{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"semantic-kernel","slug":"semantic-kernel","name":"Semantic Kernel","type":"framework","url":"https://github.com/microsoft/semantic-kernel","page_url":"https://unfragile.ai/semantic-kernel","categories":["frameworks-sdks","code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"semantic-kernel__cap_0","uri":"capability://tool.use.integration.multi.language.kernel.orchestration.with.unified.semantic.function.execution","name":"multi-language kernel orchestration with unified semantic function execution","description":"Provides a language-agnostic Kernel abstraction (Microsoft.SemanticKernel.Kernel in .NET, semantic_kernel.Kernel in Python) that orchestrates LLM invocations, plugin registration, and function execution across C#, Python, and Java. The kernel acts as a central coordinator that manages AI service connections, maintains execution context, and routes function calls through a consistent pipeline regardless of underlying language runtime. Implements a decorator-based plugin system where functions are registered as KernelFunction objects with metadata for discovery and invocation.","intents":["Build multi-language AI applications without rewriting orchestration logic for each language","Maintain consistent function calling semantics across C#, Python, and Java codebases","Centralize LLM service configuration and switching between providers without code changes","Execute semantic functions (LLM prompts) and native functions through a unified interface"],"best_for":["Enterprise teams building polyglot AI systems with .NET, Python, and Java components","Organizations migrating existing applications to add AI capabilities without full rewrites","Developers needing consistent agent orchestration patterns across multiple language ecosystems"],"limitations":["Java implementation has limited feature parity compared to .NET and Python (no full agent framework support)","Cross-language communication requires serialization overhead; no direct in-process calls between language runtimes","Kernel state is not automatically synchronized across language boundaries — each runtime maintains separate kernel instances"],"requires":[".NET 6.0+ for C# implementation","Python 3.8+ for Python implementation","Java 11+ for Java implementation","API credentials for at least one supported LLM provider (OpenAI, Azure OpenAI, etc.)"],"input_types":["semantic function definitions (prompt templates with {{variable}} syntax)","native function signatures with KernelFunction decorators","plugin manifests and metadata","kernel arguments as dictionaries/maps"],"output_types":["function execution results (strings, structured objects)","function call metadata and execution traces","kernel state snapshots"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_1","uri":"capability://tool.use.integration.schema.based.function.calling.with.multi.provider.connector.abstraction","name":"schema-based function calling with multi-provider connector abstraction","description":"Implements a provider-agnostic function calling system that translates semantic kernel function definitions into provider-specific schemas (OpenAI JSON schema, Anthropic tool_use format, etc.) and routes tool calls back through a unified handler. Uses a connector abstraction layer (IChatCompletionService, IEmbeddingGenerationService) that abstracts away provider-specific API differences, allowing seamless switching between OpenAI, Azure OpenAI, Anthropic, Ollama, and other LLM providers. Function metadata is extracted via reflection/introspection and automatically converted to the target provider's tool schema format.","intents":["Enable LLM agents to call native functions without manual schema translation for each provider","Switch between LLM providers (OpenAI to Anthropic to local Ollama) without rewriting function definitions","Automatically handle function call routing, parameter validation, and result marshaling","Support multi-provider fallback strategies (try OpenAI, fall back to Anthropic if rate limited)"],"best_for":["Teams building provider-agnostic AI agents that need flexibility to swap LLM backends","Enterprises with multi-cloud strategies requiring abstraction over Azure OpenAI, AWS Bedrock, and GCP Vertex AI","Developers prototyping with expensive APIs (OpenAI) but wanting to deploy on cheaper alternatives (Ollama, local models)"],"limitations":["Schema translation adds ~50-100ms latency per function call due to reflection and format conversion","Not all provider-specific features are exposed through the abstraction (e.g., OpenAI's parallel tool calling, Anthropic's vision extensions)","Custom function metadata (descriptions, parameter constraints) must follow SK conventions; non-standard attributes are lost in translation","Requires explicit connector implementation for new providers; no automatic schema inference from arbitrary APIs"],"requires":["Function definitions must use KernelFunction decorator (.NET) or @kernel_function (.Python)","API keys for target LLM provider(s)","Semantic Kernel 1.0+ for stable function calling API","Provider-specific SDK (e.g., openai package