Developer Quickstart Guide

Version: 6.0.0

This guide helps developers quickly understand EDDI's architecture and start building agents.

Understanding EDDI in 5 Minutes

What EDDI Is

EDDI is middleware for conversational AI—it sits between your app and AI services (OpenAI, Claude, etc.), providing:

Orchestration: Control when and how LLMs are called
Business Logic: IF-THEN rules for decision-making
State Management: Maintain conversation history and context
API Integration: Call external REST APIs from agent logic

Key Concept: The Lifecycle Pipeline

Every user message goes through a pipeline of tasks:

Input → Parser → Rules → API/LLM → Output

Each task transforms the Conversation Memory (a state object containing everything about the conversation).

Agent Composition

Agents aren't code—they're JSON configurations:

Agent (list of packages)
  └─ Workflow (list of extensions)
      ├─ Behavior Rules (.behavior.json)
      ├─ HTTP Calls (.httpcalls.json)
      ├─ LangChain (.langchain.json)
      └─ Output Templates (.output.json)

Quick Setup

Prerequisites

Java 25
Maven 3.8.4
MongoDB 6.0+
Docker (optional, recommended)

Run with Docker (Easiest)

# Clone repo
git clone https://github.com/labsai/EDDI.git
cd EDDI

# Start EDDI + MongoDB
docker-compose up

# Access dashboard
open http://localhost:7070

Run from Source

# Clone repo
git clone https://github.com/labsai/EDDI.git
cd EDDI

# Start MongoDB (or use Docker)
# On Mac: brew services start mongodb-community
# On Linux: sudo systemctl start mongod

# Run EDDI in dev mode
./mvnw compile quarkus:dev

# Access dashboard
open http://localhost:7070

💡 Secrets Vault: If you plan to store API keys through the Manager UI or use ${eddivault:...} references, set the vault master key first:
export EDDI_VAULT_MASTER_KEY=my-dev-passphrase   # Linux/macOS
$env:EDDI_VAULT_MASTER_KEY = "my-dev-passphrase"  # Windows PowerShell
Without this, the vault is disabled and secret endpoints return HTTP 503. Any passphrase works for local dev. See Secrets Vault for full details.

Configuring AI Tools

If you plan to use the Web Search or Weather tools in your agents, you need to set up API keys in your environment or application.properties.

Web Search (Google):

eddi.tools.websearch.provider=google
eddi.tools.websearch.google.api-key=...
eddi.tools.websearch.google.cx=...

Weather (OpenWeatherMap):

eddi.tools.weather.openweathermap.api-key=...

See LangChain Documentation for details.

Your First Agent (via API)

1. Create a Dictionary

Dictionaries define what users can say:

curl -X POST http://localhost:7070/regulardictionarystore/regulardictionaries \
  -H "Content-Type: application/json" \
  -d '{
    "words": [
      {
        "word": "hello",
        "expressions": "greeting(hello)",
        "frequency": 0
      },
      {
        "word": "hi",
        "expressions": "greeting(hi)",
        "frequency": 0
      }
    ],
    "phrases": []
  }'

Response: Dictionary ID (e.g., eddi://ai.labs.parser.dictionaries.regular/regulardictionarystore/regulardictionaries/abc123?version=1)

2. Create Behavior Rules

Rules define what the agent does:

curl -X POST http://localhost:7070/behaviorstore/behaviorsets \
  -H "Content-Type: application/json" \
  -d '{
    "behaviorGroups": [
      {
        "name": "Greetings",
        "behaviorRules": [
          {
            "name": "Welcome",
            "conditions": [
              {
                "type": "inputmatcher",
                "configs": {
                  "expressions": "greeting(*)",
                  "occurrence": "currentStep"
                }
              }
            ],
            "actions": ["welcome_action"]
          }
        ]
      }
    ]
  }'

Response: Behavior set ID

3. Create Output Templates

curl -X POST http://localhost:7070/outputstore/outputsets \
  -H "Content-Type: application/json" \
  -d '{
    "outputSet": [
      {
        "action": "welcome_action",
        "timesOccurred": 0,
        "outputs": [
          {
            "valueAlternatives": [
              "Hello! How can I help you today?"
            ]
          }
        ]
      }
    ]
  }'

Response: Output set ID

4. Create a Workflow

Workflows bundle extensions together:

curl -X POST http://localhost:7070/packagestore/packages \
  -H "Content-Type: application/json" \
  -d '{
    "packageExtensions": [
      {
        "type": "eddi://ai.labs.parser.dictionaries.regular",
        "extensions": {
          "uri": "eddi://ai.labs.regulardictionary/regulardictionarystore/regulardictionaries/abc123?version=1"
        }
      },
      {
        "type": "eddi://ai.labs.behavior",
        "extensions": {
          "uri": "eddi://ai.labs.behavior/behaviorstore/behaviorsets/def456?version=1"
        },
        "config": {
          "appendActions": true
        }
      },
      {
        "type": "eddi://ai.labs.output",
        "extensions": {
          "uri": "eddi://ai.labs.output/outputstore/outputsets/ghi789?version=1"
        }
      }
    ]
  }'

