Flowire AI Nodes

AI/LLM nodes for Flowire workflow automation. This package provides nodes for sending prompts to LLM providers and getting responses.

Installation

cd flowire-app/backend
uv pip install fw-nodes-ai

Then enable the package in your .env file:

# .env (comma-separated list)
INSTALLED_NODE_PACKAGES=fw-nodes-core,fw-nodes-ai

Included Nodes

Supported Providers

Usage Example

LLM Prompt

Send a prompt to any supported LLM provider:

# Node configuration:
{
    "credential_id": "your-llm-credential",
    "model": "gpt-4o",
    "system_prompt": "You are a helpful assistant.",
    "user_message": "Summarize this text: {{previous-node.output}}",
    "temperature": 0.7,
    "response_format": "text"
}

# Outputs:
# - response: LLM response (text string or parsed JSON object)
# - model: Model that was used
# - usage: Token usage statistics (prompt_tokens, completion_tokens, total_tokens)
# - finish_reason: Why the LLM stopped generating
# - rate_limits: Provider rate limit status

JSON Mode

Request structured JSON output:

{
    "credential_id": "your-llm-credential",
    "model": "gpt-4o",
    "user_message": "Extract the name and age from: John is 30 years old",
    "temperature": 0.0,
    "response_format": "json",
    "json_schema": {
        "name": "extraction",
        "strict": true,
        "schema": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "age": {"type": "integer"}
            },
            "required": ["name", "age"]
        }
    }
}

# Output response will be a parsed JSON object:
# {"name": "John", "age": 30}

Development

# Install with dev dependencies
uv sync --all-extras

# Run linter
just check

# Run tests
just test

Project Structure

fw-nodes-ai/
├── fw_nodes_ai/
│   ├── __init__.py
│   ├── credentials.py
│   ├── providers.py
│   └── nodes/
│       ├── __init__.py
│       └── llm_prompt.py
├── tests/
├── pyproject.toml
└── README.md

License

This project is licensed under the MIT License.