> ## Documentation Index
> Fetch the complete documentation index at: https://docs.emergence.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Langfuse Setup
> Step-by-step guide to deploying Langfuse and connecting it to all CRAFT solutions for LLM observability.
# Langfuse Setup Guide
This guide walks through deploying Langfuse and connecting it to your CRAFT solutions. Langfuse provides LLM call tracing, evaluation, and prompt management for Data Insights, Data Governance, and Semiconductor solutions.
## Prerequisites
* A running CRAFT deployment (any version)
* Docker and Docker Compose (for Docker deployment) or a Kubernetes cluster
* Domain name for the Langfuse instance (recommended for production)
## Step 1: Deploy Langfuse
```bash theme={null}
# Clone the Langfuse repository (provided in your deployment kit)
git clone
cd langfuse
cp .env .env.local # Edit .env.local with your configuration
```
Edit `.env.local` with your configuration:
```env theme={null}
# Required
NEXTAUTH_URL=https://langfuse.your-domain.com
NEXTAUTH_SECRET=
SALT=
# Database (auto-provisioned by compose)
DATABASE_URL=postgresql://postgres:postgres@langfuse-db:5432/langfuse
# Encryption
ENCRYPTION_KEY=
```
Start the services:
```bash theme={null}
docker compose up -d
docker compose ps # verify all services are healthy
```
Langfuse is now available at `http://localhost:3000` (or your configured domain).
```bash theme={null}
helm repo add langfuse https://langfuse.github.io/langfuse-k8s
helm repo update
# Create secrets
kubectl create secret generic langfuse-secrets \
--namespace langfuse \
--from-literal=nextauth-secret=$(openssl rand -hex 32) \
--from-literal=salt=$(openssl rand -hex 32) \
--from-literal=encryption-key=$(openssl rand -hex 32) \
--from-literal=pg-password=
# Install
helm install langfuse langfuse/langfuse \
--namespace langfuse \
--create-namespace \
--set langfuse.nextauth.url="https://langfuse.example.com" \
--set langfuse.nextauth.existingSecret=langfuse-secrets \
--set langfuse.nextauth.existingSecretKey=nextauth-secret \
--set langfuse.salt.existingSecret=langfuse-secrets \
--set langfuse.salt.existingSecretKey=salt
```
## Step 2: Create a Langfuse Project
1. Navigate to your Langfuse instance
2. Sign up for an account (or log in with the admin account)
3. Create a new **Project** for each solution you want to trace:
* `data-insights`, for Data Insights traces
* `data-governance`, for Data Governance traces
* `semiconductor`, for Semiconductor traces
4. In each project, go to **Settings → API Keys** and copy the **Public Key** and **Secret Key**
## Step 3: Store Credentials
Store Langfuse credentials as Kubernetes Secrets (or in your Secrets backend):
```bash theme={null}
# Create one secret per solution (use separate Langfuse projects for isolation):
kubectl create secret generic langfuse-api-keys \
--namespace em-runtime \
--from-literal=public-key= \
--from-literal=secret-key=
kubectl create secret generic langfuse-api-keys-dr \
--namespace em-runtime \
--from-literal=public-key= \
--from-literal=secret-key=
kubectl create secret generic langfuse-api-keys-semi \
--namespace em-runtime \
--from-literal=public-key= \
--from-literal=secret-key=
```
## Step 4: Configure Solutions
Set the Langfuse environment variables in each solution's Helm values:
### Data Insights
```yaml theme={null}
# Helm values key for em-talk2data (Data Insights solution)
em-talk2data:
extraEnvVars:
- name: LANGFUSE_HOST
value: "https://langfuse.example.com"
- name: LANGFUSE_PUBLIC_KEY
valueFrom:
secretKeyRef:
name: langfuse-api-keys
key: public-key
- name: LANGFUSE_SECRET_KEY
valueFrom:
secretKeyRef:
name: langfuse-api-keys
key: secret-key
```
### Data Governance
Same configuration, referencing the data-governance project keys:
```yaml theme={null}
# Helm values key for em-data-readiness (Data Governance solution)
em-data-readiness:
extraEnvVars:
- name: LANGFUSE_HOST
value: "https://langfuse.example.com"
- name: LANGFUSE_PUBLIC_KEY
valueFrom:
secretKeyRef:
name: langfuse-api-keys-dr
key: public-key
- name: LANGFUSE_SECRET_KEY
valueFrom:
secretKeyRef:
name: langfuse-api-keys-dr
key: secret-key
```
### Semiconductor
Apply to both the Backend API and AI Agent services:
```yaml theme={null}
# Helm values key for em-semi (Semiconductor solution)
em-semi:
extraEnvVars:
- name: LANGFUSE_HOST
value: "https://langfuse.example.com"
- name: LANGFUSE_PUBLIC_KEY
valueFrom:
secretKeyRef:
name: langfuse-api-keys-semi
key: public-key
- name: LANGFUSE_SECRET_KEY
valueFrom:
secretKeyRef:
name: langfuse-api-keys-semi
key: secret-key
```
## Step 5: Verify Traces
After deploying the configuration, trigger an LLM call in any solution:
1. In Data Insights: ask a natural language question
2. In Data Governance: run a profiling job
3. In Semiconductor: send a chat message to the AI agent
Then check the **Traces** tab in Langfuse, you should see the call appear within seconds.
## Troubleshooting
1. Verify `LANGFUSE_HOST`, `LANGFUSE_PUBLIC_KEY`, and `LANGFUSE_SECRET_KEY` are set correctly in the pod
2. Check that the Langfuse host is reachable from the pod: `kubectl exec -it -- curl -I https://langfuse.example.com`
3. Check solution logs for Langfuse callback errors
4. Verify the public key corresponds to the correct Langfuse project
1. Check pod status: `kubectl get pods -n langfuse`
2. Check worker logs: `kubectl logs -n langfuse -l app=langfuse-worker`
3. Verify the `NEXTAUTH_URL` matches the URL you're accessing