#!/usr/bin/env python3 """ LangChain Form Generator Agent =============================== This example shows a LangChain agent that generates AgentForms dynamically from natural-language prompts — e.g. "Build me a job application form for a restaurant" or "Create a checkout form for event tickets." Pattern: 1. Human provides a prompt describing the desired form. 2. The LangChain LLM parses the prompt and defines the fields. 3. The agent calls the AgentForms SDK to create the form. 4. A shareable URL is returned to the human. 5. The agent optionally polls for submissions and summarises results. Prerequisites: pip install langchain langchain-openai agentforms requests # Configure your LLM provider (e.g. OPENAI_API_KEY) # Configure AgentForms: # export AGENTFORMS_API_KEY='***' """ import os import sys import time from typing import Optional # --------------------------------------------------------------------------- # AgentForms integration # --------------------------------------------------------------------------- try: from agentforms import AgentForms, FieldDefinition except ImportError: print("ERROR: agentforms SDK not installed. Run: pip install agentforms") sys.exit(1) # --------------------------------------------------------------------------- # LangChain tool definition # --------------------------------------------------------------------------- try: from langchain.tools import tool from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI LANGCHAIN_AVAILABLE = True except ImportError: LANGCHAIN_AVAILABLE = False # Fallback stubs so the file is readable even without langchain installed. class _FakeTool: def __init__(self, name, description, **kw): self.name = name self.description = description def __call__(self, *a, **k): return None tool = _FakeTool # --------------------------------------------------------------------------- # AgentForms tool for LangChain # --------------------------------------------------------------------------- @tool def create_agentform( name: str, fields: list, metadata: Optional[dict] = None, ) -> str: """ Create an AgentForm and return its share URL. Args: name: Human-readable form name. fields: List of dicts with keys: - name (str): snake_case field id - label (str): display label - type (str): one of text, email, tel, textarea, select, number, date, hidden - required (bool): optional, default False - options (list): optional, for select-type fields - placeholder (str): optional hint text - default (str): optional default value Returns: Short share URL the human can open in a browser. """ client = AgentForms(api_key=os.environ["AGENTFORMS_API_KEY"]) form = client.forms.create(name=name, fields=fields, metadata=metadata) return form.share_url @tool def generate_agentform(prompt: str, form_name: Optional[str] = None) -> str: """ Use AgentForms' built-in AI to generate a form from a natural-language prompt. Args: prompt: e.g. "A feedback survey for a product launch event" form_name: optional override for the AI-generated name Returns: Short share URL for the generated form. """ client = AgentForms(api_key=os.environ["AGENTFORMS_API_KEY"]) form = client.forms.generate(prompt=prompt, form_name=form_name) return form.share_url @tool def poll_submissions( form_url: str, timeout_seconds: int = 60, poll_interval: int = 10, ) -> str: """ Wait for submissions on a form and return them as a summary string. Args: form_url: The share URL returned by create_agentform. timeout_seconds: How long to wait before giving up. poll_interval: Seconds between API calls. Returns: Markdown summary of collected submissions. """ client = AgentForms(api_key=os.environ["AGENTFORMS_API_KEY"]) # Extract token from share URL: https://agentforms.io/abc123 token = form_url.rstrip("/").split("/")[-1] deadline = time.time() + timeout_seconds while time.time() < deadline: result = client.submissions.list(form_token=token, limit=10) submissions = result.get("submissions", []) if submissions: lines = [f"## Submissions collected ({len(submissions)})"] for i, sub in enumerate(submissions, 1): lines.append(f"\n### Submission {i} (id={sub.id})") for key, value in sub.dynamic_data.items(): lines.append(f"- **{key}**: {value}") return "\n".join(lines) time.sleep(poll_interval) return "No submissions received within the timeout window." # --------------------------------------------------------------------------- # Build the LangChain chain # --------------------------------------------------------------------------- def build_form_agent(temperature: float = 0.3): """ Assemble a LangChain chat chain that uses the AgentForms tools. Returns: A runnable chain (langchain Runnable) and the tools list. """ if not LANGCHAIN_AVAILABLE: raise ImportError( "LangChain is not installed. Run: pip install langchain langchain-openai" ) llm = ChatOpenAI(model="gpt-4o", temperature=temperature) tools = [generate_agentform, create_agentform, poll_submissions] prompt = ChatPromptTemplate.from_messages([ ("system", """You are a Form Builder Assistant. Your job is to help users create data-collection forms by calling the AgentForms API. Available tools: - generate_agentform(prompt, form_name): AI-powered form generation from a natural language prompt. Use this when the user's request is broad or open-ended. - create_agentform(name, fields, metadata): Programmatic form creation when you already know the exact fields. Build the fields list as dicts with: name, label, type, required, options, placeholder, default. Supported types: text, email, tel, textarea, select, number, date, hidden. - poll_submissions(form_url, timeout_seconds, poll_interval): Wait for and summarise form submissions. Workflow: 1. Ask clarifying questions if the request is vague. 2. Call generate_agentform or create_agentform to build the form. 3. Share the returned URL with the user. 4. Optionally call poll_submissions to collect responses. Always explain what form was created and which fields it contains.""",), ("human", "{input}"), ]) chain = prompt | llm | StrOutputParser() return chain, tools # --------------------------------------------------------------------------- # Standalone demo (runs without LangChain if not installed) # --------------------------------------------------------------------------- def demo_direct_api(): """ Minimal demo that uses the AgentForms SDK directly (no LangChain dependency). This is useful for verifying the SDK integration works on its own. """ client = AgentForms(api_key=os.environ["AGENTFORMS_API_KEY"]) print("📝 Creating a form via the SDK directly...\n") # Approach A: Programmatic creation form1 = client.forms.create( name="Quick Contact Form", fields=[ {"name": "full_name", "label": "Full Name", "type": "text", "required": True}, {"name": "email", "label": "Email Address", "type": "email", "required": True}, {"name": "company", "label": "Company", "type": "text", "required": False}, {"name": "topic", "label": "Topic", "type": "select", "options": ["Partnership", "Support", "Sales", "Other"], "required": True}, {"name": "message", "label": "Message", "type": "textarea", "placeholder": "Tell us how we can help...", "required": False}, ], metadata={"source": "demo", "pattern": "langchain-form-agent"}, ) print(f"✅ Form created: {form1.name}") print(f" Share URL: {form1.share_url}") print(f" Public URL: {form1.public_url}") print(f" Fields: {len(form1.fields)}\n") for field in form1.fields: req = "⚠️ " if field.required else " " print(f" {req} [{field.type}] {field.label} ({field.name})") print("\n" + "-" * 60 + "\n") # Approach B: AI-powered generation (requires Starter+ tier) try: print("🤖 Generating a form with AI...\n") form2 = client.forms.generate( prompt="A food preference and allergy checklist for a corporate catering order", form_name="Catering Preferences", ) print(f"✅ AI-generated form: {form2.name}") print(f" Share URL: {form2.share_url}") print(f" Fields: {len(form2.fields)}\n") for field in form2.fields: req = "⚠️ " if field.required else " " print(f" {req} [{field.type}] {field.label} ({field.name})") except Exception as e: print(f"⚠️ AI generation skipped (may require higher tier): {e}") return form1, form2 if "form2" in dir() else None # --------------------------------------------------------------------------- # Full LangChain agent demo # --------------------------------------------------------------------------- def demo_langchain_agent(): """ Interactive demo of the LangChain form agent. If LangChain is installed, this builds the agent and runs a sample query. """ if not LANGCHAIN_AVAILABLE: print("LangChain not installed — running direct SDK demo instead.") print("Install with: pip install langchain langchain-openai\n") demo_direct_api() return print("🔗 Building LangChain Form Agent...\n") chain, tools = build_form_agent() sample_prompt = ( "Create a registration form for a tech conference. " "Include name, email, company, job title, " "and a dropdown for which workshops they want to attend." ) print(f"Prompt: {sample_prompt}\n") result = chain.invoke({"input": sample_prompt}) print("Agent response:\n") print(result) print() # Show available tools print("Available tools:") for t in tools: print(f" - {t.name}: {t.description[:80]}...") # --------------------------------------------------------------------------- # Entry point # --------------------------------------------------------------------------- if __name__ == "__main__": if "AGENTFORMS_API_KEY" not in os.environ: print("Set the AGENTFORMS_API_KEY environment variable and try again.") print(" export AGENTFORMS_API_KEY='***'") sys.exit(1) mode = sys.argv[1] if len(sys.argv) > 1 else "direct" if mode == "langchain": demo_langchain_agent() else: demo_direct_api()