{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-ibm-wxflows","slug":"ibm-wxflows","name":"IBM wxflows","type":"mcp","url":"https://github.com/IBM/wxflows/tree/main/examples/mcp/javascript","page_url":"https://unfragile.ai/ibm-wxflows","categories":["mcp-servers","deployment-infra","testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-ibm-wxflows__cap_0","uri":"capability://tool.use.integration.graphql.based.tool.schema.definition.and.rest.api.mapping","name":"graphql-based tool schema definition and rest api mapping","description":"Enables developers to define tools as GraphQL types with @rest directives that automatically map GraphQL queries/mutations to external REST APIs. The system parses wxflows.toml configuration files and tools.graphql schema definitions to generate a unified GraphQL endpoint that abstracts away REST complexity, handling request/response transformation, authentication headers, and parameter binding automatically.","intents":["I want to expose a REST API as a GraphQL tool without writing boilerplate integration code","I need to define data models and operations that map to external APIs with automatic type safety","I want to create reusable tool definitions that can be shared across multiple AI workflows"],"best_for":["Teams building AI agents that need to call external APIs","Developers creating RAG applications with structured data sources","Organizations standardizing on GraphQL for tool integration across multiple LLM providers"],"limitations":["REST API mapping via @rest directives may not support complex authentication flows (OAuth2 with refresh tokens, mutual TLS)","GraphQL schema generation is one-directional — changes to external REST APIs require manual schema updates","No built-in support for streaming responses from REST endpoints","Limited to REST APIs — SOAP, gRPC, or proprietary protocols require custom adapters"],"requires":["Python 3.7+ for CLI","Node.js 16+ for JavaScript SDK","Valid wxflows.toml configuration file","External REST API endpoint with documented schema"],"input_types":["GraphQL schema definitions (SDL)","REST API specifications (OpenAPI/Swagger optional)","Configuration files (TOML)"],"output_types":["GraphQL endpoint (HTTP)","Executable tool definitions","Type-safe SDK bindings"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.orchestration.with.unified.tool.calling.interface","name":"multi-provider llm orchestration with unified tool calling interface","description":"Abstracts differences between LLM providers (OpenAI, Anthropic, IBM watsonx, local Ollama) through a unified tool-calling interface. The wxflows engine translates tool definitions into provider-specific function-calling schemas (OpenAI functions, Anthropic tools, watsonx tool_use format) and handles provider-specific response parsing, token counting, and retry logic automatically.","intents":["I want to switch between LLM providers without rewriting tool calling code","I need to run the same agent with multiple LLM backends for comparison or fallback","I want to use local models (Ollama) for sensitive data while keeping the same tool interface"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Enterprises requiring on-premise LLM deployment with cloud fallback","Developers building multi-model agents that route requests based on complexity"],"limitations":["Provider-specific features (vision in Claude, structured outputs in GPT-4) require conditional logic or provider detection","Token counting varies by provider — cost estimation may be inaccurate when switching models","Some providers have different tool-calling semantics (parallel vs sequential) that may affect agent behavior","Rate limiting and quota management is per-provider and not centralized"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, IBM watsonx, or local Ollama instance)","Node.js 16+ or Python 3.7+","@wxflows/sdk or wxflows Python package"],"input_types":["Tool definitions (GraphQL schema)","LLM provider configuration (API keys, model names)","User prompts (text)"],"output_types":["LLM responses (text)","Tool execution results","Structured agent outputs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_10","uri":"capability://code.generation.editing.configuration.validation.and.schema.generation.from.wxflows.toml","name":"configuration validation and schema generation from wxflows.toml","description":"Automatically validates wxflows.toml configuration files, generates GraphQL schemas from tool definitions, and produces type-safe SDK bindings. The system parses TOML configuration, validates tool definitions against GraphQL schema rules, generates executable GraphQL schemas, and produces language-specific type definitions. Validation catches configuration errors at development time before deployment.","intents":["I want to catch configuration errors before deploying my flows","I need to generate type definitions from my flow definitions automatically","I want to validate that my tool definitions are compatible with my LLM providers"],"best_for":["Teams using wxflows in CI/CD pipelines with automated validation","Developers who want early error detection in configuration","Organizations enforcing configuration standards across projects"],"limitations":["Validation is schema-based — semantic errors (invalid API endpoints, missing credentials) are not caught","Type generation is one-directional — changes to external APIs require manual schema updates","Validation errors may be cryptic for developers unfamiliar with GraphQL schema rules","No support for conditional configuration or environment-specific overrides in validation"],"requires":["wxflows.toml configuration file","wxflows CLI","Valid GraphQL schema definitions"],"input_types":["TOML configuration files","GraphQL schema definitions"],"output_types":["Validation results (success/error)","Generated