{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smolagents","slug":"smolagents","name":"Smolagents","type":"repo","url":"https://github.com/huggingface/smolagents","page_url":"https://unfragile.ai/smolagents","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smolagents__cap_0","uri":"capability://code.generation.editing.code.first.agent.execution.with.python.code.generation","name":"code-first agent execution with python code generation","description":"LLM generates executable Python code snippets instead of JSON tool calls, which are parsed by parse_code_blobs() utility and executed directly by LocalPythonExecutor or RemotePythonExecutor. This approach reduces agent steps by ~30% compared to JSON-based tool calling by allowing the LLM to compose multi-step logic in a single code block, improving reasoning efficiency and reducing token overhead from intermediate parsing cycles.","intents":["Build agents that solve complex tasks in fewer LLM calls by having the model write composable Python code","Reduce token consumption and latency by eliminating JSON parsing and validation overhead between agent steps","Enable agents to leverage Python's full expressiveness (loops, conditionals, variable assignment) rather than flat tool sequences"],"best_for":["Teams building research-grade agents where step efficiency and reasoning quality matter more than sandboxing","Developers comfortable with Python who want agents that write code they can inspect and debug","Applications requiring multi-step logic composition within a single agent turn"],"limitations":["Code execution requires a Python runtime (local or remote), adding security surface vs pure JSON tool calling","LLM must be capable of generating syntactically correct Python; weaker models may produce unparseable code","No built-in sandboxing in LocalPythonExecutor — arbitrary code execution possible if LLM is compromised or adversarial","Debugging generated code requires understanding both agent logic and LLM output quality"],"requires":["Python 3.9+","LLM capable of code generation (Claude 3+, GPT-4, Llama 2 70B or better)","LocalPythonExecutor or custom RemotePythonExecutor implementation for code execution"],"input_types":["natural language task description","tool definitions (function signatures with docstrings)","agent memory/context (previous steps, observations)"],"output_types":["executable Python code (as strings)","execution results (stdout, return values, exceptions)","agent observations (structured or unstructured)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_1","uri":"capability://planning.reasoning.multi.agent.orchestration.with.planning.intervals","name":"multi-agent orchestration with planning intervals","description":"Framework supports multi-agent systems where agents can be composed hierarchically or sequentially with configurable planning intervals that determine when agents hand off to other agents or pause for human input. Agents maintain shared memory state and can observe each other's outputs, enabling collaborative problem-solving patterns where specialized agents handle subtasks and a coordinator agent manages the overall workflow.","intents":["Decompose complex tasks across multiple specialized agents (e.g., research agent, analysis agent, writing agent)","Implement hierarchical agent structures where a manager agent delegates to worker agents","Enable human-in-the-loop workflows where agents pause at planning intervals for human review or input"],"best_for":["Teams building complex automation systems requiring task decomposition across multiple LLM calls","Applications needing human oversight at specific decision points in multi-agent workflows","Researchers exploring multi-agent coordination patterns and emergent behaviors"],"limitations":["Planning interval configuration is manual — no automatic task decomposition or agent selection","Shared memory state requires careful management to avoid context explosion across multiple agents","No built-in load balancing or parallel execution — agents execute sequentially by default","Debugging multi-agent workflows is complex; requires tracing state changes across agent boundaries"],"requires":["Python 3.9+","Multiple LLM model instances or API keys if agents use different models","Agent memory/state management implementation (built-in via agent.memory)"],"input_types":["task description","agent definitions (CodeAgent or ToolCallingAgent instances)","planning interval configuration (step counts or decision points)"],"output_types":["final task result","agent execution traces (all intermediate steps and observations)","shared memory state (accessible to all agents in the system)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_10","uri":"capability://text.generation.language.prompt.templating.and.system.instruction.customization","name":"prompt templating and system instruction customization","description":"Agents use customizable system prompts that define the agent's role, available tools, and reasoning instructions. Prompts are templates that can be overridden per-agent instance, allowing teams to