{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-semantic-kernel","slug":"pypi-semantic-kernel","name":"semantic-kernel","type":"framework","url":"https://pypi.org/project/semantic-kernel/","page_url":"https://unfragile.ai/pypi-semantic-kernel","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-semantic-kernel__cap_0","uri":"capability://text.generation.language.llm.agnostic.prompt.composition.and.execution","name":"llm-agnostic prompt composition and execution","description":"Semantic Kernel abstracts LLM interactions through a unified kernel interface that decouples prompt definitions from specific model implementations. Prompts are defined as semantic functions with templating support (Handlebars/Jinja2), and the kernel routes execution to configurable LLM services (OpenAI, Azure OpenAI, Anthropic, local models) without changing function code. This enables switching between models and providers by configuration alone.","intents":["Build LLM applications that aren't locked into a single provider","Define reusable prompt templates that work across multiple model backends","Switch between OpenAI, Azure, and open-source models without refactoring code","Compose complex multi-step prompts with consistent error handling"],"best_for":["Teams building production LLM applications requiring provider flexibility","Enterprises with multi-cloud or hybrid LLM strategies","Developers prototyping with multiple models to find optimal cost/performance"],"limitations":["Abstraction layer adds ~50-100ms overhead per LLM call due to kernel routing","Model-specific features (vision, function calling nuances) require custom adapters","No built-in fallback or retry logic — must be implemented at application layer","Template syntax limited to Handlebars/Jinja2; no custom expression languages"],"requires":["Python 3.9+","API key for at least one LLM provider (OpenAI, Azure OpenAI, Anthropic, or local Ollama/LM Studio)","semantic-kernel package installed via pip"],"input_types":["text prompts","structured prompt templates with variables","conversation history (for multi-turn)","JSON configuration for model parameters"],"output_types":["text completions","structured JSON (with schema validation)","streaming token sequences","function call specifications"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_1","uri":"capability://planning.reasoning.semantic.function.definition.and.memory.integration","name":"semantic function definition and memory integration","description":"Semantic Kernel allows developers to define semantic functions (LLM-powered functions) that can be stored, retrieved, and executed with automatic context injection from memory systems. Functions are defined via YAML/JSON manifests or Python decorators, and the kernel manages function registration, parameter binding, and memory context enrichment (RAG-style). This creates a unified namespace where functions can reference stored knowledge without explicit retrieval code.","intents":["Define reusable LLM-powered functions that automatically enrich context from knowledge bases","Build function libraries that can be discovered and invoked dynamically","Compose multi-function workflows where outputs feed into subsequent function inputs","Manage function versioning and parameter schemas centrally"],"best_for":["Teams building modular LLM applications with function composition patterns","Applications requiring dynamic function discovery and invocation","Developers implementing RAG systems where functions need automatic context injection"],"limitations":["Function state is ephemeral — no built-in persistence across kernel instances","Parameter binding is string-based; complex type marshalling requires custom serializers","No native support for async function composition chains (requires manual await handling)","Memory context injection happens at function call time, not at definition time, limiting optimization"],"requires":["Python 3.9+","semantic-kernel package","Memory connector implementation (e.g., vector store for embeddings)","LLM service configured in kernel"],"input_types":["function definitions (YAML/JSON manifests or Python decorators)","function parameters (strings, numbers, structured objects)","memory context (embeddings, retrieved documents)"],"output_types":["function results (text, JSON, structured data)","function metadata (schema, description, version)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_10","uri":"capability://tool.use.integration.connector.based.llm.service.abstraction","name":"connector-based llm service abstraction","description":"Semantic Kernel abstracts LLM service interactions through pluggable connectors (OpenAI, Azure OpenAI, Anthropic, Ollama, HuggingFace) that implement a common interface. Connectors handle authentication, request formatting, response parsing, and error handling for each provider. This enables switching between providers by changing configuration, and adding new providers by implementing the connector interface without modifying kernel code.","intents":["Switch between LLM providers (OpenAI, Azure, Anthropic) without code changes","Add support for custom or self-hosted LLM services via custom connectors","Implement provider-agnostic applications that work with any LLM service","Manage multiple LLM services simultaneously (e.g., GPT-4 for