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Integrates with OpenRouter's infrastructure to provide consistent streaming behavior across multiple model providers, with support for temperature, top-p, and max-tokens constraints applied at generation time.","intents":["I need to stream model responses to users in real-time without waiting for full completion","I want to control generation parameters (temperature, top-p) per request without redeploying","I need to build chat interfaces that display tokens as they arrive for better UX"],"best_for":["web application developers building chat UIs","teams implementing real-time AI assistants","builders creating streaming API wrappers around LLMs"],"limitations":["Streaming adds ~50-100ms latency overhead vs batch generation due to HTTP chunking","Token-level control parameters (temperature, top-p) cannot be adjusted mid-stream","No built-in retry logic for dropped connections — requires client-side implementation"],"requires":["OpenRouter API key with streaming endpoint access","HTTP client with streaming/chunked transfer encoding support (e.g., fetch with ReadableStream, axios with responseType: 'stream')","Ability to parse Server-Sent Events (SSE) or newline-delimited JSON"],"input_types":["text (prompts, messages)","structured JSON (conversation history with roles)"],"output_types":["streaming text tokens (SSE or newline-delimited JSON)","structured metadata (finish_reason, usage stats)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4.7-flash__cap_2","uri":"capability://text.generation.language.multi.turn.conversation.with.role.based.context","name":"multi-turn-conversation-with-role-based-context","description":"Maintains multi-turn conversations using role-based message formatting (system, user, assistant) with full context preservation across turns. The model processes the entire conversation history to generate contextually coherent responses, with support for system prompts that define behavior and constraints. Architecture relies on stateless API calls where the client manages conversation state and sends full history with each request.","intents":["I need to build a chatbot that remembers previous messages in a conversation","I want to define system-level instructions that persist across all turns in a conversation","I need to implement multi-turn reasoning where the model builds on previous responses"],"best_for":["chatbot and conversational AI developers","teams building customer support agents","developers creating interactive coding assistants"],"limitations":["Stateless design requires sending full conversation history with each request, causing linear token cost growth with conversation length","No built-in conversation pruning or summarization — very long conversations may exceed context windows","System prompt changes require new conversation context — cannot be updated mid-conversation without losing coherence"],"requires":["OpenRouter API key","Client-side conversation state management (array of message objects)","Understanding of role-based message format (system/user/assistant)"],"input_types":["structured JSON messages with role and content fields","system prompts (text)","user messages (text)"],"output_types":["assistant messages (text)","structured conversation metadata"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4.7-flash__cap_3","uri":"capability://tool.use.integration.function.calling.with.structured.tool.schemas","name":"function-calling-with-structured-tool-schemas","description":"Enables the model to request execution of external functions by generating structured function calls with parameters, using JSON schema definitions to specify available tools. The model learns to invoke functions based on task requirements and can chain multiple function calls in sequence. Implementation relies on providing tool definitions in the system prompt or via dedicated function-calling parameters, with the model outputting structured JSON that clients parse and execute.","intents":["I need the model to decide when to call external APIs or functions based on user requests","I want to build an agent that chains multiple tool calls together to solve complex problems","I need type-safe function invocation where the model respects parameter schemas"],"best_for":["developers building autonomous agents with tool access","teams creating API orchestration layers powered by LLMs","builders implementing retrieval-augmented generation (RAG) with tool-based document access"],"limitations":["Function calling reliability decreases with schema complexity — deeply nested or union-type parameters may cause parsing errors","No built-in function execution — requires client-side implementation of tool runners and result feeding back to model","Schema validation is client-side responsibility — model may generate invalid function calls that require error handling and retry loops"],"requires":["OpenRouter API key","JSON schema definitions for each available function","Client-side function execution runtime","Error handling for invalid function calls (parsing, validation, execution failures)"],"input_types":["structured JSON function schemas (OpenAI function-calling