r105 — Beyond the prompt.

r105 is a local-first AI harness built on Textual. It connects to any OpenAI-compatible API (llama.cpp, Ollama, OpenAI, vLLM, Groq, and others) and provides an interactive chat TUI with streaming SSE responses, a slash-command system, fuzzy command palette, local tool execution, secure sandboxing, MCP integration, plugin extensibility, session persistence, and multiple themes.

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Table of Contents

• Prerequisites
• Installation
• Quick Start
• CLI Usage
• Backends
• Interactive TUI
  • Layout
  • Command Palette
  • Slash Commands
  • Keybindings
• Built-in Tools
• Sandbox & Security
• Skills
• Plugins
• MCP — Model Context Protocol
• Sessions & Export
• Themes
• Auto-Compaction
• Configuration
• Docker
• Updating & Uninstalling
• Testing
• Architecture
• Documentation
• License

---

Prerequisites

• Python 3.12+ when installing with pip or pipx
• One local or cloud OpenAI-compatible provider. llama-router remains the default when no provider is selected; direct connections can use llama.cpp, Ollama, LM Studio, vLLM, OpenAI, Groq, OpenRouter, DeepSeek, Together, or a custom endpoint.

---

Installation

pipx (recommended)

pipx install r105

# With optional export support (docx/pptx/pdf):
pipx install "r105[export]"

Homebrew (macOS / Linux)

brew install bnelabs/tap/r105

Linux and FreeBSD packages

The release page includes native x86_64 packages with the standard extension for each package manager. Set VERSION to the release you downloaded:

VERSION=0.8.1

# Ubuntu / Debian
sudo apt install "./r105_${VERSION}_amd64.deb"

# Arch Linux
sudo pacman -U "r105-${VERSION}-1-x86_64.pkg.tar.zst"

# Fedora / RHEL-like distributions
sudo dnf install "./r105-${VERSION}-1.x86_64.rpm"

# FreeBSD
sudo pkg add "./r105-${VERSION}-freebsd-amd64.pkg"

# Alpine Linux (verify SHA256SUMS before installing an unsigned local APK)
sudo apk add --allow-untrusted "./r105-${VERSION}-r0.apk"

The Ubuntu, Arch, and Fedora packages use the Linux x86_64 binary built on Ubuntu 22.04. The FreeBSD and Alpine packages are built natively on their respective systems. The Alpine job is best effort because it targets a separate musl ABI; if that native build is unavailable, the other release assets are still published.

Standalone binary

Pre-built single-file executables are attached to GitHub Releases as archives for Linux x86_64, macOS x86_64 and ARM64, and Windows x86_64 and ARM64. Verify SHA256SUMS, extract the archive, make the Unix executable runnable, and place it on your PATH — no Python install needed. The archives are named r105-linux-x64.tar.gz, r105-macos-x64.tar.gz, r105-macos-arm64.tar.gz, r105-windows-x64.zip, and r105-windows-arm64.zip.

From source

git clone https://github.com/bnelabs/r105.git
cd r105
python -m venv .venv
.venv/bin/pip install -e .
ln -sf "$(pwd)/bin/r105" ~/.local/bin/r105

Docker

docker build -t r105 .
docker run -it --rm \
  -v ~/.config/r105:/root/.config/r105 \
  -v ~/r105-workspace:/root/r105-workspace \
  r105 chat

For a quick-start stack with llama-router, use the included docker-compose.yml:

docker compose up -d llama-router
docker compose run r105 chat

---

Quick Start

# Launch the TUI (uses llama-router on http://127.0.0.1:8010 by default)
r105 chat

# One-shot: ask a question and get a response without the TUI
r105 send "explain quicksort in 3 sentences"

# Connect to a different router or any OpenAI-compatible API at startup
r105 --url http://my-router:8010 chat
OPENAI_API_KEY=sk-... r105 --backend direct --url https://api.openai.com/v1 chat

# Or select a provider from the running TUI with /connect:
# /connect llamacpp
# /connect ollama
# /connect openai

---

CLI Usage

r105 supports both one-shot prompts and management commands at the command line.