reasoning, GPT-3.5 for speed)"],"best_for":["Teams building provider-agnostic LLM applications","Enterprises with multi-cloud or hybrid LLM strategies","Developers integrating custom or self-hosted LLM services"],"limitations":["Connector abstraction hides provider-specific features (vision, function calling variants) behind lowest-common-denominator interface","Adding new providers requires implementing full connector interface — no partial implementations","Provider-specific optimizations (batching, caching) are not exposed through abstraction","Error handling is generic — provider-specific errors are normalized, losing diagnostic detail"],"requires":["Python 3.9+","semantic-kernel package","API credentials for chosen LLM provider(s)","Custom connector implementation for non-standard providers"],"input_types":["LLM service configuration (API key, endpoint, model name)","prompts (text)","function calling schemas"],"output_types":["LLM responses (text, function calls)","usage metadata (tokens, cost)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_11","uri":"capability://data.processing.analysis.structured.output.parsing.and.schema.validation","name":"structured output parsing and schema validation","description":"Semantic Kernel can enforce structured outputs from LLMs by specifying JSON schemas and parsing/validating responses against them. The kernel can request LLMs to return JSON (via prompting or function calling), parse the response, and validate it against a schema. This enables type-safe LLM outputs that can be directly used in downstream code without manual parsing or error handling.","intents":["Extract structured data from LLM responses (JSON, objects) with schema validation","Ensure LLM outputs conform to expected types before using them in code","Build pipelines where LLM outputs are automatically parsed and validated","Create type-safe agent patterns where function parameters are guaranteed valid"],"best_for":["Teams building data extraction pipelines with LLMs","Applications requiring guaranteed structured outputs from LLMs","Developers implementing type-safe agent patterns"],"limitations":["Schema validation is post-hoc (after LLM generation) — doesn't guarantee LLM will follow schema","LLM compliance with schema requests is probabilistic — may require retries or fallbacks","Complex schemas (unions, recursive types) may confuse LLMs or require custom prompting","No built-in retry logic for schema validation failures"],"requires":["Python 3.9+","semantic-kernel package","JSON schema definitions (JSON Schema format)","LLM service supporting JSON mode or function calling"],"input_types":["prompts requesting structured output","JSON schemas (JSON Schema format)","LLM responses (text, JSON)"],"output_types":["parsed objects (Python dicts, dataclasses)","validation errors (schema mismatch)","structured data (JSON)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_2","uri":"capability://tool.use.integration.plugin.based.skill.composition.and.orchestration","name":"plugin-based skill composition and orchestration","description":"Semantic Kernel implements a plugin architecture where native functions (Python code) and semantic functions (LLM-powered) are registered as skills within a unified plugin system. Plugins are discoverable collections of functions that can be composed into multi-step workflows. The kernel handles function resolution, parameter binding, and execution order, enabling complex orchestration patterns like function chaining and conditional branching without explicit workflow DSLs.","intents":["Organize LLM and native functions into logical plugin groups for reusability","Compose multi-step workflows by chaining function outputs to inputs","Build applications where LLM functions can call native functions and vice versa","Create extensible systems where new skills can be added without modifying core code"],"best_for":["Teams building extensible LLM agents with plugin ecosystems","Applications requiring mixed native/semantic function orchestration","Developers implementing skill-based architectures (e.g., agent toolkits)"],"limitations":["Plugin discovery is manual registration — no automatic scanning of function directories","No built-in dependency resolution between plugins (circular dependencies possible)","Workflow orchestration is imperative (code-based) rather than declarative (YAML/JSON)","Error handling and retry logic must be implemented per-plugin, not globally"],"requires":["Python 3.9+","semantic-kernel package","Native functions must be Python callables with type hints","LLM service configured for semantic functions"],"input_types":["Python functions (native skills)","Semantic function definitions (LLM-powered skills)","Plugin manifests (metadata, descriptions)"],"output_types":["composed function results","execution traces (function call order, parameters, results)","structured workflow outputs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_3","uri":"capability://memory.knowledge.memory.and.embedding.management.with.vector.store.abstraction","name":"memory and embedding management with vector store abstraction","description":"Semantic Kernel provides a memory abstraction layer that manages embeddings and vector storage through pluggable connectors (Azure Cognitive Search, Pinecone, Weaviate, in-memory). The