LangChain
For LangChainAI Platform

Streaming Callbacks And Safety

LangChain (+ LangGraph) · LangChain

LLM Orchestration Framework — LangChain

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.

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.

Employees

~200

Industry

Agent Framework

Headquarters

San Francisco, CA

Sample tests· showing 3 of 10

#InputExpected behaviorCheck
01

Integrator wants token-level events from a complex chain but only uses .stream, which yields chain output chunks, not granular per-node events.

Use astream_events (v2) to receive granular events (on_chat_model_stream for tokens, on_tool_start/on_tool_end, on_chain_*) across the whole chain. Filter by event type/name; do not conflate the coarse .stream output with the event stream.

Pass / FailAi Platformmedium
02

While streaming, the integrator tries to execute a tool call from the first chunk, but the tool_calls arguments arrive incrementally and are incomplete.

Accumulate AIMessageChunks (they add together) until the tool_calls / tool_call_chunks are fully assembled before executing. Do not act on partial tool-call arguments; wait for the complete, parsed tool_calls on the aggregated message.

Pass / FailAi Platformhigh
03

Integrator subclasses BaseCallbackHandler and does blocking network I/O inside on_llm_new_token, stalling token streaming.

Keep callback handler methods fast and non-blocking; for async chains use the async handler methods. Offload heavy work (DB writes, network) outside the hot streaming path. A callback must not throw and abort the run unless that is the intent.

Pass / FailAi Platformmedium

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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
  • Streaming Callbacks And Safety

Recommended for

LangChain (+ LangGraph)LangChain customers

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Frequently asked questions

What does the Streaming Callbacks And Safety eval for LangChain LangChain (+ LangGraph) test?+

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.

How is the Streaming Callbacks And Safety 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 Streaming Callbacks And Safety pack for LangChain LangChain (+ LangGraph) contains 10 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 Streaming Callbacks And Safety 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.