---

name: provider-plugins description: "Create, package, register, test, and troubleshoot custom LangExtract provider plugins." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

LangExtract Provider Plugins

Use this sub-skill when a task is about adding a new LangExtract model backend as an external Python package, making it discoverable through the provider router, or debugging plugin discovery/registration/schema integration.

Route elsewhere

• Built-in Gemini, OpenAI, Ollama, model IDs, API keys, provider kwargs, or batch configuration: use ../providers/SKILL.md.
• Prompt descriptions, few-shot examples, output schema design for extraction tasks, chunking, resolver, or tokenizer behavior: use ../extraction/SKILL.md.
• Saving JSONL, loading AnnotatedDocument results, or HTML visualization: use ../visualization/SKILL.md.
• Community provider registry maintenance is out of scope unless explicitly requested; this sub-skill records it as an omission rather than bundling maintainer-only registry validation.

Authoring workflow

1. Decide whether the user needs a plugin at all. If they only need an existing Gemini/OpenAI/Ollama backend or provider kwargs, route to ../providers/SKILL.md.
2. Create an external package scaffold, preferably with the bundled generator:

   python scripts/create_provider_plugin.py MyProvider --with-schema --patterns '^my-model' --output-dir ./plugins
   

   The generator writes a package under the selected output directory and does not install anything unless --install-and-test is explicitly passed.
3. Register the plugin in pyproject.toml with [project.entry-points."langextract.providers"] pointing to a package object that imports the provider class.
4. Implement a provider class that inherits langextract.core.base_model.BaseLanguageModel and whose infer() yields one sequence of langextract.core.types.ScoredOutput objects for each prompt.
5. Register model-ID regex patterns with @router.register(...). Use narrow patterns and explicit priority choices to avoid conflicts with built-ins.
6. If structured output is required, implement a BaseSchema subclass plus get_schema_class(), from_examples(), to_provider_config(), requires_raw_output, and apply_schema(None) handling.
7. Validate in an isolated user environment: install the plugin, call langextract.providers.load_plugins_once(), inspect router.list_entries(), resolve by model ID and explicit provider name, and run a mock or credential-gated smoke.

See references/plugin-authoring.md for the detailed package/API/schema contract and references/troubleshooting.md for discovery, conflict, dependency, and schema failure modes.

Required safety posture

• Keep plugin packages external to LangExtract; do not edit LangExtract core unless the user is doing maintainer work.
• Do not embed API keys or machine-specific paths in generated plugin files.
• Treat live provider calls as optional credentialed smoke tests; registration, routing, schema, and mock inference can be validated without network access.
• Do not run original LangExtract native tests/examples from this sub-skill. Native evidence candidates for later verification are tests/provider_plugin_test.py, tests/registry_test.py, and examples/custom_provider_plugin/test_example_provider.py.