{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-magentic","slug":"pypi-magentic","name":"magentic","type":"framework","url":"https://pypi.org/project/magentic/","page_url":"https://unfragile.ai/pypi-magentic","categories":["app-builders"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-magentic__cap_0","uri":"capability://code.generation.editing.decorator.based.llm.function.wrapping","name":"decorator-based llm function wrapping","description":"Converts Python functions into LLM-powered equivalents using a @prompt decorator that intercepts function calls and routes them to language models. The decorator preserves function signatures, type hints, and docstrings while transparently replacing execution with LLM inference, enabling developers to define LLM behavior through standard Python function definitions rather than prompt templates or API calls.","intents":["I want to define an LLM task as a regular Python function without learning a new DSL or prompt framework","I need type-safe LLM calls that respect my function's input/output types","I want to seamlessly swap between local and remote LLM backends without changing function code"],"best_for":["Python developers building LLM-integrated applications","Teams wanting to treat LLM calls as first-class Python functions","Developers migrating from manual API calls to declarative LLM integration"],"limitations":["Decorator overhead adds ~50-100ms per function call for wrapper initialization","Type hints must be compatible with LLM serialization (complex nested types may require custom handlers)","Async function decoration requires separate async-aware decorator variant"],"requires":["Python 3.9+","Valid API key for at least one supported LLM provider (OpenAI, Anthropic, Ollama, etc.)","Function docstrings for prompt context (optional but recommended)"],"input_types":["Python function with type-annotated parameters","Docstring describing task intent","Type hints (str, int, list, dict, custom dataclasses)"],"output_types":["Python objects matching return type annotation","Structured data (dataclasses, TypedDict, Pydantic models)","Primitive types (str, int, bool, list, dict)"],"categories":["code-generation-editing","python-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.backend.abstraction","name":"multi-provider llm backend abstraction","description":"Provides a unified interface to multiple LLM providers (OpenAI, Anthropic, Ollama, local models) through a pluggable backend system that abstracts provider-specific API differences. Developers specify the LLM provider once (via environment variable or explicit parameter) and the same decorated function works across all supported backends without code changes, handling differences in API formats, token counting, and response parsing internally.","intents":["I want to switch between OpenAI, Anthropic, and local Ollama models without rewriting my code","I need to test my LLM application against multiple providers to compare quality and cost","I want to use a local model in development and cloud models in production with the same function definitions"],"best_for":["Teams evaluating multiple LLM providers","Developers building cost-optimized applications that need provider flexibility","Organizations with multi-cloud or hybrid on-prem/cloud LLM strategies"],"limitations":["Provider-specific features (vision, function calling, streaming) may not be uniformly supported across all backends","Response quality and latency vary significantly between providers; no automatic optimization","Custom provider integration requires implementing the backend interface (non-trivial for proprietary APIs)"],"requires":["Python 3.9+","API credentials for target provider (OpenAI key, Anthropic key, Ollama endpoint, etc.)","Environment variables or explicit provider configuration"],"input_types":["Provider identifier (string: 'openai', 'anthropic', 'ollama')","Model name (provider-specific: 'gpt-4', 'claude-3-opus', 'llama2')","API endpoint URL (for self-hosted or custom providers)"],"output_types":["Standardized response objects (text, structured data)","Provider-agnostic error messages and status codes"],"categories":["tool-use-integration","abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_2","uri":"capability://data.processing.analysis.structured.output.parsing.with.type.coercion","name":"structured output parsing with type coercion","description":"Automatically parses LLM text responses into Python objects matching the function's return type annotation using a combination of prompt engineering (instructing the LLM to output structured formats like JSON) and post-processing validation. Supports dataclasses, TypedDict, Pydantic models, and primitive types, with intelligent fallback strategies when LLM output doesn't match the expected schema (retry with clarified prompt, partial parsing, or error propagation).","intents":["I want the LLM to return structured data (JSON, dataclass) instead of raw text that I have to parse manually","I need type-safe LLM responses that my IDE can autocomplete and type-check","I want automatic validation that the LLM's response matches my expected data structure"],"best_for":["Developers building data extraction pipelines with LLMs","Teams needing type-safe LLM integration with existing Python codebases","Applications where LLM output feeds directly into downstream systems expecting structured data"],"limitations":["LLM