> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anyapi.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Langflow Integration

> Build visual AI workflows with AnyAPI and Langflow's drag-and-drop interface

# Langflow Integration

Langflow is a visual framework for building multi-agent and RAG applications. It provides a drag-and-drop interface to create complex AI workflows without writing code. AnyAPI integrates seamlessly with Langflow, giving you access to all AnyAPI models through Langflow's visual interface.

## Overview

Langflow enables you to:

* **Visual workflow building** - Drag-and-drop components to create AI pipelines
* **Multi-agent systems** - Build complex agent interactions and coordination
* **RAG applications** - Create retrieval-augmented generation workflows
* **Real-time monitoring** - Track workflow execution and performance
* **Easy deployment** - Deploy workflows as APIs or web applications

<CardGroup cols={2}>
  <Card title="Visual Builder" icon="puzzle-piece">
    Drag-and-drop interface for AI workflows
  </Card>

  <Card title="Multi-Agent" icon="users">
    Build complex agent interaction systems
  </Card>

  <Card title="RAG Pipelines" icon="database">
    Create retrieval-augmented generation flows
  </Card>

  <Card title="Real-time Deploy" icon="rocket">
    Deploy workflows as live applications
  </Card>
</CardGroup>

## Installation

Install Langflow and required dependencies:

```bash theme={"system"}
# Install Langflow
pip install langflow

# Or install with all optional dependencies
pip install langflow[all]

# For development with additional tools
pip install langflow[dev]
```

Start Langflow:

```bash theme={"system"}
# Run Langflow server
langflow run

# Or specify custom host and port
langflow run --host 0.0.0.0 --port 7860
```

## Quick Start

### Setting Up AnyAPI in Langflow

1. **Open Langflow Interface**
   Navigate to `http://localhost:7860` in your browser

2. **Create New Flow**
   Click "New Flow" to start building your workflow

3. **Add AnyAPI Component**
   * Drag the "OpenAI" component from the Models section
   * Configure it to use AnyAPI endpoints

### Basic Configuration

Configure the OpenAI component to use AnyAPI:

<Tabs>
  <Tab title="OpenAI Component">
    ```json theme={"system"}
    {
      "model": "gpt-4o",
      "openai_api_base": "https://api.anyapi.ai/v1",
      "openai_api_key": "your-anyapi-key",
      "temperature": 0.7,
      "max_tokens": 1000
    }
    ```
  </Tab>

  <Tab title="Environment Variables">
    ```bash theme={"system"}
    # Set in your environment
    export OPENAI_API_KEY="your-anyapi-key"
    export OPENAI_API_BASE="https://api.anyapi.ai/v1"
    ```
  </Tab>

  <Tab title="Custom Component">
    ```python theme={"system"}
    # Create custom AnyAPI component
    from langflow import CustomComponent
    from langchain.llms import OpenAI

    class AnyAPIComponent(CustomComponent):
        def build_config(self):
            return {
                "model": {
                    "display_name": "Model",
                    "options": ["gpt-4o", "claude-3-5-sonnet", "gemini-pro"]
                },
                "api_key": {
                    "display_name": "API Key",
                    "password": True
                }
            }
        
        def build(self, model: str, api_key: str):
            return OpenAI(
                model=model,
                openai_api_key=api_key,
                openai_api_base="https://api.anyapi.ai/v1"
            )
    ```
  </Tab>
</Tabs>

## Building Workflows

### Simple Chat Flow

Create a basic chat workflow:

1. **Add Components**:
   * Text Input (for user messages)
   * OpenAI/AnyAPI LLM (configured with AnyAPI)
   * Text Output (for responses)

2. **Connect Components**:
   * Connect Text Input → LLM Input
   * Connect LLM Output → Text Output

3. **Configuration**:
   ```json theme={"system"}
   {
     "llm_config": {
       "model": "gpt-4o",
       "temperature": 0.7,
       "system_message": "You are a helpful assistant."
     }
   }
   ```

### RAG Workflow

Build a retrieval-augmented generation pipeline:

```mermaid theme={"system"}
graph LR
    A[Document Input] --> B[Text Splitter]
    B --> C[Embeddings]
    C --> D[Vector Store]
    E[User Query] --> F[Retriever]
    D --> F
    F --> G[Context]
    G --> H[LLM/AnyAPI]
    E --> H
    H --> I[Response]
```

