eyecite
Tool for extracting legal citations from text strings.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a specific and well-defined problem for legal text processing. It is production-stable and already proven at scale by major legal research projects. Install it if you need to extract or parse legal citations from American legal documents.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; depends on courts-db and reporters-db databases which are installed as dependencies.
- Low friction install with six runtime dependencies.
- Actively maintained with a recent release; last commit was 2026-08-14 and the project has been in active development since 2021.
License · maintenance · safety
BSD-2-Clause (permissive) — BSD-2-Clause permissive license allows use in most projects, including commercial ones, with minimal restrictions beyond attribution.
last release 2026-07-01 (44 days) · last repo commit 2026-08-14 · 268 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 381,356 downloads/mo, #7,095 on PyPI
Alternatives
Verify before relying
pip install eyecite
from eyecite import get_citations
text = "Foo v. Bar, 1 U.S. 2, 3-4 (1999). Id. at 5."
citations = get_citations(text)
for citation in citations:
print(citation)- Whether the package handles citations from non-U.S. legal systems or only American law
- Performance characteristics when processing very large documents or batches
- Accuracy rates or benchmarks against real-world legal corpora
What it is and what it does
eyecite is a citation extraction tool designed to recognize and parse legal citations from American legal text. It identifies full case citations (e.g., "Bush v. Gore, 531 U.S. 98"), statutory references, law journal citations, and reference forms like "supra" and "Id." The package is built on a database trained against over 55 million existing citations and is used by CourtListener and Harvard's Caselaw Access Project to process millions of legal documents.
The package provides four core functions: extraction (finding citations in text), aggregation (linking related citations like "supra" to their antecedents), annotation (marking up citations with custom markup), and text cleaning. It depends on courts-db and reporters-db for citation pattern databases, plus lxml, pyahocorasick, regex, and fast-diff-match-patch for text processing. It requires Python 3.10 or later.
Use it for
- Extract citations from court opinions or legal briefs for indexing or linking to case databases
- Identify statutory references in legal documents to cross-reference legislation
- Resolve short-form citations like "Id." and "supra" back to their original case references
- Annotate legal text with markup around each citation for downstream processing or display
- Preprocess and clean legal text before analysis or machine learning workflows
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, uses a permissive license, and solves a specific and well-defined problem for legal text processing. It is production-stable and already proven at scale by major legal research projects. Install it if you need to extract or parse legal citations from American legal documents.
Install
eyecite on PyPI
Before you install
Low friction install with six runtime dependencies. Actively maintained with a recent release; last commit was 2026-08-14 and the project has been in active development since 2021.
Requires Python 3.10 or later; depends on courts-db and reporters-db databases which are installed as dependencies.
License in practice
BSD-2-Clause permissive license allows use in most projects, including commercial ones, with minimal restrictions beyond attribution.
Quickstart
pip install eyecite
from eyecite import get_citations
text = "Foo v. Bar, 1 U.S. 2, 3-4 (1999). Id. at 5."
citations = get_citations(text)
for citation in citations:
print(citation)Verify before relying
- Whether the package handles citations from non-U.S. legal systems or only American law
- Performance characteristics when processing very large documents or batches
- Accuracy rates or benchmarks against real-world legal corpora
Package facts
| License | BSD-2-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagescourts-dbfast-diff-match-patchlxmlpyahocorasickregexreporters-db |
| Maintenance | Actively maintained 44 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 381,356 / month, #7,095 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Libraries :: Python Modules |
Evidence: eyecite-2.7.8-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “legal citation extraction”
- eyeciteExtracts legal citations from text, recognizing case names, statutory…
- reporters-dbProvides a comprehensive database of U.S. court reporters and legal…
- courts-dbProvides a searchable database of current and historical courts with…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also reporters-db · courts-db · textract · citeproc-py · sphinxcontrib-bibtex · pdfminer.six · goose3 · cohere-melody · langextract · MainContentExtractor