Response: Workflow ID

5. Create an Agent

curl -X POST http://localhost:7070/agentstore/agents \
  -H "Content-Type: application/json" \
  -d '{
    "packages": [
      "eddi://ai.labs.package/packagestore/packages/xyz123?version=1"
    ]
  }'

Response: Agent ID (e.g., agent-abc-123)

6. Deploy the Agent

curl -X POST "http://localhost:7070/administration/production/deploy/agent-abc-123?version=1"

7. Chat with Your Agent

# Start conversation
curl -X POST http://localhost:7070/agents/agent-abc-123/start \
  -H "Content-Type: application/json" \
  -d '{"input": "hello"}'

# Response includes conversationId
# {
#   "conversationId": "conv-123",
#   "conversationState": "READY",
#   "conversationOutputs": [
#     {"output": ["Hello! How can I help you today?"]}
#   ]
# }

# Continue conversation
curl -X POST http://localhost:7070/agents/conv-123 \
  -H "Content-Type: application/json" \
  -d '{"input": "hi"}'

Adding an LLM (OpenAI Example)

1. Create LangChain Configuration

curl -X POST http://localhost:7070/langchainstore/langchains \
  -H "Content-Type: application/json" \
  -d '{
    "tasks": [
      {
        "actions": ["send_to_ai"],
        "id": "openai_chat",
        "type": "openai",
        "description": "OpenAI ChatGPT integration",
        "parameters": {
          "apiKey": "your-openai-api-key",
          "modelName": "gpt-4o",
          "temperature": "0.7",
          "systemMessage": "You are a helpful assistant",
          "sendConversation": "true",
          "addToOutput": "true"
        }
      }
    ]
  }'

2. Add LangChain to Workflow

Add this extension to your package:

{
  "type": "eddi://ai.labs.llm",
  "extensions": {
    "uri": "eddi://ai.labs.llm/langchainstore/langchains/langchain-id?version=1"
  }
}

3. Create Behavior Rule to Trigger LLM

{
  "name": "Ask AI",
  "conditions": [
    {
      "type": "inputmatcher",
      "configs": {
        "expressions": "question(*)",
        "occurrence": "currentStep"
      }
    }
  ],
  "actions": ["send_to_ai"]
}

Now when users ask questions, the LLM is automatically called!

Understanding the Flow

Let's trace what happens when a user says "hello":

1. API Request

POST /agents/agent-abc-123/start
{"input": "hello"}

2. RestAgentEngine

Validates agent ID
Creates/loads conversation memory
Submits to ConversationCoordinator

3. ConversationCoordinator

Ensures sequential processing (no race conditions)
Queues message for this conversation

4. LifecycleManager Executes Pipeline

Parser Task:

Input: "hello"
→ Parses using dictionary
→ Output: expressions = ["greeting(hello)"]
→ Stores in memory

Behavior Rules Task:

Reads: expressions = ["greeting(hello)"]
→ Evaluates rules
→ Rule matches: "if greeting(*) then welcome_action"
→ Output: actions = ["welcome_action"]
→ Stores in memory

Output Task:

Reads: actions = ["welcome_action"]
→ Looks up output template for "welcome_action"
→ Output: "Hello! How can I help you today?"
→ Stores in memory

5. Save & Return

Memory saved to MongoDB
Response returned to user

Key Architectural Components

IConversationMemory

The state object passed through the pipeline:

IConversationMemory memory = ...;

// Read user input
String input = memory.getCurrentStep().getLatestData("input").getResult();

// Store parsed data
memory.getCurrentStep().storeData(
    dataFactory.createData("expressions", expressions)
);

// Access conversation properties
String userName = memory.getConversationProperties().get("userName");

ILifecycleTask

Interface all tasks implement:

public class MyTask implements ILifecycleTask {
    @Override
    public void execute(IConversationMemory memory, Object component) {
        // 1. Read from memory
        String input = memory.getCurrentStep().getLatestData("input").getResult();

        // 2. Process
        String result = process(input);

        // 3. Write to memory
        memory.getCurrentStep().storeData(
            dataFactory.createData("myResult", result)
        );
    }
}

ConversationCoordinator

Ensures messages are processed in order:

// Messages for same conversation execute sequentially
coordinator.submitInOrder(conversationId, () -> {
    processMessage(memory, input);
    return null;
});

Common Patterns

Pattern 1: Conditional LLM Invocation

Only call LLM for complex queries:

{
  "behaviorRules": [
    {
      "name": "Simple Greeting",
      "conditions": [
        { "type": "inputmatcher", "configs": { "expressions": "greeting(*)" } }
      ],
      "actions": ["simple_greeting"]
    },
    {
      "name": "Complex Question",
      "conditions": [
        { "type": "inputmatcher", "configs": { "expressions": "question(*)" } }
      ],
      "actions": ["send_to_ai"]
    }
  ]
}

Pattern 2: API Call Before LLM

Fetch data, then ask LLM to format it:

{
  "behaviorRules": [
    {
      "name": "Weather Query",
      "conditions": [
        {
          "type": "inputmatcher",
          "configs": { "expressions": "entity(weather)" }
        }
      ],
      "actions": ["httpcall(weather-api)", "send_to_ai"]
    }
  ]
}

The LLM receives the API response in memory and can format it naturally.

Pattern 3: Context-Aware Responses

Use context passed from your app:

curl -X POST http://localhost:7070/agents/agent-abc-123/start \
  -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 output template:

Hello {context.userName}!

Next Steps

Learn More

Architecture Overview - Deep dive into design
Behavior Rules - Master decision logic
HTTP Calls - Integrate external APIs
LangChain Integration - Configure LLMs
Agent Father Deep Dive - Real-world example

Use the Dashboard

Visit http://localhost:7070 to:

Create agents visually
Test conversations interactively
Browse configurations
Monitor deployments

Explore Examples

Check the examples/ folder for:

Weather agent (API integration)
Support agent (multi-turn conversations)
E-commerce agent (context management)

Build Your Own Task

Create a custom lifecycle task:

@ApplicationScoped
public class MyCustomTask implements ILifecycleTask {
    @Override
    public String getId() {
        return "ai.labs.mycompany.customtask";
    }

    @Override
    public String getType() {
        return "custom_processing";
    }

    @Override
    public void execute(IConversationMemory memory, Object component) {
        // Your logic here
    }
}

Troubleshooting

Agent doesn't respond

Check deployment status: GET /administration/deploy/{agentId}
Check conversation state: GET /conversationstore/conversations/{conversationId}
Check logs for errors

Rules not matching

Verify dictionary expressions match your input
Check rule conditions are correct
Use occurrence: "anyStep" to match across conversation

LLM not being called

Ensure behavior rule triggers the LLM action
Check LangChain configuration is in the package
Verify API key is correct

Memory not persisting

Ensure MongoDB is running
Check connection string in config
Use correct scope (conversation not step)

Getting Help

Documentation: https://github.com/labsai/EDDI/tree/main/docs
GitHub: https://github.com/labsai/EDDI
Issues: https://github.com/labsai/EDDI/issues

Summary

EDDI's power comes from its configurable pipeline architecture:

Agents are JSON configurations, not code
Everything flows through Conversation Memory
Tasks are pluggable and reusable
LLMs are orchestrated, not just proxied

Start simple, then add complexity as needed. The architecture scales from basic agents to sophisticated multi-API workflows.

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Last updated 17 hours ago

Was this helpful?

hashtagUnderstanding EDDI in 5 Minutes

hashtagWhat EDDI Is

hashtagKey Concept: The Lifecycle Pipeline

hashtagAgent Composition

hashtagQuick Setup

hashtagPrerequisites

hashtagRun with Docker (Easiest)

hashtagRun from Source

hashtagConfiguring AI Tools

hashtagYour First Agent (via API)

hashtag1. Create a Dictionary

hashtag2. Create Behavior Rules

hashtag3. Create Output Templates

hashtag4. Create a Workflow

hashtag5. Create an Agent

hashtag6. Deploy the Agent

hashtag7. Chat with Your Agent

hashtagAdding an LLM (OpenAI Example)

hashtag1. Create LangChain Configuration

hashtag2. Add LangChain to Workflow

hashtag3. Create Behavior Rule to Trigger LLM

hashtagUnderstanding the Flow

hashtag1. API Request

hashtag2. RestAgentEngine

hashtag3. ConversationCoordinator

hashtag4. LifecycleManager Executes Pipeline

hashtag5. Save & Return

hashtagKey Architectural Components

hashtagIConversationMemory

hashtagILifecycleTask

hashtagConversationCoordinator

hashtagCommon Patterns

hashtagPattern 1: Conditional LLM Invocation

hashtagPattern 2: API Call Before LLM

hashtagPattern 3: Context-Aware Responses

hashtagNext Steps

hashtagLearn More

hashtagUse the Dashboard

hashtagExplore Examples

hashtagBuild Your Own Task

hashtagTroubleshooting

hashtagAgent doesn't respond

hashtagRules not matching

hashtagLLM not being called

hashtagMemory not persisting

hashtagGetting Help

hashtagSummary