# Agent Father: LangChain Tools Guide

## Quick Start

When creating a new connector agent via Agent Father, you'll now be asked three additional questions about LangChain task features:

### 1. Enable Built-in Tools

**Question:** "Would you like to enable built-in tools (calculator, websearch, datetime, weather, etc.) for this agent?"

**Options:**

* **Yes, enable tools** - Activates AI agent mode with access to built-in tools
* **No, just simple chat** - Keeps simple chat mode (default)

### 2. Tools Whitelist (only if tools enabled)

**Question:** "Which specific tools would you like to enable?"

**Quick Reply Options:**

* **Enable all tools** - Makes all 8 tools available
* **Calculator & Web Search** - Enables only `calculator` and `websearch`
* **Calculator, Web, DateTime** - Enables `calculator`, `websearch`, and `datetime`

**Manual Entry:** You can also type a custom JSON array:

```json
["calculator", "websearch", "datetime", "weather"]
```

### 3. Conversation History Limit

**Question:** "How many conversation turns would you like to include in the context?"

**Quick Reply Options:**

* **10 turns (recommended)** - Balances context and performance
* **20 turns** - More context for complex conversations
* **Unlimited (-1)** - All conversation history (may impact performance)

**Manual Entry:** Type any number:

* `-1` = unlimited history
* `0` = no history
* `10-20` = recommended range

***

## Available Built-in Tools

| Tool                | Identifier       | Description                                     | Example Use Case                     |
| ------------------- | ---------------- | ----------------------------------------------- | ------------------------------------ |
| **Calculator**      | `calculator`     | Safe math evaluation (sandboxed)                | "What's 15% tip on $84.50?"          |
| **Date/Time**       | `datetime`       | Get current date, time, timezone                | "What time is it in Tokyo?"          |
| **Web Search**      | `websearch`      | Search the web (Wikipedia, news)                | "What's the weather forecast today?" |
| **Data Formatter**  | `dataformatter`  | Format JSON, CSV, XML                           | "Convert this JSON to CSV"           |
| **Web Scraper**     | `webscraper`     | Extract content from web pages (SSRF-protected) | "Get the content from example.com"   |
| **Text Summarizer** | `textsummarizer` | Summarize long text                             | "Summarize this article"             |
| **PDF Reader**      | `pdfreader`      | Extract text from PDF URLs (SSRF-protected)     | "Read this PDF file"                 |
| **Weather**         | `weather`        | Get weather information                         | "What's the weather in Paris?"       |

***

## Tool Configuration (Server-Side)

Some tools require API keys or external configuration to function. These are configured via **Environment Variables** or `application.properties` on the EDDI server, not in the agent configuration.

### Web Search Tool

By default, the tool uses **DuckDuckGo** (HTML scraping), which requires no configuration.

To use **Google Custom Search** (more reliable/structured), configure these properties:

```properties
# In application.properties
eddi.tools.websearch.provider=google
eddi.tools.websearch.google.api-key=YOUR_GOOGLE_API_KEY
eddi.tools.websearch.google.cx=YOUR_CUSTOM_SEARCH_ENGINE_ID
```

**Docker Environment Variables:**

* `EDDI_TOOLS_WEBSEARCH_PROVIDER=google`
* `EDDI_TOOLS_WEBSEARCH_GOOGLE_API_KEY=...`
* `EDDI_TOOLS_WEBSEARCH_GOOGLE_CX=...`

### Weather Tool

The weather tool uses **OpenWeatherMap**. You must provide an API key:

```properties
# In application.properties
eddi.tools.weather.openweathermap.api-key=YOUR_OWM_API_KEY
```

**Docker Environment Variables:**

* `EDDI_TOOLS_WEATHER_OPENWEATHERMAP_API_KEY=...`

***

## Configuration Examples

### Example 1: Customer Support Agent with Tools

```json
{
  "enableBuiltInTools": true,
  "builtInToolsWhitelist": ["calculator", "websearch", "datetime"],
  "conversationHistoryLimit": 15
}
```

**Use Case:** Customer support agent that can calculate discounts, search for product info, and provide time-based responses.

### Example 2: Simple Chat Agent (No Tools)

```json
{
  "enableBuiltInTools": false,
  "builtInToolsWhitelist": [],
  "conversationHistoryLimit": 10
}
```

**Use Case:** Basic chat agent for general conversation without external tool access.

### Example 3: Research Assistant with All Tools

```json
{
  "enableBuiltInTools": true,
  "builtInToolsWhitelist": [],
  "conversationHistoryLimit": 20
}
```

**Use Case:** Research assistant with access to all tools and extended conversation memory.

### Example 4: Math Tutor (Calculator Only)

```json
{
  "enableBuiltInTools": true,
  "builtInToolsWhitelist": ["calculator"],
  "conversationHistoryLimit": 10
}
```

**Use Case:** Math tutor that can perform calculations but doesn't need web access.

***

## Tool Execution Pipeline

All tool invocations flow through a unified pipeline that provides enterprise-grade controls. These settings can be added to the `langchain.json` task configuration:

| Setting                    | Type    | Default   | Description                                  |
| -------------------------- | ------- | --------- | -------------------------------------------- |
| `enableRateLimiting`       | boolean | `true`    | Token-bucket rate limiting per tool          |
| `defaultRateLimit`         | int     | `100`     | Default calls/minute for each tool           |
| `toolRateLimits`           | map     | `{}`      | Per-tool overrides, e.g. `{"websearch": 30}` |
| `enableToolCaching`        | boolean | `true`    | Cache identical tool calls                   |
| `enableCostTracking`       | boolean | `true`    | Track cost per conversation                  |
| `maxBudgetPerConversation` | number  | unlimited | Maximum spend per conversation               |

