{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-smolagents","slug":"pypi-smolagents","name":"smolagents","type":"repo","url":"https://pypi.org/project/smolagents/","page_url":"https://unfragile.ai/pypi-smolagents","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-smolagents__cap_0","uri":"capability://code.generation.editing.python.code.generation.for.tool.invocation","name":"python code generation for tool invocation","description":"Agents generate executable Python code as their primary reasoning mechanism, where each tool call is expressed as a Python function invocation within a code block. The LLM outputs raw Python that the runtime parses and executes, enabling agents to compose tool calls with arbitrary Python logic (loops, conditionals, variable assignment) rather than being constrained to sequential JSON-based function calls. This approach treats code generation as the agent's native language for orchestration.","intents":["I want my agent to call multiple tools in sequence with conditional logic based on results","I need agents to perform intermediate computations or data transformations between tool calls","I want to leverage Python's expressiveness for complex multi-step reasoning without custom DSLs"],"best_for":["Python developers building LLM agents who are comfortable with code-as-orchestration patterns","Teams building agents that need flexible control flow beyond simple function calling","Prototyping scenarios where rapid iteration on agent logic is critical"],"limitations":["Requires LLM capable of generating syntactically correct Python (hallucination risk for complex logic)","No built-in sandboxing — executing arbitrary LLM-generated code poses security risks in untrusted environments","Debugging agent reasoning requires reading generated code, which can be verbose and hard to trace","Performance overhead from parsing and executing Python code vs direct function call protocols"],"requires":["Python 3.9+","LLM API access (OpenAI, Anthropic, Hugging Face, or local model via Ollama)","Tool definitions compatible with Python function signatures"],"input_types":["tool definitions (Python callables or JSON schemas)","user queries (text)","system prompts (text)"],"output_types":["Python code (agent reasoning steps)","tool call results (structured or unstructured)","final agent response (text)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Provides a unified agent interface that abstracts away provider-specific API differences (OpenAI, Anthropic, Hugging Face, Ollama, etc.), allowing agents to swap LLM backends without code changes. The library handles prompt formatting, token counting, and response parsing for each provider's conventions, exposing a single agent API that works across proprietary and open-source models. This enables cost optimization and model experimentation without refactoring agent logic.","intents":["I want to switch between OpenAI GPT-4 and Anthropic Claude without rewriting my agent code","I need to run agents on local open-source models (Llama, Mistral) for privacy or cost reasons","I want to A/B test different LLM providers to find the best cost/quality tradeoff for my use case"],"best_for":["Teams evaluating multiple LLM providers for production agents","Developers building cost-sensitive applications who want to switch between expensive and cheap models","Organizations with privacy requirements needing to run agents on local or self-hosted models"],"limitations":["Abstraction layer adds ~50-100ms latency per request due to provider-specific formatting and parsing","Not all providers support identical feature sets (e.g., vision capabilities, function calling schemas) — fallback behavior may degrade gracefully","Token counting estimates vary by provider; exact token usage only known after API call","Prompt engineering may need tuning per provider due to different instruction-following capabilities"],"requires":["Python 3.9+","API keys for at least one provider (OpenAI, Anthropic, Hugging Face, etc.)","Network access to provider APIs or local Ollama instance"],"input_types":["provider configuration (API keys, model names, endpoints)","agent prompts (text)","tool definitions"],"output_types":["LLM responses (text)","structured agent outputs"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_10","uri":"capability://safety.moderation.observability.and.execution.tracing","name":"observability and execution tracing","description":"Provides detailed execution traces of agent reasoning, including generated code, tool calls, results, and LLM interactions. The library logs each step of the agentic loop (code generation, parsing, tool invocation, result processing) with structured metadata, enabling debugging, monitoring, and analysis of agent behavior. Traces can be exported to external observability platforms (e.g., Langfuse, Arize) for centralized monitoring.","intents":["I want to debug agent failures by seeing the exact code it generated and tools it called","I need to monitor agent performance and identify bottlenecks","I want to analyze agent behavior patterns to improve prompts or tool definitions"],"best_for":["Production agents where debugging and monitoring are critical","Teams