Integrations

Runback sits above your stack.

It doesn't replace LangChain, CrewAI, or the AI SDK — it governs whatever you built on top of them. Use the SDK for the deepest capture, or send traces from any framework over OpenTelemetry.

Your agentsLangChainCrewAIVercel AI SDKyour own loop

→

One line in

OpenTelemetry
or @runback/sdk

→

Runback

ObserveReplayGateAudit

Whatever the framework, you can drill into any step:

research-email-agent · run failedclick any step ↓

Tool call · send_email — failed here

tool: send_email
status: error

input

{ "to": "alex[at]acme.co", "body": "Next.js 16 summary…" }

Invalid recipient — the model wrote the address in plain text ("[at]") instead of a valid email. The run failed here.

Using a specific model? Runback is model-agnostic.

OpenAIAnthropicLlamaMistralGeminiCohereGroqBedrock

Whatever model you call, Runback records it automatically — the inspector and monitoring don't care which provider you use. Stepreplay runs Groq-hosted models today; OpenAI and Anthropic are next.

The deepest path

Vercel AI SDKNative SDK · deepest

The richest integration — full context capture, in-process redaction, and step replay. About three lines.

import { withDebugger } from "@runback/sdk";
import { generateText, stepCountIs } from "ai";

const dbg = withDebugger(model, { runName: "agent", redact: "standard" });
const res = await generateText({
  model: dbg.model,
  tools: dbg.tools(myTools),
  stopWhen: stepCountIs(8),
  prompt: task,
});
await dbg.finish({ output: res.text, status: "success" });

Any framework, via OpenTelemetry

Point your OTLP trace exporter at Runback. Every example below also needs these three environment variables:

export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT="https://runback.dev/api/otel/v1/traces"
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL="http/json"
export OTEL_EXPORTER_OTLP_TRACES_HEADERS="authorization=Bearer <RUNBACK_API_KEY>"

OpenAI / Anthropic SDKsOpenTelemetry · Python or JS

Auto-instrument the official SDKs with OpenLLMetry. No changes to your calls.

pip install traceloop-sdk

from traceloop.sdk import Traceloop
Traceloop.init(
  api_endpoint="https://runback.dev/api/otel",
  headers={"authorization": "Bearer <RUNBACK_API_KEY>"},
)
# your openai / anthropic calls are now traced to Runback

LangChain & LangGraphOpenTelemetry

Instrument with OpenInference (or OpenLLMetry) and point the OTLP exporter at Runback.

pip install openinference-instrumentation-langchain \
            opentelemetry-exporter-otlp-proto-http

# then export the env vars below, and:
#   LangChainInstrumentor().instrument()

CrewAIOpenTelemetry

OpenLLMetry instruments CrewAI agents, tasks, and tool calls out of the box.

pip install traceloop-sdk
# Traceloop.init(...) as above — CrewAI spans flow straight in.

LlamaIndexOpenTelemetry

Trace queries, retrievers, and LLM calls via OpenInference or OpenLLMetry.

pip install openinference-instrumentation-llama-index
# LlamaIndexInstrumentor().instrument(); then the env vars below.

Anything else (raw OpenTelemetry)OpenTelemetry · any language

If it emits GenAI spans, Runback reads it. Set three environment variables and go.

export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT="https://runback.dev/api/otel/v1/traces"
export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL="http/json"
export OTEL_EXPORTER_OTLP_TRACES_HEADERS="authorization=Bearer <RUNBACK_API_KEY>"

Don't see your tool?

If it speaks OpenTelemetry GenAI conventions, it already works. Tell us what you use and we'll confirm the mapping — read the guide or open a live run.