tune agent behavior without code changes. System prompts include tool schemas (auto-generated from function signatures) and instructions for the agent paradigm (e.g., 'write Python code' for CodeAgent, 'call tools' for ToolCallingAgent). Prompt engineering is transparent; teams can inspect and modify prompts to improve agent performance.","intents":["Customize agent behavior and reasoning style by modifying system prompts","Tune agent performance for specific domains by adding domain-specific instructions","Experiment with different prompting strategies without code changes"],"best_for":["Teams optimizing agent performance through prompt engineering","Applications requiring domain-specific agent behavior (e.g., medical, legal, financial)","Researchers studying the impact of prompting strategies on agent reasoning"],"limitations":["Prompt engineering is manual and empirical; no automated optimization","Large prompts increase token usage and latency; no automatic prompt compression","Prompt changes can have unpredictable effects on agent behavior; requires testing","No built-in A/B testing or prompt versioning; teams must manage prompt variants manually"],"requires":["Python 3.9+","Understanding of LLM prompting best practices"],"input_types":["system prompt template (string with placeholders)","tool schemas (auto-generated)","agent-specific instructions"],"output_types":["final system prompt (rendered with tool schemas and instructions)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_11","uri":"capability://automation.workflow.agent.persistence.and.hugging.face.hub.integration","name":"agent persistence and hugging face hub integration","description":"Agents can be serialized and saved to Hugging Face Hub, enabling sharing and reuse of agent configurations, prompts, and tool definitions. Persistence includes agent class, model configuration, system prompt, and tool definitions. Agents can be loaded from Hub by name, automatically downloading and deserializing the configuration. This enables teams to build agent libraries and share agents across projects without code duplication.","intents":["Share agent configurations and prompts with team members or the community via Hugging Face Hub","Reuse pre-built agents across multiple projects without reimplementing configuration","Version agent configurations and track changes over time"],"best_for":["Teams building reusable agent libraries","Organizations sharing agents across projects","Researchers publishing agent configurations for reproducibility"],"limitations":["Persistence is configuration-only; agent state (memory, execution history) is not persisted","Requires Hugging Face Hub account and authentication","Tool definitions must be serializable (Python functions with type hints); complex tools may not persist cleanly","No versioning or rollback; overwriting an agent configuration is permanent"],"requires":["Python 3.9+","Hugging Face Hub account and API token","huggingface_hub library"],"input_types":["agent instance (CodeAgent or ToolCallingAgent)","Hub repository name"],"output_types":["serialized agent configuration (JSON)","Hub repository URL"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_12","uri":"capability://automation.workflow.gradio.web.ui.for.agent.interaction.and.monitoring","name":"gradio web ui for agent interaction and monitoring","description":"Framework includes a Gradio-based web interface that allows non-technical users to interact with agents through a chat-like UI. The UI displays agent reasoning steps, tool calls, and results in real-time, providing visibility into agent behavior. Streaming is supported, showing agent thoughts and tool outputs as they arrive. The UI is auto-generated from agent configuration; no custom UI code required. Teams can deploy agents as web services without building custom frontends.","intents":["Deploy agents as web services accessible to non-technical users","Provide visibility into agent reasoning and tool calls through a user-friendly interface","Enable rapid prototyping and testing of agents without custom UI development"],"best_for":["Teams prototyping agents and wanting quick user feedback","Non-technical stakeholders who need to interact with agents","Applications requiring a simple web interface for agent interaction"],"limitations":["Gradio UI is basic; customization requires modifying Gradio code","No built-in authentication or access control; requires external security layer for production","UI is single-user by default; scaling to multiple concurrent users requires custom deployment","Streaming support depends on model and network; may not work reliably with all providers"],"requires":["Python 3.9+","Gradio library","Agent instance (CodeAgent or ToolCallingAgent)"],"input_types":["user message (text)","agent configuration"],"output_types":["agent response (text)","reasoning steps (displayed in UI)","tool calls and results (displayed in UI)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_13","uri":"capability://planning.reasoning.error.handling.and.recovery.with.step.level.retry.logic","name":"error handling