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Documentation
Managing Output Parsers
for Structured Data
Transform raw model outputs into clean, typed, validated data structures with composable parser primitives — built for production pipelines.
🧩
Composable Parsers
Chain parsers together to handle complex nested schemas with automatic fallback and retry logic.
🌸
Type Validation
Every field is validated at parse time using Pydantic-compatible schemas with rich error messages.
🌿
Streaming Support
Parse incremental token streams in real-time without waiting for the full model response to complete.
✨
Auto-Retry
Built-in retry logic re-prompts the model when output doesn’t match the expected schema or format.
💎
Format Agnostic
Parse JSON, XML, YAML, Markdown tables, and custom delimited formats with the same unified API.
🪄
Zero Config
Works out of the box with sensible defaults. Drop in your schema and start extracting in minutes.
▸ Quick Example
# Define your target schema from output_parsers import StructuredParser, Field from pydantic import BaseModel class ProductInfo(BaseModel): name: str price: float tags: list[str] in_stock: bool # Attach parser to your chain parser = StructuredParser(schema=ProductInfo, retries=3) result = parser.parse(model_output) # Fully typed, validated output ✓ print(result.name, result.price)
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