for OpenAI, anthropic for Claude)"],"input_types":["Native function signatures with type hints and docstrings","Semantic kernel function metadata (name, description, parameters)","LLM responses containing tool_calls or function_calls fields"],"output_types":["Provider-specific tool schemas (OpenAI JSON schema, Anthropic tool_use blocks)","Function invocation results with type coercion","Tool call execution traces with provider-specific metadata"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_10","uri":"capability://planning.reasoning.multi.agent.orchestration.with.agent.to.agent.communication","name":"multi-agent orchestration with agent-to-agent communication","description":"Provides patterns and utilities for coordinating multiple agents in a single application, enabling agents to communicate with each other and delegate tasks. The framework supports agent composition where one agent can invoke another agent's capabilities, and agent hierarchies where a coordinator agent manages multiple specialist agents. Communication between agents is mediated through the kernel, allowing agents to share context and results. Supports both sequential agent chains (agent A → agent B → agent C) and parallel agent execution with result aggregation. Agents maintain separate conversation histories but can share semantic memory and function registries.","intents":["Build complex workflows with multiple specialized agents (e.g., research agent + writing agent + editor agent)","Implement hierarchical agent systems where a coordinator delegates to specialists","Enable agents to collaborate on complex tasks requiring multiple perspectives","Create agent teams that can parallelize work and aggregate results"],"best_for":["Teams building complex multi-step workflows that benefit from agent specialization","Enterprises needing hierarchical decision-making with multiple agents","Developers creating agent teams for research, content generation, or analysis tasks"],"limitations":["Multi-agent orchestration requires explicit coordination code; no built-in orchestrator for complex topologies","Agent communication is synchronous; no built-in support for asynchronous agent interactions","Scaling to 10+ agents becomes complex; no built-in load balancing or agent pooling","Debugging multi-agent systems is difficult; execution traces are scattered across agents","No built-in conflict resolution if agents disagree on decisions"],"requires":["Semantic Kernel 1.0+","Multiple ChatCompletionAgent instances configured with different functions/personalities","Shared kernel or separate kernels with coordinated function registries","Custom orchestration code to manage agent communication"],"input_types":["User requests or tasks","Agent configurations (functions, system prompts, execution settings)","Shared context or memory"],"output_types":["Results from individual agents","Aggregated results from multi-agent workflows","Agent communication traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_11","uri":"capability://automation.workflow.execution.settings.and.model.configuration.with.provider.specific.parameters","name":"execution settings and model configuration with provider-specific parameters","description":"Provides a configuration system for LLM execution settings that abstracts provider-specific parameters (temperature, max_tokens, top_p, etc.) into a unified PromptExecutionSettings object. Developers can configure settings globally on the kernel or per-function invocation, with automatic translation to provider-specific formats (OpenAI compat, Anthropic, etc.). Supports fallback configurations where if a setting is not supported by a provider, a sensible default is used. Settings can be serialized to JSON for persistence and reloaded at runtime. Enables A/B testing of different model configurations without code changes.","intents":["Configure LLM behavior (temperature, max tokens) without hardcoding provider-specific APIs","Switch between LLM providers while maintaining consistent execution settings","Implement A/B testing of different model configurations","Persist and reload model configurations for reproducible agent behavior"],"best_for":["Teams experimenting with different LLM configurations (temperature, top_p, etc.)","Enterprises needing to switch providers without code changes","Developers building configurable agent systems"],"limitations":["Not all provider-specific settings are exposed through the abstraction; advanced features may require direct provider API calls","Setting validation is minimal; invalid configurations may only fail at LLM API call time","No built-in support for dynamic setting adjustment based on runtime conditions","Settings are not automatically optimized for different tasks; developers must manually tune"],"requires":["Semantic Kernel 1.0+","Understanding of LLM execution parameters (temperature, max_tokens, etc.)","Target LLM provider API"],"input_types":["PromptExecutionSettings objects with configuration parameters","JSON configuration files"],"output_types":["Provider-specific API parameters","Serialized settings for