Eval directory
Evals for LangChain
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for LangChain AI products.
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.
How complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
8/8 packs
Scoring metrics
0/8 packs
Judge rubrics
8/8 packs
Use-case maps
0/8 packs
Pack context
8/8 packs
Available eval packs for LangChain
8 packs ready to run.
Agents Langgraph
Evaluates LangChain's Agents (LangGraph) 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.
Chat Models And Messages
Evaluates LangChain's Chat Models & Messages 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.
Lcel And Runnables
Evaluates LangChain's LCEL & Runnables 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.
Memory And State Langgraph
Knowledge RetentionEvaluates LangChain's Memory & State (LangGraph) 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.
Retrieval And Vector Stores
Answer RelevanceEvaluates LangChain's Retrieval & Vector Stores 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.
Streaming Callbacks And Safety
Evaluates LangChain's Streaming, Callbacks & Safety across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's LLM Orchestration Framework eval coverage.
Structured Output And Parsers
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.
Tools And Tool Calling
Evaluates LangChain's Tools & Tool Calling 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.
Why eval LangChain AI
LangChain's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.
The Corsac eval library for LangChain measures four dimensions teams care about most when deploying ai platform agents:
- Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
- Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
- Safety gates — does it escalate or refuse when it should, and only then?
- Operator quality — does it preserve analyst trust by surfacing the right context at the right time?
Every eval pack above is hand-authored against LangChain's public product surface and runnable in Corsac with your own data.