optimizing agent performance and prompt engineering","Compliance scenarios requiring detailed audit trails of agent decisions"],"limitations":["Detailed tracing adds overhead (50-200ms per agent step) due to logging and serialization","Storing full traces can consume significant storage — requires log rotation or sampling strategies","Sensitive data in traces (API keys, user data) requires careful redaction before exporting","Trace analysis tools are external — library provides tracing but not built-in analytics"],"requires":["Python 3.9+","Logging infrastructure (Python logging module or external platform)","Optional: observability platform (Langfuse, Arize, etc.)"],"input_types":["agent execution events (code generation, tool calls, results)","tracing configuration (verbosity, sampling rate)"],"output_types":["structured execution traces (JSON or similar)","exported traces to observability platforms"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_11","uri":"capability://image.visual.vision.and.multimodal.input.support","name":"vision and multimodal input support","description":"Enables agents to process multimodal inputs including images, documents, and audio, allowing them to reason about visual content and extract information from documents. Agents can invoke vision tools that analyze images (OCR, object detection, scene understanding) or document processing tools that extract structured data from PDFs and scanned documents. This extends agent capabilities beyond text-only reasoning.","intents":["I want agents to analyze images and extract information from visual content","I need agents to process documents (PDFs, scans) and extract structured data","I want agents to reason about multimodal content (text + images + documents)"],"best_for":["Document processing workflows where agents need to extract data from PDFs or scans","Visual reasoning tasks (image analysis, scene understanding, object detection)","Multimodal applications combining text, images, and documents"],"limitations":["Vision model inference is slow (1-5 seconds per image) compared to text-only reasoning","Image encoding and transmission adds latency and bandwidth overhead","Vision models have varying accuracy depending on image quality and content type","Multimodal context (images + text) increases token usage and costs significantly"],"requires":["Python 3.9+","Vision-capable LLM (GPT-4V, Claude 3 with vision, etc.)","Image processing libraries (PIL, OpenCV) for preprocessing"],"input_types":["images (PNG, JPEG, WebP)","documents (PDF, TIFF)","text queries about images/documents"],"output_types":["extracted text from images (OCR)","structured data from documents","visual analysis results"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_2","uri":"capability://tool.use.integration.tool.registry.with.schema.based.validation","name":"tool registry with schema-based validation","description":"Agents discover and invoke tools through a registry system that validates tool schemas (input parameters, output types) before execution. Tools are registered as Python callables with type hints or JSON schemas, and the registry enforces that LLM-generated code calls tools with valid arguments, preventing runtime errors from malformed tool invocations. This enables safe tool composition and provides agents with introspectable tool metadata for reasoning about available capabilities.","intents":["I want to ensure agents can only call tools with valid argument combinations","I need agents to discover available tools and their signatures dynamically","I want to prevent runtime errors from agents calling tools with wrong argument types"],"best_for":["Developers building production agents where tool invocation reliability is critical","Teams with large tool ecosystems who need centralized tool management and validation","Scenarios requiring audit trails of which tools agents attempted to call"],"limitations":["Schema validation happens at runtime after code generation, not during LLM inference — agents can still generate invalid code that fails validation","Complex nested schemas or union types may not translate cleanly between Python type hints and JSON schemas","No built-in versioning for tool schemas — breaking changes to tool signatures require careful migration","Validation overhead adds ~10-50ms per tool call depending on schema complexity"],"requires":["Python 3.9+","Tool definitions with type hints or JSON schemas","Pydantic or similar for schema validation (if using structured schemas)"],"input_types":["tool callables (Python functions)","tool schemas (JSON or Python type hints)","agent-generated code with tool calls"],"output_types":["validated tool call results","validation error messages"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_3","uri":"capability://planning.reasoning.agent.composition.and.hierarchical.delegation","name":"agent composition and hierarchical delegation","description":"Agents can invoke other agents as tools, enabling hierarchical task decomposition where complex problems are delegated to specialized sub-agents. The library treats agents as first-class tools that can be registered in the tool registry, allowing parent agents to