kernel automatically handles embedding generation, storage, and retrieval without requiring developers to manage embedding models or vector databases directly. Memory is integrated with semantic functions, enabling automatic context enrichment for RAG patterns.","intents":["Store and retrieve document embeddings without managing embedding models separately","Implement RAG (Retrieval-Augmented Generation) patterns with automatic context injection","Switch between vector stores (cloud, self-hosted, in-memory) via configuration","Build semantic search capabilities that enhance LLM prompts with relevant context"],"best_for":["Teams implementing RAG systems with flexible vector store backends","Applications requiring semantic search over document collections","Developers building knowledge-augmented LLM applications"],"limitations":["Embedding model is decoupled from memory layer — requires separate configuration and API calls","No built-in batch embedding optimization — each document embedded individually","Memory retrieval is synchronous only; no streaming or async batch operations","Vector store abstraction hides provider-specific features (filtering, metadata search) behind lowest-common-denominator interface"],"requires":["Python 3.9+","semantic-kernel package","Embedding service (OpenAI, Azure, local model)","Vector store connector (Azure Cognitive Search, Pinecone, Weaviate, or in-memory)","API credentials for chosen vector store"],"input_types":["text documents (strings)","embedding vectors (numpy arrays, lists)","query strings for semantic search","metadata (JSON objects for filtering)"],"output_types":["retrieved documents with scores","embedding vectors","memory collection metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_4","uri":"capability://tool.use.integration.function.calling.and.native.code.execution.with.schema.validation","name":"function calling and native code execution with schema validation","description":"Semantic Kernel enables LLMs to call native Python functions through a schema-based function calling mechanism. The kernel exposes native functions to the LLM via JSON schemas, the LLM generates function call specifications, and the kernel validates and executes them. This creates a closed loop where LLMs can invoke arbitrary Python code with automatic parameter validation and type coercion, enabling agent patterns where LLMs decide which tools to use.","intents":["Allow LLMs to invoke native Python functions dynamically based on task requirements","Build agents that decide which tools to use and execute them with validated parameters","Create tool-use patterns where LLM output is automatically parsed and executed","Implement multi-turn agent loops where LLM results trigger subsequent function calls"],"best_for":["Teams building LLM agents with dynamic tool selection","Applications requiring LLM-driven automation of native code","Developers implementing ReAct or similar agent patterns"],"limitations":["Schema generation is automatic but may not capture complex type constraints (unions, generics)","Function execution is synchronous — no native async/await support in LLM-driven calls","No built-in sandboxing — arbitrary Python code can be executed if LLM is compromised","Parameter validation is schema-based only; custom validation logic requires wrapper functions"],"requires":["Python 3.9+","semantic-kernel package","Native functions with type hints (required for schema generation)","LLM service supporting function calling (OpenAI, Anthropic, Azure OpenAI)"],"input_types":["Python functions (with type hints)","LLM-generated function call specifications (JSON)","function parameters (strings, numbers, objects)"],"output_types":["function execution results","validation errors (schema mismatch)","execution traces (function name, parameters, result)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_5","uri":"capability://text.generation.language.prompt.templating.with.variable.substitution.and.filters","name":"prompt templating with variable substitution and filters","description":"Semantic Kernel provides a templating engine (Handlebars/Jinja2) for defining prompts with variable placeholders, conditional logic, and filters. Templates support dynamic variable injection from kernel context, memory retrieval, and function outputs. This enables parameterized prompts that adapt to runtime context without string concatenation or manual formatting, reducing prompt injection vulnerabilities and improving maintainability.","intents":["Define reusable prompt templates with variable placeholders for dynamic content","Inject context from memory, function outputs, or kernel state into prompts automatically","Create conditional prompts that adapt based on runtime parameters","Build prompt libraries that are version-controlled and reusable across projects"],"best_for":["Teams managing large prompt libraries requiring version control and reusability","Applications with dynamic prompts that adapt to user input or context","Developers seeking to reduce prompt injection vulnerabilities through structured templating"],"limitations":["Template syntax is limited to Handlebars/Jinja2 — no custom expression languages","No built-in prompt validation or testing framework","Template rendering adds ~10-20ms latency per prompt","Complex