Bbs

FastMCP-based telnet client for BBS (Bulletin Board System) interactions with auto-learning capabilities. Enables AI agents to interact with legacy telnet-based systems through the Model Context Protocol (MCP).

⚠️ AI-Generated Code Disclaimer

This project was generated using AI assistance. While functional, it may contain bugs, security vulnerabilities, or unexpected behavior. Use at your own risk. The author assumes no responsibility or liability for any issues, damages, or losses resulting from the use of this software. Review the code thoroughly before using in any production environment.

Overview

bbsbot bridges modern AI agents with vintage BBS systems by providing:

• Full terminal emulation with ANSI/CP437 support
• Pattern-based screen reading and navigation
• Automatic discovery and documentation of menus and prompts
• Session logging for analysis and replay
• MCP tool exposure for seamless AI integration

Architecture

bbsbot uses a clean layered architecture with proper separation of concerns:

graph TB
    subgraph "AI Agent"
        LLM[Claude/LLM]
        MCP[MCP Client]
    end

    subgraph "bbsbot Server"
        App[FastMCP App]
        SM[SessionManager]

        subgraph "Core Layer"
            Session[Session]
        end

        subgraph "Transport Layer"
            Transport[TelnetTransport]
        end

        subgraph "Terminal Layer"
            Emulator[TerminalEmulator<br/>pyte]
        end

        subgraph "Learning Layer"
            Engine[LearningEngine]
            Discovery[Menu Discovery]
        end

        subgraph "Logging Layer"
            Logger[SessionLogger]
        end
    end

    subgraph "BBS System"
        Telnet[Telnet Server :23]
        BBS[BBS Software]
    end

    subgraph "Knowledge Base"
        Prompts[prompt-catalog.md]
        Menus[menu-map.md]
        Flows[navigation-flows.md]
    end

    LLM --> MCP
    MCP -->|MCP Tools| App
    App --> SM
    SM --> Session
    Session --> Transport
    Session --> Emulator
    Session --> Engine
    Session --> Logger
    Transport -->|RFC 854 + IAC Escaping| Telnet
    Telnet --> BBS
    BBS -->|ANSI/CP437| Telnet
    Telnet -->|Raw Bytes| Transport
    Transport --> Emulator
    Engine --> Discovery
    Engine --> Prompts
    Engine --> Menus
    Engine --> Flows

Architecture Layers

• Transport Layer (transport/): Protocol abstraction (telnet with RFC 854 compliance, IAC byte escaping)
• Terminal Layer (terminal/): ANSI/CP437 terminal emulation using pyte
• Core Layer (core/): Session management with resource limits and state isolation
• Learning Layer (learning/): Auto-discovery of menus/prompts, knowledge base management
• Logging Layer (logging/): Structured JSONL session logging with raw bytes

Features

• Telnet Client: Full RFC 854 telnet protocol with option negotiation (BINARY, SGA, NAWS, TTYPE)
• Terminal Emulation: Complete ANSI/CP437 terminal with 80x25 (configurable) screen buffer
• Screen Reading: Extract text, match patterns, wait for prompts with timeout control
• Auto-Learning: Discover menus [A] Option, prompts Enter name:, and document navigation flows
• Session Logging: JSONL format with timestamps, context, and raw bytes for replay/analysis
• MCP Integration: 25+ tools for connection, navigation, learning, and session management
• Keepalive: Configurable interval to prevent idle disconnections

Installation

Prerequisites

Install uv (recommended package installer):

curl -LsSf https://astral.sh/uv/install.sh | sh

Or see uv installation docs for other methods.

Installing bbsbot

bbsbot is an MCP server that must be configured in your MCP client (Claude Desktop, Cline, etc.).

Option 1: Install as a tool (recommended)

uv tool install bbsbot

Then add to your MCP client configuration:

{
  "mcpServers": {
    "bbsbot_local_tw2002": {
      "command": "bbsbot"
    }
  }
}

Local TW2002 MCP vs External MCP Connectors

bbsbot provides TW2002 tools locally, but those tools only exist in the MCP server process you start from this repository/environment. In multi-connector clients, you may also have unrelated MCP connectors installed; those will not expose tw2002_* tools.

