langextract

library1.5.0pypypi✓ verified 85d ago

LangExtract is a Python library for robustly extracting structured data from large language models (LLMs). It handles automatic chunking, multi-language support, and provides integrations with various LLM providers (e.g., OpenAI, Vertex AI, Ollama). The current version is 1.2.1, with a frequent release cadence, often introducing new providers, features, and bug fixes.

llm-agents ai-ml

pip install langextract

github ↗package ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · LANGEXTRACT

langextract

llm-agentspythonv1.5.0

Install

16.5s avg

Import

2268ms

Disk

243MB

Pass rate

8/ 10

Env Coverage8 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.5.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 19.4s

py 3.11

✓ —

✓ 16.7s

py 3.12

✓ —

✓ 14.7s

py 3.13

✓ —

✓ 15.2s

py 3.9

✕ build_error

243MB installed

● package 243MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

extract

✓ from langextract import extract

ModelConfig

✓ from langextract import ModelConfig

ProviderConfig

✓ from langextract import ProviderConfig

langextract_schema

✓ from langextract.dataclasses import langextract_schema

LangExtractSchema

✓ from langextract.dataclasses import LangExtractSchema

This quickstart defines a schema using `langextract_schema` and then uses the `extract` function to parse structured data from a given text, demonstrating a basic usage pattern. Ensure your LLM provider's API key is available (e.g., `OPENAI_API_KEY`).

import os from langextract import extract, dataclasses # Define a simple schema for extraction @dataclasses.langextract_schema class Person(dataclasses.LangExtractSchema): name: str age: int occupation: str text = "John Doe is a software engineer aged 30." # Use a dummy OpenAI key for quickstart, actual key from env os.environ['OPENAI_API_KEY'] = os.environ.get('OPENAI_API_KEY', 'sk-dummy-key') # Extract data using the schema result = extract( text=text, schema=Person, model_name="gpt-3.5-turbo" # Or any other supported model ) print(result.extracted_data) # Expected: Person(name='John Doe', age=30, occupation='software engineer')

Debug

Known issues

breakingPrior to v1.0.4, `OllamaLanguageModel` used the `model` parameter. This was changed to `model_id` in v1.0.4 for consistency.

fix

Update to `langextract>=1.0.4` and ensure your `ModelConfig` for Ollama uses `model_id='llama2'` instead of `model='llama2'`.

affects: <1.0.4

gotchaUsing `reasoning_effort` with certain OpenAI models (e.g., o1, o3, o4-mini, gpt-5) in versions <1.2.1 would cause an `unexpected keyword argument 'reasoning'` error due to incorrect parameter passing.

fix

Upgrade to `langextract>=1.2.1` to fix the parameter passing, or avoid using `reasoning_effort` for affected models if an upgrade is not possible.

affects: <1.2.1

gotchaWhen specifying an LLM provider by name (e.g., `ModelConfig(provider='ollama')`), versions <1.2.0 could fail to load built-in providers correctly, leading to an `InferenceConfigError`.

fix

Upgrade to `langextract>=1.2.0` to ensure proper loading and resolution of model providers.

affects: <1.2.0

gotchaFor users of Gemini Batch API with Vertex AI, versions <1.1.1 might have encountered a 'Required parameter: project' error due to incorrect parameter handling.

fix

Upgrade to `langextract>=1.1.1` to ensure the `project` parameter is correctly passed to the Vertex AI Batch API.

affects: <1.1.1

gotchaThe `debug` parameter in `extract()` defaulted to `True` in versions prior to v1.0.9, resulting in verbose output. It now defaults to `False`.

fix

If you relied on the `debug=True` behavior, explicitly set `debug=True` in your `extract()` calls after upgrading to `langextract>=1.0.9`.

affects: <1.0.9

Errors

Common errors & fixes

unexpected keyword argument 'reasoning'

Incorrect parameter passing for `reasoning_effort` with specific OpenAI models (o1, o3, o4-mini, gpt-5) in `langextract` versions prior to 1.2.1.

fix

Upgrade `langextract` to version 1.2.1 or newer (`pip install --upgrade langextract`).

InferenceConfigError: Could not resolve model provider for 'ollama'

Built-in model providers were not correctly loaded when specified by name in `ModelConfig` for `langextract` versions prior to 1.2.0.

fix

Upgrade `langextract` to version 1.2.0 or newer (`pip install --upgrade langextract`).

ValueError: Required parameter: project

Missing or incorrect `project` parameter handling for Vertex AI Gemini Batch API calls in `langextract` versions prior to 1.1.1.

fix

Upgrade `langextract` to version 1.1.1 or newer (`pip install --upgrade langextract`).

TypeError: OllamaLanguageModel.__init__() got an unexpected keyword argument 'model'

The `OllamaLanguageModel` parameter for specifying the model ID changed from `model` to `model_id` in `langextract` v1.0.4.

fix

Update your `ModelConfig` for Ollama to use `model_id` instead of `model`, e.g., `ModelConfig(provider='ollama', model_id='llama2')`.

Upgrade

Version history

1.5.0latest on PyPI · released May 20, 2026

Audit

Dependencies

No dependency data recorded yet.

Agent activity

11 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/google/langextract ↗

packagehttps://pypi.org/project/langextract/ ↗

docshttps://github.com/google/langextract/blob/main/README.md ↗

changeloghttps://github.com/google/langextract/releases ↗