hallucination can produce invalid JSON or mismatched field types; validation failures require retry logic or fallback handling","Complex nested structures may confuse LLMs; deeply nested dataclasses have lower success rates","No built-in schema versioning; breaking changes to return types require careful migration"],"requires":["Python 3.9+","Return type annotation (dataclass, TypedDict, Pydantic model, or primitive type)","LLM provider that supports instruction-following (most modern models do)"],"input_types":["Python type annotation (dataclass, TypedDict, Pydantic BaseModel)","Function docstring with task description"],"output_types":["Python dataclass instance","TypedDict-compatible dict","Pydantic model instance","Primitive types (str, int, list, dict)"],"categories":["data-processing-analysis","type-safety"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_3","uri":"capability://text.generation.language.streaming.response.handling.with.iterative.token.consumption","name":"streaming response handling with iterative token consumption","description":"Enables streaming LLM responses token-by-token through Python iterators, allowing applications to display partial results in real-time without waiting for full completion. Internally manages provider-specific streaming protocols (Server-Sent Events for OpenAI, streaming for Anthropic) and yields tokens as they arrive, with optional buffering for structured output types that require complete responses for parsing.","intents":["I want to display LLM responses to users in real-time as tokens arrive instead of waiting for full completion","I need to build chat interfaces or content generation tools that feel responsive","I want to cancel long-running LLM requests mid-stream when users stop waiting"],"best_for":["Web applications and chat interfaces requiring real-time user feedback","Content generation tools (writing assistants, code generators) where partial output is useful","Applications with strict latency requirements where token-by-token feedback improves perceived performance"],"limitations":["Structured output types (dataclass, JSON) cannot be streamed; must buffer entire response before parsing","Streaming adds complexity to error handling; mid-stream failures may yield partial/invalid data","Token-level granularity varies by provider; some batch tokens into larger chunks, reducing perceived responsiveness"],"requires":["Python 3.9+","LLM provider with streaming support (OpenAI, Anthropic, Ollama all support this)","Async-capable event loop for non-blocking token consumption (optional but recommended)"],"input_types":["Streaming-enabled LLM provider configuration","Function decorated with @prompt and streaming=True parameter"],"output_types":["Python iterator yielding string tokens","Async iterator for async functions","Accumulated full response after iteration completes"],"categories":["text-generation-language","streaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_4","uri":"capability://text.generation.language.function.parameter.injection.into.prompts","name":"function parameter injection into prompts","description":"Automatically incorporates function parameters into the LLM prompt by introspecting function arguments at call time and embedding them as context. The decorator extracts parameter names, types, and values, then constructs a prompt that includes both the function's docstring (task description) and the actual parameter values, enabling the LLM to make decisions based on dynamic input without requiring manual string formatting or f-string construction.","intents":["I want to pass user input to an LLM function and have it automatically included in the prompt context","I need the LLM to process different inputs with the same task logic without redefining the function","I want to avoid manual prompt construction with f-strings or format() calls"],"best_for":["Developers building parameterized LLM tasks (classification, extraction, transformation)","Applications processing variable user input through fixed LLM logic","Teams wanting clean separation between task definition (docstring) and runtime input (parameters)"],"limitations":["Large parameter values (long text, large lists) can exceed token limits; no automatic truncation or summarization","Parameter ordering and naming conventions must be consistent; refactoring function signatures requires prompt re-tuning","Complex parameter types (nested objects, custom classes) may not serialize cleanly into prompts"],"requires":["Python 3.9+","Function parameters with type annotations","Docstring describing the task (parameter descriptions optional but recommended)"],"input_types":["Function parameters (any type: str, int, list, dict, custom objects)","Type annotations for parameters"],"output_types":["Constructed prompt string (internal; not directly exposed)","LLM response based on prompt + parameters"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_5","uri":"capability://automation.workflow.asynchronous.llm.function.execution","name":"asynchronous llm function execution","description":"Provides async/await support for LLM function calls through async-decorated variants, enabling non-blocking execution in async Python applications. Internally uses asyncio to manage concurrent requests to LLM providers, allowing multiple LLM calls to execute in parallel without blocking the event loop, with proper error propagation and