{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pydantic-ai","slug":"pydantic-ai","name":"Pydantic AI","type":"framework","url":"https://github.com/pydantic/pydantic-ai","page_url":"https://unfragile.ai/pydantic-ai","categories":["ai-agents","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pydantic-ai__cap_0","uri":"capability://planning.reasoning.type.safe.agent.definition.with.pydantic.validation","name":"type-safe agent definition with pydantic validation","description":"Defines agents using Python dataclasses and Pydantic models with full type annotations, enabling compile-time validation of agent state, inputs, and outputs. The Agent class wraps model providers and enforces schema validation on all LLM responses through Pydantic V2's validation engine, catching type mismatches before runtime. This approach moves validation errors from production into development, leveraging IDE type checking and mypy/pyright for static analysis.","intents":["I want to define an agent with guaranteed type safety so invalid LLM outputs are caught before they reach my application logic","I need IDE autocomplete and type hints for all agent inputs, outputs, and state transitions","I want to validate structured LLM responses (JSON, objects) against a schema automatically"],"best_for":["teams building production LLM applications that require reliability and maintainability","developers using mypy or pyright for static type checking","projects where LLM output validation is critical (financial, healthcare, compliance)"],"limitations":["Pydantic validation adds ~50-100ms overhead per response for complex schemas with nested models","Type annotations are required for all agent inputs/outputs — cannot use untyped dicts or Any types without losing validation benefits","Validation errors from LLMs may require prompt engineering to resolve; no automatic recovery mechanism"],"requires":["Python 3.9+","Pydantic V2.0+","Type annotations in agent definition"],"input_types":["Python dataclasses","Pydantic BaseModel subclasses","TypedDict","native Python types (str, int, list, dict)"],"output_types":["Pydantic BaseModel instances","dataclass instances","validated Python objects matching declared types"],"categories":["planning-reasoning","type-safety"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_1","uri":"capability://tool.use.integration.model.agnostic.provider.abstraction.with.unified.interface","name":"model-agnostic provider abstraction with unified interface","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, DeepSeek, Groq, Ollama) behind a single ModelClient interface, allowing agents to switch providers by changing a single parameter. Each provider has a dedicated integration module that handles API-specific details (authentication, request formatting, streaming protocols, token counting) while exposing a consistent run() and stream() API. The framework automatically handles provider-specific quirks like Anthropic's tool_choice syntax vs OpenAI's function_calling format.","intents":["I want to build an agent that works with any LLM provider without rewriting code","I need to switch between OpenAI and Anthropic models for cost optimization or latency testing","I want to use local models (Ollama) in development and cloud models (OpenAI) in production with the same agent code"],"best_for":["teams evaluating multiple LLM providers for cost/performance tradeoffs","applications requiring fallback providers for reliability","developers building multi-tenant SaaS where customers choose their own model provider"],"limitations":["Not all providers support identical feature sets — vision/multimodal support varies by provider, requiring conditional code paths","Token counting is provider-specific and approximate; exact counts require provider APIs (adds latency)","Streaming implementations differ by provider; some providers have higher latency for first token"],"requires":["API keys or credentials for chosen provider(s)","Provider-specific SDK (openai, anthropic, google-generativeai, boto3, etc.)","Network access to provider endpoints or local Ollama instance"],"input_types":["provider name string (e.g., 'openai', 'anthropic')","model identifier (e.g., 'gpt-4-turbo', 'claude-3-opus')","API credentials (keys, endpoints)"],"output_types":["unified ModelResponse objects","streaming token iterators","structured outputs validated by Pydantic"],"categories":["tool-use-integration","model-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_10","uri":"capability://planning.reasoning.multi.agent.orchestration.and.agent.to.agent.communication","name":"multi-agent orchestration and agent-to-agent communication","description":"Enables multiple agents to communicate and coordinate through a message-passing protocol. Agents can invoke other agents as tools, passing context and receiving results. The framework handles agent discovery, message routing, and result aggregation, allowing complex multi-agent workflows (e.g., supervisor agent delegating tasks to specialist agents). Supports both synchronous and asynchronous agent-to-agent communication.","intents":["I want to decompose a complex task into subtasks handled by specialist agents","I need a supervisor agent that delegates work to multiple worker agents and aggregates results","I want