format or similar)","natural language task descriptions","conversation history with previous function calls and results"],"output_types":["structured function calls (JSON with function name and parameters)","function execution results (fed back as assistant messages)","final text response after tool use"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4.7-flash__cap_4","uri":"capability://code.generation.editing.code.understanding.and.analysis.with.context.awareness","name":"code-understanding-and-analysis-with-context-awareness","description":"Analyzes code snippets and full codebases with awareness of language-specific syntax, semantics, and architectural patterns. The model can identify bugs, suggest refactorings, explain code behavior, and understand dependencies between functions and modules. Implementation leverages the 30B parameter scale and code-specific training to maintain coherence across multi-file contexts and recognize common patterns (design patterns, anti-patterns, security issues).","intents":["I need the model to review code and identify bugs or security issues","I want to understand what a complex function does and how it fits into the larger codebase","I need suggestions for refactoring code to improve readability or performance"],"best_for":["code review automation tools","IDE plugins for code analysis and suggestions","teams implementing automated code quality checks"],"limitations":["Context window limits analysis to codebases under ~50KB of code per request","Analysis quality varies by language — better for popular languages (Python, JavaScript, Java) than niche languages","No execution context — cannot detect runtime bugs or performance issues that require actual execution"],"requires":["OpenRouter API key","Code snippets or full file contents as text input","Optional: language hints in prompts to improve analysis accuracy"],"input_types":["code snippets (single or multiple files)","natural language questions about code","structured code analysis requests (e.g., 'find security issues')"],"output_types":["analysis results (text explanations, bug reports)","refactoring suggestions (code diffs or new implementations)","structured findings (JSON with issue types and locations)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4.7-flash__cap_5","uri":"capability://planning.reasoning.natural.language.reasoning.with.chain.of.thought","name":"natural-language-reasoning-with-chain-of-thought","description":"Generates step-by-step reasoning traces that decompose complex problems into intermediate reasoning steps before arriving at final answers. The model can be prompted to 'think aloud' using chain-of-thought patterns, enabling transparency into decision-making and improving accuracy on multi-step reasoning tasks. Implementation relies on prompting techniques (e.g., 'Let's think step by step') that activate the model's reasoning capabilities without requiring special model modifications.","intents":["I need the model to show its reasoning process for debugging or transparency","I want to improve answer accuracy on complex questions by enabling step-by-step reasoning","I need to extract intermediate reasoning steps for educational or auditing purposes"],"best_for":["educational AI applications requiring reasoning transparency","teams building explainable AI systems","developers creating complex question-answering systems"],"limitations":["Chain-of-thought reasoning increases token generation by 2-5x, raising latency and cost","Reasoning quality depends heavily on prompt engineering — generic prompts may not activate reasoning","No guarantee of correct reasoning — model may generate plausible-sounding but incorrect intermediate steps"],"requires":["OpenRouter API key","Prompts designed to elicit chain-of-thought (e.g., 'Let's think step by step')","Ability to parse and extract reasoning traces from model output"],"input_types":["natural language questions or problems","prompts with chain-of-thought instructions"],"output_types":["reasoning traces (step-by-step explanations)","final answers with supporting reasoning","structured reasoning (if parsed from output)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4.7-flash__cap_6","uri":"capability://tool.use.integration.api.based.model.access.with.provider.abstraction","name":"api-based-model-access-with-provider-abstraction","description":"Provides access to the GLM-4.7-Flash model through OpenRouter's unified API, abstracting away provider-specific implementation details and offering consistent request/response formats across multiple underlying models. Clients make HTTP requests to OpenRouter endpoints with standard JSON payloads, and OpenRouter handles routing, rate limiting, and provider-specific protocol translation. This enables easy model switching and multi-model fallback strategies without code changes.","intents":["I need to use a specific model (GLM-4.7-Flash) without managing API keys or provider-specific SDKs","I want to build applications that can switch between models without code changes","I need a unified API that works across multiple model providers"],"best_for":["application developers avoiding vendor lock-in","teams building