<h1 align="center">
</h1>
<p align="center">
  <p align="center"><b>End-to-end open-source voice agents platform</b>: Quickly build voice firsts conversational assistants through a json. </p>
</p>

<h4 align="center">
  <a href="https://discord.gg/59kQWGgnm8">Discord</a> |
  <a href="https://docs.bolna.ai">Hosted Docs</a> |
  <a href="https://bolna.ai">Website</a>
</h4>

<h4 align="center">
  <a href="https://discord.gg/59kQWGgnm8">
      <img src="https://img.shields.io/static/v1?label=Chat%20on&message=Discord&color=blue&logo=Discord&style=flat-square" alt="Discord">
  </a>
  <a href="https://github.com/bolna-ai/bolna/blob/main/LICENSE">
    <img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="Bolna is released under the MIT license." />
  </a>
  <a href="https://github.com/bolna-ai/bolna/blob/main/CONTRIBUTING.md">
    <img src="https://img.shields.io/badge/PRs-Welcome-brightgreen" alt="PRs welcome!" />
  </a>
</h4>

> [!NOTE]
> We are actively looking for maintainers.

## Introduction

**[Bolna](https://bolna.ai)** is the end-to-end open source production ready framework for quickly building LLM based voice driven conversational applications.

## Demo
https://github.com/bolna-ai/bolna/assets/1313096/2237f64f-1c5b-4723-b7e7-d11466e9b226


## What is this repository?
This repository contains the entire orchestration platform to build voice AI applications. It technically orchestrates voice conversations using combination of different ASR+LLM+TTS providers and models over websockets.


## Components
Bolna helps you create AI Voice Agents which can be instructed to do tasks beginning with:

1. Orchestration platform (this open source repository)
2. Hosted APIs (https://docs.bolna.ai/api-reference/introduction) built on top of this orchestration platform [currently closed source]
3. No-code UI playground at https://platform.bolna.ai/ using the hosted APIs + tailwind CSS [currently closed source]


## Development philosophy
1. Any integration, enhancement or feature initially lands on this open source package since it forms the backbone of our Hosted APIs and dashboard
2. Post that we expose APIs or make changes to existing APIs as required for the same
3. Thirdly, we push it to the UI dashboard

```mermaid
graph LR;
    A[Bolna open source] -->B[Hosted APIs];
    B[Hosted APIs] --> C[Hosted Playground]
```

## Supported providers and models
1. Initiating a phone call using telephony providers like `Twilio`, `Plivo`, `Exotel` (coming soon), `Vonage` (coming soon) etc.
2. Transcribing the conversations using `Deepgram`, `Azure` etc.
3. Using LLMs like `OpenAI`, `DeepSeek`, `Llama`, `Cohere`, `Mistral`,  etc to handle conversations
4. Synthesizing LLM responses back to telephony using `AWS Polly`, `ElevenLabs`, `Deepgram`, `OpenAI`, `Azure`, `Cartesia`, `Smallest` etc.


Refer to the [docs](https://docs.bolna.ai/providers) for a deepdive into all supported providers.


## Local example setup [will be moved to a different repository]
A basic local setup includes usage of [Twilio](local_setup/telephony_server/twilio_api_server.py) or [Plivo](local_setup/telephony_server/plivo_api_server.py) for telephony. We have dockerized the setup in `local_setup/`. One will need to populate an environment `.env` file from `.env.sample`.

The setup consists of four containers:

1. Telephony web server:
   * Choosing Twilio: for initiating the calls one will need to set up a [Twilio account](https://www.twilio.com/docs/usage/tutorials/how-to-use-your-free-trial-account)
   * Choosing Plivo: for initiating the calls one will need to set up a [Plivo account](https://www.plivo.com/)
2. Bolna server: for creating and handling agents 
3. `ngrok`: for tunneling. One will need to add the `authtoken` to `ngrok-config.yml`
4. `redis`: for persisting agents & prompt data

### Quick Start

The easiest way to get started is to use the provided script:

```bash
cd local_setup
chmod +x start.sh
./start.sh
```

This script will check for Docker dependencies, build all services with BuildKit enabled, and start them in detached mode.

### Manual Setup

Alternatively, you can manually build and run the services:

1. Make sure you have Docker with Docker Compose V2 installed
2. Enable BuildKit for faster builds:
   ```bash
   export DOCKER_BUILDKIT=1
   export COMPOSE_DOCKER_CLI_BUILD=1
   ```
3. Build the images:
   ```bash
   docker compose build
   ```
4. Run the services:
   ```bash
   docker compose up -d
   ```

To run specific services only:

```bash
docker compose up -d bolna-app twilio-app
# or
docker compose up -d bolna-app plivo-app
```

Once the docker containers are up, you can now start to create your agents and instruct them to initiate calls.