orchestrate sub-agents' execution and aggregate their results. This pattern enables building multi-agent systems where each agent specializes in a domain (e.g., search agent, calculation agent, summarization agent) and higher-level agents coordinate their work.","intents":["I want to decompose a complex task across multiple specialized agents","I need agents to delegate subtasks to other agents and use their results","I want to build hierarchical agent systems where agents can call agents"],"best_for":["Teams building complex multi-agent systems with clear task decomposition","Scenarios where different agents specialize in different domains (search, math, summarization)","Applications requiring agent composition without building custom orchestration frameworks"],"limitations":["Nested agent calls increase latency linearly with depth — deep hierarchies (3+ levels) may cause timeout issues","Error propagation through agent hierarchies can be opaque — debugging failures across multiple agents is difficult","No built-in load balancing or concurrency — sequential agent execution can be slow for independent subtasks","Context passing between agents requires explicit serialization, risking information loss in deep hierarchies"],"requires":["Python 3.9+","Multiple agent instances configured with same or different LLM providers","Clear task decomposition strategy before implementing hierarchies"],"input_types":["parent agent queries (text)","sub-agent definitions (Agent objects)","tool registry with agents registered as tools"],"output_types":["aggregated results from sub-agents","parent agent final response"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_4","uri":"capability://automation.workflow.streaming.agent.execution.with.incremental.output","name":"streaming agent execution with incremental output","description":"Agents can stream their reasoning steps and intermediate results in real-time as they execute, rather than waiting for complete execution before returning results. The library exposes streaming APIs that yield agent steps (code generation, tool calls, results) incrementally, enabling UI updates, progressive disclosure of reasoning, and early termination if intermediate results are unsatisfactory. This is particularly useful for long-running agents where users benefit from seeing progress.","intents":["I want to show users agent reasoning steps as they happen, not wait for final results","I need to stream agent outputs to a UI for real-time feedback","I want to allow users to interrupt agents after seeing intermediate results"],"best_for":["Web applications and chatbots where real-time feedback improves UX","Long-running agents where users need visibility into progress","Interactive applications where early termination based on intermediate results is valuable"],"limitations":["Streaming adds complexity to error handling — failures mid-stream may leave clients in inconsistent states","Token counting and cost estimation become approximate during streaming (exact costs only known after completion)","Buffering and flushing streaming responses adds ~50-200ms latency overhead","Not all LLM providers support streaming equally well — fallback to non-streaming may be necessary"],"requires":["Python 3.9+","LLM provider with streaming API support (OpenAI, Anthropic, Hugging Face)","Client-side streaming support (WebSockets, Server-Sent Events, or similar)"],"input_types":["agent queries (text)","streaming configuration (chunk size, timeout)"],"output_types":["streaming agent steps (code, tool calls, results)","final agent response"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_5","uri":"capability://planning.reasoning.agentic.loop.with.error.recovery.and.retry.logic","name":"agentic loop with error recovery and retry logic","description":"Implements a robust agentic loop that handles tool call failures, invalid code generation, and LLM errors with automatic recovery mechanisms. When agents generate invalid code or tools fail, the loop captures error messages, feeds them back to the LLM as context, and allows the agent to retry with corrected logic. This pattern reduces manual intervention and enables agents to self-correct from common failures (syntax errors, wrong argument types, tool timeouts).","intents":["I want agents to automatically recover from tool call failures without human intervention","I need agents to retry failed operations with corrected logic based on error messages","I want to reduce manual debugging of agent failures by enabling self-correction"],"best_for":["Production agents where reliability and uptime are critical","Scenarios with unreliable tools or external services that may fail transiently","Applications where agent self-correction reduces operational overhead"],"limitations":["Retry loops can mask underlying tool issues — agents may retry indefinitely on persistent failures","Error messages fed back to LLM can be noisy or misleading, causing agents to learn incorrect recovery patterns","Configurable retry limits and backoff strategies add complexity to agent tuning","Infinite retry loops are possible if agents generate the same invalid code repeatedly — requires timeout safeguards"],"requires":["Python 