Langflow

Stepflow Langflow Integration

A Python package for seamlessly integrating Langflow workflows with Stepflow, enabling conversion and execution of Langflow components within the Stepflow ecosystem.

✨ Features

🔄 One-Command Conversion & Execution: Convert Langflow JSON to Stepflow YAML and execute in a single command
🚀 Real Langflow Execution: Execute actual Langflow components with full type preservation
🔧 Tweaks System: Modify component configurations (API keys, parameters) at execution time
🛡️ Security First: Safe JSON-only parsing without requiring full Langflow installation

🚀 Quick Start

Installation

cd integrations/langflow
uv sync --dev

# Or with pip
pip install -e .

Basic Execution

# Execute with real Langflow components (requires OPENAI_API_KEY)
export OPENAI_API_KEY="your-api-key-here"
uv run stepflow-langflow execute tests/fixtures/langflow/basic_prompting.json '{"message": "Write a haiku about coding"}'

# Execute without environment variables using tweaks
uv run stepflow-langflow execute tests/fixtures/langflow/basic_prompting.json \
  '{"message": "Write a haiku"}' \
  --tweaks '{"LanguageModelComponent-xyz123": {"api_key": "your-api-key-here"}}'

Using Tweaks for Configuration

The tweaks system allows you to modify component configurations at execution time, perfect for:

Setting API keys without environment variables
Adjusting model parameters
Debugging component configurations

# Basic tweaks example - modify API key and temperature
uv run stepflow-langflow execute my-workflow.json '{"message": "Hello"}' \
  --tweaks '{"OpenAIComponent-abc123": {"api_key": "sk-...", "temperature": 0.7}}'

# Multiple component tweaks
uv run stepflow-langflow execute my-workflow.json '{"message": "Hello"}' \
  --tweaks '{
    "LanguageModelComponent-xyz": {"api_key": "sk-..."},
    "EmbeddingComponent-def": {"api_key": "sk-...", "model": "text-embedding-3-small"}
  }'

Debugging and Inspection

# Dry-run to see converted workflow without execution
uv run stepflow-langflow execute my-workflow.json --dry-run

# Apply tweaks and see the modified workflow
uv run stepflow-langflow execute my-workflow.json --dry-run \
  --tweaks '{"LanguageModelComponent-xyz": {"temperature": 0.9}}'

# Keep temporary files for debugging
uv run stepflow-langflow execute my-workflow.json '{"message": "Hello"}' \
  --keep-files --output-dir ./debug

# Analyze workflow structure before execution
uv run stepflow-langflow analyze my-workflow.json

# Convert to Stepflow YAML for inspection
uv run stepflow-langflow convert my-workflow.json output.yaml

Finding Component IDs for Tweaks

To apply tweaks, you need the component IDs from your Langflow workflow:

# Method 1: Analyze the workflow to see component structure
uv run stepflow-langflow analyze my-workflow.json

# Method 2: Dry-run to see the converted Stepflow YAML with component IDs
uv run stepflow-langflow execute my-workflow.json --dry-run

# Method 3: Open the Langflow JSON file and look for node IDs
# Component IDs follow the pattern: ComponentType-nodeId (e.g., "LanguageModelComponent-kBOja")

🛠️ CLI Reference

`execute` Command

The primary command for converting and executing Langflow workflows:

uv run stepflow-langflow execute [OPTIONS] INPUT_FILE [INPUT_JSON]

Arguments:

INPUT_FILE: Path to Langflow JSON workflow file
INPUT_JSON: JSON input data for the workflow (default: {})

Options:

--config PATH: Use custom stepflow configuration file
--stepflow-binary PATH: Path to stepflow binary (auto-detected if not specified)
--timeout INTEGER: Execution timeout in seconds (default: 60)
--dry-run: Only convert, don't execute (shows converted YAML)
--keep-files: Keep temporary files after execution
--output-dir PATH: Directory to save temporary files

Examples:

# Basic execution with real Langflow components
uv run stepflow-langflow execute examples/simple_chat.json '{"message": "Hi there!"}'

# Execution with custom timeout
uv run stepflow-langflow execute examples/basic_prompting.json '{"message": "Write a haiku"}' --timeout 120

# Debug with file preservation
uv run stepflow-langflow execute examples/memory_chatbot.json --dry-run --keep-files

`submit-batch` Command

Execute a Langflow workflow with multiple inputs using batch processing. This command converts the workflow once and then processes multiple inputs efficiently.

uv run stepflow-langflow submit-batch [OPTIONS] INPUT_FILE INPUTS_JSONL

Arguments:

