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Chatbot Studio is an open-source, self-hostable project for configuring AI agents, testing them against their saved settings, and publishing them as website chatbots or connecting them to WhatsApp. Its central design choice is to keep an agent’s intelligence separate from the channel that presents it: the same configured agent can be reused across different publishing surfaces. The capabilities and implementation described here are those reported by project author Mohammad Joud Julius, not independently verified test results.
Why separate an agent from its channel?
A chatbot is more than its model prompt. It may also need tools, knowledge, memory, guardrails and rules for when a person should take over. Meanwhile, a website widget needs its own appearance, welcome message, allowed domains and usage limits. Chatbot Studio treats these as two related but distinct configurations.
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The agent holds the model and provider settings, instructions, tools, MCP servers, knowledge, memory, skills, guardrails, sandbox settings and human-in-the-loop behavior. A published chatbot holds channel-specific choices such as its appearance, launcher, welcome experience, suggested prompts, domain allowlist, usage limits and publishing state. Julius sums up the principle: “The agent should.”
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →That separation is useful when an organization wants to maintain one agent while presenting it in different places. It also means channel setup does not replace agent setup: the agent’s instructions and capabilities still need to be configured and tested.
#1 Best Overall
- E-Paper-Like Display: 4.2-inch fully reflective RLCD screen (300×400 resolution), low power consumption, no backlight, faster refresh rate, providing an eye-friendly reading experience similar to an e-ink screen.
- High-Performance Processor: Equipped with an ESP32-S3 dual-core processor (240MHz), supporting 2.4GHz Wi-Fi and Bluetooth 5 (LE) , built-in antenna, easily enabling IoT connectivity and AI applications.
- Supports AI Voice Interaction: Integrated with an SHTC3 high-precision temperature and humidity sensor and a dual-microphone array (supporting noise reduction/echo cancellation), accurately achieving voice recognition and AI voice interaction, compatible with Xiaozhi AI and large models such as Doubao/DeepSeek/GPT.
- Long Batt Life and Strong Expandability: Supports 186-50 Li Batt power + R-T-C backup Batt, Micro SD card slot for data storage, and reserved rich interfaces such as UART/I2C/GPIO for easy expansion of DIY projects. (Note: This version doesn't include 186-50 Li Batt)
- Suitable for DIY Creative Projects and Prototype Development: It can be used to create electronic calendars, smart desktop ornaments, AI intelligent agents, etc., taking into account learning, development and practical application.
What the project says it can do
Configure and test agents
Julius describes support for OpenAI, Anthropic, Google Gemini, Groq, OpenRouter, Ollama and OpenAI-compatible endpoints. An agent can be configured with tools, MCP servers, knowledge, memory, skills and guardrails. The built-in test chat is described as using the saved agent configuration, so operators can try the configured behavior before publishing.
Add knowledge and external tools
Reported knowledge inputs include text, URLs, PDF, DOCX, Markdown, CSV and JSON. The project describes processing these materials into chunks and embeddings for retrieval during conversations. This is a retrieval-augmented generation (RAG) approach: relevant portions of a knowledge source can be supplied to the model when it responds.
For connections to external tool servers, the article lists MCP transports including stdio, SSE and streamable HTTP. That is the project author’s stated support, not a compatibility audit across MCP servers or deployments.
Rank #2
- Talk to Your Hardware – Control sensors, servos, buzzers, and OLED displays using natural language. No complex coding required – just tell the AI what you want to do
- Powerful AI Agent Onboard – Built around UNO Q with 4GB RAM and 32GB eMMC storage. Runs the EmbodiQ AI Agent HAT, enabling real-time reasoning and multi-step task execution with conditional logic
- Versatile Sensor Suite – Includes soil moisture sensor, raindrop sensor, 9g servo motor, and OLED output. Perfect for smart gardening, weather stations, robotics, and automation projects
- Flexible AI Provider Support – Works with OpenAI, OpenRouter, MiniMax, and any OpenAI-compatible API. Choose your preferred model and switch easily via the web-based interface or terminal REPL
- Dual‑Architecture & Ready to Use – Python + Arduino co-processing ensures responsive performance. Comes with acrylic mounting bracket for tidy assembly – ideal for makers, educators, and AI enthusiasts
Publish a website chatbot
The website widget is described as a browser-native custom element using Shadow DOM, intended to isolate its presentation from the host page. The project reports generated integration examples for native HTML, React, Vue, Angular and WordPress. The channel configuration can include a domain allowlist, launcher and welcome message, while the agent configuration remains separate.