and recovery with step-level retry logic","description":"Agents implement error handling at the step level: if a tool call fails or code execution raises an exception, the error is captured as an observation and passed back to the LLM for recovery. The LLM can then decide to retry the tool, try a different approach, or report failure. No automatic retries; the LLM controls recovery strategy. Error messages are included in agent memory, allowing the LLM to learn from failures within a single agent run.","intents":["Enable agents to recover from transient tool failures (network errors, rate limits) by retrying","Allow agents to adapt their strategy when a tool call fails","Provide agents with error context to improve reasoning and recovery decisions"],"best_for":["Agents calling external APIs or tools that may fail transiently","Applications where agent recovery is more valuable than immediate failure","Teams wanting agents to learn from failures within a single run"],"limitations":["Recovery depends on LLM quality; weak models may not recover effectively from errors","No automatic retry logic; teams must implement custom retry strategies if needed","Error handling is step-level only; no cross-step recovery or rollback","Repeated errors can consume tokens and increase latency without guaranteeing recovery"],"requires":["Python 3.9+","LLM capable of reasoning about errors and adapting strategy"],"input_types":["tool call or code execution","error message (exception traceback)"],"output_types":["error observation (included in agent memory)","LLM-generated recovery action"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_14","uri":"capability://automation.workflow.async.and.streaming.agent.execution","name":"async and streaming agent execution","description":"Framework supports async agent execution via async/await syntax, allowing agents to run concurrently with other code. Streaming is supported for real-time agent output — agents can stream intermediate results (thoughts, tool calls, observations) to the client as they execute. Streaming is implemented via callbacks that emit events as the agent progresses.","intents":["Run multiple agents concurrently without blocking","Stream agent output in real-time to web frontends or CLIs","Build responsive agent applications that don't freeze while thinking"],"best_for":["Teams building web applications with agents","Projects requiring real-time agent feedback","Developers building concurrent agent systems"],"limitations":["Async support is basic; complex concurrent patterns may require custom code","Streaming requires client support (WebSockets, Server-Sent Events)","Streaming adds latency (events must be serialized and sent)"],"requires":["Python 3.9+","async/await support in calling code","For streaming: WebSocket or SSE client"],"input_types":["async task description"],"output_types":["async result or streamed events"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_15","uri":"capability://automation.workflow.agent.persistence.and.hugging.face.hub.integration","name":"agent persistence and hugging face hub integration","description":"Agents can be saved to disk or pushed to Hugging Face Hub for sharing and versioning. Persistence includes agent configuration, memory, and step history. Hub integration allows agents to be discovered and reused by other developers. This enables reproducibility and collaboration on agent development.","intents":["Save agent state and resume from checkpoints","Share agents with team members or the community via Hub","Version control agent configurations and behavior"],"best_for":["Teams collaborating on agent development","Projects requiring reproducibility and versioning","Developers sharing agents with the community"],"limitations":["Persistence is optional; no automatic checkpointing","Hub integration requires Hugging Face account and authentication","Large agent states (long memory) may be expensive to persist"],"requires":["Python 3.9+","For Hub integration: Hugging Face account and huggingface_hub library"],"input_types":["agent object"],"output_types":["saved agent file or Hub repository"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_16","uri":"capability://automation.workflow.human.in.the.loop.agent.workflows","name":"human-in-the-loop agent workflows","description":"Framework supports pausing agents at specific steps to request human input or approval. Callbacks can pause execution and wait for human feedback before continuing. This enables workflows where agents handle routine tasks but escalate decisions to humans. Human input is fed back into agent memory and used for subsequent reasoning.","intents":["Build agents that escalate decisions to humans for approval","Implement workflows where humans and agents collaborate","Enable human oversight of agent decisions for safety and compliance"],"best_for":["Teams building agents for high-stakes domains (finance, healthcare, legal)","Projects requiring human oversight for compliance","Developers building collaborative human-AI systems"],"limitations":["Human-in-the-loop adds latency; agents must