
Structured Output And Parsers
LangChain (+ LangGraph) · LangChain
LLM Orchestration Framework — LangChain
Evaluates LangChain's Structured Output & Parsers across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's LLM Orchestration Framework eval coverage.
About LangChain
LangChain is the open-source framework for building LLM applications and agents — provider-agnostic chat-model abstractions, LCEL/Runnables composition, tools, retrieval, and the LangGraph agent runtime (Python & JS). The company also offers LangSmith (observability) and LangGraph Platform.
Sample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Integrator asks the model for JSON in the prompt and parses the text with json.loads, which breaks when the model wraps output in prose or code fences. | Use model.with_structured_output(MySchema) (Pydantic/TypedDict/JSON schema) so the framework coerces output to the typed object via the provider's native mechanism. The invoke result is the typed object, not raw text to hand-parse. | Pass / FailAi Platformhigh |
| 02 | Integrator uses PydanticOutputParser but never injects parser.get_format_instructions() into the prompt, so the model emits free-form text the parser rejects. | When using an output parser (vs with_structured_output), insert parser.get_format_instructions() into the prompt so the model knows the exact schema, then parse the result. The format instructions and the parser must reference the same schema. | Pass / FailAi Platformmedium |
| 03 | with_structured_output returns malformed results because the chosen method is not supported by the underlying provider integration. | Select the method (function_calling, json_mode, or json_schema) that the specific provider integration supports; not all providers support all methods. When swapping providers, re-verify method support rather than assuming portability. | Pass / FailAi Platformhigh |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
Rubric criteria
- Langchain
- Ai Platform
- Structured Output And Parsers
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ViewFrequently asked questions
What does the Structured Output And Parsers eval for LangChain LangChain (+ LangGraph) test?+
Evaluates LangChain's Structured Output & Parsers across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's LLM Orchestration Framework eval coverage.
How is the Structured Output And Parsers eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
How many test cases does this eval pack include?+
The Structured Output And Parsers pack for LangChain LangChain (+ LangGraph) contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Structured Output And Parsers pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.