Connect WhatsApp
For WhatsApp, Julius describes a separate Node.js bridge built around Baileys. Reported functions include QR pairing, persisted authentication, inbound and outbound messages, quoted replies, typing state and debounce windows. The bridge is also described as supporting audio transcription and optional generated voice replies. These are project-described capabilities; their availability and behavior should be checked against the current repository and the specific deployment.
Hand a conversation to a person
The article describes queues such as General Support, Technical Support, Sales and Billing. When a human is assigned a conversation, the stated behavior is that the person takes ownership and the assistant stops responding as though no handoff had occurred. That ownership transition matters: a handoff is only useful if the automated agent does not continue talking over the person handling the case.
Rank #3
- High-Performance RISC-V Core and Tri-Mode Wireless Communication---Equipped with an ESP32-C6 32-bit RISC-V processor with a 160MHz clock speed, it features 512KB HP SRAM, 16KB LP SRAM, 320KB ROM, and an external 16MB Flash memory. It supports Wi-Fi 6, Bluetooth 5, and IEEE 802.15.4 (Zigbee 3.0 and Thread), and includes an onboard antenna for excellent RF performance.
- 2.16-inch AMOLED High-Definition Touchscreen---Features a 2.16-inch capacitive AMOLED touchscreen with a 480×480 resolution and 16.7 million colors. It utilizes a CO5300 driver chip (QSPI interface) and a CST9220 touch chip (I2C interface), minimizing pin usage. AMOLED offers high contrast, wide viewing angles, rich colors, fast response, and a slim, low-power design.
- AI Voice Dialogue and Sensing Functionality---Designed specifically for the development and functional verification of AI voice dialogue intelligent agent prototypes, it features onboard dual microphones and an audio codec chip, supporting Xiaozhi AI and DeepSeek. The QMI8658 six-axis IMU (3-axis accelerometer, 3-axis gyroscope) supports motion posture detection and step counting. The PCF85063 RTC connects to the batt via the AXP2101 for uninterrupted power supply. (Batt is not included)
- Power Management and Abundant Interfaces---The AXP2101 power management system supports multiple output voltages, charging management, batt management, and lifespan optimization. It features an onboard 3.7V MX1.25 lithium batt charging/discharging interface. It includes a Type-C interface and programmable side buttons for KEY and BOOT. One I2C, one UART, and one USB pad are provided for easy external connection and debugging. (Batt is not included)
- CNC Metal Chassis and Development Scenarios---The CNC unibody metal casing is robust and provides excellent heat dissipation. Suitable for AI voice dialogue intelligent agent prototype development and functional verification scenarios.
Coordinate agents and workflows
The project reports visual workflows with directed acyclic graph (DAG) execution, conditional paths, input mapping, human approval, persisted runs, scheduled execution and execution traces. Its workflow editor uses XYFlow. The article also presents agent teams as a way to coordinate work; the precise behavior of those features depends on the project implementation and configuration.
Evaluate and observe operation
Julius describes repeatable evaluation suites and monitoring for token usage, cost, latency, tool calls, traces, errors, sessions and visitor feedback. The project also describes a prompt-optimization process in which proposed changes are reviewed by a human. Together, these functions are intended to help operators inspect what happened and assess changes rather than relying only on an informal chat test.
How the described workflow fits together
- Configure the agent. Choose a model provider and define instructions, tools, knowledge, memory and relevant controls.
- Test the saved configuration. Use the studio’s test chat to try the configured agent, then use the reported evaluations and traces to examine behavior and failures.
- Set up a channel. Configure a website chatbot’s presentation and access settings, or connect the WhatsApp bridge.
- Publish and monitor. Make the channel available, then inspect sessions, tool calls, errors and visitor feedback as described by the project.
This is the project’s stated lifecycle, not a claim that every step has been independently exercised or that a particular deployment will behave reliably in production.
Rank #4
- This is an AIoT microcontroller development board based on ESP32-S3 with double eye LCD displays, designed for makers and electronics enthusiasts, supporting 2.4GHz Wi-Fi and Bluetooth BLE 5.