GraphQL schemas","Type definitions (TypeScript, Python)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_2","uri":"capability://automation.workflow.flow.orchestration.and.execution.engine.with.wxflows.toml.configuration","name":"flow orchestration and execution engine with wxflows.toml configuration","description":"Central orchestration platform that processes flow definitions from wxflows.toml configuration files, manages tool registry, generates GraphQL schemas, and executes multi-step AI workflows. The engine handles flow state management, tool execution sequencing, error handling, and exposes flows as GraphQL endpoints for client consumption. Flows can chain multiple tools, LLM calls, and data transformations in a declarative configuration format.","intents":["I want to define multi-step AI workflows declaratively without writing orchestration code","I need to expose my AI workflows as GraphQL APIs that clients can query","I want to manage tool execution order, error handling, and state across multiple steps"],"best_for":["Teams building complex AI agents with multiple tool dependencies","Organizations exposing AI capabilities as APIs to internal/external clients","Developers who prefer declarative configuration over imperative code for workflow definition"],"limitations":["Flow definitions are static at deployment time — dynamic flow generation or conditional branching requires code generation or custom extensions","No built-in support for long-running workflows or async job scheduling — suitable for request-response patterns only","State management is request-scoped — no persistent workflow state across multiple client requests without external storage","Debugging complex flows requires understanding wxflows.toml syntax and GraphQL schema generation"],"requires":["wxflows.toml configuration file with flow definitions","Python 3.7+ for CLI","Node.js 16+ for JavaScript SDK","Deployed wxflows engine instance or local development server"],"input_types":["TOML configuration files","GraphQL schema definitions","Tool definitions"],"output_types":["GraphQL endpoint","Flow execution results","Structured JSON responses"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_3","uri":"capability://memory.knowledge.rag.application.scaffolding.with.vector.collection.management","name":"rag application scaffolding with vector collection management","description":"Provides templates and CLI commands (wxflows collection deploy) to build Retrieval-Augmented Generation applications with integrated vector storage. The system handles document ingestion, embedding generation, vector collection creation, and semantic search integration. Developers can scaffold RAG applications with pre-configured retrieval tools that automatically embed queries and search vector collections, then pass results to LLMs for generation.","intents":["I want to build a RAG application without implementing vector storage and embedding pipelines from scratch","I need to deploy document collections as searchable tools in my AI workflows","I want to integrate semantic search with LLM generation in a single flow"],"best_for":["Teams building question-answering systems over proprietary documents","Developers creating chatbots with knowledge base retrieval","Organizations deploying RAG systems with IBM watsonx as the LLM backend"],"limitations":["Vector collection deployment is tightly coupled to IBM watsonx infrastructure — limited support for alternative vector databases (Pinecone, Weaviate, Milvus)","Embedding model selection is limited to IBM watsonx offerings — no support for custom embedding models","Document ingestion pipeline is basic — no built-in support for complex document parsing (PDFs with tables, scanned images)","No built-in reranking or hybrid search (keyword + semantic) — retrieval quality depends entirely on embedding quality"],"requires":["IBM watsonx API credentials","Document collection in supported format (text, JSON, or CSV)","Python 3.7+ for CLI","wxflows CLI installed and authenticated"],"input_types":["Documents (text, JSON, CSV)","Document metadata","Query text"],"output_types":["Vector collection (deployed to IBM watsonx)","Retrieved documents","LLM-generated answers"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_4","uri":"capability://planning.reasoning.agent.system.scaffolding.with.multi.turn.conversation.management","name":"agent system scaffolding with multi-turn conversation management","description":"Provides templates and examples for building AI agents with multi-turn conversation capabilities, tool calling loops, and conversation history management. The system handles conversation state, tool execution within agent loops, and integration with LLM providers. Agents can iteratively call tools, process results, and generate responses based on accumulated context across multiple user turns.","intents":["I want to build a conversational agent that can call tools across multiple turns","I need to manage conversation history and context without implementing state management myself","I want to create agents that can reason about tool results and decide next steps"],"best_for":["Teams building chatbots with tool-calling capabilities","Developers creating autonomous agents for task automation","Organizations deploying conversational AI systems with multi-step reasoning"],"limitations":["Conversation history is stored in memory — no built-in persistence across server restarts or distributed deployments","Agent loop termination requires explicit stopping conditions — no automatic detection of task completion","Token limit management is manual — developers must implement context windowing to prevent token overflow","No built-in support for multi-agent collaboration or hierarchical agent structures"],"requires":["Node.js 16+ or Python 3.7+","@wxflows/sdk or wxflows Python package","LLM