#### Components Configuration:

<Tabs>
  <Tab title="Document Processing">
    ```json theme={"system"}
    {
      "text_splitter": {
        "chunk_size": 1000,
        "chunk_overlap": 200,
        "separator": "\n\n"
      },
      "embeddings": {
        "model": "text-embedding-3-large",
        "api_base": "https://api.anyapi.ai/v1",
        "api_key": "your-anyapi-key"
      }
    }
    ```
  </Tab>

  <Tab title="Vector Store">
    ```json theme={"system"}
    {
      "vector_store": {
        "type": "FAISS",
        "distance_strategy": "cosine",
        "index_name": "knowledge_base"
      }
    }
    ```
  </Tab>

  <Tab title="Retrieval Chain">
    ```json theme={"system"}
    {
      "retriever": {
        "search_type": "similarity",
        "k": 5,
        "score_threshold": 0.7
      },
      "llm": {
        "model": "gpt-4o",
        "temperature": 0.3,
        "system_message": "Answer questions based on the provided context. If you cannot find the answer in the context, say so clearly."
      }
    }
    ```
  </Tab>
</Tabs>

### Multi-Agent Workflow

Create a multi-agent system with specialized roles:

```mermaid theme={"system"}
graph TD
    A[User Query] --> B[Router Agent]
    B --> C[Research Agent]
    B --> D[Analysis Agent]
    B --> E[Writing Agent]
    C --> F[Synthesis Agent]
    D --> F
    E --> F
    F --> G[Final Response]
```

#### Agent Configurations:

```python theme={"system"}
# Router Agent
router_prompt = """
You are a router agent. Analyze the user query and determine which specialist agents should handle it:
- Research Agent: For factual information gathering
- Analysis Agent: For data analysis and interpretation  
- Writing Agent: For content creation and editing

User Query: {query}
Route to: [agents_needed]
"""

# Research Agent
research_prompt = """
You are a research agent. Your job is to gather accurate, up-to-date information about the topic.
Focus on facts, data, and credible sources.

Research Query: {query}
Findings: [your_research]
"""

# Analysis Agent  
analysis_prompt = """
You are an analysis agent. Your job is to interpret data, identify patterns, and draw insights.
Be analytical and objective in your assessment.

Data to Analyze: {data}
Analysis: [your_analysis]
"""

# Writing Agent
writing_prompt = """
You are a writing agent. Your job is to create clear, engaging, and well-structured content.
Adapt your writing style to the intended audience and purpose.

Content Brief: {brief}
Written Content: [your_content]
"""
```

## Advanced Features

### Custom Components

Create reusable custom components for AnyAPI:

```python theme={"system"}
from langflow import CustomComponent
from langchain.llms import OpenAI
from langchain.schema import BaseRetriever
import requests

class AnyAPIEmbeddings(CustomComponent):
    display_name = "AnyAPI Embeddings"
    description = "Generate embeddings using AnyAPI"
    
    def build_config(self):
        return {
            "api_key": {
                "display_name": "API Key",
                "password": True,
                "required": True
            },
            "model": {
                "display_name": "Embedding Model",
                "options": [
                    "text-embedding-3-large",
                    "text-embedding-3-small",
                    "text-embedding-ada-002"
                ],
                "value": "text-embedding-3-large"
            },
            "batch_size": {
                "display_name": "Batch Size",
                "value": 100
            }
        }
    
    def build(self, api_key: str, model: str, batch_size: int):
        return AnyAPIEmbeddingsWrapper(
            api_key=api_key,
            model=model,
            batch_size=batch_size
        )

class AnyAPIMultiModal(CustomComponent):
    display_name = "AnyAPI Vision"
    description = "Process images and text with AnyAPI vision models"
    
    def build_config(self):
        return {
            "api_key": {
                "display_name": "API Key", 
                "password": True,
                "required": True
            },
            "model": {
                "display_name": "Vision Model",
                "options": ["gpt-4o", "gpt-4-vision-preview"],
                "value": "gpt-4o"
            },
            "max_tokens": {
                "display_name": "Max Tokens",
                "value": 1000
            }
        }
    
    def build(self, api_key: str, model: str, max_tokens: int):
        return AnyAPIVisionWrapper(
            api_key=api_key,
            model=model,
            max_tokens=max_tokens
        )
```