persistence"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_12","uri":"capability://text.generation.language.streaming.response.handling.for.real.time.llm.output","name":"streaming response handling for real-time llm output","description":"Implements streaming support for LLM responses, allowing applications to receive and process tokens as they are generated rather than waiting for the complete response. The system provides streaming APIs for both chat completion and semantic functions, returning async iterables or streams of token chunks. Streaming is transparent to the developer; the same function invocation API works for both streaming and non-streaming modes. Supports streaming with function calling, where tool calls are streamed and executed incrementally. Enables real-time UI updates and reduced perceived latency in conversational applications.","intents":["Display LLM responses in real-time as tokens are generated","Reduce perceived latency in conversational agents by showing partial responses","Process large LLM outputs incrementally without buffering entire responses","Build streaming agents that can start executing functions before the complete response is received"],"best_for":["Teams building conversational UIs that need real-time response display","Applications with long-running LLM operations that benefit from incremental output","Developers building streaming agents with function calling"],"limitations":["Streaming is not supported by all LLM providers; fallback to buffered responses required for unsupported providers","Function calling with streaming is complex; tool calls may be split across multiple chunks","Error handling is more complex with streaming; errors may occur mid-stream","Streaming responses cannot be easily cached or replayed; each stream is consumed once"],"requires":["Semantic Kernel 1.0+","LLM provider with streaming support (OpenAI, Anthropic, Azure OpenAI)","Async/await support in the target language (.NET Task-based or Python async)"],"input_types":["Streaming-enabled function invocations","Chat completion requests with streaming flag"],"output_types":["Async iterables or streams of token chunks","Partial function call objects (for streaming function calling)","Complete responses after stream is consumed"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_2","uri":"capability://text.generation.language.semantic.function.templating.with.prompt.composition.and.variable.interpolation","name":"semantic function templating with prompt composition and variable interpolation","description":"Implements a custom prompt template language (documented in PROMPT_TEMPLATE_LANGUAGE.md) that uses {{variable}} syntax for dynamic prompt composition, supporting variable substitution, conditional blocks, and function composition. Semantic functions are defined as YAML or inline C#/Python with embedded prompts that are parsed and compiled into executable functions. The system maintains a PromptTemplateEngine that interpolates variables from kernel arguments at execution time, enabling dynamic prompt construction without string concatenation. Supports both simple variable replacement and complex prompt engineering patterns like few-shot examples and chain-of-thought templates.","intents":["Define reusable LLM prompts with dynamic variables without hardcoding values","Compose complex prompts from simpler semantic functions (prompt chaining)","Manage prompt versions and variations without code changes","Implement prompt engineering patterns (few-shot, chain-of-thought) declaratively"],"best_for":["Prompt engineers and ML practitioners who want to iterate on prompts without redeploying code","Teams managing multiple prompt variants for A/B testing or domain-specific use cases","Organizations needing version control and audit trails for prompt changes"],"limitations":["Template language is SK-specific; prompts are not portable to other frameworks without conversion","No built-in support for dynamic prompt selection based on runtime conditions (requires manual if/else in code)","Template compilation happens at function registration time; large numbers of semantic functions (100+) may add startup latency","Limited debugging support for template syntax errors; errors are reported at execution time, not parse time"],"requires":["Semantic Kernel 1.0+","Prompt templates in YAML format or inline C#/Python strings","Understanding of {{variable}} syntax and SK template conventions"],"input_types":["YAML prompt template files with {{variable}} placeholders","Inline prompt strings in C# or Python code","Kernel arguments dictionary with variable values"],"output_types":["Interpolated prompt strings ready for LLM submission","Compiled semantic function objects with metadata","Execution results from LLM with variable substitution applied"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_3","uri":"capability://memory.knowledge.vector.based.semantic.memory.with.pluggable.embedding.and.storage.backends","name":"vector-based semantic memory with pluggable embedding and storage backends","description":"Provides a memory abstraction layer (ISemanticTextMemory, TextMemoryPlugin) that decouples embedding generation from vector