AI ERP with agents inside the systems you run
AI phone and voice agents for real calls
LLM.coPrivate, self-hosted LLM deployments
Legal AI infrastructure for firms
AI RFP discovery and response drafting
Automatic.coBusiness process automation
CyberAttack.aiThreat detection and defense
Secure AI virtual data roomslangextract
LangExtract is a Google-authored Python library that extracts structured information from unstructured documents using LLMs, with precise source grounding (character-level mapping) and interactive HTML visualization. It supports Gemini, OpenAI, and local models via Ollama, handling long documents through intelligent chunking and multi-pass processing.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | google/langextract |
| Owner | |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 37.1k |
| Forks | 2.6k |
| Open issues | 106 |
| Latest release | v1.6.0 (2026-07-02) |
| Last updated | 2026-07-02 |
| Source | https://github.com/google/langextract |
What langextract is
LangExtract provides schema-enforced extraction via few-shot prompting and controlled generation (Gemini/OpenAI). It maps extractions to source character intervals for grounding verification, processes documents in parallel chunks with configurable passes, and outputs JSONL-serializable results with optional HTML visualization. Built for NLP/information extraction workflows with LLM flexibility.
Get the langextract source
Clone the repository and explore it locally.
git clone https://github.com/google/langextract.gitcd langextract# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Extraction quality depends heavily on prompt clarity and example quality; invest time in few-shot design and validate on representative samples before scaling.
- Source grounding (char_interval) fails for hallucinated content; filter results with `[e for e in result.extractions if e.char_interval]` to keep only verifiable extractions.
- Long documents (>100K tokens) incur higher API costs and latency; tuning `extraction_passes`, chunk size, and model choice (Flash vs. Pro) impacts cost-quality trade-off.
- Gemini models have defined retirement dates; monitor official model lifecycle documentation and plan migrations for production deployments.
- Output schema constraints (enums, nested structures) are supported on Gemini and OpenAI; fallback behavior on other models requires testing.
When to avoid it — and what to weigh
- Offline-Only Environments — Primary workflow requires cloud LLM APIs (Gemini, OpenAI) or external Ollama setup; not suitable for air-gapped or fully on-premise systems without additional infrastructure.
- Sub-Millisecond Latency Requirements — LLM-based extraction inherently introduces network and model inference latency; inappropriate for real-time stream processing or microsecond-scale applications.
- Highly Sensitive Data Without Strict Data Residency — Cloud-based LLMs (Gemini, OpenAI) process text on external servers; use local Ollama models if data cannot leave your infrastructure for compliance/privacy reasons.
- Fully Deterministic or Rule-Based Extraction — LLM outputs introduce variability and occasional hallucinations; regex/rule engines are more predictable for rigid, unambiguous patterns.
License & commercial use
Apache License 2.0 (Apache-2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution with attribution and indemnification clauses. Suitable for proprietary and open-source projects.
Apache-2.0 explicitly permits commercial use. However, commercial viability depends on external LLM API costs (Gemini, OpenAI) and data residency constraints. Requires paid API tiers for production volume. Ensure compliance review of your LLM provider's terms and data handling for your use case.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
Text sent to cloud LLMs (Gemini, OpenAI) is processed on external servers; review provider data retention and privacy policies before processing sensitive data (PII, health info, confidential docs). Local Ollama models avoid external data transfer. No claims made about model robustness against adversarial inputs. Validate grounded extractions in regulated domains (healthcare, legal) to prevent LLM hallucinations affecting downstream decisions.
Alternatives to consider
OpenAI Structured Outputs / GPT Function Calling
Direct schema-constrained generation without grounding; lower per-call cost but no source mapping; suitable if verification/audit trails not required.
Hugging Face Transformers (fine-tuned extraction models)
Fully on-premise, deterministic outputs; requires labeled training data and GPU; better for closed-domain tasks where data residency is non-negotiable.
Apache Tika / GATE / spaCy (rule/ML pipelines)
Lightweight, offline-first; no LLM dependency; limited adaptability to new domains; better for high-volume deterministic extraction (structured formats, known patterns).
Build on langextract with DEV.co software developers
Explore LangExtract on GitHub, try the quick-start guide with Gemini or Ollama, and visualize extractions in minutes. Review Apache-2.0 license terms for your use case.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
langextract FAQ
Does LangExtract require GPU or special hardware?
How much do Gemini API calls cost?
Can I use LangExtract offline?
What happens if an extraction cannot be found in the source text?
From evaluation to production software
Bringing langextract into production usually means new services, new interfaces, and new failure modes. DEV.co is a software development agency that builds all three and stays on for the ai frameworks maintenance.
Ready to Extract Structured Data from Your Documents?
Explore LangExtract on GitHub, try the quick-start guide with Gemini or Ollama, and visualize extractions in minutes. Review Apache-2.0 license terms for your use case.