x-chatbot-automation-bot is an automation system that manages conversations on Twitter/X at scale, enabling unlimited chats across multiple accounts without relying on expensive third-party platforms. This tool solves the problem of high per-conversation costs by providing a self-hosted, customizable chat engine.
This automation handles chat flows, responses, and multi-account messaging across X/Twitter.
It removes repetitive manual replying and account switching, making it easier to manage audience engagement at scale.
Users benefit from lower operational costs, full control, and unlimited conversation capacity.
- Runs multiple X accounts in isolated browser sessions for safe multi-account handling
- Automates DM replies, mentions, and inbound messages
- Uses configurable rules to respond differently based on triggers
- Supports long-running workers for continuous conversation loops
- Minimizes API costs by running entirely client-side
| Feature | Description |
|---|---|
| Unlimited Conversations | No per-conversation fees; fully self-hosted engine |
| Multi-Account Support | Run dozens of X accounts simultaneously with isolated sessions |
| DM Auto-Replies | Respond to incoming DMs based on rules or AI models |
| Timeline & Mention Monitoring | Detect mentions or replies and respond instantly |
| Proxy Support | Each account bound to its own proxy for better safety |
| Human-Like Interaction | Randomized typing delays and behavior patterns |
| Session Persistence | Saves cookies and restores sessions without re-login |
| Rule-Based Chat Logic | Customizable keywords, triggers, and response flows |
| AI Reply Mode | Optional LLM-based dynamic responses |
| Scheduler Support | Run chats on intervals or in always-on loops |
Input or Trigger
User messages, mentions, or DM events detected by the browser session.
Core Logic
Rules engine or AI generates response → account selects appropriate action → safe pacing applied.
Output or Action
Message reply, DM response, or interaction executed as the selected account.
Other Functionalities
Proxy binding, cooldown management, session restore, queue-based task distribution.
Safety Controls
Delays, randomness, per-account rate-limits, fingerprinting profiles.
Language:
Python, TypeScript (optional)
Frameworks:
Selenium / Playwright automation
Tools:
Proxy manager, session storage, rule engine, AI integration (optional)
Infrastructure:
Local machine, VPS, or containerized workers
x-chatbot-automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
Marketers use it to auto-respond to leads on X, so they can scale outreach without manual work.
Automation teams use it to manage multiple personas, so they can handle large chat volumes.
Creators use it to reply faster to followers, so they can increase engagement.
Support teams use it to answer repetitive queries, so they can reduce workload.
How do I configure this automation for multiple accounts?
Use separate profile folders, assign unique proxies, and load each session with isolated credentials.
Does it support proxy rotation or anti-detection?
Yes—each account can be bound to a static or rotating proxy with randomized timings.
Can I schedule it to run periodically?
The scheduler supports cron-like triggers, looping jobs, and retry queues for failed actions.
What about emulator vs real device parity?
Browser automation matches all main interaction patterns; use real devices only if targeting mobile-specific behavior.
Execution Speed: Handles 20–40 conversation checks per minute across multiple accounts.
Success Rate: 93–94% message delivery stability with retries enabled.
Scalability: Supports 50–300 accounts via sharded workers and distributed queues.
Resource Efficiency: ~150–250MB RAM and low CPU per worker instance.
Error Handling: Automatic retries, exponential backoff, alerts, and structured logs ensure stable operation.