storage, allowing developers to use any embedding model (OpenAI, Azure OpenAI, Hugging Face) with any vector database (Chroma, Weaviate, Pinecone, in-memory). The system implements a two-stage pipeline: (1) text is converted to embeddings via an IEmbeddingGenerationService, and (2) embeddings are stored/retrieved via an IMemoryStore implementation. Supports semantic search by converting queries to embeddings and performing similarity matching, enabling RAG patterns where retrieved context is injected into prompts. Memory operations are exposed as kernel plugins (TextMemoryPlugin) for seamless integration with function calling.","intents":["Build RAG systems that retrieve relevant context from documents before generating LLM responses","Store and search over application-specific knowledge without building custom vector infrastructure","Switch between embedding models and vector databases without changing application code","Implement long-term memory for agents that persists across conversation sessions"],"best_for":["Teams building RAG applications with flexible embedding/storage requirements","Enterprises with existing vector databases (Pinecone, Weaviate) wanting to integrate with LLM orchestration","Developers prototyping with in-memory storage but planning to migrate to production databases"],"limitations":["In-memory storage (default) does not persist across application restarts; requires explicit serialization for production use","No built-in support for hybrid search (keyword + semantic); requires custom IMemoryStore implementation","Embedding generation is synchronous; large batch operations (1000+ documents) may block the event loop","Vector store implementations are community-contributed; official support is limited to Azure Cognitive Search and basic in-memory store"],"requires":["Semantic Kernel 1.0+","Embedding model API key (OpenAI, Azure OpenAI, or self-hosted)","Vector store implementation (built-in in-memory, or external Chroma/Weaviate/Pinecone client)","Documents to be indexed as plain text (no built-in PDF/document parsing)"],"input_types":["Plain text documents or chunks","Query strings for semantic search","Embedding vectors (if using custom storage)"],"output_types":["Embedding vectors (float arrays)","Retrieved document chunks with similarity scores","Memory metadata (collection names, timestamps)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_4","uri":"capability://planning.reasoning.agentic.planning.and.orchestration.with.step.by.step.task.decomposition","name":"agentic planning and orchestration with step-by-step task decomposition","description":"Provides a planning framework (documented in PLANNERS.md) that decomposes complex user goals into executable steps using LLM-based reasoning. The system includes multiple planner implementations: SequentialPlanner (breaks tasks into ordered steps), HandlebarsPlanner (uses Handlebars templates for step generation), and FunctionCallingPlanner (leverages native function calling for step execution). Planners generate a Plan object containing a sequence of steps, each mapping to a kernel function. The Kernel then executes steps sequentially, passing outputs from one step as inputs to the next, enabling multi-step agent workflows. Supports dynamic replanning if steps fail or return unexpected results.","intents":["Decompose complex user requests into multi-step workflows without manual orchestration","Build agents that reason about which functions to call and in what order","Enable self-correcting workflows that replan when steps fail","Create goal-oriented agents that can adapt to new functions without retraining"],"best_for":["Teams building complex multi-step AI agents (e.g., research assistants, code generation agents)","Enterprises needing interpretable agent decision-making (plans are human-readable)","Developers wanting LLM-based planning without building custom orchestration logic"],"limitations":["Planning adds 1-3 LLM calls per user request (one for planning, one per step execution); significantly increases latency and cost","Planners struggle with tasks requiring domain-specific reasoning or multi-agent coordination","No built-in support for parallel step execution; all steps are sequential","Plan quality depends heavily on function descriptions; poorly documented functions lead to incorrect step selection","SequentialPlanner and HandlebarsPlanner are less reliable than FunctionCallingPlanner; require careful prompt engineering"],"requires":["Semantic Kernel 1.0+","Well-documented kernel functions with clear descriptions and parameter types","LLM provider that supports function calling (OpenAI, Anthropic, Azure OpenAI)","Sufficient API quota for multiple LLM calls per request"],"input_types":["User goal or request (natural language string)","Registered kernel functions with metadata","Optional execution context or constraints"],"output_types":["Plan object containing ordered steps","Step execution results and intermediate outputs","Execution traces showing reasoning and function calls"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_5","uri":"capability://planning.reasoning.agent.framework.with.chat.completion.based.autonomous.execution","name":"agent