### Dynamic Workflows

Create workflows that adapt based on input:

```python theme={"system"}
class AdaptiveWorkflow(CustomComponent):
    display_name = "Adaptive Workflow"
    description = "Dynamically route tasks based on content analysis"
    
    def build_config(self):
        return {
            "input_analyzer": {
                "display_name": "Input Analyzer",
                "component_type": "LLM"
            },
            "workflow_routes": {
                "display_name": "Workflow Routes",
                "multiline": True,
                "value": """
                creative: Creative writing and ideation
                analytical: Data analysis and research
                technical: Code and technical documentation
                conversational: General chat and Q&A
                """
            }
        }
    
    def build(self, input_analyzer, workflow_routes: str):
        routes = self.parse_routes(workflow_routes)
        
        def route_input(user_input: str):
            # Analyze input to determine best route
            analysis = input_analyzer.predict(
                f"Categorize this input: {user_input}\nCategories: {list(routes.keys())}"
            )
            
            # Extract category from analysis
            category = self.extract_category(analysis, routes.keys())
            
            return {
                "category": category,
                "route": routes.get(category, "conversational"),
                "input": user_input
            }
        
        return route_input
```

### Integration with External APIs

Connect Langflow workflows to external services:

```python theme={"system"}
class ExternalAPIComponent(CustomComponent):
    display_name = "External API Connector"
    description = "Connect to external APIs within workflows"
    
    def build_config(self):
        return {
            "api_endpoint": {
                "display_name": "API Endpoint",
                "required": True
            },
            "headers": {
                "display_name": "Headers (JSON)",
                "multiline": True,
                "value": '{"Content-Type": "application/json"}'
            },
            "method": {
                "display_name": "HTTP Method",
                "options": ["GET", "POST", "PUT", "DELETE"],
                "value": "POST"
            }
        }
    
    def build(self, api_endpoint: str, headers: str, method: str):
        import json
        import requests
        
        def make_request(data):
            parsed_headers = json.loads(headers)
            
            response = requests.request(
                method=method,
                url=api_endpoint,
                headers=parsed_headers,
                json=data if method in ["POST", "PUT"] else None,
                params=data if method == "GET" else None
            )
            
            return response.json()
        
        return make_request
```

## Workflow Templates

### Content Generation Pipeline

Complete content creation workflow:

```yaml theme={"system"}
# content-generation-template.yaml
name: "Content Generation Pipeline"
description: "End-to-end content creation with research, writing, and review"

components:
  1_topic_input:
    type: "TextInput"
    config:
      placeholder: "Enter content topic..."
  
  2_research_agent:
    type: "AnyAPI_LLM"
    config:
      model: "gpt-4o"
      system_message: "You are a research specialist. Gather comprehensive information about the given topic."
      temperature: 0.3
  
  3_outline_generator:
    type: "AnyAPI_LLM"
    config:
      model: "claude-3-5-sonnet"
      system_message: "Create a detailed outline based on the research provided."
      temperature: 0.5
  
  4_content_writer:
    type: "AnyAPI_LLM"
    config:
      model: "gpt-4o"
      system_message: "Write engaging content following the provided outline and research."
      temperature: 0.7
  
  5_editor_reviewer:
    type: "AnyAPI_LLM"
    config:
      model: "claude-3-5-sonnet"
      system_message: "Review and edit the content for clarity, flow, and engagement."
      temperature: 0.3

connections:
  - from: "1_topic_input.output"
    to: "2_research_agent.input"
  - from: "2_research_agent.output"
    to: "3_outline_generator.input"
  - from: "3_outline_generator.output"
    to: "4_content_writer.context"
  - from: "2_research_agent.output"
    to: "4_content_writer.research"
  - from: "4_content_writer.output"
    to: "5_editor_reviewer.input"
```