agents to collaborate on a task without explicit orchestration code"],"best_for":["complex applications requiring task decomposition and delegation","systems with multiple specialized agents (research, analysis, writing, etc.)","applications where agent collaboration improves output quality"],"limitations":["Agent-to-agent communication adds latency (each agent call is a full model invocation)","No built-in load balancing or agent pool management; manual agent instantiation required","Debugging multi-agent workflows is complex; requires tracing across multiple agent runs"],"requires":["Multiple agent definitions (one per agent type)","Message passing protocol (built-in to framework)","Coordination logic (supervisor agent or orchestration code)"],"input_types":["agent definitions (same as single-agent)","agent invocation requests (agent name, input)"],"output_types":["agent results (same as single-agent)","aggregated results from multiple agents"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_11","uri":"capability://safety.moderation.evaluation.framework.with.datasets.and.automated.testing","name":"evaluation framework with datasets and automated testing","description":"Provides a built-in evaluation framework (pydantic-evals) for testing agents against datasets of test cases. Supports defining test datasets with inputs, expected outputs, and evaluation metrics. Includes pre-built evaluators (exact match, semantic similarity, LLM-as-judge) and enables custom evaluators. Generates evaluation reports with pass/fail rates, latency metrics, and cost analysis. Integrates with CI/CD for automated agent testing.","intents":["I want to test my agent against a dataset of test cases to measure quality","I need to track agent performance over time as I iterate on prompts and tools","I want to evaluate agents using semantic similarity or LLM-as-judge scoring"],"best_for":["teams iterating on agent prompts and tools with data-driven feedback","applications requiring quality gates before deployment","research and experimentation with different agent configurations"],"limitations":["Evaluation requires labeled test datasets; no automatic test generation","LLM-as-judge evaluators are subjective and may not align with human judgment","Evaluation latency scales with dataset size; large datasets require significant compute"],"requires":["Test dataset with inputs and expected outputs","Evaluation metrics definition (exact match, semantic similarity, custom)","Model credentials for LLM-as-judge evaluators"],"input_types":["test case datasets (JSON, CSV, or Python objects)","agent definition to evaluate","evaluation metrics configuration"],"output_types":["evaluation reports (JSON, HTML)","pass/fail rates per test case","latency and cost metrics"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_12","uri":"capability://planning.reasoning.graph.based.agent.workflows.with.pydantic.graph","name":"graph-based agent workflows with pydantic-graph","description":"Provides pydantic-graph library for defining agent workflows as directed acyclic graphs (DAGs) where nodes are agents or functions and edges represent data flow. Nodes execute in topological order with automatic dependency resolution. Supports conditional branching, loops, and parallel execution. Graphs are visualized as Mermaid diagrams and can be persisted for replay and debugging. Integrates with the core agent framework for seamless execution.","intents":["I want to define complex agent workflows as graphs instead of imperative code","I need to visualize agent workflows and understand data flow between agents","I want to execute agents in parallel when they have no dependencies"],"best_for":["complex workflows with multiple agents and conditional logic","teams preferring declarative workflow definitions over imperative code","applications requiring workflow visualization and debugging"],"limitations":["Graph-based workflows add complexity for simple sequential tasks","Debugging graph execution is harder than imperative code; requires understanding topological ordering","Parallel execution is limited by model rate limits and token budgets"],"requires":["pydantic-graph library","Graph definition using node and edge APIs","Type annotations for node inputs/outputs"],"input_types":["graph definition (nodes, edges, types)","initial input data"],"output_types":["graph execution results (final node outputs)","execution history (node execution order, timing)","Mermaid diagram (workflow visualization)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_13","uri":"capability://image.visual.multimodal.input.support.with.vision.and.image.processing","name":"multimodal input support with vision and image processing","description":"Supports multimodal inputs including text, images, and other media types. Images can be passed as URLs, base64-encoded data, or file paths, and are automatically converted to provider-specific formats (OpenAI's image_url, Anthropic's image blocks). The framework handles image validation, format conversion, and provider-specific constraints (e.g., image size limits). Supports vision-capable models (GPT-4V, Claude 3 Vision, Gemini Vision) with automatic model selection.","intents":["I want to pass images to agents for analysis, OCR, or visual reasoning","I need to