For swarm runtime/ROI diagnostics and anti-collapse control references, see:

• docs/guides/SWARM_OPERATIONS_TELEMETRY.md

Use an explicit local alias and tool filter for TW2002 operations:

{
  "mcpServers": {
    "bbsbot_local_tw2002": {
      "command": "bbsbot",
      "args": ["serve", "--tools", "tw2002"]
    }
  }
}

If tw2002_* tools are missing, first verify the configured command includes serve --tools tw2002.

Option 2: Install with pip

pip install bbsbot

Then configure your MCP client to run bbsbot as a server.

Development Installation

git clone https://github.com/livingstaccato/bbsbot.git
cd bbsbot
uv pip install -e ".[dev]"

Quick Start Example

Here's a complete example of an AI agent connecting to a BBS, navigating menus, and reading messages:

from fastmcp import Client
from fastmcp.mcp_config import StdioMCPServer

# Start bbsbot server
server = StdioMCPServer(command="bbsbot", args=[])

async with Client(server.to_transport()) as client:
    # Connect to BBS
    await client.call_tool("bbs_connect", {
        "host": "bbs.example.com",
        "port": 23,
        "cols": 80,
        "rows": 25,
        "term": "ANSI",
        "send_newline": True
    })

    # Wait for main menu
    screen = await client.call_tool("bbs_read_until_pattern", {
        "pattern": r"\[M\] Main Menu",
        "timeout_ms": 5000
    })

    # Navigate to messages
    await client.call_tool("bbs_send", {"keys": "M\r"})

    # Read until message list appears
    screen = await client.call_tool("bbs_read_until_pattern", {
        "pattern": r"Message #\d+",
        "timeout_ms": 3000
    })

    print(screen["screen"])  # Display the screen

    # Disconnect
    await client.call_tool("bbs_disconnect", {})

Usage

As MCP Server

Run as an MCP server to expose BBS tools:

# Start the server (stdio transport)
bbsbot

# Or specify config
bbsbot --host localhost --port 2002

Programmatic Usage

Direct Python API usage without MCP:

from bbsbot.core.session_manager import SessionManager

manager = SessionManager(max_sessions=10)

# Connect and create session
session_id = await manager.create_session(
    host="bbs.example.com",
    port=23,
    cols=80,
    rows=25,
    term="ANSI",
    send_newline=True,
    reuse=False
)

# Get session
session = await manager.get_session(session_id)

# Read screen with timeout
snapshot = await session.read(timeout_ms=250, max_bytes=8192)

# Snapshot contains:
# - screen: formatted text (80x25)
# - screen_hash: SHA256 of screen text
# - cursor: {x, y} position
# - cols, rows, term

print(snapshot["screen"])
print(f"Cursor at: {snapshot['cursor']}")

# Send keys
await session.send("A\r\n")

# Wait for specific pattern (manual implementation)
import re
import asyncio

pattern = re.compile(r"Enter your name:")
timeout = 5.0
interval = 0.1
start_time = asyncio.get_event_loop().time()

matched = False
while asyncio.get_event_loop().time() - start_time < timeout:
    snapshot = await session.read(timeout_ms=int(interval * 1000), max_bytes=8192)
    if pattern.search(snapshot["screen"]):
        matched = True
        break
    await asyncio.sleep(interval)

if matched:
    print("Found prompt!")
    await session.send("Alice\r")

# Disconnect
await manager.close_session(session_id)

Note: For convenience, use the MCP tools which provide higher-level operations like bbs_read_until_pattern. The direct API is lower-level and requires manual pattern matching loops.

MCP Tools Reference

The following tools are exposed when running as an MCP server:

Note: In v0.2.0, we removed duplicate *_screen variants and utility wrappers to simplify the API. Use bbs_read()["screen"] to extract screen text, and compose operations instead of using convenience wrappers like bbs_expect or bbs_play_step.

Connection Management

bbs_connect

Connect to a BBS via telnet.

{
  "host": "bbs.example.com",
  "port": 23,
  "cols": 80,
  "rows": 25,
  "term": "ANSI",
  "send_newline": true,
  "reuse": false
}

bbs_disconnect

Disconnect from the BBS. Always call before exit.

{}

bbs_status

Get connection status, session ID, last RX/TX timestamps, keepalive info.