## Example agents to create, use and start making calls
You may try out different agents from [example.bolna.dev](https://examples.bolna.dev).

## Programmatic usage (minimal example)

You can also build and run an agent directly in Python without the local telephony setup.

Example script: `examples/simple_assistant.py`

```python
import asyncio
from bolna.assistant import Assistant
from bolna.models import (
    Transcriber,
    Synthesizer,
    ElevenLabsConfig,
    LlmAgent,
    SimpleLlmAgent,
)


async def main():
    assistant = Assistant(name="demo_agent")

    # Configure audio input (ASR)
    transcriber = Transcriber(provider="deepgram", model="nova-2", stream=True, language="en")

    # Configure LLM
    llm_agent = LlmAgent(
        agent_type="simple_llm_agent",
        agent_flow_type="streaming",
        llm_config=SimpleLlmAgent(
            provider="openai",
            model="gpt-4o-mini",
            temperature=0.3,
        ),
    )

    # Configure audio output (TTS)
    synthesizer = Synthesizer(
        provider="elevenlabs",
        provider_config=ElevenLabsConfig(
            voice="George", voice_id="JBFqnCBsd6RMkjVDRZzb", model="eleven_turbo_v2_5"
        ),
        stream=True,
        audio_format="wav",
    )

    # Build a single coherent pipeline: transcriber -> llm -> synthesizer
    assistant.add_task(
        task_type="conversation",
        llm_agent=llm_agent,
        transcriber=transcriber,
        synthesizer=synthesizer,
        enable_textual_input=False,
    )

    # Stream results
    async for chunk in assistant.execute():
        print(chunk)


if __name__ == "__main__":
    asyncio.run(main())
```

How to run:

```bash
export OPENAI_API_KEY=...
export DEEPGRAM_AUTH_TOKEN=...
export ELEVENLABS_API_KEY=...
python examples/simple_assistant.py
```

This demonstrates orchestration and streaming output. For telephony, use the services in `local_setup/`.

Note: For REST-based usage (Agent CRUD over HTTP), see `API.md` in the repo root.

Expected output shape: `assistant.execute()` is an async generator yielding per-task result dicts (event-like chunks). The exact keys depend on configured tools/providers; treat it as a stream and process incrementally.

### Text-only pipeline example

If you want a text-only flow (no transcriber/synthesizer), you can enable a text-only pipeline:

Example script: `examples/text_only_assistant.py`

```python
import asyncio
from bolna.assistant import Assistant
from bolna.models import LlmAgent, SimpleLlmAgent


async def main():
    assistant = Assistant(name="text_only_agent")

    llm_agent = LlmAgent(
        agent_type="simple_llm_agent",
        agent_flow_type="streaming",
        llm_config=SimpleLlmAgent(
            provider="openai",
            model="gpt-4o-mini",
            temperature=0.2,
        ),
    )

    # No transcriber/synthesizer; enable a text-only pipeline
    assistant.add_task(
        task_type="conversation",
        llm_agent=llm_agent,
        enable_textual_input=True,
    )

    async for chunk in assistant.execute():
        print(chunk)


if __name__ == "__main__":
    asyncio.run(main())
```

How to run (text-only):

```bash
export OPENAI_API_KEY=...
python examples/text_only_assistant.py
```

Expected output shape: `assistant.execute()` yields streaming dicts per task step; fields vary by configuration. Handle chunk-by-chunk.


## Using your own providers
You can populate the `.env` file to use your own keys for providers.

<details>

<summary>ASR Providers</summary><br>
These are the current supported ASRs Providers:

| Provider     | Environment variable to be added in `.env` file |
|--------------|-------------------------------------------------|
| Deepgram     | `DEEPGRAM_AUTH_TOKEN`                           |

</details>
&nbsp;<br>

<details>
<summary>LLM Providers</summary><br>
Bolna uses LiteLLM package to support multiple LLM integrations.