provider API credentials","Tool definitions (GraphQL schema)"],"input_types":["User messages (text)","Tool definitions","LLM provider configuration"],"output_types":["Agent responses (text)","Tool execution results","Conversation history"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_5","uri":"capability://automation.workflow.cli.based.project.initialization.and.deployment.pipeline","name":"cli-based project initialization and deployment pipeline","description":"Command-line interface (wxflows init, wxflows deploy, wxflows collection deploy) that scaffolds new projects from templates, manages authentication, and deploys flows to cloud endpoints. The CLI handles project structure creation, configuration validation, authentication token management, and remote deployment orchestration. Developers use CLI commands to initialize projects, authenticate with IBM platform, and deploy flows as GraphQL endpoints.","intents":["I want to quickly scaffold a new wxflows project with sensible defaults","I need to deploy my flows to a cloud endpoint without manual infrastructure setup","I want to manage authentication and credentials securely through the CLI"],"best_for":["Teams using IBM watsonx as their primary LLM platform","Developers who prefer CLI-driven workflows over web dashboards","Organizations deploying AI applications to IBM Cloud infrastructure"],"limitations":["CLI is Python-based — requires Python 3.7+ installation, adding dependency management complexity","Deployment is tightly coupled to IBM Cloud infrastructure — no support for deploying to AWS, GCP, or on-premise Kubernetes","Authentication tokens are stored locally — no built-in support for CI/CD pipeline authentication or service accounts","No rollback mechanism for deployed flows — version management requires manual tracking"],"requires":["Python 3.7+ installed on system","IBM Cloud account with watsonx.ai access","wxflows CLI wheel file (downloaded separately)","Valid IBM Cloud API credentials"],"input_types":["Project template selection","Configuration parameters","wxflows.toml files"],"output_types":["Project directory structure","Deployed GraphQL endpoint URL","Authentication tokens"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_6","uri":"capability://tool.use.integration.javascript.and.python.sdk.with.graphql.client.capabilities","name":"javascript and python sdk with graphql client capabilities","description":"Provides language-specific SDKs (@wxflows/sdk for JavaScript, wxflows package for Python) that enable client applications to query deployed flows as GraphQL endpoints. The SDKs handle GraphQL query construction, authentication header injection, response parsing, and tool result handling. Clients can invoke flows, pass parameters, and receive structured results without manually constructing HTTP requests or managing authentication.","intents":["I want to call deployed wxflows from my JavaScript/Python application without writing GraphQL queries manually","I need type-safe access to flow inputs and outputs in my client code","I want to handle authentication and error responses consistently across my application"],"best_for":["JavaScript/Node.js applications consuming wxflows endpoints","Python applications integrating with wxflows backends","Teams building multi-language applications that need consistent wxflows integration"],"limitations":["SDKs are language-specific — no support for Go, Rust, Java, or other languages without manual GraphQL client setup","Type generation requires schema introspection — types are not automatically updated when flow definitions change","No built-in caching or request deduplication — clients must implement their own caching strategies","Error handling is basic — provider-specific errors (rate limits, authentication failures) require custom handling"],"requires":["Node.js 16+ (for JavaScript SDK) or Python 3.7+ (for Python SDK)","Deployed wxflows endpoint URL","Valid authentication credentials (API key or token)","npm or pip package manager"],"input_types":["Flow parameters (JSON)","Authentication credentials","GraphQL query definitions"],"output_types":["Flow execution results (JSON)","Structured response objects","Error messages"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_7","uri":"capability://tool.use.integration.tool.calling.integration.with.langchain.and.langgraph.frameworks","name":"tool calling integration with langchain and langgraph frameworks","description":"Provides examples and integration patterns for using wxflows tools within LangChain chains and LangGraph state machines. The system enables developers to invoke wxflows tools from LangChain agents and LangGraph workflows, treating wxflows as a tool provider alongside native LangChain tools. Integration handles tool schema translation, result parsing, and error propagation between frameworks.","intents":["I want to use wxflows tools in my existing LangChain agent without rewriting tool definitions","I need to combine wxflows tools with native LangChain tools in a single agent","I want to invoke wxflows flows from LangGraph state machines"],"best_for":["Teams with existing LangChain/LangGraph investments who want to integrate wxflows","Developers building hybrid systems that combine wxflows and LangChain tools","Organizations migrating from pure LangChain to wxflows-managed tools"],"limitations":["Integration is example-based rather than officially supported — no guarantee of compatibility across LangChain versions","Tool schema translation may lose provider-specific features (OpenAI vision, Anthropic batch processing)","Error handling between frameworks requires custom wrapper code — no automatic error propagation","Performance overhead from cross-framework calls — additional latency compared to native tool calling"],"requires":["Python 3.7+","LangChain 0.1.0+ or LangGraph 