{}

Screen Interaction

bbs_read

The primary read method. Reads from telnet stream and returns full snapshot with screen text, raw output, cursor position, and hash. Always logs raw bytes to JSONL so LLM can refer back if uncertain about screen content.

Use timeout_ms=0 to get current screen state without waiting for new data.

{
  "timeout_ms": 250,
  "max_bytes": 8192
}

Returns:

{
  "screen": "formatted 80x25 text...",
  "raw": "raw terminal output",
  "raw_bytes_b64": "base64 encoded raw bytes",
  "screen_hash": "sha256 of screen text",
  "cursor": {"x": 0, "y": 0},
  "cols": 80,
  "rows": 25,
  "term": "ANSI"
}

Important: Every bbs_read call logs the full snapshot including raw_bytes_b64 to session.jsonl (if logging enabled). If the LLM misinterprets screen content, it can consult the log file for the exact raw bytes received.

bbs_read_until_nonblank

Keep reading until screen has non-whitespace content or timeout.

{
  "timeout_ms": 5000,
  "interval_ms": 100,
  "max_bytes": 8192
}

bbs_read_until_pattern

Read until screen matches regex pattern.

{
  "pattern": "Enter your name:",
  "timeout_ms": 5000,
  "interval_ms": 100,
  "max_bytes": 8192
}

Returns: snapshot with additional "matched": true/false

bbs_send

Send keystrokes to BBS (CP437 encoded).

{
  "keys": "A\r\n"
}

Use \r for Enter, \n for Line Feed, \x1b for Escape.

bbs_wake

Try multiple keystroke sequences until screen changes (useful for idle timeouts).

{
  "timeout_ms": 5000,
  "interval_ms": 250,
  "max_bytes": 8192,
  "keys_sequence": ["\r", " ", "\r\n"]
}

Auto-Learning

bbs_auto_learn_enable

Enable/disable automatic learning of prompts and menus.

{
  "enabled": true
}

bbs_auto_learn_prompts

Configure rules to auto-detect prompts.

{
  "rules": [
    {
      "prompt_id": "username",
      "regex": "Enter your name:",
      "input_type": "text",
      "example_input": "Alice"
    }
  ]
}

bbs_auto_learn_menus

Configure rules to auto-detect menu options.

{
  "rules": [
    {
      "menu_id": "main",
      "regex": "\\[M\\] Main Menu"
    }
  ]
}

bbs_auto_learn_discover

Enable automatic discovery of [X] style menu options.

{
  "enabled": true
}

bbs_learn_menu

Manually document a menu.

{
  "menu_id": "main",
  "title": "Main Menu",
  "options": [
    {"key": "M", "label": "Read Messages"},
    {"key": "P", "label": "Post Message"},
    {"key": "Q", "label": "Quit"}
  ],
  "prompt": "Your choice:"
}

bbs_learn_prompt

Manually document a prompt.

{
  "prompt_id": "username",
  "pattern": "Enter your name:",
  "input_type": "text",
  "example_input": "Alice",
  "notes": "Username for login"
}

bbs_learn_flow

Document navigation between screens.

{
  "from_screen": "main_menu",
  "action": "M",
  "to_screen": "message_list",
  "notes": "Press M to read messages"
}

Session Management

bbs_log_start

Start JSONL session logging. Highly recommended - allows LLM to refer back to raw session data if it misreads the screen.

{
  "path": "session.jsonl"
}

bbs_log_stop

Stop session logging.

{}

bbs_log_note

Add structured note to log for debugging.

{
  "note": "Starting message read loop",
  "context": "messages"
}

bbs_set_context

Set metadata attached to all subsequent log entries.

{
  "context": {
    "menu": "main",
    "action": "reading_messages"
  }
}

Keepalive

bbs_keepalive

Configure automatic keepalive to prevent idle timeout.

{
  "interval_s": 30.0,
  "keys": "\r"
}

Set interval_s to 0 or null to disable.

