jambo
Jambo - JSON Schema to Pydantic Converter
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need dynamic schema-to-model conversion and can tolerate alpha-stage software. The package has no known vulnerabilities, permissive licensing, and reasonable download volume (488061 monthly). However, the 212-day gap since last release and alpha status mean you should verify stability for your use case and be prepared to maintain a fork if needed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; pydantic, jsonschema, and email-validator must be installed.
- High install friction due to three runtime dependencies (email-validator, jsonschema, pydantic).
- Package is in alpha status with aging maintenance signal—last release 212 days ago, though repository remains active and not archived.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without licensing concerns.
last release 2026-01-14 (212 days) · last repo commit 2026-01-14 · 94 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 488,061 downloads/mo, #6,382 on PyPI
Alternatives
Verify before relying
from jambo import SchemaConverter
schema = {
"title": "Person",
"type": "object",
"properties": {"name": {"type": "string"}, "age": {"type": "integer"}},
"required": ["name"],
}
Person = SchemaConverter.build(schema)
obj = Person(name="Alice", age=30)- Whether the package handles all JSON Schema draft versions or only specific ones
- Performance characteristics when converting large or deeply nested schemas
- Stability guarantees given alpha development status
What it is and what it does
Jambo is a converter that takes JSON Schema definitions and generates Pydantic models from them automatically. It bridges the gap between schema-first design and Python type safety by eliminating manual model writing. The package supports a broad range of JSON Schema features including nested objects, references, unions (anyOf, oneOf, allOf), enums, and validation constraints like minLength, maxLength, pattern, minimum, and maximum.
It offers two APIs: a static convenience method for one-off conversions and an instance-based API with reference caching for scenarios where you need to reuse generated subtypes or manage schema namespaces. The package is designed for frameworks like LangChain and CrewAI that need to dynamically generate models at runtime, though it works for any use case requiring schema-driven validation.
Use it for
- Generate Pydantic models from OpenAPI or JSON Schema specifications without manual model definition.
- Build dynamic validation layers in AI frameworks that accept arbitrary schema inputs from users or APIs.
- Convert third-party schema definitions into type-safe Python models for data processing pipelines.
- Reuse schema-derived subtypes across multiple converter instances using the reference cache.
- Enforce JSON Schema constraints (pattern, bounds, length limits) through Pydantic validation automatically.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need dynamic schema-to-model conversion and can tolerate alpha-stage software. The package has no known vulnerabilities, permissive licensing, and reasonable download volume (488061 monthly). However, the 212-day gap since last release and alpha status mean you should verify stability for your use case and be prepared to maintain a fork if needed.
Install
jambo on PyPI
Before you install
High install friction due to three runtime dependencies (email-validator, jsonschema, pydantic). Package is in alpha status with aging maintenance signal—last release 212 days ago, though repository remains active and not archived.
Requires Python 3.10 or later; pydantic, jsonschema, and email-validator must be installed.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for most projects without licensing concerns.
Quickstart
from jambo import SchemaConverter
schema = {
"title": "Person",
"type": "object",
"properties": {"name": {"type": "string"}, "age": {"type": "integer"}},
"required": ["name"],
}
Person = SchemaConverter.build(schema)
obj = Person(name="Alice", age=30)Verify before relying
- Whether the package handles all JSON Schema draft versions or only specific ones
- Performance characteristics when converting large or deeply nested schemas
- Stability guarantees given alpha development status
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | High. Source build required |
| Runtime dependencies | 3 packagesemail-validatorjsonschemapydantic |
| Maintenance | Aging 212 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 488,061 / month, #6,382 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: jambo-0.1.7.tar.gz
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See also jsonschema-pydantic-converter · dydantic · json-schema-to-pydantic · redis-om · jsonschema-pydantic · marshmallow-jsonschema · dataclasses-avroschema · py-automapper · sparkdantic · dataclasses-jsonschema