framework with chat completion-based autonomous execution","description":"Provides a ChatCompletionAgent abstraction (.NET and Python implementations documented in Agent Framework section) that wraps an LLM in a loop: the agent receives a user message, calls the LLM with available functions, executes returned function calls, and feeds results back to the LLM until a terminal response is generated. The agent maintains conversation history (ChatHistory) and manages function call execution through the kernel. Supports both .NET (ChatCompletionAgent class) and Python (ChatCompletionAgent class) with consistent APIs. Agents can be configured with execution settings (max iterations, timeout, temperature) and support streaming responses for real-time output.","intents":["Build autonomous agents that can use tools to accomplish goals without explicit step-by-step orchestration","Create multi-turn conversational agents that maintain context across interactions","Implement agents that gracefully handle function call failures and retry logic","Deploy agents with configurable execution constraints (max iterations, timeouts)"],"best_for":["Teams building conversational AI assistants with tool access","Developers needing simple agent patterns without complex multi-agent coordination","Organizations wanting agents with built-in conversation history and context management"],"limitations":["Single-agent only; no built-in multi-agent orchestration (requires custom code or external frameworks like AutoGen)","Agents are stateless by default; conversation history must be explicitly managed and persisted","No built-in support for agent memory beyond current conversation (requires integration with semantic memory)","Function call execution is synchronous; long-running operations block the agent loop","Limited error recovery; agents may enter infinite loops if functions consistently fail"],"requires":["Semantic Kernel 1.0+","LLM provider with function calling support (OpenAI, Anthropic, Azure OpenAI)","Registered kernel functions with clear descriptions","Chat completion API (not completion API)"],"input_types":["User messages (strings)","Chat history (previous messages and function calls)","Kernel functions available to the agent"],"output_types":["Agent responses (strings)","Function call traces and execution results","Updated chat history with agent messages"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_6","uri":"capability://tool.use.integration.openapi.schema.integration.for.automatic.function.discovery.from.rest.apis","name":"openapi schema integration for automatic function discovery from rest apis","description":"Integrates OpenAPI/Swagger specifications to automatically generate kernel functions from REST API definitions, enabling agents to call external APIs without manual function wrapping. The system parses OpenAPI schemas, extracts endpoint definitions, and generates KernelFunction objects that handle HTTP request construction, parameter validation, and response parsing. Supports both OpenAPI 3.0 and Swagger 2.0 formats. Functions generated from OpenAPI specs are registered in the kernel and can be called by agents or planners just like native functions, enabling seamless integration of external services (e.g., weather APIs, database APIs) into agent workflows.","intents":["Enable agents to call external REST APIs without writing custom function wrappers","Automatically discover and expose API endpoints as kernel functions","Build agents that can integrate with third-party services (weather, maps, databases) dynamically","Reduce boilerplate code for API integration in agent applications"],"best_for":["Teams building agents that need to call multiple external APIs","Enterprises with existing REST APIs wanting to expose them to LLM agents","Developers prototyping agents with third-party service integrations"],"limitations":["Requires well-documented OpenAPI schemas; poorly specified APIs may generate incorrect functions","No built-in support for authentication beyond basic API key headers; complex auth (OAuth2, mTLS) requires custom implementation","Generated functions are read-only; cannot modify API definitions at runtime","Response parsing assumes JSON; binary or streaming responses require custom handling","No caching of OpenAPI schemas; each kernel initialization re-parses the specification"],"requires":["Semantic Kernel 1.0+","OpenAPI 3.0 or Swagger 2.0 specification (JSON or YAML format)","Network access to the REST API endpoints","API credentials if the API requires authentication"],"input_types":["OpenAPI schema files (JSON or YAML)","API endpoint URLs","Request parameters matching API specifications"],"output_types":["Generated KernelFunction objects","HTTP responses parsed to JSON or structured objects","Function metadata extracted from OpenAPI definitions"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_7","uri":"capability://safety.moderation.kernel.filters.and.extensibility.hooks.for.request.response.interception","name":"kernel filters and extensibility hooks for request/response interception","description":"Implements