### Example: Restrict web search rate and set a budget

```json
{
  "enableBuiltInTools": true,
  "builtInToolsWhitelist": ["calculator", "websearch"],
  "enableRateLimiting": true,
  "defaultRateLimit": 100,
  "toolRateLimits": { "websearch": 20 },
  "enableCostTracking": true,
  "maxBudgetPerConversation": 2.0
}
```

***

## Security

Tools that accept URLs (PDF Reader, Web Scraper) are protected against **SSRF** (Server-Side Request Forgery):

* Only `http://` and `https://` URLs accepted
* Private / internal IP ranges blocked (127.x, 10.x, 192.168.x, 172.16-31.x)
* Cloud metadata endpoints blocked (169.254.169.254, metadata.google.internal)
* Internal hostnames rejected (localhost, \_.local, \_.internal)

The **Calculator** tool uses a sandboxed recursive-descent parser — no script engine, no code injection risk.

See the [Security documentation](https://docs.labs.ai/security-and-compliance/security) for full details.

***

## Best Practices

### When to Enable Tools

✅ **Enable tools when:**

* Agent needs to perform calculations
* Agent should search for current information
* Agent needs to access external data
* You want agentic behavior (autonomous tool use)

❌ **Keep tools disabled when:**

* Simple conversational agent
* Controlled, predictable responses needed
* Security/privacy concerns about external access
* Cost optimization (tools may increase API costs)

### Choosing History Limit

* **Short conversations (0-5 turns):** Use for FAQ agents or single-turn interactions
* **Medium conversations (10-15 turns):** Recommended for most use cases
* **Long conversations (20+ turns):** Use for complex problem-solving or tutoring
* **Unlimited (-1):** Only for special cases; may cause performance issues

### Tools Whitelist Strategy

1. **Start specific:** Begin with only the tools you need
2. **Add gradually:** Enable more tools as requirements grow
3. **Monitor usage:** Check which tools are actually being used
4. **Security first:** Only enable tools that match your security requirements

***

## Provider Support

All LLM providers in Agent Father now support these features:

| Provider         | Tools Support | History Limit | Notes                                      |
| ---------------- | ------------- | ------------- | ------------------------------------------ |
| OpenAI           | ✅             | ✅             | Best tool calling support                  |
| Anthropic/Claude | ✅             | ✅             | Requires `includeFirstAgentMessage: false` |
| Gemini           | ✅             | ✅             | Google AI Studio                           |
| Gemini Vertex    | ✅             | ✅             | Google Cloud Vertex AI                     |
| Hugging Face     | ✅             | ✅             | Model-dependent                            |
| Ollama           | ✅             | ✅             | Local models                               |
| Jlama            | ✅             | ✅             | Local Java-based models                    |

***

## Troubleshooting

### Tools Not Working

**Problem:** Agent doesn't use tools even when enabled **Solution:**

1. Verify `enableBuiltInTools` is set to `true`
2. Check the system message encourages tool use
3. Ensure the LLM model supports tool calling
4. Try with explicit tool-related prompts

### Too Many Tools Enabled

**Problem:** Agent is using unexpected tools **Solution:**

1. Use `builtInToolsWhitelist` to restrict tools
2. Adjust system message to guide tool usage
3. Review conversation logs to see tool invocations

### Performance Issues

**Problem:** Agent is slow or timing out **Solution:**

1. Reduce `conversationHistoryLimit` (try 5-10)
2. Reduce number of enabled tools
3. Increase API timeout in parameters
4. Use faster LLM model

### Context Length Errors

**Problem:** "Context length exceeded" errors **Solution:**

1. Lower `conversationHistoryLimit`
2. Use model with larger context window
3. Summarize conversation history periodically

***

## API Configuration Reference

When Agent Father creates the langchain configuration, it generates:

```json
{
  "tasks": [
    {
      "actions": ["send_message"],
      "id": "model-id",
      "type": "provider-type",
      "description": "Integration description",
      "parameters": {
        "systemMessage": "Your agent's system prompt",
        "addToOutput": "true",
        "apiKey": "your-api-key",
        "modelName": "model-name",
        "timeout": "15000",
        "temperature": "0.7",
        "logRequests": "true",
        "logResponses": "true"
      },
      "enableBuiltInTools": true,
      "builtInToolsWhitelist": ["calculator", "websearch"],
      "conversationHistoryLimit": 10
    }
  ]
}
```

***

## Next Steps

1. **Create a test agent** using Agent Father with tools enabled
2. **Experiment** with different tool combinations
3. **Monitor** agent behavior and tool usage
4. **Optimize** configuration based on actual usage
5. **Review** the [LangChain Integration Documentation](https://docs.labs.ai/agent-configuration/langchain) for advanced features

***

## Related Documentation

* [LangChain Integration](https://docs.labs.ai/agent-configuration/langchain) - Complete LangChain task documentation
* [Security](https://docs.labs.ai/security-and-compliance/security) - SSRF protection, sandboxed evaluation, tool hardening
* [Agent Father Deep Dive](https://docs.labs.ai/advanced-concepts/agent-father-deep-dive) - Agent Father architecture
* [Behavior Rules](https://docs.labs.ai/agent-configuration/behavior-rules) - Understanding behavior rules
* [Output Configuration](https://docs.labs.ai/agent-configuration/output-configuration) - Configuring agent outputs

***

**Last Updated:** March 2026\
**EDDI Version:** 6.0.0\
**Agent Father Version:** 3.0.1