0.1.0+","wxflows Python SDK","Deployed wxflows endpoint"],"input_types":["LangChain tool definitions","wxflows tool schemas","LangGraph state objects"],"output_types":["Tool execution results","LangChain agent responses","LangGraph state updates"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_8","uri":"capability://data.processing.analysis.webpage.summarization.and.content.extraction.workflow.template","name":"webpage summarization and content extraction workflow template","description":"Provides a pre-built application template that demonstrates web scraping, content extraction, and LLM-based summarization in a single flow. The template handles fetching web content, parsing HTML, extracting relevant text, and generating summaries using configured LLM providers. Developers can customize the template for their specific content sources and summarization requirements.","intents":["I want to build a web scraper that summarizes content without implementing extraction logic","I need to create a workflow that fetches and processes web content in a single flow","I want to demonstrate wxflows capabilities with a concrete, working example"],"best_for":["Developers learning wxflows through practical examples","Teams building content aggregation and summarization tools","Organizations creating knowledge extraction pipelines from web sources"],"limitations":["HTML parsing is basic — may fail on complex, JavaScript-heavy websites","No built-in handling of authentication-required content (paywalls, login pages)","Summarization quality depends entirely on LLM provider — no fine-tuning or domain-specific models","No support for multi-page content extraction or pagination handling"],"requires":["Node.js 16+ or Python 3.7+","LLM provider API credentials","wxflows CLI and SDK","Web URLs to process"],"input_types":["Web URLs (text)","LLM provider configuration"],"output_types":["Extracted content (text)","Generated summaries (text)","Structured metadata"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-ibm-wxflows__cap_9","uri":"capability://tool.use.integration.pre.built.tool.library.with.common.integrations","name":"pre-built tool library with common integrations","description":"Offers a collection of pre-built tools for common operations (web search, database queries, API calls, document processing) that developers can import and use in their flows without implementing integration code. Tools are defined as GraphQL types with @rest directives and can be composed into larger workflows. The library abstracts authentication, error handling, and response parsing for popular external services.","intents":["I want to add web search to my agent without implementing search API integration","I need database query tools that work with my existing wxflows flows","I want to reuse common integrations across multiple projects"],"best_for":["Teams building agents quickly with common tool requirements","Developers who want to avoid reimplementing standard integrations","Organizations standardizing on wxflows for tool management"],"limitations":["Pre-built tools cover only popular services — niche integrations require custom tool development","Tool configurations may not support all features of underlying APIs — advanced use cases require customization","Authentication is simplified — complex flows (OAuth2 with refresh tokens) may not be fully supported","Tool library updates are decoupled from wxflows releases — version mismatches may cause compatibility issues"],"requires":["wxflows CLI and SDK","API credentials for integrated services","wxflows.toml configuration to import tools"],"input_types":["Tool parameters (JSON)","API credentials"],"output_types":["Tool execution results","Structured data from external services"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ for CLI","Node.js 16+ for JavaScript SDK","Valid wxflows.toml configuration file","External REST API endpoint with documented schema","API keys for at least one LLM provider (OpenAI, Anthropic, IBM watsonx, or local Ollama instance)","Node.js 16+ or Python 3.7+","@wxflows/sdk or wxflows Python package","wxflows.toml configuration file","wxflows CLI","Valid GraphQL schema definitions"],"failure_modes":["REST API mapping via @rest directives may not support complex authentication flows (OAuth2 with refresh tokens, mutual TLS)","GraphQL schema generation is one-directional — changes to external REST APIs require manual schema updates","No built-in support for streaming responses from REST endpoints","Limited to REST APIs — SOAP, gRPC, or proprietary protocols require custom adapters","Provider-specific features (vision in Claude, structured outputs in GPT-4) require conditional logic or provider detection","Token counting varies by provider — cost estimation may be inaccurate when switching models","Some providers have different tool-calling semantics (parallel vs sequential) that may affect agent behavior","Rate limiting and quota management is per-provider and not centralized","Validation is schema-based — semantic errors (invalid API endpoints, missing credentials) are not caught","Type generation is one-directional — changes to external APIs require manual schema updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.6,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"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":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=ibm-wxflows","compare_url":"https://unfragile.ai/compare?artifact=ibm-wxflows"}},"signature":"/k18mgEfNK56EJvSIhvZClvQUXgfy7yxDquIyrQhTTC+gRcLQnJIFJVCPtM/s52F5ucPbbmOXHQCihqSDGuvDA==","signedAt":"2026-06-20T14:08:01.647Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ibm-wxflows","artifact":"https://unfragile.ai/ibm-wxflows","verify":"https://unfragile.ai/api/v1/verify?slug=ibm-wxflows","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"}}