- It integrates high-capacity Flash and PSRAM, onboard Dual 1.28inch LCD 240 × 240 resolution displays which can smoothly run GUI programs such as LVGL. Additionally, it also integrates a microphone, speaker header, Lithium battery recharge circuit, and reserves a TF card slot and DIY expansion connectors.
- It is suitable for the quick development based on ESP32-S3 such as HMI (Human-Machine Interface), double eye robotic agents, and AI voice-interactive toys. Whether you want to build a robot that can "wink", create an intelligent IoT Interface, design touch-controlled games, or develop futuristic wearable devices, this board is an ideal choice.
- Onboard ES8311 audio codec and ES7210 audio ADC chip, equipped with standard microphone and speaker header, Supports AI speech interaction. Allows access to online large model platforms such as ChatGPT, DeepSeek, Doubao, etc.
- Onboard TF card slot for convenient local storage expansion, and supports the storing and reading of data, images, audio files, and more. Onboard Lithium battery recharge management module, reserved 3.7V Lithium battery power supply header. Onboard SH1.0 14PIN connector, adapting UART, I2C and some IO interfaces, for easy DIY customization.
Reported stack and local setup
The author’s October 1, 2026 article identifies the following stack and versions. They reflect the article’s description on that date; software versions and repository instructions can change.
| Part | Reported technologies |
|---|---|
| Frontend | Next.js 16, React 19, TypeScript, Tailwind CSS, Zustand and XYFlow |
| API and runtime | Python 3.12+, FastAPI, Pydantic, Motor, MongoDB, APScheduler and MCP |
| Website widget | A separate JavaScript package built with esbuild |
| WhatsApp transport | A separate Node.js service |
| Container setup | Docker Compose described as combining Next.js, FastAPI, MongoDB and the WhatsApp bridge, with an optional nginx SSL profile |
For local setup, the article lists Node.js 20+, Python 3.12+, uv and MongoDB 7. Its described routes are to clone the repository, create a .env file, install dependencies, start MongoDB, and run npm run dev, or use docker compose up --build. These are the author’s setup instructions, not a verified installation guide; consult the live Chatbot Studio repository for current commands and configuration. The article warns that placeholder secrets must be replaced before a real deployment.
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Security claims and what they do—and do not—establish
The project author reports encrypted provider credentials and MCP secrets, JWT authentication, optional TOTP two-factor authentication, short-lived widget sessions, domain allowlists, rate limiting, visitor IP hashing, role-based access and queue-based conversation access. These are project-reported mechanisms. Their presence does not by itself establish how they are implemented, configured or protected in a particular installation.
Best Value
- Built for Custom Integration: Keep control of the enclosure, mounting and final device layout. The open-board format fits robots, kiosks, custom voice devices and embedded prototypes where flexible mechanical integration matters.
- Onboard Voice Processing: XVF3800 performs AEC, beamforming, de-reverberation, DoA, VAD, AGC and noise suppression before audio reaches your application, helping reduce downstream audio preprocessing.
- 360° Far-Field Voice Capture: Four MEMS microphones in a circular array support speech pickup from different directions at distances up to 5 m, so users do not need to speak toward one fixed microphone position.
- XIAO ESP32S3 for Embedded Voice: The pre-soldered XIAO adds Wi-Fi, Bluetooth Low Energy and MCU-side control for connected voice interfaces, local wake-word projects and custom embedded applications.
- Firmware Options: Ships with Standard I2S firmware for XIAO ESP32S3 and is not a USB audio device by default; switch to USB firmware for host audio or use dedicated 48 kHz HA I2S firmware for Home Assistant and ESPHome Voice; configurations are separate.
The available project descriptions do not establish an independent security review, penetration-test result, vulnerability assessment or production reliability record. Anyone considering a deployment that handles customer conversations or credentials should evaluate the code and configuration, review its threat model, and decide whether it meets their security and operational requirements.
Who might find Chatbot Studio useful?
It is aimed at developers who want to configure agents and publish them through multiple channels while keeping channel presentation separate from agent behavior. The project’s reported evaluation, workflow and observability features also speak to teams that want more than a prompt editor. Self-hosting is part of its positioning, but it comes with operational work: the listed setup involves multiple runtimes and services, and production use requires careful configuration and maintenance.
The project is presented as MIT licensed, according to Julius and the repository description. Readers should check the repository’s current license file and terms before adopting or redistributing the software. The project page is live and may change; its descriptions are not independent confirmation that every listed feature works in a running installation.
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