langextract
Python library for extracting structured information from unstructured text using LLMs
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
73views this month
Decision brief
Langextract is a Python library designed for extracting structured data from unstructured text using large language models, offering precise source grounding and interactive visualization.
Good fit when
- When you need to extract structured data from unstructured text with precise source grounding and interactive visualization capabilities.
- If your project requires integration with cloud-hosted models like Gemini, as langextract supports these with API key configuration.
Avoid when
- If your project does not require the use of large language models for information extraction, as langextract is specifically designed around this functionality.
- When you do not have access to or do not wish to use cloud-hosted models like Gemini, as langextract requires an API key for these services.
- Pricing:
- freemium - The library itself is free to use, but using cloud-hosted models like Gemini may incur costs based on the provider's pricing model.
- Requirements:
- Min 4 GB RAM; Requires Docker; Installation requires Python and pip.; For cloud-hosted models, an API key is required and must be configured as per the documentation.
Observed Sep 18, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (4d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Google. Display-only - separate from trust and ranking.
- Company
- Google·GitHub org profile·Jul 11, 2026
- Employees
- 47,756·Wikidata (P1128 employees)·Jul 11, 2026
- Commercial model
- Pure OSS·GitHub org profile (public repos)·Jul 11, 2026
Install
pip install langextractHow it fits your stack(16)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Integrates
Depends on
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides tools for extracting structured data from unstructured text leveraging large language models with interactive visualization and precise source grounding.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Sep 18, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Sep 18, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 18, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 18, 2026)
docker run --rm -e LANGEXTRACT_API_KEY="your-api-key" langextract python your_script.pySource link
Tags
README
Quick Start Note: Using cloud hosted models like Gemini requires an API key. See the API Key Setup section for instructions on how to get and configure your key. Extract structured information with just a few lines of code. For basic installation: pip install e . Docker Install w...
For agents
This page has a .md twin and JSON over the API.