support multiple image formats (PNG, JPEG, WebP) without manual conversion","I want to use vision models automatically when images are provided"],"best_for":["applications requiring image analysis (document processing, visual QA, etc.)","agents that need to understand visual context alongside text","systems processing user-uploaded images"],"limitations":["Vision models are more expensive and slower than text-only models","Image size limits vary by provider; large images must be resized or compressed","Not all models support vision; automatic fallback to text-only models is not implemented"],"requires":["Vision-capable model (GPT-4V, Claude 3 Vision, Gemini Vision, etc.)","Image in supported format (PNG, JPEG, WebP, GIF)","Provider support for multimodal inputs"],"input_types":["text strings","image URLs (http/https)","base64-encoded images","file paths to local images"],"output_types":["text analysis of images","structured outputs (Pydantic models) based on image content","OCR results"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_14","uri":"capability://text.generation.language.direct.model.requests.without.agent.framework.overhead","name":"direct model requests without agent framework overhead","description":"Provides a low-level API (model.request_schema()) for making direct requests to models without the agent framework overhead. Useful for simple tasks that don't require tools, message history, or agent state management. Supports the same provider abstraction and output validation as agents, but with minimal latency and memory overhead. Enables mixing direct model calls with agent-based workflows.","intents":["I want to make a simple model request without creating an agent","I need low-latency model calls for high-throughput applications","I want to use the same provider abstraction for both agents and direct calls"],"best_for":["simple, stateless model requests (classification, extraction, summarization)","high-throughput applications where agent overhead matters","mixing direct calls with agent-based workflows"],"limitations":["No tool support; direct calls cannot invoke functions","No message history; each call is independent","No streaming support in some providers"],"requires":["Model instance (same as agents)","Input prompt and output schema"],"input_types":["text prompt","Pydantic model for output validation"],"output_types":["validated Pydantic model instance","text string"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_2","uri":"capability://planning.reasoning.dependency.injection.and.runtime.context.management","name":"dependency injection and runtime context management","description":"Provides a RunContext object that flows through agent execution, carrying dependencies (database connections, API clients, user context) and runtime state without passing them as function parameters. Dependencies are registered via the Agent.run() method or through a context manager, and are injected into tool functions and system prompts via parameter inspection. This pattern decouples tool implementations from dependency management and enables testing by swapping dependencies at runtime.","intents":["I want to pass database connections or API clients to tools without adding them to every function signature","I need to inject user context (user ID, permissions) into tools for authorization checks","I want to test tools in isolation by mocking dependencies without modifying tool code"],"best_for":["applications with complex dependency graphs (databases, caches, external APIs)","multi-tenant systems requiring per-request context isolation","teams practicing dependency injection and testability patterns"],"limitations":["Dependencies must be explicitly declared in tool function signatures using type hints; implicit dependencies are not discovered","RunContext is thread-local or async-local; concurrent agent runs require separate context instances","Circular dependencies are not detected; misconfigured dependency graphs fail at runtime, not at definition time"],"requires":["Type annotations on tool function parameters matching dependency types","Dependencies must be serializable if using durable execution (Temporal, DBOS)","Python 3.9+ for type inspection capabilities"],"input_types":["dependency objects (any Python class)","type-annotated function parameters","context manager objects"],"output_types":["RunContext object with injected dependencies","tool function results with dependencies resolved"],"categories":["planning-reasoning","dependency-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_3","uri":"capability://tool.use.integration.tool.registration.and.function.calling.with.schema.inference","name":"tool registration and function calling with schema inference","description":"Registers Python functions as tools using the @agent.tool decorator, which automatically extracts parameter types, docstrings, and return types to generate OpenAI/Anthropic function schemas. The framework handles tool invocation, parameter validation, and error handling, including support for deferred execution (tools that require user approval before running) and async tools. Tool schemas are generated once at agent definition time and reused across all model calls, reducing overhead.","intents":["I want to expose Python functions