These are the current supported LLM Provider Family:
https://github.com/bolna-ai/bolna/blob/10fa26e5985d342eedb5a8985642f12f1cf92a4b/bolna/providers.py#L30-L47

For LiteLLM based LLMs, add either of the following to the `.env` file depending on your use-case:<br><br>
`LITELLM_MODEL_API_KEY`: API Key of the LLM<br>
`LITELLM_MODEL_API_BASE`: URL of the hosted LLM<br>
`LITELLM_MODEL_API_VERSION`: API VERSION for LLMs like Azure

For LLMs hosted via VLLM, add the following to the `.env` file:<br>
`VLLM_SERVER_BASE_URL`: URL of the hosted LLM using VLLM

</details>
&nbsp;<br>

<details>

<summary>TTS Providers</summary><br>
These are the current supported TTS Providers:
https://github.com/bolna-ai/bolna/blob/c8a0d1428793d4df29133119e354bc2f85a7ca76/bolna/providers.py#L7-L14

| Provider   | Environment variable to be added in `.env` file  |
|------------|--------------------------------------------------|
| AWS Polly  | Accessed from system wide credentials via ~/.aws |
| Elevenlabs | `ELEVENLABS_API_KEY`                             |
| OpenAI     | `OPENAI_API_KEY`                                 |
| Deepgram   | `DEEPGRAM_AUTH_TOKEN`                            |
| Cartesia   | `CARTESIA_API_KEY`                            |
| Smallest   | `SMALLEST_API_KEY`                            |

</details>
&nbsp;<br>

<details>

<summary>Telephony Providers</summary><br>
These are the current supported Telephony Providers:

| Provider | Environment variable to be added in `.env` file                                                                                                                    |
|----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Twilio   | `TWILIO_ACCOUNT_SID`, `TWILIO_AUTH_TOKEN`, `TWILIO_PHONE_NUMBER`|
| Plivo    | `PLIVO_AUTH_ID`, `PLIVO_AUTH_TOKEN`, `PLIVO_PHONE_NUMBER`|

</details>

## Open-source v/s Hosted APIs
**We have in the past tried to maintain both the open source and the hosted solution (via APIs and a UI dashboard)**.

We have fluctuated b/w maintaining this repository purely from a point of time crunch and not interest.

Currently, we are continuing to maintain it for the community and improving the adoption of Voice AI.

Though the repository is completely open source, you can connect with us if interested in managed hosted offerings or more customized solutions.
<a href="https://calendly.com/bolna/30min"><img alt="Schedule a meeting" src="https://cdn.cookielaw.org/logos/122ecfc3-4694-42f1-863f-2db42d1b1e68/0bcbbcf4-9b83-4684-ba59-bc913c0d5905/c21bea90-f4f1-43d1-8118-8938bbb27a9d/logo.png" /></a>

## Extending with other Telephony Providers
In case you wish to extend and add some other Telephony like Vonage, Telnyx, etc. following the guidelines below:
1. Make sure bi-directional streaming is supported by the Telephony provider
2. Add the telephony-specific input handler file in [input_handlers/telephony_providers](https://github.com/bolna-ai/bolna/tree/master/bolna/input_handlers/telephony_providers) writing custom functions extending from the [telephony.py](https://github.com/bolna-ai/bolna/blob/master/bolna/input_handlers/telephony.py) class
   1. This file will mainly contain how different types of event packets are being ingested from the telephony provider
3. Add telephony-specific output handler file in [output_handlers/telephony_providers](https://github.com/bolna-ai/bolna/tree/master/bolna/output_handlers/telephony_providers) writing custom functions extending from the [telephony.py](https://github.com/bolna-ai/bolna/blob/master/bolna/output_handlers/telephony.py) class
   1. This mainly concerns converting audio from the synthesizer class to a supported audio format and streaming it over the websocket provided by the telephony provider
4. Lastly, you'll have to write a dedicated server like the example [twilio_api_server.py](https://github.com/bolna-ai/bolna/blob/master/local_setup/telephony_server/twilio_api_server.py) provided in [local_setup](https://github.com/bolna-ai/bolna/blob/master/local_setup/telephony_server) to initiate calls over websockets.


## Contributing
We love all types of contributions: whether big or small helping in improving this community resource.

1. There are a number of [open issues present](https://github.com/bolna-ai/bolna/issues) which can be good ones to start with
2. If you have suggestions for enhancements, wish to contribute a simple fix such as correcting a typo, or want to address an apparent bug, please feel free to initiate a new issue or submit a pull request
2. If you're contemplating a larger change or addition to this repository, be it in terms of its structure or the features, kindly begin by creating a new issue [open a new issue :octocat:](https://github.com/bolna-ai/bolna/issues/new) and outline your proposed changes. This will allow us to engage in a discussion before you dedicate a significant amount of time or effort. Your cooperation and understanding are appreciated
