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.
Available eval packs for LangChain
8 packs ready to run.
Agents Langgraph
LangChain evals — Agents (LangGraph) (relift v3 InfraRed)
Chat Models And Messages
LangChain evals — Chat Models & Messages (relift v3 InfraRed)
Lcel And Runnables
LangChain evals — LCEL & Runnables (relift v3 InfraRed)
Memory And State Langgraph
Knowledge RetentionLangChain evals — Memory & State (LangGraph) (relift v3 InfraRed)
Retrieval And Vector Stores
Answer RelevanceLangChain evals — Retrieval & Vector Stores (relift v3 InfraRed)
Streaming Callbacks And Safety
LangChain evals — Streaming, Callbacks & Safety (relift v3 InfraRed)
Structured Output And Parsers
LangChain evals — Structured Output & Parsers (relift v3 InfraRed)
Tools And Tool Calling
LangChain evals — Tools & Tool Calling (relift v3 InfraRed)
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.