### Customer Support Automation

Intelligent customer support workflow:

```yaml theme={"system"}
# customer-support-template.yaml  
name: "Customer Support Automation"
description: "Automated customer support with escalation and knowledge base"

components:
  1_customer_input:
    type: "TextInput"
    config:
      placeholder: "Customer inquiry..."
  
  2_intent_classifier:
    type: "AnyAPI_LLM"
    config:
      model: "gpt-4o-mini"
      system_message: "Classify customer inquiries: billing, technical, general, complaint"
      temperature: 0.1
  
  3_knowledge_base:
    type: "VectorStore"
    config:
      embeddings: "anyapi_embeddings"
      store_type: "FAISS"
  
  4_retriever:
    type: "VectorStoreRetriever"
    config:
      search_type: "similarity"
      k: 3
  
  5_response_generator:
    type: "AnyAPI_LLM"
    config:
      model: "gpt-4o"
      system_message: "Provide helpful customer support responses based on knowledge base context."
      temperature: 0.4
  
  6_escalation_checker:
    type: "AnyAPI_LLM"
    config:
      model: "claude-3-5-sonnet"
      system_message: "Determine if this inquiry requires human escalation."
      temperature: 0.2

routing_logic:
  - condition: "escalation_required == true"
    route: "human_agent"
  - condition: "intent == 'billing'"
    route: "billing_specialist"
  - condition: "intent == 'technical'"
    route: "technical_support"
  - default: "automated_response"
```

### Data Analysis Workflow

Automated data analysis and reporting:

```yaml theme={"system"}
# data-analysis-template.yaml
name: "Data Analysis Workflow"
description: "Automated data analysis with insights and visualization"

components:
  1_data_input:
    type: "FileInput"
    config:
      accepted_types: [".csv", ".xlsx", ".json"]
  
  2_data_processor:
    type: "PythonFunction"
    config:
      function: "process_data"
      imports: ["pandas", "numpy"]
  
  3_statistical_analyzer:
    type: "AnyAPI_LLM"
    config:
      model: "claude-3-5-sonnet"
      system_message: "Analyze data statistics and identify key patterns."
      temperature: 0.3
  
  4_insight_generator:
    type: "AnyAPI_LLM"
    config:
      model: "gpt-4o"
      system_message: "Generate business insights from data analysis."
      temperature: 0.6
  
  5_report_writer:
    type: "AnyAPI_LLM"
    config:
      model: "claude-3-5-sonnet"
      system_message: "Create comprehensive data analysis report."
      temperature: 0.4
  
  6_visualization_generator:
    type: "PythonFunction"
    config:
      function: "create_visualizations"
      imports: ["matplotlib", "seaborn", "plotly"]
```

## Deployment Options

### API Deployment

Deploy workflows as REST APIs:

```bash theme={"system"}
# Deploy as API
langflow run --api-only --port 8000

# Or with specific configuration
langflow run --config api_config.yaml
```

Access deployed workflow:

```python theme={"system"}
import requests

# Call deployed workflow
response = requests.post(
    "http://localhost:8000/api/v1/run/workflow_id",
    json={
        "input": "What is machine learning?",
        "config": {
            "model": "gpt-4o",
            "temperature": 0.7
        }
    }
)

result = response.json()
print(result["output"])
```

### Web Application Deployment

Deploy as interactive web application:

```bash theme={"system"}
# Deploy with web interface
langflow run --frontend-only

# Or full deployment
langflow run --host 0.0.0.0 --port 7860
```

### Docker Deployment

Deploy using Docker:

```dockerfile theme={"system"}
# Dockerfile
FROM langflowai/langflow:latest

COPY workflows/ /app/workflows/
COPY config/ /app/config/

ENV ANYAPI_API_KEY=your-api-key
ENV LANGFLOW_CONFIG_DIR=/app/config

EXPOSE 7860

CMD ["langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
```

```bash theme={"system"}
# Build and run
docker build -t my-langflow-app .
docker run -p 7860:7860 my-langflow-app
```

## Monitoring and Analytics

### Workflow Monitoring

Track workflow performance:

```python theme={"system"}
class WorkflowMonitor(CustomComponent):
    display_name = "Workflow Monitor"
    description = "Monitor workflow execution and performance"
    
    def build_config(self):
        return {
            "metrics_endpoint": {
                "display_name": "Metrics Endpoint",
                "value": "http://localhost:8080/metrics"
            },
            "log_level": {
                "display_name": "Log Level",
                "options": ["INFO", "DEBUG", "WARNING"],
                "value": "INFO"
            }
        }
    
    def build(self, metrics_endpoint: str, log_level: str):
        import logging
        import time
        import requests
        
        logging.basicConfig(level=getattr(logging, log_level))
        logger = logging.getLogger(__name__)
        
        def monitor_execution(func):
            def wrapper(*args, **kwargs):
                start_time = time.time()
                
                try:
                    result = func(*args, **kwargs)
                    execution_time = time.time() - start_time
                    
                    # Log success metrics
                    metrics = {
                        "status": "success",
                        "execution_time": execution_time,
                        "timestamp": time.time()
                    }
                    
                    logger.info(f"Workflow executed successfully in {execution_time:.2f}s")
                    
                    # Send to metrics endpoint
                    requests.post(metrics_endpoint, json=metrics)
                    
                    return result
                    
                except Exception as e:
                    execution_time = time.time() - start_time
                    
                    # Log error metrics
                    metrics = {
                        "status": "error",
                        "error": str(e),
                        "execution_time": execution_time,
                        "timestamp": time.time()
                    }
                    
                    logger.error(f"Workflow failed after {execution_time:.2f}s: {e}")
                    
                    # Send to metrics endpoint
                    requests.post(metrics_endpoint, json=metrics)
                    
                    raise e
            
            return wrapper
        
        return monitor_execution
```

