LangChain

Language support

Setup option	Python	TypeScript
Agnost AI skill	✓	✓
Agnost AI SDK	✓	✓
OpenTelemetry through LangSmith	✓	✓

Choose a setup method

Method	Use it when
Agnost AI skill	You want your coding agent to inspect the app, make the change, and verify it.
Agnost AI SDK	You want explicit control over the interaction boundaries and fields.
OpenTelemetry	The framework already emits useful spans or you operate an OTel pipeline.

Start with one method for a call path. Combining SDK tracking with framework OpenTelemetry on the same call can create duplicate interactions.

Recommended: Agnost AI skill

Recommended. Choose this path when you want your coding agent to inspect the project, select a supported transport, make the smallest instrumentation change, send a test event, and verify that it reached Agnost AI.

Install the skill once in your coding-agent environment:

npx skills add AgnostAI/skills --skill agnost-ai

Run the framework-specific prompt below from the application root. Review the resulting diff before deploying it.

Use the agnost-ai skill to add Agnost AI analytics to this LangChain or LangGraph application.
Org ID: your-org-id
Instrument the real chain, graph, or agent invocation and verify one fresh interaction.

Manual setup: Agnost AI SDK

pip install agnost

npm install agnostai

import agnost

agnost.init("your-org-id")
interaction = agnost.begin(user_id="u-42", agent_name="langchain-support", input=prompt)
try:
    result = chain.invoke({"input": prompt})
    interaction.end(output=str(result))
except Exception as exc:
    interaction.end(output=str(exc), success=False)
    raise
finally:
    agnost.shutdown()

import * as agnost from 'agnostai';

agnost.init('your-org-id');
const interaction = agnost.begin({ userId: 'u-42', agentName: 'langchain-support', input: prompt });
try {
  const result = await chain.invoke({ input: prompt });
  interaction.end(JSON.stringify(result));
} catch (error) {
  interaction.end(String(error), false);
  throw error;
} finally {
  await agnost.shutdown();
}

Manual setup: OpenTelemetry

LangChain emits OTel through LangSmith. Register the OTel exporter pointing at Agnost AI before building chains or agents, enable LangSmith’s OTel mode, then pass session_id / user_id per call.

1. Install

Already have LangSmith and an OTLP exporter installed? Skip. No setup yet?

pip install "langsmith[otel]" langchain \
            opentelemetry-sdk opentelemetry-exporter-otlp-proto-http

npm install langsmith @opentelemetry/api @opentelemetry/context-async-hooks \
            @opentelemetry/sdk-trace-base @opentelemetry/exporter-trace-otlp-proto

2. Wire LangSmith OTel pointing at Agnost AI

The provider must be registered before importing or constructing LangChain objects. Already have an OTel TracerProvider? Append Agnost AI as an additional span processor:

Python

import os
from opentelemetry import trace
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://otel.agnost.ai/v1/traces",
            headers={"X-Agnost-Org-ID": os.environ["AGNOST_ORG_ID"]},
        )
    )
)

os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_OTEL_ONLY"] = "true"

No OTel yet? Full setup:

import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://otel.agnost.ai/v1/traces",
            headers={"X-Agnost-Org-ID": os.environ["AGNOST_ORG_ID"]},
        )
    )
)
trace.set_tracer_provider(provider)

os.environ["LANGSMITH_OTEL_ENABLED"] = "true"
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_OTEL_ONLY"] = "true"

# Import LangChain AFTER the provider is registered.
from langchain_openai import ChatOpenAI

import { trace, SpanStatusCode } from '@opentelemetry/api';

process.env.LANGSMITH_TRACING = 'true';
process.env.LANGCHAIN_TRACING_V2 = 'true';
process.env.LANGSMITH_TRACING_MODE = 'otel';

const { initializeOTEL } = await import('langsmith/experimental/otel/setup');

initializeOTEL({
  exporterConfig: {
    url: 'https://otel.agnost.ai/v1/traces',
    headers: { 'X-Agnost-Org-ID': process.env.AGNOST_ORG_ID! },
  },
});

For TypeScript, langsmith/experimental/vercel is the Vercel AI SDK 7 adapter. LangChain JS/TS should use langsmith/experimental/otel/setup. For LangChain JS/TS, keep one active OTel span around the actual invoke. The LangSmith OTel translator attaches LangChain run data to that active span, and the GenAI attributes below give Agnost AI exact Chat View input/output text.

