Instructor
Open-source Python library for structured LLM outputs using Pydantic response models, validation, retries, streaming, and provider adapters.
Open the source and read safety notes before installing.
Safety notes
- Instructor validates structure and field constraints, but schema-valid LLM output can still be factually wrong, hallucinated, incomplete, biased, or unsafe for automated decisions.
- Automatic retries can increase model cost, latency, rate-limit pressure, and repeated exposure of prompts or failed outputs, so retries should be bounded and observable.
- Do not let successful parsing directly trigger irreversible writes, approvals, billing changes, or user-facing actions without domain checks, provenance, and fallback review.
Privacy notes
- Prompts, source text, extracted fields, validation errors, failed outputs, retry messages, and typed response objects may contain sensitive user or business data.
- Provider calls send extraction inputs and retry context to the configured LLM backend unless a local model path is used.
- Application logs, traces, eval datasets, examples, and debugging output can retain structured records that are easier to query and exfiltrate than raw prose.
Prerequisites
- Python LLM application or extraction pipeline that needs typed structured outputs rather than free-form text parsing.
- Pydantic response models, validation rules, retry policy, and downstream error-handling behavior reviewed before production use.
- Model provider credentials and provider-specific configuration for OpenAI, Anthropic, Google, Ollama, Groq, or another supported backend.
Schema details
- Install type
- copy
- Troubleshooting
- No
- Scope
- Source repo
- Website
- https://useinstructor.com/
- Pricing
- open-source
- Disclosure
- editorial
- Application category
- DeveloperApplication
- Operating system
- macOS, Windows, Linux
Full copyable content
## Editorial notes
Instructor is useful when Claude-adjacent apps need structured data from LLMs without hand-rolled JSON parsing. It lets developers define Pydantic response models, call supported providers through a common interface, validate model output, retry failed validations with error context, and stream partial structured objects for extraction-heavy workflows.
## Source notes
- The official documentation explains structured outputs as a way to make LLM responses consistent and machine-readable, with Instructor using Pydantic models to define expected output structure.
- The validation docs describe constraint checks, required fields, type safety, automatic retry with validation feedback, and domain-specific validation patterns.
- The GitHub README describes Instructor as "Structured Outputs for LLMs" and says it is built on Pydantic for validation, type safety, and IDE support.
- The repository documents provider adapters for OpenAI, Anthropic, Google, Ollama, and Groq-style usage, plus automatic retries, streaming partial objects, nested objects, and MIT licensing.
## Duplicate check
Checked current `content/tools/`, `content/mcp/`, guides, skills, agents, open pull requests, live HeyClaude `llms-full.txt`, and repository-wide content for `Instructor`, `useinstructor`, `567-labs`, `github.com/567-labs/instructor`, `python.useinstructor.com`, `structured output`, `structured outputs`, `Pydantic`, `response_model`, `validation retry`, and `JSON schema`. Guardrails AI is already listed as a broader input/output guardrail and validator framework, while existing FastAPI, Zod, and JSON Schema entries are skills for application/API validation rather than Instructor's LLM structured-output extraction library. No dedicated Instructor tools entry, source URL duplicate, or open duplicate PR was found.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.About this resource
Editorial notes
Instructor is useful when Claude-adjacent apps need structured data from LLMs without hand-rolled JSON parsing. It lets developers define Pydantic response models, call supported providers through a common interface, validate model output, retry failed validations with error context, and stream partial structured objects for extraction-heavy workflows.
Source notes
- The official documentation explains structured outputs as a way to make LLM responses consistent and machine-readable, with Instructor using Pydantic models to define expected output structure.
- The validation docs describe constraint checks, required fields, type safety, automatic retry with validation feedback, and domain-specific validation patterns.
- The GitHub README describes Instructor as "Structured Outputs for LLMs" and says it is built on Pydantic for validation, type safety, and IDE support.
- The repository documents provider adapters for OpenAI, Anthropic, Google, Ollama, and Groq-style usage, plus automatic retries, streaming partial objects, nested objects, and MIT licensing.
Duplicate check
Checked current content/tools/, content/mcp/, guides, skills, agents, open pull requests, live HeyClaude llms-full.txt, and repository-wide content for Instructor, useinstructor, 567-labs, github.com/567-labs/instructor, python.useinstructor.com, structured output, structured outputs, Pydantic, response_model, validation retry, and JSON schema. Guardrails AI is already listed as a broader input/output guardrail and validator framework, while existing FastAPI, Zod, and JSON Schema entries are skills for application/API validation rather than Instructor's LLM structured-output extraction library. No dedicated Instructor tools entry, source URL duplicate, or open duplicate PR was found.
Disclosure
Editorial listing. No paid placement or affiliate link is used.
Source citations
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