INPUT_FILE: Path to Langflow JSON workflow file
INPUTS_JSONL: Path to JSONL file with batch inputs (one JSON object per line)

Options:

--url TEXT: Stepflow server URL (required, e.g., http://localhost:7837/api/v1)
--tweaks TEXT: JSON tweaks to apply to the workflow (same format as execute command)
--max-concurrent INTEGER: Maximum number of concurrent executions (optional)
--output PATH: Path to output JSONL file for results (optional)

JSONL Input Format:

Each line in the inputs JSONL file should be a complete JSON object representing one execution input:

{"message": "Write a haiku about coding"}
{"message": "Explain quantum computing"}
{"message": "What is machine learning?"}
{"message": "Tell me a joke"}
{"message": "Summarize photosynthesis"}

Output Format:

When --output is specified, results are written as JSONL with the following structure:

{"index": 0, "status": "completed", "result": {"outcome": "success", "result": {...}}}
{"index": 1, "status": "failed", "result": {"outcome": "failure", "error": {...}}}
{"index": 2, "status": "completed", "result": {"outcome": "success", "result": {...}}}

Each result includes:

index: Input index (0-based)
status: Either "completed" or "failed"
result: Workflow execution result or error details

Examples:

# Basic batch execution with running server
uv run stepflow-langflow submit-batch \
  examples/basic_prompting.json \
  inputs.jsonl \
  --url http://localhost:7837/api/v1

# Batch execution with tweaks (API keys, parameters)
uv run stepflow-langflow submit-batch \
  examples/basic_prompting.json \
  inputs.jsonl \
  --url http://localhost:7837/api/v1 \
  --tweaks '{"LanguageModelComponent-kBOja": {"api_key": "sk-...", "temperature": 0.7}}'

# Batch execution with concurrency limit and output file
uv run stepflow-langflow submit-batch \
  examples/vector_store_rag.json \
  queries.jsonl \
  --url http://localhost:7837/api/v1 \
  --max-concurrent 5 \
  --output results.jsonl

# Complete workflow: start server, run batch, analyze results
# Terminal 1: Start Stepflow server
stepflow-server --port 7837 --config stepflow-config.yml

# Terminal 2: Submit batch job
uv run stepflow-langflow submit-batch \
  examples/basic_prompting.json \
  batch_inputs.jsonl \
  --url http://localhost:7837/api/v1 \
  --output batch_results.jsonl

# Analyze results
cat batch_results.jsonl | jq '.result.result'

Exit Codes:

Returns 0 if all executions completed successfully
Returns non-zero if any executions failed (results still written to output file)

Performance Characteristics:

Workflow conversion happens once (not per input)
Tweaks are applied once (not per input)
Inputs are processed concurrently by the Stepflow server
Use --max-concurrent to control resource usage

Other Commands

# Analyze workflow dependencies and structure
uv run stepflow-langflow analyze workflow.json

# Convert with validation and pretty printing
uv run stepflow-langflow convert workflow.json --validate --pretty

# Validate workflow structure
uv run stepflow-langflow validate workflow.json

# Start component server for Stepflow integration
uv run stepflow-langflow serve

🐍 Python API

from stepflow_langflow_integration.converter.translator import LangflowConverter

# Basic conversion
converter = LangflowConverter()
stepflow_yaml = converter.convert_file("workflow.json")

# Analyze workflow structure
with open("workflow.json") as f:
    langflow_data = json.load(f)

analysis = converter.analyze(langflow_data)
print(f"Nodes: {analysis['node_count']}")
print(f"Component types: {analysis['component_types']}")

# Save converted workflow
with open("output.yaml", "w") as f:
    f.write(stepflow_yaml)

🧪 Testing & Development

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=stepflow_langflow_integration --cov-report=html

# Run integration tests with example flows
uv run pytest tests/integration/

Development Setup

# Development installation with all dependencies
uv sync --dev

# Code formatting and linting
uv run ruff format
uv run ruff check --fix

# Type checking
uv run mypy src tests

📚 Example Flows

The tests/fixtures/langflow/ directory contains example workflows from official Langflow starter projects that work with real Langflow component execution.

