FAQs

How to...?

...add an LLM to my agent?

1. Create a LangChain configuration with your provider settings

2. Add a behavior rule that triggers the LLM action (e.g., send_to_ai)

3. Add the LangChain extension to your package/workflow

{
  "tasks": [
    {
      "actions": ["send_to_ai"],
      "id": "openai_chat",
      "type": "openai",
      "parameters": {
        "apiKey": "${eddivault:OPENAI_KEY}",
        "modelName": "gpt-4o",
        "systemMessage": "You are a helpful assistant",
        "sendConversation": "true",
        "addToOutput": "true"
      }
    }
  ]
}

See LLM Integration for the complete guide with all 12 supported providers.

---

...store API keys securely?

Use the Secrets Vault. API keys and other sensitive values are encrypted at rest using envelope encryption (AES-256-GCM + PBKDF2).

Via the Manager UI: Navigate to Secrets in the sidebar. Enter a key name and value — values are write-only and can never be retrieved through the API.

Via REST API:

curl -X PUT http://localhost:7070/secretstore/secrets/MY_API_KEY \
  -H "Content-Type: text/plain" \
  -d "sk-abc123..."

In LangChain configs, reference secrets using vault syntax:

{
  "parameters": {
    "apiKey": "${eddivault:MY_API_KEY}"
  }
}

See Secrets Vault for full documentation.

---

...use context to pass data from my app?

Send context with each message:

curl -X POST http://localhost:7070/agents/conv-123 \
  -H "Content-Type: application/json" \
  -d '{
    "input": "What is my name?",
    "context": {
      "userName": {"type": "string", "value": "John"},
      "userId": {"type": "string", "value": "user-123"}
    }
  }'

Access in templates: {context.userName}

See Passing Context Information for full documentation.

---

...set up monitoring?

EDDI exposes Prometheus metrics at /q/metrics and includes pre-built Grafana dashboards.

Quick setup with Docker Compose:

docker compose -f docker-compose.yml -f docker-compose.monitoring.yml up

Then open Grafana at http://localhost:3000 (admin/admin).

See Metrics & Monitoring for details.

---

...deploy to Kubernetes?

# One-command quickstart
kubectl apply -f https://raw.githubusercontent.com/labsai/EDDI/main/k8s/quickstart.yaml

# Or use Kustomize overlays / Helm charts for production
kubectl apply -k k8s/overlays/mongodb/

See Kubernetes for complete deployment options.

---

...start a conversation with a welcome / intro message?

You will need behavior rules and an outputset for that.

For the behavior rules, you have three possibilities (ordered by recommendation):

1) Match for the action CONVERSATION_START

{
  "behaviorGroups": [
    {
      "name": "Onboarding",
      "behaviorRules": [
        {
          "name": "Welcome",
          "actions": [
            "welcome"
          ],
          "conditions": [
            {
              "type": "actionmatcher",
              "configs": {
                "actions": "CONVERSATION_START"
              }
            }
          ]
        }
      ]
    }
  ]
}

2) Check if the triggered action has never been triggered before

{
  "behaviorGroups": [
    {
      "name": "Onboarding",
      "behaviorRules": [
        {
          "name": "Welcome",
          "actions": [
            "welcome"
          ],
          "conditions": [
            {
              "type": "actionmatcher",
              "configs": {
                "actions": "welcome",
                "occurrence": "never"
              }
            }
          ]
        }
      ]
    }
  ]
}

3) Check how often this rule has succeeded before

{
  "behaviorGroups": [
    {
      "name": "Onboarding",
      "behaviorRules": [
        {
          "name": "Welcome",
          "actions": [
            "welcome"
          ],
          "conditions": [
            {
              "type": "occurrence",
              "configs": {
                "maxTimesOccurred": "0",
                "behaviorRuleName": "Welcome"
              }
            }
          ]
        }
      ]
    }
  ]
}

Output set:

{
  "outputSet": [
    {
      "action": "welcome",
      "timesOccurred": 0,
      "outputs": [
        {
          "valueAlternatives": [
            {
              "type": "text",
              "text": "Some output here...",
              "delay": 3000
            }
          ]
        }
      ],
      "quickReplies": []
    }
  ]
}

---

...say something based on what the agent previously said?

(Think of a form-like behavior, asking a couple of questions and sending these results somewhere.)

Check whether a certain action had been triggered in the previous conversation step.

{
  "behaviorGroups": [
    {
      "name": "Onboarding",
      "behaviorRules": [
        {
          "name": "Ask for Name",
          "actions": [
            "ask_for_name"
          ],
          "conditions": [
            {
              "type": "actionmatcher",
              "configs": {
                "actions": "some_previous_action",
                "occurrence": "lastStep"
              }
            }
          ]
        }
      ]
    }
  ]
}

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