Typical Workflow

sequenceDiagram
    participant LLM as AI Agent (LLM)
    participant MCP as bbsbot
    participant BBS as BBS System
    participant Log as session.jsonl
    participant KB as Knowledge Base

    LLM->>MCP: bbs_log_start("session.jsonl")
    MCP->>Log: Create log file

    LLM->>MCP: bbs_connect(host, port, ...)
    MCP->>BBS: Telnet connection
    BBS-->>MCP: Welcome screen (ANSI)
    MCP->>Log: Log connection + raw bytes

    LLM->>MCP: bbs_auto_learn_enable(true)
    LLM->>MCP: bbs_auto_learn_discover(true)

    LLM->>MCP: bbs_read_until_pattern("Main Menu")
    MCP->>BBS: Read telnet stream
    BBS-->>MCP: Raw bytes + ANSI codes
    MCP->>MCP: Parse terminal, extract text
    MCP->>Log: Log screen + raw bytes
    MCP->>KB: Discover menu options [A], [B]
    MCP-->>LLM: screen + cursor + hash

    Note over LLM: LLM analyzes screen,<br/>decides to press 'M'

    LLM->>MCP: bbs_send("M\r")
    MCP->>BBS: Send 'M' + Enter
    MCP->>Log: Log keystroke

    LLM->>MCP: bbs_read(250, 8192)
    BBS-->>MCP: Message list screen
    MCP->>Log: Log screen + raw bytes
    MCP-->>LLM: Full snapshot

    Note over LLM: If LLM is uncertain about<br/>screen content, it can<br/>refer to session.jsonl<br/>for raw bytes

    LLM->>MCP: bbs_disconnect()
    MCP->>BBS: Close connection
    MCP->>Log: Log disconnect
    LLM->>MCP: bbs_log_stop()

Key Points

1. Always start session logging (bbs_log_start) - creates complete record with raw bytes
2. Use bbs_read for everything - single method that always logs raw data in JSONL
3. Enable auto-learning early - builds knowledge base for future sessions
4. LLM can refer to logs if uncertain - every bbs_read includes raw_bytes_b64 in session log
5. No separate "get screen" method - use bbs_read(timeout_ms=0) to get current state
6. Knowledge base accumulates - menus, prompts, flows documented in .bbs-knowledge/

Why Single Read Method?

Previously, there were separate methods for reading new data vs. getting current screen. This was confusing and error-prone. Now:

• Single source of truth: bbs_read always reads, always logs raw bytes
• LLM safety: If unsure about screen content, LLM can inspect raw_bytes_b64 from session.jsonl
• Consistent logging: Every screen observation is recorded with full context
• Simpler API: No confusion about which method to use

Configuration

Knowledge Base

By default, learned knowledge is stored in platform-specific user data directories following the XDG Base Directory Specification:

• Linux/BSD: ~/.local/share/bbsbot (or $XDG_DATA_HOME/bbsbot)
• macOS: ~/Library/Application Support/bbsbot
• Windows: %LOCALAPPDATA%\bbsbot

Override the default location with:

export BBSBOT_KNOWLEDGE_ROOT=/path/to/knowledge

To keep knowledge bases per-project (instead of user-wide):

export BBSBOT_KNOWLEDGE_ROOT=$(pwd)/.bbs-knowledge

Terminal Settings

Configure terminal size and keepalive via MCP tools:

# Using MCP client
await client.call_tool("bbs_set_size", {"cols": 80, "rows": 25})
await client.call_tool("bbs_keepalive", {"interval_s": 30.0, "keys": "\r"})

Or programmatically:

# Using SessionManager
session = await manager.get_session(session_id)
await session.set_size(cols=80, rows=25)
# Keepalive is configured per-session through the session manager

Development

Setup

uv pip install -e ".[dev]"

Code Quality

This project uses modern Python 3.11+ features and strict quality tools:

• Type Checking: mypy src/bbsbot
• Linting: ruff check src/bbsbot
• Formatting: ruff format src/bbsbot
• Testing: pytest
• Type Validation: ty src/bbsbot

Run All Checks

ruff check src/bbsbot
ruff format --check src/bbsbot
mypy src/bbsbot
pytest

Regenerate Diagrams

The architecture and workflow diagrams are generated from Mermaid files using mermaid-py.

