@aerograph/adapter-langchain

This adapter bridges LangChain.js workflows into the AeroGraph.

Deterministic Mapping

LangChain's complex callback hierarchy is deterministically mapped to the minimal Flight Recorder contracts as follows:

| LangChain Callback | AFR Event Kind | Metadata Mapped | |---|---|---| | handleLLMStart | prompt | messages/prompts, model.name, parentSpanId, runId -> spanId | | handleLLMEnd | response | generations, streaming metrics, usage tokens, durationMs, runId -> spanId | | handleLLMError | error | error message, durationMs, runId -> spanId | | handleToolStart | tool_call | input string/JSON, runId -> spanId | | handleToolEnd | tool_result | output, durationMs, runId -> spanId | | handleToolError | error | error message, durationMs, runId -> spanId | | handleChainEnd | note | emits payload.event = "chain_end" and includes output key summary |

Installation

npm install @aerograph/adapter-langchain @aerograph/sdk

(Requires Node.js >= 18.18.0)

Quick Start

The adapter provides a callback handler that you inject into your LangChain invocations.

import { FlightRecorder } from "@aerograph/sdk";
import { AeroGraphCallbackHandler } from "@aerograph/adapter-langchain";
import { ChatOpenAI } from "@langchain/openai";

const recorder = new FlightRecorder({
  endpoint: "http://localhost:4317",
  actor: { id: "my-langchain-agent" }
});

const handler = new AeroGraphCallbackHandler(recorder);
const model = new ChatOpenAI({ modelName: "gpt-4" });

// The handler automatically maps LangChain callbacks to AeroGraph events
await model.invoke("Hello, how are you?", {
  callbacks: [handler]
});

Supported Features

• LLM Call Tracking (Prompts & Responses)
• Streaming Telemetry (TTFT, tokens/sec)
• RAG Retriever Document Payloads
• Tool Calls & Results
• LangGraph State Snapshots

License

Apache-2.0 | handleAgentAction| (ignored) | Caught by tool/llm events |

For Phase 1 MVP, we focus strictly on LLMs and Tools plus lightweight chain boundary notes to keep the graph comprehensible.