wait for human input","No built-in UI for human feedback; developers must implement custom interfaces","Scaling human-in-the-loop to many agents is challenging"],"requires":["Python 3.9+","Custom callback to pause and request human input","UI for human feedback (web form, CLI, etc.)"],"input_types":["agent state requiring human input"],"output_types":["human feedback or approval"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_17","uri":"capability://automation.workflow.gradio.web.ui.for.agent.interaction","name":"gradio web ui for agent interaction","description":"Framework includes a built-in Gradio web interface for interacting with agents. The UI allows users to input tasks, view agent reasoning in real-time, and see step-by-step execution. The Gradio UI is automatically generated from agent configuration and supports streaming output. This enables non-technical users to interact with agents without writing code.","intents":["Provide a user-friendly interface for non-technical users to interact with agents","Visualize agent reasoning and step-by-step execution","Deploy agents as web applications without custom UI development"],"best_for":["Teams deploying agents to non-technical users","Projects needing quick prototyping of agent UIs","Developers building demo applications"],"limitations":["Gradio UI is basic; complex interactions may require custom UI","Styling and customization are limited","Scaling to many concurrent users requires careful deployment"],"requires":["Python 3.9+","Gradio library"],"input_types":["task description (text)"],"output_types":["agent result and step history (displayed in UI)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_2","uri":"capability://planning.reasoning.react.loop.with.memory.and.callback.hooks","name":"react loop with memory and callback hooks","description":"Implements the ReAct (Reasoning + Acting) loop as the core agent execution pattern in MultiStepAgent, where agents alternate between reasoning steps (LLM generates thought + action) and observation steps (tool execution + result capture). Memory is maintained as a list of (action, observation) tuples, and callback hooks (via AgentLogger and Monitor) fire at each lifecycle event (step start, tool call, error, completion), enabling observability, debugging, and custom monitoring without modifying core agent logic.","intents":["Build agents that explicitly separate reasoning from action, improving interpretability and error recovery","Instrument agent execution with custom logging, metrics, and monitoring without framework modifications","Replay agent execution traces for debugging or analysis by inspecting the full memory history"],"best_for":["Teams needing transparent agent behavior for compliance, debugging, or research","Applications requiring custom monitoring, error handling, or step-level callbacks","Developers building agent observability tools or dashboards"],"limitations":["Memory grows linearly with agent steps; no automatic summarization or pruning for long-running agents","Callback hooks are synchronous — blocking callbacks can slow down agent execution","No built-in persistence of memory state — requires custom serialization for agent replay across sessions","ReAct loop assumes sequential step execution; no native support for parallel reasoning or action branches"],"requires":["Python 3.9+","LLM model instance (API-based or local)","Tool definitions for the agent to act upon"],"input_types":["task description (initial prompt)","tool definitions","callback handlers (optional, via AgentLogger or Monitor)"],"output_types":["agent memory (list of action-observation tuples)","final result","callback events (step started, tool called, error occurred, etc.)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_3","uri":"capability://tool.use.integration.tool.definition.and.validation.with.schema.based.function.calling","name":"tool definition and validation with schema-based function calling","description":"Tools are defined as Python functions with type hints and docstrings; the framework automatically extracts function signatures and docstrings to generate tool schemas (JSON Schema or OpenAI function calling format). Tool validation occurs at definition time (checking for required docstrings, type hints) and at call time (validating arguments against the schema). Both CodeAgent and ToolCallingAgent use these schemas, but CodeAgent passes them as context to the LLM while ToolCallingAgent uses them for structured output validation.","intents":["Define reusable tools once and use them with both code-generating and tool-calling agents","Automatically generate tool schemas from Python function signatures without manual JSON definition","Validate tool arguments at runtime to catch LLM hallucinations or malformed tool calls"],"best_for":["Python developers who want to define tools as regular functions without boilerplate","Teams building tool libraries that need to work with multiple agent paradigms","Applications requiring runtime validation of LLM-generated tool calls"],"limitations":["Tool schemas are generated from Python