ConversationalFilter gives you the full power of any LLM with focused, scope-aware output. Stop trimming. Start building.
Open source under MIT · Commercial license for production use · API Status
Remember when Python HTTP was painful? Then requests made it clean.
LLM responses have the same problem. We fix it the same way.
You: How do I connect to a database? LLM: Great question! Databases are fundamental to modern software. Let me explain 8 different database types, 12 ORMs, connection pooling theory, sharding strategies, the history of SQL, NoSQL vs SQL debates, and here are 47 links you didn't ask for... (800+ words for a 7-word question)
You: How do I connect to a database?
LLM: For Python with SQLite:
import sqlite3
conn = sqlite3.connect('database.db')
Want me to dive deeper?
(Scope creep detected, trimmed to core answer)
ScopeAnalyzer calculates the complexity ratio between your question and the AI's response using word count, sentence structure, and technical density.
If the response is disproportionately complex compared to the question, the system flags it as scope creep based on your configured threshold.
The response is trimmed to its core answer, and a clarifying question is added so the user can request more detail if they want it.
Measures elaboration ratio between question complexity and response complexity. Configurable thresholds.
Cuts responses at natural sentence boundaries. No mid-sentence breaks. Preserves the core answer.
Predefined profiles like CONCISE_LEARNER or TUTORIAL_LEARNER. Create custom profiles matching your style.
Production-ready API hosted on Railway. Send a question and response, get back the filtered version.
Install with pip and integrate directly into your application. Works with any LLM provider.
Built-in license validation via Lemonsqueezy. Automatic key generation on purchase.
As clean as import requests — three lines to start filtering
from conversational_filter import ConversationalFilter from conversational_filter.user_profile import CONCISE_LEARNER # Create a filter with your preferred style cf = ConversationalFilter(user_profile=CONCISE_LEARNER) # Filter any LLM response result = cf.filter_response( question="How do I authenticate users?", response=verbose_llm_output ) print(result.filtered_response) # Concise, focused answer - no scope creep print(result.clarifying_question) # "Want me to dive deeper?"
Keep your chatbot responses focused and on-topic. Prevent users from getting overwhelmed with information they didn't request.
Integrate into IDE plugins, CLI tools, or coding assistants to keep AI code suggestions concise and relevant.
Match response depth to the learner's level. Beginners get simple answers; advanced users get technical detail on demand.
Enforce response quality standards across your organization's AI-powered tools with consistent filtering policies.
Open source for personal use. Commercial license for production.
For solo developers
For growing teams
ConversationalFilter is dual-licensed. The core library is open source under MIT for personal and non-commercial use. Commercial use requires a license. Install and try it now:
pip install conversational-filter