3. Pass user_id / session_id per call

result = agent.invoke(
    {"messages": [{"role": "user", "content": "Hello"}]},
    config={
        "metadata": {
            "session_id": "conv-abc123",
            "user_id": "user-42",
        },
    },
)

const tracer = trace.getTracer('langchain-ts');
const userMessage = 'Hello';

const result = await tracer.startActiveSpan('langchain.invoke', {
  attributes: {
    'langsmith.traceable': 'true',
    'langsmith.metadata.session_id': 'conv-abc123',
    'langsmith.metadata.user_id': 'user-42',
    'gen_ai.operation.name': 'chat',
    'gen_ai.system': 'langchain',
    'gen_ai.input.messages': JSON.stringify([{ role: 'user', content: userMessage }]),
  },
}, async (span) => {
  try {
    const result = await chain.invoke(
      { messages: [{ role: 'user', content: userMessage }] },
      {
        metadata: {
          session_id: 'conv-abc123',
          user_id: 'user-42',
        },
        tags: ['agnost', 'langchain-ts'],
      },
    );
    const answer = typeof result.content === 'string' ? result.content : JSON.stringify(result);
    span.setAttribute('gen_ai.output.messages', JSON.stringify([{ role: 'assistant', content: answer }]));
    span.setStatus({ code: SpanStatusCode.OK });
    return result;
  } catch (error) {
    span.recordException(error as Error);
    span.setStatus({ code: SpanStatusCode.ERROR, message: error instanceof Error ? error.message : String(error) });
    throw error;
  } finally {
    span.end();
  }
});

LangChain prefixes metadata as langsmith.metadata.*: Agnost AI reads langsmith.metadata.session_id and langsmith.metadata.user_id natively for user / session grouping. For Chat View, keep the assistant answer in the normal LangChain output: message content, generations, or a chain result wrapper such as answer, output, or result. Agnost AI stores the raw OTel payload, then collapses those common output shapes to readable text for the dashboard.

What appears in Agnost AI

Conversations grouped by session_id.
User-level analytics grouped by user_id.
Events for LangSmith OTel spans.

Verify

Run one agent.invoke, then open Events and confirm langsmith.metadata.session_id and langsmith.metadata.user_id exist.

Getting started

Agent frameworks

Voice agents

Custom applications

MCP server analytics

Platforms

Language support

Choose a setup method

Recommended: Agnost AI skill

Manual setup: Agnost AI SDK

Manual setup: OpenTelemetry

1. Install

2. Wire LangSmith OTel pointing at Agnost AI

3. Pass user_id / session_id per call

What appears in Agnost AI

Verify

Troubleshooting

References

Next steps

​Language support

​Choose a setup method

​Recommended: Agnost AI skill

​Manual setup: Agnost AI SDK

​Manual setup: OpenTelemetry

​1. Install

​2. Wire LangSmith OTel pointing at Agnost AI

​3. Pass user_id / session_id per call

​What appears in Agnost AI

​Verify

​Troubleshooting

​References

​Next steps

Language support

Choose a setup method

Recommended: Agnost AI skill

Manual setup: Agnost AI SDK

Manual setup: OpenTelemetry

1. Install

2. Wire LangSmith OTel pointing at Agnost AI

3. Pass user_id / session_id per call

What appears in Agnost AI

Verify

Troubleshooting

References

Next steps