Available Example Flows

All workflows accept a "message" field as their primary input:

Workflow	Description	Status
`simple_chat.json`	Direct message passthrough	✅ Working
`basic_prompting.json`	LLM with prompt template	✅ Working
`memory_chatbot.json`	Conversational AI with memory	✅ Working
`document_qa.json`	Document-based Q&A	✅ Working
`vector_store_rag.json`	Retrieval augmented generation	✅ Working
`simple_agent.json`	AI agent with tools	⚠️ Use --timeout 180

Running Example Flows

With Environment Variables

# Set API key in environment
export OPENAI_API_KEY="sk-your-key-here"

# Run any example flow
uv run stepflow-langflow execute tests/fixtures/langflow/basic_prompting.json \
  '{"message": "Write a haiku about coding"}'

With Tweaks (No Environment Variables)

# First, find the component ID that needs API key configuration
uv run stepflow-langflow analyze tests/fixtures/langflow/basic_prompting.json

# Then execute with tweaks
uv run stepflow-langflow execute tests/fixtures/langflow/basic_prompting.json \
  '{"message": "Write a haiku about coding"}' \
  --tweaks '{"LanguageModelComponent-kBOja": {"api_key": "sk-your-key-here"}}'

Complex Example: Vector Store RAG

This example demonstrates using tweaks with multiple components:

# Analyze to find component IDs
uv run stepflow-langflow analyze tests/fixtures/langflow/vector_store_rag.json

# Execute with multiple component tweaks
uv run stepflow-langflow execute tests/fixtures/langflow/vector_store_rag.json \
  '{"message": "Explain machine learning"}' \
  --tweaks '{
    "LanguageModelComponent-abc123": {
      "api_key": "sk-your-openai-key",
      "temperature": 0.7
    },
    "EmbeddingComponent-def456": {
      "api_key": "sk-your-openai-key",
      "model": "text-embedding-3-small"
    }
  }'

Workflow Input Patterns

All workflows expect JSON input with a message field:

Input Example	Use Case
`{"message": "Hello there!"}`	Basic greeting or question
`{"message": "Write a poem about nature"}`	Creative prompts for LLM
`{"message": "Remember I like coffee"}`	Conversational input with memory
`{"message": "Summarize this document"}`	Questions about documents
`{"message": "Calculate 42 * 17"}`	Tasks requiring tools/calculations
`{"message": "Explain machine learning"}`	Knowledge retrieval queries

Debugging Workflows

# Analyze workflow structure and find component IDs
uv run stepflow-langflow analyze tests/fixtures/langflow/vector_store_rag.json

# View converted workflow without execution
uv run stepflow-langflow execute tests/fixtures/langflow/basic_prompting.json --dry-run

# Keep temporary files for inspection
uv run stepflow-langflow execute tests/fixtures/langflow/memory_chatbot.json \
  '{"message": "Test"}' --keep-files --output-dir ./debug

🔧 Advanced Usage

Working with Your Own Flows

# Convert your Langflow export to Stepflow
uv run stepflow-langflow convert your-workflow.json your-workflow.yaml

# Analyze your workflow structure
uv run stepflow-langflow analyze your-workflow.json

# Execute with custom configuration
uv run stepflow-langflow execute your-workflow.json \
  '{"your_input": "value"}' \
  --tweaks '{"YourComponent-id": {"api_key": "sk-..."}}'

Tweaks System Reference

Tweaks modify component configurations at execution time:

{
  "ComponentType-nodeId": {
    "field_name": "new_value",
    "api_key": "sk-your-key",
    "temperature": 0.8,
    "model": "gpt-4"
  }
}

Common tweak patterns:

API Keys: {"api_key": "sk-your-key"}
Model Parameters: {"temperature": 0.7, "max_tokens": 1000}
Model Selection: {"model": "gpt-4", "provider": "openai"}
Custom Settings: Any component field can be modified

Manual Stepflow Integration

For advanced use cases with custom Stepflow configurations:

# Convert workflow
uv run stepflow-langflow convert workflow.json workflow.yaml

# Create custom stepflow-config.yml
# Run with Stepflow directly
cargo run -- run --flow workflow.yaml --input input.json --config stepflow-config.yml

🆘 Support

Examples: Comprehensive examples in tests/fixtures/langflow/
Issues: Report bugs via GitHub Issues
Testing: Use --dry-run mode for safe experimentation

📄 License

This project follows the same Apache 2.0 license as the main Stepflow project.

✨ Features​

🚀 Quick Start​

Installation​

Basic Execution​

Using Tweaks for Configuration​

Debugging and Inspection​

Finding Component IDs for Tweaks​

🛠️ CLI Reference​

execute Command​

submit-batch Command​

Other Commands​

🐍 Python API​

🧪 Testing & Development​

Running Tests​

Development Setup​

📚 Example Flows​

Available Example Flows​

Running Example Flows​

With Environment Variables​

With Tweaks (No Environment Variables)​

Complex Example: Vector Store RAG​

Workflow Input Patterns​

Debugging Workflows​

🔧 Advanced Usage​

Working with Your Own Flows​

Tweaks System Reference​

Manual Stepflow Integration​

🆘 Support​

📄 License​