cancellation support through Python's asyncio.Task interface.","intents":["I want to call multiple LLM functions concurrently in an async application without blocking","I need to integrate LLM calls into async web frameworks (FastAPI, Starlette) without thread pools","I want to cancel in-flight LLM requests when users disconnect or timeouts occur"],"best_for":["Async web applications (FastAPI, Starlette, aiohttp)","High-concurrency services handling multiple simultaneous LLM requests","Applications requiring responsive cancellation and timeout handling"],"limitations":["Async decorator requires separate function definition; cannot mix sync and async in same function","Error handling in async context is more complex; exceptions in concurrent tasks require careful propagation","Provider support for async varies; some backends may fall back to thread pools, negating concurrency benefits"],"requires":["Python 3.9+","Async-capable event loop (asyncio, uvloop, etc.)","Async-compatible LLM provider (most modern providers support this)"],"input_types":["Async function with @prompt decorator","Coroutine parameters (optional)"],"output_types":["Coroutine object (awaitable)","Result matching return type annotation"],"categories":["automation-workflow","async-concurrency"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_6","uri":"capability://text.generation.language.prompt.template.customization.with.docstring.parsing","name":"prompt template customization with docstring parsing","description":"Allows developers to customize how prompts are constructed by parsing function docstrings and extracting task descriptions, parameter documentation, and output format instructions. The decorator interprets docstring conventions (Google-style, NumPy-style, or plain text) to build context-aware prompts that include parameter descriptions and expected output formats, with optional hooks for custom prompt builders that override default behavior.","intents":["I want to control how my function's docstring is converted into an LLM prompt","I need to add special instructions or formatting guidelines to the LLM without changing the function signature","I want to document my LLM task in a way that's both human-readable and machine-parseable"],"best_for":["Teams with specific prompt engineering practices or conventions","Developers building domain-specific LLM applications with custom prompt requirements","Organizations needing consistent prompt formatting across many LLM functions"],"limitations":["Docstring parsing is heuristic-based; non-standard formats may not parse correctly","Custom prompt builders add complexity; incorrect implementations can degrade LLM output quality","No built-in versioning for prompt templates; changes to docstrings affect all function calls immediately"],"requires":["Python 3.9+","Well-formatted docstring (Google, NumPy, or plain text style)","Optional custom prompt builder function"],"input_types":["Function docstring (string)","Optional custom prompt builder callable"],"output_types":["Constructed prompt string (internal)","LLM response based on customized prompt"],"categories":["text-generation-language","prompt-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_7","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Provides built-in error handling for LLM API failures, rate limits, and malformed responses through configurable retry strategies with exponential backoff. When an LLM call fails (network error, rate limit, invalid response), the decorator automatically retries with increasing delays, with customizable retry counts, backoff multipliers, and jitter to prevent thundering herd problems in concurrent scenarios.","intents":["I want LLM calls to automatically retry on transient failures without my code handling it","I need to handle rate limits gracefully without crashing or losing requests","I want to configure retry behavior per-function based on criticality and latency requirements"],"best_for":["Production applications requiring resilience to LLM provider outages","High-volume applications susceptible to rate limiting","Teams building fault-tolerant LLM pipelines without manual retry logic"],"limitations":["Retries increase total latency; exponential backoff can delay responses by minutes for high retry counts","Idempotency is assumed; retrying non-idempotent operations (e.g., generating unique content) may produce duplicates","No circuit breaker pattern; repeated failures continue retrying rather than fast-failing"],"requires":["Python 3.9+","LLM provider with transient failure modes (network errors, rate limits)"],"input_types":["Retry configuration (max_retries, backoff_factor, jitter)","Exception types to retry on (configurable)"],"output_types":["Successful LLM response after retries","Exception raised after max retries exceeded"],"categories":["automation-workflow","error-handling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_8","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation","name":"token counting and cost estimation","description":"Estimates the number of tokens consumed by LLM calls before execution and calculates approximate costs based on provider pricing models. Uses provider-specific tokenizers (OpenAI's tiktoken, Anthropic's token counter) to count input and output tokens, then multiplies by per-token rates to estimate costs, enabling developers to monitor and optimize LLM spending without waiting