Open-Sourcing ConversationalBot: A Low-Latency Voice Assistant for AI Conversations

Today, we’re excited to announce the open-source release of NesterConversationalBot — our production-tested framework for building voice-first AI applications. After months of refinement across real-world deployments, we’re making this available to the community.

Why Another Voice Framework?

Most existing voice AI solutions fall into two categories: either overly simplistic (think basic IVR systems) or prohibitively complex enterprise platforms. We built NesterConversationalBot to fill the gap — a framework that’s production-ready yet accessible.

The key differentiator: ~1-1.5 second response times for complete voice interactions, including speech recognition, LLM reasoning, and speech synthesis. This isn’t a demo metric — it’s what we achieve in production with real users.

Core Architecture

NesterConversationalBot is built on four tightly integrated components, each optimized for its specific role in the voice pipeline.

1. Speech-to-Text (STT)

The system streams audio in chunks and outputs both partial and final transcription results. This streaming approach is crucial — we don’t wait for complete utterances before starting processing.

Primary providers:

• Deepgram: Our default choice for production deployments (sub-300ms latency)
• OpenAI Whisper: Higher accuracy for complex audio, slightly higher latency

Additional providers supported via Pipecat integration: AWS Transcribe, Azure Speech, Google Cloud Speech.

2. LLM Integration & Conversation Management

At the heart of the system is a ConversationManager that handles:

• Session state persistence across turns
• Context window management for long conversations
• Tool/function invocation coordination
• Memory retrieval from vector stores

The architecture enables bots that reason and act, not just respond. When a user asks “What’s my appointment tomorrow?”, the system doesn’t just generate text — it calls the calendar API, processes the result, and formulates a natural response.

3. Function Calling & RAG Integration

Voice interactions often require real-time data: checking inventory, looking up customer records, querying knowledge bases. NesterConversationalBot includes first-class support for:

• Dynamic tool invocation: Database queries, API calls, and custom functions execute mid-conversation
• RAG (Retrieval-Augmented Generation): Semantic search against document stores for context-aware responses
• Streaming function results: Long-running operations don’t block the conversation

The critical insight: tools must be used selectively and strategically. Every function call adds latency. Our framework includes intelligent routing that only invokes tools when necessary, maintaining the sub-1.5 second target.

4. Text-to-Speech (TTS)

For synthesis, we selected ElevenLabs as the primary provider based on three criteria:

• Voice quality: Natural-sounding output across different speaking styles
• Indian voice support: Critical for our multilingual deployments
• Streaming capability: Audio generation begins before the full response is complete
• Emotion modeling: Built-in support for tone variation

Key Features

Real-Time Voice Streaming

The entire pipeline operates in streaming mode. Audio flows from the user’s microphone → STT → LLM → TTS → speaker in a continuous stream. There’s no “processing...” pause that plagues traditional voice systems.

Modular LLM Execution

Swap LLM providers without changing your application logic. The framework supports:

• OpenAI GPT-4 / GPT-4o
• Anthropic Claude
• Google Gemini
• Local models via Ollama

Multilingual Support

Built-in support for multilingual conversations, including code-switching. Users can seamlessly switch between English and Hindi (Hinglish) within the same conversation — the system handles it transparently.

Performance Monitoring

Production voice systems need observability. The framework includes built-in metrics for:

• End-to-end latency per turn
• STT/LLM/TTS latency breakdown
• Tool invocation timing
• Conversation session analytics

Getting Started

Getting up and running takes minutes:

# Clone the repository
git clone https://github.com/nesterlabs-ai/NesterConversationalBot.git
cd NesterConversationalBot

# Install dependencies
pip install -r requirements.txt

# Configure your API keys
cp env.example .env
# Edit .env with your Deepgram, OpenAI, and ElevenLabs keys

# Start the WebSocket server
python websocket_server.py

The server exposes a WebSocket endpoint that accepts audio streams and returns synthesized responses. Connect any frontend — web, mobile, or embedded devices.

Architecture Deep Dive

For teams building production systems, here’s how the components interact:

The Pipeline Flow

1. Audio Input: WebSocket receives audio chunks (16kHz, 16-bit PCM)
2. Streaming STT: Deepgram processes chunks, emitting partial transcripts
3. Intent Detection: On utterance completion, the ConversationManager determines required actions
4. Tool Execution: If needed, functions execute in parallel where possible
5. LLM Generation: Response generation begins with all context assembled
6. Streaming TTS: Audio synthesis starts immediately, streaming back to the client

Latency Optimization Techniques

Achieving sub-1.5 second latency required specific optimizations:

• Speculative execution: Start likely tool calls before the utterance completes
• Response chunking: Begin TTS on the first sentence while generating the rest
• Connection pooling: Maintain warm connections to all external services
• Intelligent caching: Cache frequent queries and tool results

Use Cases

NesterConversationalBot powers several production deployments:

• Healthcare intake: Patient scheduling and information collection
• Customer support: Technical troubleshooting with knowledge base integration
• Sales assistance: Product recommendations with CRM integration
• Internal tools: Voice interfaces for enterprise applications

Enterprise Support

The open-source framework covers most use cases, but enterprise deployments often need additional capabilities:

• Custom voice cloning and branding
• On-premise deployment options
• Advanced analytics and compliance logging
• SLA-backed support

Contact our team for enterprise licensing and support options.

What’s Next

This release is v1.0, but development continues. On the roadmap:

• Emotion detection: Real-time sentiment analysis for adaptive responses
• Multi-party conversations: Support for conference-style interactions
• Visual context: Integration with vision models for multi-modal interactions
• Edge deployment: Optimized builds for on-device inference

Contributing

We welcome contributions from the community. Whether it’s bug fixes, new features, or documentation improvements, check out our GitHub repository to get started.