@raindrop-ai/langchain

Raindrop integration for LangChain. Automatically captures LLM calls, tool usage, chains, retrievers, and agent actions via LangChain's callback system.

Installation

npm install @raindrop-ai/langchain @langchain/core

Usage

import { createRaindropLangChain } from "@raindrop-ai/langchain";
import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";

const raindrop = createRaindropLangChain({
  writeKey: "your-write-key",
  userId: "user-123",
});

const model = new ChatOpenAI({ model: "gpt-4o" });

const result = await model.invoke(
  [new HumanMessage("Hello!")],
  { callbacks: [raindrop.handler] },
);

await raindrop.flush();

What gets captured

• LLM calls: model name, input, output, token usage
• Tool calls: tool name, input arguments, output
• Chains: execution spans with parent-child nesting
• Retrievers: query and document count
• Errors: captured with OTLP error status

Options

| Option | Type | Default | Description | |--------|------|---------|-------------| | writeKey | string | - | Raindrop API write key (omit to disable telemetry) | | endpoint | string | https://api.raindrop.ai/v1/ | API endpoint | | userId | string | - | Associate all events with a user | | convoId | string | - | Group events into a conversation | | projectId | string | - | Route events to a specific project (slug); omit for the default Production project | | debug | boolean | false | Enable verbose logging | | traceChains | boolean | true | Create spans for chain execution | | traceRetrievers | boolean | true | Create spans for retriever calls | | filterLangGraphInternals | boolean | true | Filter LangGraph-internal chain events and deduplicate LLM callbacks | | maxTextFieldChars | number | 1000000 | Per-field cap for event input/output and serialized span payloads, enforced before/during serialization (truncated values end with ...[truncated by raindrop]; a stricter OTEL_SPAN_ATTRIBUTE_VALUE_LENGTH_LIMIT env var is honored) |

Projects

If your org has multiple projects, route events to a specific one by passing its slug as projectId:

const raindrop = createRaindropLangChain({
  writeKey: "your-write-key",
  projectId: "support-prod",
});

This sets the X-Raindrop-Project-Id header on every event. Omit it (or pass "default") to use your org's default Production project — the existing behavior. Single-project orgs need nothing new.

LangGraph Support

Works with LangGraph out of the box. The handler automatically:

• Filters LangGraph-internal chain events (graph executor, __start__, __end__, channel nodes)
• Deduplicates LLM callbacks that LangGraph fires multiple times with the same runId

Pass the handler both at graph.invoke() and inside your LLM node. See examples/langchain-langgraph-basic/ for a full example.

Best practices:

• Pass callbacks to the model inside your node, not to graph.invoke() — this avoids duplicate events from LangGraph's internal callback propagation
• Create a new handler instance per request in server environments

LangSmith Coexistence

Raindrop and LangSmith can run simultaneously — both receive the same LangChain callbacks independently. Set LANGSMITH_TRACING=false to disable LangSmith if you only want Raindrop.

Testing

pnpm test

Tests use MSW to intercept HTTP requests — no real LLM calls are made.