for actual API billing.","intents":["I want to estimate how much an LLM call will cost before executing it","I need to track token consumption across my application to optimize costs","I want to set budgets or alerts when LLM spending exceeds thresholds"],"best_for":["Cost-conscious teams building LLM applications with budget constraints","Applications processing large volumes of text where token counting is critical","Developers optimizing prompts to reduce token consumption and costs"],"limitations":["Token count estimates are approximate; actual usage may differ due to tokenizer differences or provider-specific overhead","Pricing models change frequently; hardcoded rates become stale and require manual updates","Output token estimation is inherently uncertain (LLM may produce variable-length responses)"],"requires":["Python 3.9+","Provider-specific tokenizer library (tiktoken for OpenAI, etc.)","Current pricing data for target provider"],"input_types":["Function input (text, parameters)","Provider and model name"],"output_types":["Token count estimate (input + output)","Cost estimate (USD or other currency)","Breakdown by input/output tokens"],"categories":["data-processing-analysis","cost-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-magentic__cap_9","uri":"capability://memory.knowledge.context.window.management.with.automatic.truncation","name":"context window management with automatic truncation","description":"Manages LLM context windows by tracking token consumption and automatically truncating or summarizing input when approaching provider limits. Monitors cumulative tokens from function parameters, system prompts, and conversation history, then applies truncation strategies (sliding window, summarization, priority-based filtering) to keep total tokens within the model's maximum context window without exceeding limits.","intents":["I want to process long documents without hitting token limits","I need to maintain conversation history while staying within context windows","I want automatic fallback strategies when input exceeds available context"],"best_for":["Applications processing variable-length documents (PDFs, articles, logs)","Conversational AI systems maintaining multi-turn dialogue history","Teams building RAG systems where context selection is critical"],"limitations":["Automatic truncation may lose important information; no semantic awareness of what to keep vs. discard","Summarization adds latency and cost (requires additional LLM calls)","Context window limits vary by model; no automatic model selection based on input size"],"requires":["Python 3.9+","Token counter for target provider","Knowledge of model context window size"],"input_types":["Long text input (document, conversation history)","Context window size (tokens)","Truncation strategy (sliding window, summarize, priority-based)"],"output_types":["Truncated input within context limits","Metadata about what was removed","LLM response based on truncated context"],"categories":["memory-knowledge","context-management"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","Valid API key for at least one supported LLM provider (OpenAI, Anthropic, Ollama, etc.)","Function docstrings for prompt context (optional but recommended)","API credentials for target provider (OpenAI key, Anthropic key, Ollama endpoint, etc.)","Environment variables or explicit provider configuration","Return type annotation (dataclass, TypedDict, Pydantic model, or primitive type)","LLM provider that supports instruction-following (most modern models do)","LLM provider with streaming support (OpenAI, Anthropic, Ollama all support this)","Async-capable event loop for non-blocking token consumption (optional but recommended)","Function parameters with type annotations"],"failure_modes":["Decorator overhead adds ~50-100ms per function call for wrapper initialization","Type hints must be compatible with LLM serialization (complex nested types may require custom handlers)","Async function decoration requires separate async-aware decorator variant","Provider-specific features (vision, function calling, streaming) may not be uniformly supported across all backends","Response quality and latency vary significantly between providers; no automatic optimization","Custom provider integration requires implementing the backend interface (non-trivial for proprietary APIs)","LLM hallucination can produce invalid JSON or mismatched field types; validation failures require retry logic or fallback handling","Complex nested structures may confuse LLMs; deeply nested dataclasses have lower success rates","No built-in schema versioning; breaking changes to return types require careful migration","Structured output types (dataclass, JSON) cannot be streamed; must buffer entire response before parsing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.3,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:13.887Z","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-magentic","compare_url":"https://unfragile.ai/compare?artifact=pypi-magentic"}},"signature":"g9OkbXudCKc/KXCESWmfdDG0gU5pvu3n43OIWETalXfXSKgMqiinlv/L5l78iXaSZUnOG7/o3R+usklYrngkDQ==","signedAt":"2026-06-22T00:22:06.216Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-magentic","artifact":"https://unfragile.ai/pypi-magentic","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-magentic","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"}}