type hints; complex types (nested objects, unions) may not translate cleanly to JSON Schema","Docstrings are required for all tools; missing or poorly formatted docstrings degrade LLM understanding","No built-in tool versioning or deprecation — breaking changes to tool signatures require agent retraining","Tool validation is permissive; type coercion may mask LLM errors (e.g., string '5' coerced to int 5)"],"requires":["Python 3.9+","Type hints on all tool function parameters","Docstrings on all tool functions (required for schema generation)"],"input_types":["Python function definitions","type hints (int, str, list, dict, custom classes)","docstrings (for tool description and parameter documentation)"],"output_types":["JSON Schema (for CodeAgent context)","OpenAI function calling format (for ToolCallingAgent)","tool execution results (any Python object)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_4","uri":"capability://code.generation.editing.local.and.remote.python.code.execution.with.security.boundaries","name":"local and remote python code execution with security boundaries","description":"LocalPythonExecutor runs generated code in the current Python process with access to the agent's tool namespace, while RemotePythonExecutor (abstract base class) enables custom implementations for sandboxed or distributed execution. Code is executed via exec() with a restricted namespace containing only imported tools and safe builtins, preventing access to filesystem or network unless explicitly granted through tool definitions. Remote executors can implement additional security measures (containerization, timeouts, resource limits) at the cost of higher latency.","intents":["Execute LLM-generated Python code safely by restricting the namespace to only necessary tools","Support both local development (fast iteration) and production deployment (sandboxed execution)","Implement custom execution environments (Docker, Kubernetes, serverless) by extending RemotePythonExecutor"],"best_for":["Development teams prototyping agents locally with LocalPythonExecutor","Production systems requiring sandboxed execution via custom RemotePythonExecutor implementations","Teams building agent platforms that need to support multiple execution backends"],"limitations":["LocalPythonExecutor has no true sandboxing — code runs in the main process and can access globals, imports, or filesystem if tools expose them","Code execution is synchronous; long-running code blocks the agent loop (no async support in LocalPythonExecutor)","No built-in timeout or resource limits in LocalPythonExecutor; runaway code can hang the agent indefinitely","RemotePythonExecutor is abstract; teams must implement their own remote execution backend (adds complexity and latency ~100-500ms per call)"],"requires":["Python 3.9+","For LocalPythonExecutor: tools must be importable in the current Python environment","For RemotePythonExecutor: custom implementation with execution backend (Docker, Lambda, etc.)"],"input_types":["Python code (as string, extracted from LLM output)","tool namespace (dict of tool_name -> tool_function)","execution context (variables, imports)"],"output_types":["execution result (return value or stdout)","execution error (exception traceback)","execution metadata (duration, memory usage if tracked)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_5","uri":"capability://tool.use.integration.model.abstraction.with.multi.provider.support.and.streaming","name":"model abstraction with multi-provider support and streaming","description":"Agents accept a model parameter that implements a minimal Model interface (forward() method for inference, optional stream() for streaming). Built-in implementations support OpenAI, Anthropic, Hugging Face Inference API, and local models via Ollama or vLLM. Models are instantiated with provider-specific configuration (API keys, base URLs, model names) and handle prompt formatting, token counting, and response parsing internally. Streaming is optional and model-dependent; agents can consume streamed tokens for real-time output without waiting for full completion.","intents":["Switch between different LLM providers (OpenAI, Anthropic, local) without changing agent code","Use streaming models to display agent reasoning in real-time as tokens arrive","Support both API-based models (cloud) and local models (on-premises, offline) in the same agent framework"],"best_for":["Teams evaluating multiple LLM providers and wanting to benchmark agents across models","Applications requiring on-premises or offline LLM inference via Ollama or vLLM","Real-time agent applications (chatbots, live dashboards) where streaming improves perceived latency"],"limitations":["Model interface is minimal; advanced features (function calling, vision, structured output) must be implemented per-provider","Streaming support is optional and model-dependent; not all providers support token-level streaming","Token counting is approximate and provider-specific; actual token usage may differ from estimates","Model switching requires reinstantiating the agent; no hot-swapping