multi-model applications","startups prototyping with different models quickly"],"limitations":["Additional latency (~50-200ms) from OpenRouter proxy layer vs. direct provider APIs","Pricing markup from OpenRouter on top of base model costs","Rate limiting and quota management handled by OpenRouter, not directly controllable by client","No access to provider-specific features (e.g., vision capabilities) unless explicitly supported by OpenRouter"],"requires":["OpenRouter API key (free or paid account)","HTTP client (curl, fetch, axios, requests, etc.)","Knowledge of OpenRouter API format (compatible with OpenAI API format)"],"input_types":["JSON request bodies with messages, parameters, and optional functions","HTTP headers with authorization"],"output_types":["JSON responses with model output, usage statistics, and metadata","streaming responses (Server-Sent Events or newline-delimited JSON)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-z-ai-glm-4.7-flash__cap_7","uri":"capability://text.generation.language.context.window.aware.text.processing","name":"context-window-aware-text-processing","description":"Processes text inputs with awareness of context window constraints, maintaining coherence within the model's maximum token capacity. The model can handle inputs up to its context window limit (typically 128K tokens for GLM-4.7-Flash) and generates outputs that fit within remaining token budget. Implementation relies on client-side token counting and context management to avoid exceeding limits, with graceful degradation when inputs approach window boundaries.","intents":["I need to process long documents without losing important context","I want to estimate token usage before making API calls to control costs","I need to handle variable-length inputs while respecting context window limits"],"best_for":["document processing and analysis applications","teams building RAG systems with large document collections","developers implementing cost-aware LLM applications"],"limitations":["Token counting requires external libraries (tiktoken, transformers) — not built into API","Context window size may vary by model version — requires checking documentation","No automatic context pruning or summarization — client must implement strategies for handling overflow","Very long documents (>100K tokens) may degrade reasoning quality even within window limits"],"requires":["OpenRouter API key","Token counting library (tiktoken for OpenAI-compatible tokenization, or model-specific tokenizer)","Client-side context management logic"],"input_types":["text of variable length (from short prompts to full documents)","structured data with metadata about token budgets"],"output_types":["text responses within remaining token budget","metadata about token usage (prompt tokens, completion tokens, total)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["OpenRouter API key","HTTP client capable of streaming responses","Support for function-calling or tool-use schema (if using agentic patterns)","OpenRouter API key with streaming endpoint access","HTTP client with streaming/chunked transfer encoding support (e.g., fetch with ReadableStream, axios with responseType: 'stream')","Ability to parse Server-Sent Events (SSE) or newline-delimited JSON","Client-side conversation state management (array of message objects)","Understanding of role-based message format (system/user/assistant)","JSON schema definitions for each available function","Client-side function execution runtime"],"failure_modes":["Long-horizon planning quality degrades with task complexity beyond ~10 sequential steps","No built-in state persistence — requires external memory store for multi-turn agent sessions","Context window limitations may constrain visibility into very large codebases during planning","Streaming adds ~50-100ms latency overhead vs batch generation due to HTTP chunking","Token-level control parameters (temperature, top-p) cannot be adjusted mid-stream","No built-in retry logic for dropped connections — requires client-side implementation","Stateless design requires sending full conversation history with each request, causing linear token cost growth with conversation length","No built-in conversation pruning or summarization — very long conversations may exceed context windows","System prompt changes require new conversation context — cannot be updated mid-conversation without losing coherence","Function calling reliability decreases with schema complexity — deeply nested or union-type parameters may cause parsing errors","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.059Z","last_scraped_at":"2026-05-03T15:20:45.776Z","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=z-ai-glm-4.7-flash","compare_url":"https://unfragile.ai/compare?artifact=z-ai-glm-4.7-flash"}},"signature":"Q3QoZGjq3gNklUENE/h84Np2m+INRpsq2VX+3ofS8ZM3TAC1RwYvgtj5eYVzJXcEOzr03D+wXSW3d7oaGZnfCw==","signedAt":"2026-06-20T10:51:47.462Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/z-ai-glm-4.7-flash","artifact":"https://unfragile.ai/z-ai-glm-4.7-flash","verify":"https://unfragile.ai/api/v1/verify?slug=z-ai-glm-4.7-flash","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"}}