formcoax 0.1.1

A developer-first open-core library for conversational data intake

FormCoax 🚀

FormCoax is a developer-first, open-core python library for conversational data intake. It replaces rigid, blocking forms with non-blocking, agent-driven conversational intake natively in SaaS and enterprise web products.

Instead of a consumer-facing landing page builder, FormCoax is a headless backend SDK that manages form states and agent-coaxing logic.

📖 Learn More: Check out our Features & Architecture Guide for a deep-dive on dynamic turn-scaling, context-aware extraction, evasion state machines, and detailed validation projections.

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1. Why FormCoax? The Anti-Pattern we Solve

Naively sending a chat history to an LLM and asking it to "figure out what's missing from the form" leads to context drift, high latency, hallucinated form fields, and a terrible user experience.

FormCoax enforces a deterministic State-Machine Driven Multi-Agent Pattern:

1. The State is the Single Source of Truth: Your form fields are tracked structurally with confidence scores, validation flags, descriptions, and depth requirements.
2. Context-Aware Evaluator Agent (Fast, Cheap): Rather than evaluating user messages in isolation, the Evaluator analyzes the complete conversation history (state.chat_history). It uses Structured Outputs to extract values, run quality validation, flag shallow/vague details (e.g. "I want it to be exciting"), and programmatically identify explicit user skips/evasions.
3. Adaptive Interviewer Agent (Empathetic, Conversational): It examines the missing or shallow checklist, utilizing precise field descriptions, validation error logs, and current values to compose context-aware follow-ups or reprompts. It also supports customizable initial greetings.
4. Intelligent Stagnation & Scaling Guardrails: Rather than a rigid hardcoded turn cap, the conversation turn limit scales dynamically based on form complexity (e.g., 3 turns per required field). Furthermore, if a user repeatedly evades or refuses questions, the system detects this stagnation and triggers an early soft-exit—submitting the partial form state gracefully.

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2. Recommended 2026 Model Selection

For optimal performance, leverage the current 2026 flagship frontier models:

Role	Recommended Model	Why?
Evaluator (Extraction)	Gemini 3.5 Flash	Low latency, sub-second response times, and near-perfect structured tool calling for a fraction of the cost.
Interviewer (Coaxer)	Gemini 3.1 Pro or Claude 3.5 Sonnet	Premium conversational nuance, high empathy, and great context retention.

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3. Installation

Install the package along with its dependencies:

pip install -e .

To install development dependencies (for pytest):

pip install -e ".[dev]"

[!IMPORTANT] LangChain Interface Dependency: FormCoax engines (EvaluatorEngine and InterviewerEngine) do not call model APIs directly. Instead, they expect LangChain-compatible ChatModel instances (such as ChatGoogleGenerativeAI, ChatOpenAI, or ChatAnthropic).

• The Evaluator requires a model supporting structured outputs via .with_structured_output(...).
• The Interviewer requires a model supporting async generation via .ainvoke(...).

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4. Key Components & How to Use Them

A. Define your Target Form Schema

You define your intake goals using a standard Pydantic v2 model. Use json_schema_extra parameter keys to configure validation depth:

from pydantic import BaseModel, Field
from typing import Optional

class StartupOnboardingForm(BaseModel):
    company_name: str = Field(..., description="The official company name.")
    project_budget: int = Field(..., description="Estimated software project budget in USD.")
    
    # Enforce depth validation: must be at least 10 words or it's flagged as 'shallow'
    problem_description: str = Field(
        ...,
        description="Detailed description of the problem you are solving.",
        json_schema_extra={"depth": "detailed", "min_words": 10}
    )

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Generate the centralized SessionState directly from your Pydantic model. By omitting max_turns, FormCoax automatically scales the conversational turn ceiling to match your schema size (3 turns per required field):

from formcoax import SessionState

state = SessionState.from_pydantic_model(
    session_id="user-session-101",
    model_class=StartupOnboardingForm,
    # max_turns is omitted here to enable dynamic scaling:
    # 3 required fields * 3 = 9 turns (with safe floor of 5, ceiling of 15)
)

print(state.max_turns)
# Output: 9

print(state.max_evasions)
# Output: 3 (Consecutive evasion limit before early soft-exit)

print(state.missing_or_shallow_fields)
# Output: ['company_name', 'project_budget', 'problem_description']

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C. State Persistence (State Stores)

FormCoax provides an abstract BaseStateStore for integrating your own database (PostgreSQL, Redis, MongoDB, etc.), alongside a default thread-safe, copying InMemoryStateStore for testing and development:

from formcoax import InMemoryStateStore

store = InMemoryStateStore()

# Save state
await store.save_state(state.session_id, state)

# Retrieve state (isolated deep-copy to prevent side-effects)
state_copy = await store.get_state(state.session_id)

To create a production database store, subclass BaseStateStore:

from formcoax import BaseStateStore, SessionState
from typing import Optional

class PostgreSQLStateStore(BaseStateStore):
    async def get_state(self, session_id: str) -> Optional[SessionState]:
        # Fetch from PostgreSQL, deserialize JSON into SessionState
        ...
        
    async def save_state(self, session_id: str, state: SessionState) -> None:
        # Serialize SessionState to JSON and upsert in PostgreSQL
        ...

    async def delete_state(self, session_id: str) -> None:
        # Delete record
        ...