3.9+","Configurable retry limits and backoff strategies","Clear error messages from tools for agents to learn from"],"input_types":["agent queries (text)","tool definitions with error handling","retry configuration (max attempts, backoff strategy)"],"output_types":["final agent response after retries","retry history and error logs"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_6","uri":"capability://safety.moderation.execution.environment.isolation.and.sandboxing","name":"execution environment isolation and sandboxing","description":"Provides configurable execution environments for agent-generated code, with optional sandboxing to limit the scope of code execution. Agents can run code in isolated Python interpreters or restricted execution contexts that prevent access to sensitive resources (filesystem, network, environment variables). This is critical for security when agents are invoked by untrusted users or in multi-tenant environments where code isolation is required.","intents":["I want to run agent-generated code safely without exposing the host system","I need to prevent agents from accessing sensitive files or environment variables","I want to limit agent code execution to specific resources (e.g., approved APIs only)"],"best_for":["Multi-tenant SaaS applications where agents run untrusted code from different users","Security-sensitive environments (healthcare, finance) where code isolation is mandatory","Public-facing agents where malicious users might try to exploit code execution"],"limitations":["Sandboxing adds significant overhead (100-500ms per code execution) due to process isolation or restricted interpreters","Sandboxed environments may not support all Python libraries — agents may fail if they try to import restricted modules","Escaping sandboxes is possible with sophisticated attacks — sandboxing is a defense-in-depth measure, not a complete security solution","Debugging agent code in sandboxed environments is difficult — error messages may be limited or obfuscated"],"requires":["Python 3.9+","Optional: containerization (Docker) or process isolation libraries (e.g., RestrictedPython, Pyodide)","Security audit of sandboxing approach before production deployment"],"input_types":["agent-generated code (Python)","sandboxing configuration (allowed modules, resource limits)"],"output_types":["execution results from sandboxed code","security violation logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_7","uri":"capability://text.generation.language.prompt.templating.and.dynamic.context.injection","name":"prompt templating and dynamic context injection","description":"Supports dynamic prompt construction where agent system prompts, tool descriptions, and user queries are templated with context variables that are injected at runtime. This enables agents to adapt their behavior based on user context (user role, permissions, available tools), conversation history, or external state without requiring code changes. Templates support variable substitution, conditional sections, and formatting for different LLM providers.","intents":["I want agents to adapt their behavior based on user roles or permissions","I need to inject conversation history or external context into agent prompts dynamically","I want to reuse agent logic across different contexts by parameterizing prompts"],"best_for":["Multi-user applications where agents need to respect user permissions or roles","Conversational agents that need to maintain context across turns","Applications where agent behavior needs to adapt to external state (available tools, user preferences)"],"limitations":["Template variable injection can introduce prompt injection vulnerabilities if user input is not sanitized","Large context injections (e.g., full conversation history) increase token usage and latency","Template syntax errors can be hard to debug — malformed templates may fail silently or produce unexpected prompts","No built-in caching of templated prompts — regenerating prompts for each request adds overhead"],"requires":["Python 3.9+","Template engine (Jinja2 or similar)","Context variables defined before agent execution"],"input_types":["prompt templates (text with variables)","context variables (dict or object)","user queries (text)"],"output_types":["rendered prompts (text)","agent responses"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_8","uri":"capability://memory.knowledge.tool.result.caching.and.memoization","name":"tool result caching and memoization","description":"Caches tool execution results based on input arguments, reducing redundant tool calls when agents invoke the same tool with identical inputs. The library maintains an in-memory or persistent cache of tool results, allowing agents to reuse cached results instead of re-executing expensive operations (API calls, database queries, computations). This optimization is particularly valuable for agents that explore multiple solution paths or retry operations.","intents":["I want to avoid redundant tool calls when agents invoke the same tool multiple times","I need to reduce API costs by caching expensive tool results","I want to speed up agent execution by reusing cached computation