as LLM-callable tools without manually writing JSON schemas","I need tools that require user approval before executing (e.g., financial transactions)","I want to use async tools that make database queries or API calls without blocking the agent"],"best_for":["agents that need to interact with databases, APIs, or external systems","applications requiring audit trails or approval workflows for tool execution","high-performance agents using async/await for I/O-bound operations"],"limitations":["Tool schemas are inferred from Python type hints; complex types (Union, Literal with many values) may generate verbose schemas that confuse models","Deferred execution requires explicit user interaction; no automatic approval logic","Tool error handling is basic; exceptions are converted to strings and sent back to the model, which may not recover gracefully"],"requires":["Type annotations on all tool function parameters","Docstrings for tool descriptions (required for good model behavior)","Return type annotation (can be Any if tool output is unstructured)"],"input_types":["Python functions (sync or async)","function parameters matching agent input types","tool invocation requests from LLM"],"output_types":["tool execution results (any Python type)","error messages if tool fails","deferred execution tokens for approval workflows"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_4","uri":"capability://text.generation.language.streaming.responses.with.token.by.token.output","name":"streaming responses with token-by-token output","description":"Provides streaming APIs (agent.run_stream(), agent.stream()) that yield tokens or structured chunks as they arrive from the model, enabling real-time UI updates and progressive output. The framework handles provider-specific streaming protocols (Server-Sent Events for OpenAI, streaming for Anthropic) and buffers tokens into logical chunks (complete words, sentences, or structured fields). Streaming works with both text outputs and structured Pydantic models, validating partial outputs incrementally.","intents":["I want to display LLM responses token-by-token in a web UI for better perceived latency","I need to stream structured outputs (JSON) and validate them as they arrive","I want to cancel long-running agent executions mid-stream without waiting for completion"],"best_for":["web applications and chat interfaces requiring real-time feedback","long-running agents where progressive output is valuable","applications with strict latency requirements where TTFT (time-to-first-token) matters"],"limitations":["Streaming structured outputs (Pydantic models) requires buffering until a complete object is available; partial validation is limited","Tool execution cannot be streamed; tools block the stream until completion","Some providers (e.g., Ollama) have higher streaming latency than non-streaming calls due to protocol overhead"],"requires":["Async/await support for streaming (sync streaming is limited)","WebSocket or Server-Sent Events support in client for real-time delivery","Provider support for streaming (all major providers support this)"],"input_types":["agent input (same as non-streaming)","stream configuration (chunk size, timeout)"],"output_types":["token iterators (str)","structured chunk iterators (Pydantic models)","streaming response objects with cancellation support"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_5","uri":"capability://memory.knowledge.message.history.and.multi.turn.conversation.management","name":"message history and multi-turn conversation management","description":"Maintains a message history (list of UserMessage, ModelMessage, ToolReturnMessage objects) that tracks the full conversation state across multiple agent.run() calls. Messages are immutable and typed, enabling type-safe history inspection and replay. The framework automatically manages message ordering, deduplication, and context window management, with support for message pruning strategies (e.g., keep last N messages, summarize old messages) to fit within model token limits.","intents":["I want to maintain conversation state across multiple agent.run() calls without manually managing message lists","I need to inspect the full conversation history for debugging or audit purposes","I want to implement conversation summarization to stay within token limits for long conversations"],"best_for":["multi-turn conversational agents and chatbots","applications requiring conversation audit trails or compliance logging","long-running agents where token budget management is critical"],"limitations":["Message history is in-memory by default; no built-in persistence (requires external storage)","Context window management is manual; no automatic summarization or pruning strategies built-in","Message history grows linearly with conversation length; no built-in compression or archival"],"requires":["Message history must be passed to each agent.run() call; no automatic session management","External storage required for persistence across application restarts","Token counting API to monitor context window usage"],"input_types":["list of Message objects (UserMessage, ModelMessage, ToolReturnMessage)","conversation state from previous agent.run() calls"],"output_types":["updated message history with new messages