Generate SVG diagrams using make:

make diagrams

Available targets:

• make diagrams or make diagrams-svg - Generate SVG diagrams (default)
• make diagrams-png - Generate PNG diagrams
• make clean - Remove all generated diagram files
• make help - Show all available targets

Alternatively, run the script directly:

python3 docs/generate_diagrams.py --format svg

Source files:

• docs/diagrams/architecture.mmd → architecture.svg
• docs/diagrams/workflow.mmd → workflow.svg
• docs/diagrams/session-flow.mmd → session-flow.svg

Intelligent Bot & Pattern Testing

bbsbot includes an intelligent bot system that uses prompt detection to autonomously navigate BBS games and test prompt patterns.

TW2002 Trading Bot

Run the TW2002 trading bot:

bbsbot tw2002 bot -c tw2002_bot_config.yaml

Goal Visualization (AI Strategy)

When trading.strategy: ai_strategy is active, goal progress is shown:

• A compact status line every trading.ai_strategy.visualization_interval turns
• A full timeline whenever the goal changes
• A summary report at the end of the session

To disable all goal visualization output:

trading:
  ai_strategy:
    show_goal_visualization: false

Spy / Watch Socket

You can broadcast the live terminal stream over TCP and attach to it from another terminal:

# Start bot with watch socket enabled (raw ANSI stream)
bbsbot tw2002 bot -c tw2002_bot_config.yaml --watch-socket

# Attach viewer
bbsbot spy

If you want structured JSON events (including goal visualization events), use JSON protocol:

# Start bot with JSON watch protocol
bbsbot tw2002 bot -c tw2002_bot_config.yaml --watch-socket --watch-socket-protocol json

# Attach and view JSON lines (the "viz" events include compact/timeline/summary text)
bbsbot spy --encoding utf-8

Goal visualization watch events look like:

{"event":"viz","data":{"kind":"compact|timeline|summary","text":"...","turn":123,"character_name":"..."}}

MCP “Spy” Tools (In-Process)

If you’re running the bot inside the MCP server process, you can query goal progress on demand:

• tw2002_get_goal_visualization (renders compact/timeline/summary)
• tw2002_get_goal_phases (raw phase data)
• tw2002_capabilities (returns grouped tool map + quick entrypoints)
• tw2002_list_sessions / tw2002_set_active_session (deterministic session targeting)
• tw2002_bootstrap (connect + login in one call)

Quick Start

# Run intelligent bot with pattern testing
bbsbot script play_tw2002_intelligent

# Run systematic pattern validation
bbsbot script test_all_patterns

Features

• Reactive Detection: Bot waits for prompts, detects them, and responds appropriately
• Auto-Pagination: Automatically handles "more" prompts and pagination
• Pattern Validation: Tests all 13 defined prompt patterns
• Flow Tracking: Records action→prompt sequences
• Comprehensive Reports: Generates JSON and Markdown results

Architecture

The intelligent bot uses a hybrid reactive approach:

1. Phase 1: Pure reactive - detect prompts as they appear
2. Phase 2: Track prompt sequences and flows
3. Phase 3: Add prediction for common patterns (future)

See docs/guides/INTELLIGENT_BOT.md for complete documentation.

Example Bot Usage

from bbsbot.commands.scripts.play_tw2002_intelligent import IntelligentTW2002Bot

bot = IntelligentTW2002Bot()
await bot.connect()

# Navigate to game
await bot.navigate_twgs_to_game()
await bot.enter_game_as_player("BotName")

# Test commands with automatic prompt detection
await bot.test_command("D\r", "Display computer")
await bot.test_command("I\r", "Show inventory")

# Auto-handles pagination
snapshot = await bot.send_and_wait("L\r", "Long range scan")
snapshot = await bot.handle_pagination(snapshot)

# Generate results
await bot.generate_report()

Pattern Testing

13 patterns tested:

• login_username, login_password
• twgs_main_menu, twgs_select_game
• main_menu, command_prompt_generic
• sector_command, planet_command
• press_any_key, more_prompt
• yes_no_prompt, quit_confirm
• enter_number

Results saved to logs/reports/intelligent-bot-{timestamp}.json

Known Warnings

• You may see UserWarning: Field name "validate" ... shadows an attribute from Pydantic when starting bbsbot. This is currently harmless and does not affect runtime behavior.

License

GNU Affero General Public License v3 or later - Copyright (c) 2025-2026 provide.io llc

See LICENSE for details.