results"],"best_for":["Agents with expensive tools (API calls, database queries, ML inference)","Scenarios where agents explore multiple solution paths and may call the same tool repeatedly","Cost-sensitive applications where reducing API calls directly impacts operating costs"],"limitations":["Cache invalidation is manual — stale cached results may be returned if underlying data changes","In-memory caching doesn't persist across agent restarts — distributed caching requires external storage","Cache key generation for complex tool inputs (objects, nested structures) can be error-prone","Cache size can grow unbounded without eviction policies — requires manual cleanup or LRU eviction"],"requires":["Python 3.9+","Optional: Redis or similar for distributed caching","Tool definitions with deterministic outputs (same inputs always produce same outputs)"],"input_types":["tool definitions (Python callables)","tool arguments (any serializable type)","cache configuration (TTL, max size)"],"output_types":["cached or fresh tool results","cache hit/miss metrics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-smolagents__cap_9","uri":"capability://memory.knowledge.agent.state.persistence.and.resumption","name":"agent state persistence and resumption","description":"Enables agents to save their execution state (current step, tool results, reasoning context) to persistent storage and resume from checkpoints, allowing long-running agents to survive interruptions or be paused and resumed later. The library serializes agent state including the execution history, intermediate results, and LLM context, enabling recovery without re-executing completed steps. This is valuable for agents that run for hours or days.","intents":["I want agents to resume from checkpoints if they're interrupted or timeout","I need to pause long-running agents and resume them later","I want to save agent execution history for auditing or debugging"],"best_for":["Long-running agents (hours/days) where interruptions are likely","Batch processing scenarios where agents need to be paused and resumed","Compliance-sensitive applications requiring full execution audit trails"],"limitations":["Serializing LLM context (conversation history, embeddings) can be expensive and storage-intensive","Resuming from checkpoints may produce different results if LLM behavior changes between runs","State persistence adds latency (100-500ms per checkpoint) depending on storage backend","No built-in versioning for state schema — schema changes require migration logic"],"requires":["Python 3.9+","Persistent storage backend (database, file system, cloud storage)","Serialization format for agent state (JSON, pickle, or custom)"],"input_types":["agent execution state (Agent object, execution history)","checkpoint identifier (string or UUID)"],"output_types":["serialized state (JSON or binary)","resumed agent execution"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM API access (OpenAI, Anthropic, Hugging Face, or local model via Ollama)","Tool definitions compatible with Python function signatures","API keys for at least one provider (OpenAI, Anthropic, Hugging Face, etc.)","Network access to provider APIs or local Ollama instance","Logging infrastructure (Python logging module or external platform)","Optional: observability platform (Langfuse, Arize, etc.)","Vision-capable LLM (GPT-4V, Claude 3 with vision, etc.)","Image processing libraries (PIL, OpenCV) for preprocessing","Tool definitions with type hints or JSON schemas"],"failure_modes":["Requires LLM capable of generating syntactically correct Python (hallucination risk for complex logic)","No built-in sandboxing — executing arbitrary LLM-generated code poses security risks in untrusted environments","Debugging agent reasoning requires reading generated code, which can be verbose and hard to trace","Performance overhead from parsing and executing Python code vs direct function call protocols","Abstraction layer adds ~50-100ms latency per request due to provider-specific formatting and parsing","Not all providers support identical feature sets (e.g., vision capabilities, function calling schemas) — fallback behavior may degrade gracefully","Token counting estimates vary by provider; exact token usage only known after API call","Prompt engineering may need tuning per provider due to different instruction-following capabilities","Detailed tracing adds overhead (50-200ms per agent step) due to logging and serialization","Storing full traces can consume significant storage — requires log rotation or sampling strategies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.49,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:12.847Z","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-smolagents","compare_url":"https://unfragile.ai/compare?artifact=pypi-smolagents"}},"signature":"YtQDZeSQ8VMNz0gY7rzSa1EQ0jhsqmL4pZA05fT6wKzc+KYtcf4g/c9kTrReS6qyNZBhRy/0nktO6VXkYr0UCg==","signedAt":"2026-06-22T05:22:01.570Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-smolagents","artifact":"https://unfragile.ai/pypi-smolagents","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-smolagents","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"}}