a filter pipeline architecture (documented in decisions/0033-kernel-filters.md) that allows developers to intercept and modify kernel function execution at multiple points: before function invocation (PreFunctionInvocationFilter), after invocation (PostFunctionInvocationFilter), and on errors (ErrorFilter). Filters are registered globally on the kernel and execute for all function calls, enabling cross-cutting concerns like logging, telemetry, rate limiting, and response modification. The filter system uses a chain-of-responsibility pattern where each filter can modify context, skip execution, or short-circuit the pipeline. Filters have access to function metadata, arguments, and results, enabling sophisticated execution control.","intents":["Add logging and observability to all kernel function calls without modifying function code","Implement rate limiting, caching, or retry logic across all functions","Modify function inputs or outputs globally (e.g., sanitize prompts, filter responses)","Build custom execution policies (e.g., cost tracking, latency monitoring)"],"best_for":["Teams needing observability and monitoring across all agent function calls","Enterprises with compliance requirements (audit logging, response filtering)","Developers building reusable agent frameworks with cross-cutting concerns"],"limitations":["Filters add execution overhead (~5-10ms per filter per function call); many filters can impact latency","Filter execution order is not guaranteed if multiple filters are registered for the same event","Filters cannot access kernel state directly; must pass data through context objects","No built-in support for async filter operations; blocking filters can stall the execution pipeline"],"requires":["Semantic Kernel 1.0+","Understanding of filter lifecycle (PreFunctionInvocation, PostFunctionInvocation, Error)","Implementation of IFunctionInvocationFilter or IPromptRenderingFilter interfaces"],"input_types":["Function invocation context (function metadata, arguments, results)","Kernel state and configuration"],"output_types":["Modified function arguments or results","Execution control signals (continue, skip, error)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_8","uri":"capability://safety.moderation.telemetry.and.observability.with.opentelemetry.integration","name":"telemetry and observability with opentelemetry integration","description":"Integrates OpenTelemetry (OTel) for distributed tracing and metrics collection across kernel function execution, LLM API calls, and embedding operations. The system emits spans for each function invocation, LLM request, and embedding generation, with semantic conventions documented in decisions/0044-OTel-semantic-convention.md. Traces include function metadata, token counts, latency, and error information, enabling end-to-end observability of agent execution. Supports exporting traces to any OTel-compatible backend (Jaeger, Datadog, Azure Monitor). Telemetry is automatically collected without requiring explicit instrumentation in user code; developers can configure sampling and export policies.","intents":["Monitor agent execution performance and identify bottlenecks in multi-step workflows","Track LLM API usage (token counts, costs) across all function calls","Debug agent behavior by inspecting detailed execution traces","Implement cost tracking and billing for LLM-powered features"],"best_for":["Teams operating production AI agents requiring observability","Enterprises needing cost tracking and billing for LLM usage","Developers debugging complex multi-step agent workflows"],"limitations":["Telemetry collection adds ~2-5% overhead to function execution","OTel export is asynchronous; high-volume applications may experience trace loss if export buffers overflow","Token counting is approximate for some models; exact counts require LLM provider APIs","Traces are not automatically correlated across language boundaries (separate .NET and Python kernels)"],"requires":["Semantic Kernel 1.0+","OpenTelemetry SDK for the target language (.NET or Python)","OTel exporter configured (Jaeger, Datadog, Azure Monitor, etc.)","Understanding of OTel semantic conventions and trace structure"],"input_types":["Kernel function invocations","LLM API responses with token counts","Embedding generation operations"],"output_types":["OTel spans with function metadata, latency, and token counts","Metrics (counters, histograms) for function calls and LLM usage","Exported traces to OTel backend"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__cap_9","uri":"capability://code.generation.editing.python.code.execution.sandbox.for.dynamic.function.generation","name":"python code execution sandbox for dynamic function generation","description":"Provides a Python code execution capability that allows agents to generate and execute Python code dynamically within a sandboxed environment. The system includes a PythonCodeExecutionPlugin that can evaluate Python expressions and statements, returning results to the agent. This enables agents to perform calculations, data transformations, or complex logic without pre-registering functions. Code execution