appended","typed Message objects for inspection and analysis"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_6","uri":"capability://data.processing.analysis.output.modes.and.response.formatting.text.json.structured","name":"output modes and response formatting (text, json, structured)","description":"Supports multiple output modes that control how the model formats its response: text mode (free-form text), JSON mode (structured JSON output), and structured mode (Pydantic model validation). Each mode uses provider-specific features (OpenAI's JSON mode, Anthropic's structured output) to guide the model toward the desired format. The framework automatically validates outputs against the declared schema and retries on validation failure (with configurable retry logic).","intents":["I want the model to always return valid JSON that I can parse without error handling","I need structured outputs (Pydantic models) with guaranteed schema compliance","I want to use different output formats for different agent runs without redefining the agent"],"best_for":["applications requiring structured, machine-readable outputs","APIs that need guaranteed response schemas for downstream processing","systems where output validation is critical (no error handling fallbacks)"],"limitations":["JSON mode is not available on all models (e.g., older OpenAI models); requires model-specific feature detection","Structured output validation may fail if the model generates invalid JSON; retry logic adds latency","Output mode is set at agent definition time; cannot change modes per-call without creating multiple agents"],"requires":["Model support for desired output mode (JSON mode, structured output)","Pydantic model definition for structured mode","Provider-specific configuration (some providers require explicit mode specification)"],"input_types":["output mode specification (text, json, structured)","Pydantic model for structured mode","retry configuration (max retries, backoff strategy)"],"output_types":["text strings (text mode)","JSON strings (JSON mode)","Pydantic model instances (structured mode)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_7","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.dynamic.tool.discovery","name":"model context protocol (mcp) integration for dynamic tool discovery","description":"Integrates with the Model Context Protocol (MCP) to dynamically discover and invoke tools from external MCP servers at runtime. Agents can connect to MCP servers (local or remote) and automatically expose their tools without manual registration. The framework handles MCP protocol details (JSON-RPC, stdio/HTTP transports) and tool invocation, treating MCP tools identically to @agent.tool decorated functions.","intents":["I want to use tools from external MCP servers without modifying my agent code","I need to connect to multiple MCP servers and expose all their tools to a single agent","I want to build a plugin system where tools are discovered dynamically at runtime"],"best_for":["applications requiring dynamic tool discovery and plugin architectures","teams using MCP-compatible tools (Claude Desktop, other MCP clients)","systems where tools are managed separately from agent code"],"limitations":["MCP server discovery and connection is manual; no automatic service discovery","Tool invocation latency is higher than local @agent.tool functions due to IPC/network overhead","MCP servers must be running and accessible; connection failures are not automatically recovered"],"requires":["MCP server implementation (local or remote)","MCP client library (pydantic-ai includes MCP client support)","Network access to MCP server (stdio for local, HTTP for remote)"],"input_types":["MCP server configuration (transport type, endpoint, credentials)","MCP tool invocation requests from agent"],"output_types":["dynamically discovered tools (function schemas)","tool execution results from MCP servers"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_8","uri":"capability://automation.workflow.observability.and.instrumentation.with.logfire.and.opentelemetry","name":"observability and instrumentation with logfire and opentelemetry","description":"Integrates with Pydantic Logfire and OpenTelemetry to instrument agent execution with detailed traces, metrics, and logs. Automatically captures model calls, tool invocations, token usage, latency, and errors without code changes. Traces are structured hierarchically (agent run → model call → tool invocation) and include full context (prompts, responses, dependencies) for debugging and monitoring. Supports custom instrumentation via context managers and decorators.","intents":["I want to see detailed traces of agent execution for debugging without adding logging code","I need to monitor token usage, latency, and costs across all model calls","I want to export traces to Datadog, Honeycomb, or other observability platforms"],"best_for":["production applications requiring observability and debugging","teams using Logfire or OpenTelemetry for infrastructure monitoring","applications with cost tracking requirements (token usage per request)"],"limitations":["Instrumentation adds overhead (~5-10% latency per agent run) due to trace collection and export","Sensitive data (prompts, responses) is captured in