### Usage Analytics

Track usage patterns and costs:

```python theme={"system"}
class UsageAnalytics(CustomComponent):
    display_name = "Usage Analytics"
    description = "Track usage patterns and costs"
    
    def build_config(self):
        return {
            "analytics_db": {
                "display_name": "Analytics Database",
                "value": "sqlite:///langflow_analytics.db"
            }
        }
    
    def build(self, analytics_db: str):
        import sqlite3
        import json
        from datetime import datetime
        
        # Initialize database
        conn = sqlite3.connect(analytics_db)
        conn.execute("""
            CREATE TABLE IF NOT EXISTS workflow_usage (
                id INTEGER PRIMARY KEY,
                workflow_id TEXT,
                user_id TEXT,
                model_used TEXT,
                tokens_used INTEGER,
                cost REAL,
                execution_time REAL,
                timestamp DATETIME
            )
        """)
        conn.close()
        
        def track_usage(workflow_id, user_id, model_used, tokens_used, cost, execution_time):
            conn = sqlite3.connect(analytics_db)
            conn.execute("""
                INSERT INTO workflow_usage 
                (workflow_id, user_id, model_used, tokens_used, cost, execution_time, timestamp)
                VALUES (?, ?, ?, ?, ?, ?, ?)
            """, (workflow_id, user_id, model_used, tokens_used, cost, execution_time, datetime.now()))
            conn.commit()
            conn.close()
        
        return track_usage
```

## Best Practices

### Workflow Design

1. **Modular Components**: Break complex workflows into reusable components
2. **Error Handling**: Add error handling and fallback mechanisms
3. **Performance**: Optimize for speed and resource usage
4. **Testing**: Test workflows thoroughly before deployment

### Security

1. **API Key Management**: Use environment variables for API keys
2. **Input Validation**: Validate all user inputs
3. **Access Control**: Implement proper authentication and authorization
4. **Audit Logging**: Log all workflow executions

### Scalability

1. **Caching**: Implement caching for frequently accessed data
2. **Load Balancing**: Distribute load across multiple instances
3. **Resource Limits**: Set appropriate resource limits
4. **Monitoring**: Implement comprehensive monitoring

## Troubleshooting

### Common Issues

#### Component Connection Errors

```
Error: Component output type mismatch
```

**Solution**: Ensure output types match input requirements

#### API Authentication Failures

```
Error: Invalid API key for AnyAPI
```

**Solution**: Verify API key configuration in component settings

#### Memory Issues

```
Error: Out of memory during workflow execution
```

**Solution**: Optimize workflow components and add memory limits

### Debug Mode

Enable debug logging:

```python theme={"system"}
import logging
logging.basicConfig(level=logging.DEBUG)

# Run Langflow with debug mode
langflow run --debug
```

### Performance Optimization

Monitor and optimize workflow performance:

```python theme={"system"}
# Add performance monitoring to components
@performance_monitor
def optimized_component():
    # Component logic here
    pass
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Cline Integration" icon="terminal" href="/integrations/cline">
    AI-powered code editing and automation
  </Card>

  <Card title="Continue.dev" icon="code" href="/integrations/continue-dev">
    VS Code AI coding assistant
  </Card>

  <Card title="API Reference" icon="book" href="/api-reference/text-models/overview">
    Complete API documentation
  </Card>

  <Card title="Use Cases" icon="lightbulb" href="/use-cases/create-assistant">
    Build AI assistants and workflows
  </Card>
</CardGroup>

For more information about Langflow, visit the [official documentation](https://docs.langflow.org/).