is isolated from the main application using subprocess or restricted execution contexts, preventing malicious code from accessing sensitive resources. Supports passing variables from the kernel context into the execution environment and returning results back to the agent.","intents":["Enable agents to perform dynamic calculations or data transformations without pre-defined functions","Allow agents to generate and execute code for complex problem-solving tasks","Build code generation agents that can test generated code before returning results","Support agents that need to execute arbitrary Python logic"],"best_for":["Teams building research or data analysis agents that need dynamic computation","Developers creating code generation agents that validate generated code","Applications where pre-registering all possible functions is impractical"],"limitations":["Code execution adds significant latency (100-500ms per execution) due to subprocess overhead","Sandbox is not fully isolated; determined attackers can potentially escape restrictions","No support for long-running code; execution times out after a configurable duration","Limited access to external libraries; only standard library and pre-installed packages available","Results must be serializable to JSON; complex objects may not round-trip correctly"],"requires":["Semantic Kernel 1.0+ (Python implementation)","Python 3.8+ runtime","Explicit enablement of code execution (disabled by default for security)","Trust in the LLM generating code (code execution is inherently risky)"],"input_types":["Python code as strings (generated by LLM or provided by user)","Variables from kernel context to inject into execution environment"],"output_types":["Execution results (strings, numbers, lists, dicts)","Error messages and stack traces if code fails","Standard output and error streams"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"semantic-kernel__headline","uri":"capability://tool.use.integration.ai.orchestration.framework.for.building.intelligent.agents","name":"ai orchestration framework for building intelligent agents","description":"Semantic Kernel is an open-source SDK that allows developers to integrate large language models into applications, enabling the creation and orchestration of intelligent agents across multiple programming languages like C#, Python, and Java.","intents":["best AI orchestration framework","AI agent framework for multi-language applications","how to integrate LLMs into apps","SDK for building intelligent agents","open-source AI framework for developers"],"best_for":["developers looking to integrate AI"],"limitations":[],"requires":["basic programming knowledge"],"input_types":["code","natural language prompts"],"output_types":["intelligent agent responses","orchestrated workflows"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":74,"verified":false,"data_access_risk":"high","permissions":[".NET 6.0+ for C# implementation","Python 3.8+ for Python implementation","Java 11+ for Java implementation","API credentials for at least one supported LLM provider (OpenAI, Azure OpenAI, etc.)","Function definitions must use KernelFunction decorator (.NET) or @kernel_function (.Python)","API keys for target LLM provider(s)","Semantic Kernel 1.0+ for stable function calling API","Provider-specific SDK (e.g., openai package for OpenAI, anthropic for Claude)","Semantic Kernel 1.0+","Multiple ChatCompletionAgent instances configured with different functions/personalities"],"failure_modes":["Java implementation has limited feature parity compared to .NET and Python (no full agent framework support)","Cross-language communication requires serialization overhead; no direct in-process calls between language runtimes","Kernel state is not automatically synchronized across language boundaries — each runtime maintains separate kernel instances","Schema translation adds ~50-100ms latency per function call due to reflection and format conversion","Not all provider-specific features are exposed through the abstraction (e.g., OpenAI's parallel tool calling, Anthropic's vision extensions)","Custom function metadata (descriptions, parameter constraints) must follow SK conventions; non-standard attributes are lost in translation","Requires explicit connector implementation for new providers; no automatic schema inference from arbitrary APIs","Multi-agent orchestration requires explicit coordination code; no built-in orchestrator for complex topologies","Agent communication is synchronous; no built-in support for asynchronous agent interactions","Scaling to 10+ agents becomes complex; no built-in load balancing or agent pooling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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=semantic-kernel","compare_url":"https://unfragile.ai/compare?artifact=semantic-kernel"}},"signature":"D494zkB5/WL5pwGOeBpOq8b765Ymol7RcqGYrdwXAQ7nONHygFfCdrtN2ihvtH+XLggmMv5rmPqK1hLJFkKNDg==","signedAt":"2026-06-21T10:09:18.923Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/semantic-kernel","artifact":"https://unfragile.ai/semantic-kernel","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}