traces; requires careful configuration for PII handling","OpenTelemetry export requires external collector/backend; no built-in local storage"],"requires":["Logfire account (free tier available) or OpenTelemetry collector","Pydantic Logfire SDK or OpenTelemetry Python SDK","Network access to observability backend"],"input_types":["agent execution context (model calls, tool invocations)","custom instrumentation decorators/context managers"],"output_types":["structured traces (JSON)","metrics (latency, token usage, error rates)","logs (debug, info, error levels)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__cap_9","uri":"capability://automation.workflow.durable.execution.with.temporal.and.dbos.workflow.integration","name":"durable execution with temporal and dbos workflow integration","description":"Integrates with Temporal and DBOS to enable durable agent execution that survives process crashes and network failures. Agent runs are checkpointed at tool invocation boundaries, allowing execution to resume from the last completed tool call if the process restarts. The framework handles serialization of agent state (message history, dependencies) and coordinates with workflow engines to manage retries and error recovery.","intents":["I want agent execution to survive process crashes without losing progress","I need to run long-lived agents that may take hours or days to complete","I want automatic retry logic for transient failures without implementing it myself"],"best_for":["long-running agents (hours, days, weeks)","applications requiring high reliability and fault tolerance","systems where losing progress is expensive (financial transactions, data processing)"],"limitations":["Requires Temporal or DBOS infrastructure; adds operational complexity","Agent state must be serializable (Pydantic models, basic Python types); custom objects may not serialize","Checkpointing adds latency at tool boundaries; not suitable for latency-critical applications"],"requires":["Temporal cluster or DBOS runtime","Workflow definition using Temporal SDK or DBOS decorators","Serializable agent state (Pydantic models, basic types)"],"input_types":["agent definition (same as non-durable)","workflow configuration (retry policy, timeouts)"],"output_types":["durable agent results (same as non-durable)","workflow execution history (for replay and debugging)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pydantic-ai__headline","uri":"capability://tool.use.integration.production.grade.ai.agent.framework","name":"production-grade ai agent framework","description":"Pydantic AI is a type-safe, model-agnostic framework for building production-ready AI agents in Python, ensuring reliable interactions with LLMs through robust validation and structured outputs.","intents":["best AI agent framework","AI agent framework for production","top Python frameworks for LLM interactions","model-agnostic AI agent solutions","Pydantic-based AI agent tools"],"best_for":["developers building AI agents","teams needing reliable LLM interactions"],"limitations":[],"requires":["Python"],"input_types":["text","data"],"output_types":["structured outputs","validated responses"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Pydantic V2.0+","Type annotations in agent definition","API keys or credentials for chosen provider(s)","Provider-specific SDK (openai, anthropic, google-generativeai, boto3, etc.)","Network access to provider endpoints or local Ollama instance","Multiple agent definitions (one per agent type)","Message passing protocol (built-in to framework)","Coordination logic (supervisor agent or orchestration code)","Test dataset with inputs and expected outputs"],"failure_modes":["Pydantic validation adds ~50-100ms overhead per response for complex schemas with nested models","Type annotations are required for all agent inputs/outputs — cannot use untyped dicts or Any types without losing validation benefits","Validation errors from LLMs may require prompt engineering to resolve; no automatic recovery mechanism","Not all providers support identical feature sets — vision/multimodal support varies by provider, requiring conditional code paths","Token counting is provider-specific and approximate; exact counts require provider APIs (adds latency)","Streaming implementations differ by provider; some providers have higher latency for first token","Agent-to-agent communication adds latency (each agent call is a full model invocation)","No built-in load balancing or agent pool management; manual agent instantiation required","Debugging multi-agent workflows is complex; requires tracing across multiple agent runs","Evaluation requires labeled test datasets; no automatic test generation","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":"2026-06-17T09:51:05.295Z","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=pydantic-ai","compare_url":"https://unfragile.ai/compare?artifact=pydantic-ai"}},"signature":"XERN3hTjQt78a7GVUKi3Giw+3Xep1ys4M5SGTQO1PUqYQAAAJKu6jiMeFAeVhKoX7ATHyxLpFvhGmocw1CggCQ==","signedAt":"2026-06-22T06:44:57.295Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pydantic-ai","artifact":"https://unfragile.ai/pydantic-ai","verify":"https://unfragile.ai/api/v1/verify?slug=pydantic-ai","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"}}