conditional logic in templates can become unmaintainable; better suited for simple variable substitution"],"requires":["Python 3.9+","semantic-kernel package","Prompt templates in YAML/JSON or Python string format","Variables/context to inject into templates"],"input_types":["template strings (Handlebars/Jinja2 syntax)","context variables (strings, numbers, objects)","memory retrieval results","function outputs"],"output_types":["rendered prompt strings","validation errors (missing variables, syntax errors)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_6","uri":"capability://text.generation.language.streaming.and.token.level.response.handling","name":"streaming and token-level response handling","description":"Semantic Kernel supports streaming LLM responses at the token level, enabling real-time output display and token counting without buffering entire responses. The kernel provides streaming iterators that yield tokens as they arrive from the LLM service, allowing applications to process responses incrementally. This is critical for user-facing applications requiring low latency and token-based billing calculations.","intents":["Display LLM responses in real-time as tokens arrive (for chat UIs, dashboards)","Count tokens for billing and quota management without buffering full responses","Implement progressive response processing where early tokens trigger downstream actions","Build responsive applications that don't block on full LLM response completion"],"best_for":["Teams building user-facing LLM applications requiring real-time response display","Applications with strict latency requirements or token-based billing","Developers implementing streaming chat interfaces or progressive output processing"],"limitations":["Streaming is token-level only — no structured streaming for JSON or function calls","Token counting is approximate (varies by tokenizer) — not suitable for precise billing","Streaming requires keeping connection open; incompatible with some proxy/load balancer configurations","Error handling mid-stream is complex — partial responses may be invalid"],"requires":["Python 3.9+","semantic-kernel package","LLM service supporting streaming (OpenAI, Azure OpenAI, Anthropic)","Async/await support in application code"],"input_types":["prompts (text)","streaming parameters (chunk size, timeout)"],"output_types":["token streams (iterators yielding strings)","token counts (integers)","partial responses (incomplete text)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_7","uri":"capability://memory.knowledge.conversation.history.and.multi.turn.context.management","name":"conversation history and multi-turn context management","description":"Semantic Kernel manages conversation history and multi-turn context through a chat history abstraction that tracks messages, roles (user/assistant/system), and metadata. The kernel automatically maintains context across turns, handles token limits by truncating history, and integrates history with semantic functions for context-aware responses. This simplifies building multi-turn conversational agents without manual history management.","intents":["Build multi-turn conversational agents that maintain context across interactions","Implement conversation memory that persists across kernel instances","Automatically manage token limits by truncating old messages while preserving recent context","Create role-based conversation flows (system prompts, user queries, assistant responses)"],"best_for":["Teams building conversational AI applications with multi-turn interactions","Applications requiring persistent conversation state across sessions","Developers implementing chatbots or dialogue systems"],"limitations":["History is in-memory by default — no built-in persistence (requires external storage)","Token limit handling is naive (truncates from oldest messages) — no semantic importance weighting","No built-in conversation summarization for long-running conversations","Role management is basic (user/assistant/system) — no support for custom roles or multi-agent conversations"],"requires":["Python 3.9+","semantic-kernel package","External storage for persistence (database, file system, vector store)"],"input_types":["messages (text)","roles (user, assistant, system)","metadata (timestamps, message IDs)","token limits (integers)"],"output_types":["conversation history (list of messages)","truncated history (for token limits)","context-enriched prompts (history injected into templates)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_8","uri":"capability://automation.workflow.async.await.support.for.non.blocking.llm.operations","name":"async/await support for non-blocking llm operations","description":"Semantic Kernel provides native async/await support throughout its API, enabling non-blocking LLM calls, concurrent function execution, and efficient resource utilization. All kernel operations (LLM calls, memory retrieval, function execution) have async variants, allowing applications to handle multiple requests concurrently without thread pools. This is critical for scalable server-side applications and responsive client applications.","intents":["Build scalable server applications that handle multiple concurrent LLM requests","Implement responsive client applications that don't block on LLM operations","Execute