Comparison Preset
Choose PydanticAI for its focus on stability, observability, and long-term maintainability. Its first-party support for durable execution with providers like Temporal ensures that long-running agent processes are resilient, a critical feature SmolAgents lacks. The framework is built on a permissive MIT license, has a solid 8/10 bus factor, and its OpenTelemetry-native instrumentation fits directly into enterprise monitoring stacks. PydanticAI's high commit frequency (25x/week) signals active support, contrasting with SmolAgents' slower pace (<1x/week) and CRITICAL vulnerability, making it the lower-risk choice. This framework provides the guarantees necessary to justify its adoption to stakeholders.
Overview
The bottom line — what this framework is, who it's for, and when to walk away.
Bottom Line Up Front
Pydantic AI is a Python SDK for building typed, extensible AI agents that integrate with virtually any model and provider. It supports various interfaces and durable execution, enabling complex, long-running agent workflows with robust observability.
smolagents is an open-source Python library focused on simplifying AI agent development with minimal code. It supports both code-executing and tool-calling agents, offering model, modality, and tool agnosticism. The framework integrates seamlessly with the Hugging Face Hub for sharing and loading agents.
Best For
Building typed, multi-model Python AI agents for data extraction, real-time interactions, and durable workflows.
Rapidly developing and deploying simple, composable AI agents with code execution and diverse tools.
Avoid If
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Requiring deeply custom agent internal architectures or complex, high-performance stateful orchestration.
Strengths
- +Supports virtually every model and provider, swappable with a string or through Pydantic AI Gateway for failover and cost monitoring.
- +Provides structured outputs, typed dependency injection, and typed tools, moving errors from runtime to write-time, with Pydantic Graph for typed workflows.
- +OpenTelemetry-native instrumentation integrates with Pydantic Logfire for real-time debugging, tracing, and cost tracking, and uses Pydantic Evals for testing agent behavior.
- +Uses `capabilities` as reusable units for tools, instructions, and model settings, supporting composition of complex agents like Coder and Researcher.
- +Agents can run across various interfaces, including CLI, built-in web chat, realtime speech, UI event streams (AG-UI, Vercel AI), and as an editor agent.
- +Offers first-party, co-maintained durable execution on Temporal, DBOS, or Prefect, allowing agents to survive restarts and long waits with human-in-the-loop approval.
- +Built by the Pydantic team, leveraging its validation layer which is foundational for major AI SDKs and FastAPI, ensuring robust data handling.
- +Extremely simple to use with minimal code and abstractions.
- +Native support for Code Agents executing Python code for complex, composable actions.
- +Supports secure code execution in sandboxed environments (Modal, Blaxel, E2B, Docker).
- +Includes support for traditional JSON/text-based tool-calling agents.
- +Seamlessly integrates with Hugging Face Hub for sharing and loading agents/tools.
- +Highly model-agnostic, supporting diverse LLMs via Hub, APIs, or local execution.
- +Supports multimodal inputs, including vision, video, and audio.
- +Highly tool-agnostic, integrating tools from MCP servers, LangChain, or Hub Spaces.
- +Provides CLI tools for quickly running agents without boilerplate.
Weaknesses
- −The ACP editor agent interface is marked as experimental, indicating it may not be fully stable or production-ready.
- −The `llms-full.txt` format, designed for LLMs, may be too large for some LLMs, limiting its utility for certain integrations.
- −Minimal abstractions might necessitate more manual implementation for highly custom or complex agent behaviors.
Project Health
Is this project alive, well-maintained, and safe to bet on long-term?
Bus Factor Score
Maintainers
Open Issues
Fit
Does it support the workflows, patterns, and capabilities your team actually needs?
State Management
The framework manages agent context and execution longevity using capabilities for memory and context management, and supports durable execution via integrations like Temporal to survive restarts and long waits.
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Cost & Licensing
What does it actually cost? License type, pricing model, and hidden fees.
License
Perspective
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