or cloud storage)"],"input_types":["prompt template definitions","template variables and context","A/B test configurations"],"output_types":["rendered prompts","template versions","A/B test results","prompt audit logs"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_8","uri":"capability://safety.moderation.agent.error.handling.and.recovery.strategies","name":"agent error handling and recovery strategies","description":"Implements configurable error handling and recovery strategies for agent failures including automatic retries with exponential backoff, circuit breaker patterns for cascading failures, graceful degradation when tools fail, and fallback agent invocation. Distinguishes between transient errors (network timeouts, rate limits) and permanent errors (invalid tool calls, authentication failures) with different recovery strategies for each. Provides hooks for custom error handlers and recovery logic.","intents":["I want my agent to automatically retry failed tool calls with exponential backoff","I need to prevent cascading failures when a tool or LLM provider becomes unavailable","I want agents to gracefully degrade functionality when optional tools fail","I need custom error handling for domain-specific error scenarios"],"best_for":["teams building production-grade agent systems","organizations requiring high availability for agent services","developers building resilient multi-agent systems","teams with complex error recovery requirements"],"limitations":["Retry logic may mask underlying issues — requires monitoring to detect persistent failures","Circuit breaker patterns add latency during failure detection phase","Graceful degradation requires explicit fallback definitions — no automatic fallback inference","Custom error handlers add complexity and potential for bugs in recovery logic"],"requires":["Node.js 18+","Error handler configuration (retry policies, circuit breaker thresholds, etc.)"],"input_types":["error objects from agent execution","retry policies","circuit breaker configuration","fallback agent definitions"],"output_types":["recovery action decisions","retry logs","circuit breaker state","fallback execution results"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-network-ai__cap_9","uri":"capability://tool.use.integration.agent.capability.discovery.and.dynamic.tool.binding","name":"agent capability discovery and dynamic tool binding","description":"Automatically discovers agent capabilities (supported tools, LLM models, execution modes) at runtime and dynamically binds tools based on agent requirements and availability. Implements capability negotiation between agents and tool providers, with fallback to alternative tools if preferred tools are unavailable. Supports capability constraints (e.g., 'agent requires tools with <100ms latency') and automatic tool selection based on constraints.","intents":["I want agents to automatically discover available tools at runtime without hardcoding tool lists","I need agents to select alternative tools if their preferred tools are unavailable","I want to enforce capability constraints (latency, cost, reliability) when binding tools to agents","I need to support dynamic tool registration without redeploying agents"],"best_for":["teams with dynamic tool ecosystems","organizations with tool availability constraints","developers building flexible agent systems","teams requiring runtime tool negotiation"],"limitations":["Capability discovery adds startup latency (tool enumeration and negotiation)","No built-in capability caching — each agent startup triggers full discovery","Capability constraints require explicit definition — no automatic constraint inference","Tool selection based on constraints may be suboptimal without performance profiling"],"requires":["Node.js 18+","Tool registry or discovery service","Capability metadata for tools (latency, cost, reliability)"],"input_types":["agent capability requirements","tool registry queries","capability constraints"],"output_types":["discovered tool lists","capability negotiation results","tool binding decisions","constraint satisfaction reports"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","TypeScript 4.9+ (for type safety across adapters)","At least one target framework installed (LangChain, AutoGen, CrewAI, etc.)","MCP server running if using MCP adapter","MCP server running and accessible (stdio or SSE endpoint)","MCP server implementation compatible with MCP 1.0+ spec","Testing framework (Jest, Mocha, Vitest, etc.)","Test scenario definitions","Configuration file format (JSON, YAML, TOML)","Optional: secrets management system (Vault, AWS Secrets Manager, etc.)"],"failure_modes":["Adapter coverage is framework-dependent — not all frameworks have feature parity in their adapters","Lowest-common-denominator abstraction may hide framework-specific optimizations or advanced features","Adapter maintenance burden grows linearly with number of supported frameworks","Performance overhead from translation layer adds latency per agent invocation (estimated 10-50ms per adapter call)","MCP server availability directly impacts agent reliability — no built-in circuit breaker or graceful degradation","Tool schema discovery happens at connection time; dynamic tool registration requires server restart","No caching of MCP tool schemas — each agent invocation may trigger capability re-negotiation","SSE transport mode may have higher latency than stdio for high-frequency tool calls","Mock LLM providers may not capture real LLM behavior (hallucinations, inconsistencies)","Property-based testing requires explicit property definitions — no automatic property inference","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.09334872878487055,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":429,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=network-ai","compare_url":"https://unfragile.ai/compare?artifact=network-ai"}},"signature":"WE73tQE0xhqKjWgOB35AkTJfZ+cguznbY9QFrFmsgm4x6Q68Q7SJoQZJD+5CarbGhFqpsSxrxnmes3QFSUePBQ==","signedAt":"2026-06-22T05:17:10.714Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/network-ai","artifact":"https://unfragile.ai/network-ai","verify":"https://unfragile.ai/api/v1/verify?slug=network-ai","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"}}