of models mid-execution","Local models (Ollama, vLLM) require separate infrastructure setup and may have lower quality than API models"],"requires":["Python 3.9+","API key for cloud providers (OpenAI, Anthropic, Hugging Face) OR local model server (Ollama, vLLM)","Model name/ID that the provider supports"],"input_types":["model configuration (provider, API key, model name, base URL)","prompts (system + user messages)","optional: streaming callback for token-level output"],"output_types":["model response (text)","optional: token stream (for streaming models)","metadata (token counts, latency)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_6","uri":"capability://memory.knowledge.agent.memory.and.context.management.with.observation.tracking","name":"agent memory and context management with observation tracking","description":"Agent memory is maintained as a simple list of (action, observation) tuples that grows with each agent step. The memory is passed to the LLM as context in the system prompt, allowing the model to reason over its previous actions and their outcomes. Memory can be inspected, replayed, or serialized for debugging. No automatic summarization or pruning; teams must implement custom memory management (e.g., sliding windows, importance-based truncation) if context length becomes a bottleneck.","intents":["Give agents access to their full execution history for multi-step reasoning and error recovery","Debug agent behavior by inspecting the complete trace of actions and observations","Implement custom memory strategies (summarization, filtering, prioritization) by extending the memory list"],"best_for":["Developers debugging agent behavior and needing full execution traces","Research teams studying agent reasoning patterns and decision-making","Applications with short-running agents where memory doesn't exceed context limits"],"limitations":["Memory grows linearly with steps; no automatic pruning means context length can exceed model limits on long-running agents","No built-in summarization or compression; teams must implement custom memory management for production use","Memory is in-process only; no persistence across agent restarts without custom serialization","Large memory contexts increase token usage and latency; no optimization for redundant or low-value observations"],"requires":["Python 3.9+","LLM with sufficient context length to accommodate agent memory (typically 4K+ tokens)"],"input_types":["action (tool call or code execution)","observation (tool result or execution output)"],"output_types":["memory list (list of action-observation tuples)","serialized memory (JSON or pickle for persistence)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_7","uri":"capability://tool.use.integration.tool.calling.agent.with.structured.output.validation","name":"tool calling agent with structured output validation","description":"ToolCallingAgent instructs the LLM to emit structured tool calls (JSON objects with tool_name and arguments) instead of code. The framework parses these structured outputs, validates arguments against the tool schema, and calls Tool.forward() directly. This approach works with models that support function calling APIs (OpenAI, Anthropic) and is safer than code execution because tool calls are validated before execution. Tool definitions must implement a forward() method that accepts validated arguments.","intents":["Build agents that call tools via structured JSON instead of code, reducing execution risk","Use models with native function calling support (OpenAI, Anthropic) for better tool invocation accuracy","Implement agents where tool calls are logged, audited, or rate-limited before execution"],"best_for":["Production systems requiring strict tool call validation and audit trails","Teams using models with strong function calling support (GPT-4, Claude 3)","Applications where tool execution must be controlled or monitored before running"],"limitations":["Requires models with function calling support; older models or local models may not support structured output","Tool calls are single-step; complex logic must be decomposed into multiple tool calls (less efficient than code generation)","Tool definitions must implement forward() method; not all Python functions can be easily wrapped as tools","Structured output parsing adds latency and can fail if the model produces malformed JSON"],"requires":["Python 3.9+","LLM with function calling support (OpenAI, Anthropic, or compatible API)","Tool definitions with forward() method implementation"],"input_types":["tool definitions (with forward() methods)","tool schemas (auto-generated from function signatures)","LLM output (structured tool calls in JSON format)"],"output_types":["tool execution results","structured tool call logs (tool_name, arguments, result)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_8","uri":"capability://tool.use.integration.mcp.model.context.protocol.tool.integration","name":"mcp (model context protocol) tool integration","description":"Framework supports loading tools from MCP servers, which expose tools via a