multiple semantic functions concurrently and aggregate results","Optimize resource utilization by avoiding thread overhead for I/O-bound operations"],"best_for":["Teams building production server applications with high concurrency requirements","Applications requiring responsive UIs that don't block on LLM calls","Developers implementing concurrent agent patterns or batch processing"],"limitations":["Async code is more complex than synchronous equivalents — requires understanding of event loops and coroutines","Mixing sync and async code can cause deadlocks — requires careful architecture","Some integrations (vector stores, embedding services) may not have async support","Debugging async code is harder than sync code — requires async-aware debugging tools"],"requires":["Python 3.9+","semantic-kernel package","Async runtime (asyncio, uvloop, or similar)","Understanding of Python async/await patterns"],"input_types":["async functions (coroutines)","concurrent tasks (multiple semantic functions)"],"output_types":["async results (coroutines returning values)","aggregated results from concurrent operations"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-semantic-kernel__cap_9","uri":"capability://planning.reasoning.plan.generation.and.execution.for.complex.task.decomposition","name":"plan generation and execution for complex task decomposition","description":"Semantic Kernel includes a planning system that uses LLMs to decompose complex tasks into executable plans (sequences of semantic and native functions). The planner generates plans in a structured format (e.g., YAML), the kernel validates the plan against available functions, and then executes it step-by-step. This enables agents to reason about task decomposition without explicit workflow definition, supporting dynamic planning based on available skills.","intents":["Decompose complex user requests into executable multi-step plans automatically","Build agents that reason about which functions to call and in what order","Enable dynamic planning where available skills determine possible plans","Create self-improving agents that can learn from plan execution results"],"best_for":["Teams building autonomous agents with complex task reasoning","Applications requiring dynamic task decomposition based on available skills","Developers implementing planning-based agent architectures"],"limitations":["Plan generation is non-deterministic — same input may produce different plans","No built-in plan validation or feasibility checking before execution","Plan execution is sequential only — no parallel task execution","Error recovery is limited — failed plan steps don't trigger replanning","Plans are generated fresh each time — no learning or caching of successful plans"],"requires":["Python 3.9+","semantic-kernel package","LLM service supporting function calling and reasoning (GPT-4 recommended)","Semantic and native functions registered as skills"],"input_types":["user goals (natural language descriptions)","available functions (skills registered in kernel)","context (memory, previous results)"],"output_types":["execution plans (structured sequences of function calls)","plan results (aggregated outputs from all steps)","execution traces (step-by-step results)"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API key for at least one LLM provider (OpenAI, Azure OpenAI, Anthropic, or local Ollama/LM Studio)","semantic-kernel package installed via pip","semantic-kernel package","Memory connector implementation (e.g., vector store for embeddings)","LLM service configured in kernel","API credentials for chosen LLM provider(s)","Custom connector implementation for non-standard providers","JSON schema definitions (JSON Schema format)","LLM service supporting JSON mode or function calling"],"failure_modes":["Abstraction layer adds ~50-100ms overhead per LLM call due to kernel routing","Model-specific features (vision, function calling nuances) require custom adapters","No built-in fallback or retry logic — must be implemented at application layer","Template syntax limited to Handlebars/Jinja2; no custom expression languages","Function state is ephemeral — no built-in persistence across kernel instances","Parameter binding is string-based; complex type marshalling requires custom serializers","No native support for async function composition chains (requires manual await handling)","Memory context injection happens at function call time, not at definition time, limiting optimization","Connector abstraction hides provider-specific features (vision, function calling variants) behind lowest-common-denominator interface","Adding new providers requires implementing full connector interface — no partial implementations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:13.887Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-semantic-kernel","compare_url":"https://unfragile.ai/compare?artifact=pypi-semantic-kernel"}},"signature":"tJf92RVE5XnwzJhf9da6zyu9w0CrwWKY2d1t+FpKM57lNDxsAU5oCuVUHS86gogYntu8a1ja4RZN/08/DOD0DA==","signedAt":"2026-06-21T10:27:21.162Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-semantic-kernel","artifact":"https://unfragile.ai/pypi-semantic-kernel","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-semantic-kernel","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"}}