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The EvaluatorEngine hooks up to an LLM supporting structured tool-calling. It extracts values, runs validation, detects shallow details, and flags evasions while analyzing the complete conversation history:

from langchain_google_genai import ChatGoogleGenerativeAI
from formcoax import EvaluatorEngine

# Configure Gemini 3.5 Flash (the 2026 industry-standard for fast structured calls)
llm = ChatGoogleGenerativeAI(model="gemini-3.5-flash", temperature=0.0)
evaluator = EvaluatorEngine(llm=llm, model_class=StartupOnboardingForm)

# User says: "We're Acme Corp. We need a fast backend built."
# Let's run context-aware extraction:
state = await evaluator.extract_and_hydrate(state, "We're Acme Corp. We need a fast backend built.")

# Results:
print(state.form_schema["company_name"].value)        # "Acme Corp"
print(state.form_schema["company_name"].shallow)      # False (No depth constraint)

print(state.form_schema["problem_description"].value) # "We need a fast backend built."
print(state.form_schema["problem_description"].shallow) # True (Vague details flag! Under min_words=10 quality gate)

# The checklist is updated. Since problem_description is too shallow, it remains on the list:
print(state.missing_or_shallow_fields)
# Output: ['project_budget', 'problem_description']

# If the user says: "I don't know, skip this for now"
# The Evaluator detects the evasion, updating consecutive_evasions:
state = await evaluator.extract_and_hydrate(state, "I don't know, skip this for now")
print(state.consecutive_evasions) # Output: 1

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The InterviewerEngine examines missing fields and generates empathetic questions. When a field is shallow, the Interviewer receives detailed status metadata (its current value and validation error) to frame highly specific reprompting follow-ups. It also supports customizable initial greetings:

from langchain_google_genai import ChatGoogleGenerativeAI
from formcoax import InterviewerEngine, ChatMessage

# Configure Gemini 3.1 Pro (the 2026 reasoning model)
chat_llm = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview", temperature=0.7)
interviewer = InterviewerEngine(
    llm=chat_llm,
    initial_greeting="Let's create a project. What do you want to accomplish?"
)

# Print initial opening line:
print(interviewer.initial_greeting)
# Output: "Let's create a project. What do you want to accomplish?"

# If we are in the middle of a session, it coaxes the next missing/shallow field,
# acknowledging any shallow details instead of starting from scratch:
reply = await interviewer.generate_response(state)
print(reply)
# Output: "That's a neat starting point! Could you expand on what the backend should do so we can capture the specific details?"

# Append to history and record a turn
state.chat_history.append(ChatMessage(role="assistant", content=reply))
state.turn_count += 1

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5. Standard End-to-End Orchestration Loop

This is how you integrate FormCoax inside your backend controller, API endpoint (FastAPI), or state machine (LangGraph):

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from formcoax import InMemoryStateStore, SessionState, ChatMessage, EvaluatorEngine, InterviewerEngine
from langchain_google_genai import ChatGoogleGenerativeAI

app = FastAPI()
store = InMemoryStateStore()

# Define your intake form target
class IntakeForm(BaseModel):
    user_name: str
    project_budget: int

# Initialize Agents
eval_llm = ChatGoogleGenerativeAI(model="gemini-3.5-flash", temperature=0.0)
chat_llm = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview", temperature=0.7)

evaluator = EvaluatorEngine(llm=eval_llm, model_class=IntakeForm)
interviewer = InterviewerEngine(llm=chat_llm)

class ChatRequest(BaseModel):
    session_id: str
    message: str

@app.post("/chat")
async def chat_turn(req: ChatRequest):
    # 1. Retrieve the existing state, or initialize a new one if it's a new session
    state = await store.get_state(req.session_id)
    if not state:
        # max_turns is omitted here to enable dynamic turn-scaling:
        # 2 required fields * 3 = 6 turns (with safe floor of 5, ceiling of 15)
        state = SessionState.from_pydantic_model(req.session_id, IntakeForm)

    # 2. Add user message and advance the turn counter
    state.chat_history.append(ChatMessage(role="user", content=req.message))
    state.turn_count += 1

    # 3. RUN EVALUATOR: Extract details and validate depth
    state = await evaluator.extract_and_hydrate(state, req.message)

    # 4. RUN INTERVIEWER: Generate empathetic question or soft-exit closeout
    reply = await interviewer.generate_response(state)
    state.chat_history.append(ChatMessage(role="assistant", content=reply))

    # 5. Persist state
    await store.save_state(req.session_id, state)

    return {
        "reply": reply,
        "is_complete": state.is_complete,
        "is_submitted": state.is_complete or state.is_turn_limit_reached,
        "extracted_data": {k: v.value for k, v in state.form_schema.items()}
    }

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6. Running Tests

FormCoax ships with robust, offline unit tests using mocks:

pytest

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7. Contributing & Community

FormCoax is open source and we welcome contributions of all types! Whether you are a first-time contributor or an experienced open-source developer:

• Check out our friendly Contribution Guide to set up your local development environment and submit your first pull request.
• Help us expand the SDK by adding nested schemas, branching form support, or new state persistence backends!