standardized protocol. MCP tools are wrapped into the smolagents Tool interface, allowing agents to use MCP-provided tools alongside native Python tools. This enables integration with external tool ecosystems (e.g., Anthropic's MCP server ecosystem) without reimplementing tools in Python. MCP tool loading is transparent; agents treat MCP tools identically to native tools.","intents":["Integrate agents with external tool ecosystems via MCP without reimplementing tools","Use pre-built MCP servers (e.g., for web search, database access, file operations) in agents","Build agent platforms that support pluggable tool sources (native + MCP)"],"best_for":["Teams building agent platforms that need to support multiple tool sources","Applications leveraging Anthropic's MCP ecosystem or other MCP-compatible servers","Developers wanting to reuse existing MCP tools without porting to Python"],"limitations":["Requires MCP server to be running and accessible (adds infrastructure complexity)","MCP tool schemas may not translate perfectly to smolagents' schema format (type mismatches possible)","Network latency for MCP calls adds overhead vs local Python tools (~50-200ms per call)","Debugging MCP tool failures requires understanding both MCP protocol and smolagents integration"],"requires":["Python 3.9+","MCP server running and accessible (local or remote)","MCP client library (e.g., mcp package)"],"input_types":["MCP server configuration (host, port, or connection details)","MCP tool definitions (from server)"],"output_types":["wrapped Tool objects (compatible with smolagents)","tool execution results (from MCP server)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__cap_9","uri":"capability://automation.workflow.agent.logging.and.observability.with.lifecycle.callbacks","name":"agent logging and observability with lifecycle callbacks","description":"AgentLogger captures agent lifecycle events (step started, tool called, error occurred, step completed) and logs them at configurable verbosity levels (DEBUG, INFO, WARNING, ERROR). Monitor class provides metrics collection (step count, tool call count, error rate). Both integrate with OpenTelemetry for distributed tracing and external observability platforms. Callbacks are synchronous and optional; agents work without logging but can be instrumented for debugging or production monitoring.","intents":["Debug agent behavior by inspecting detailed logs of each step, tool call, and error","Monitor agent performance in production with metrics (step count, latency, error rate)","Integrate agent execution with external observability platforms (Datadog, New Relic, etc.) via OpenTelemetry"],"best_for":["Development teams debugging agent behavior during prototyping","Production systems requiring observability and monitoring","Teams using OpenTelemetry-compatible observability platforms"],"limitations":["Logging is synchronous; verbose logging can add latency to agent execution","Metrics are in-process only; no built-in persistence or aggregation across multiple agent instances","OpenTelemetry integration requires additional setup and configuration","Callback hooks fire for every step; high-frequency agents may generate excessive logs"],"requires":["Python 3.9+","Optional: OpenTelemetry SDK for distributed tracing"],"input_types":["log level configuration (DEBUG, INFO, WARNING, ERROR)","callback handlers (optional)"],"output_types":["log messages (to stdout, file, or custom handler)","metrics (step count, tool calls, errors)","OpenTelemetry spans (if enabled)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smolagents__headline","uri":"capability://automation.workflow.lightweight.agent.framework.for.building.llm.powered.agents","name":"lightweight agent framework for building llm-powered agents","description":"Smolagents is a minimal abstraction framework that allows developers to create LLM-powered agents using Python code directly, making it simple and hackable for various automation tasks.","intents":["best lightweight agent framework","LLM-powered agents for automation","how to build agents with Python","top frameworks for multi-agent orchestration","code-first agent frameworks"],"best_for":["developers looking for simplicity"],"limitations":[],"requires":["Python"],"input_types":["Python code"],"output_types":["executed actions"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LLM capable of code generation (Claude 3+, GPT-4, Llama 2 70B or better)","LocalPythonExecutor or custom RemotePythonExecutor implementation for code execution","Multiple LLM model instances or API keys if agents use different models","Agent memory/state management implementation (built-in via agent.memory)","Understanding of LLM prompting best practices","Hugging Face Hub account and API token","huggingface_hub library","Gradio library","Agent instance (CodeAgent or ToolCallingAgent)"],"failure_modes":["Code execution requires a Python runtime (local or remote), adding security surface vs pure JSON tool calling","LLM must be capable of generating syntactically correct Python; 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