# Interactive chat (default, opens TUI)
r105 chat

# One-shot prompt
r105 send "explain quicksort in 3 sentences"

# Check selected backend health
r105 health

# List available task profiles
r105 profiles

# Diagnose environment: config, sandbox, backend, workspace
r105 doctor

# Print the supported config.json JSON Schema
r105 config-schema

# Load a saved session on startup
r105 --session my-session chat

# Auto-approve code execution (skip the confirmation gate)
r105 --yes chat

# Version info
r105 --version

CLI Flags

Flag	Default	Description
--url	http://127.0.0.1:8010	Router or API base URL
--workspace	~/r105-workspace	Workspace directory for file tools
--skills-dir	./skills	Directory containing skill .md files
--plugins-dir	~/.config/r105/plugins	Directory for custom tool plugins
--profile	auto	Force a router task profile
--model	auto	Override model selection
--quality	auto	Quality hint: fast, balanced, or best
--max-tokens	auto	Override max output tokens
--json	off	Request JSON-object responses
--backend	auto	router (profiles) or direct (any OpenAI-compatible)
--session	—	Load a saved session on startup
--yes	off	Auto-approve execute_python (skip confirmation gate)
--version	—	Print version and exit

---

Backends

r105 has two connection modes:

1. Router — llama-router at http://127.0.0.1:8010, with profiles, quality hints, and router-specific metadata.
2. Direct — any OpenAI-compatible server, including llama.cpp, Ollama, LM Studio, vLLM, OpenAI, Groq, OpenRouter, DeepSeek, Together, or a custom endpoint.

At startup, r105 uses a saved connection from ~/.config/r105/config.json, then R105_URL, then the OpenAI environment variables, and finally the local llama-router default. Override the mode with --backend router or --backend direct.

Connecting from the TUI

Press / and type /connect to open the provider command, or type one of these commands directly. The optional second argument overrides the preset base URL. API keys are read from the environment and are never accepted as slash-command arguments or written to config.json.

Command	Mode	Default base URL	Credential
/connect llamacpp	direct	http://127.0.0.1:8080/v1	none
/connect ollama	direct	http://127.0.0.1:11434/v1	none
/connect lmstudio	direct	http://127.0.0.1:1234/v1	none
/connect vllm	direct	http://127.0.0.1:8000/v1	OPENAI_API_KEY
/connect router	router	http://127.0.0.1:8010	none
/connect openai	direct	https://api.openai.com/v1	OPENAI_API_KEY
/connect groq	direct	https://api.groq.com/openai/v1	GROQ_API_KEY
/connect openrouter	direct	https://openrouter.ai/api/v1	OPENROUTER_API_KEY
/connect deepseek	direct	https://api.deepseek.com/v1	DEEPSEEK_API_KEY
/connect together	direct	https://api.together.xyz/v1	TOGETHER_API_KEY
/connect url https://host/v1	direct	custom	OPENAI_API_KEY

For llama.cpp, start its OpenAI-compatible server and then select it in r105:

llama-server -m /path/to/model.gguf --host 127.0.0.1 --port 8080
r105 chat
# In the TUI: /connect llamacpp

/provider is an alias for /connect; /connect status shows the active mode and URL. The provider switch is applied to the running TUI immediately, and /health checks the newly selected backend.

Backend Capabilities

Feature	Router backend	Direct backend
Profiles (task routing)	✅	—
Quality hints	✅	—
Model listing/switching	✅	✅
Tool calling	✅	✅
SSE streaming	✅	✅
Conversation compaction	✅	✅

---

Interactive TUI

Layout

┌─────────────────────────────────────────────┐
│  Header: model, profile, context usage      │
├───────────────────────┬─────────────────────┤
│                       │                     │
│    Chat View          │   File Explorer     │
│    (messages,         │   (workspace tree)  │
│     streaming)        │                     │
│                       │                     │
├───────────────────────┴─────────────────────┤
│  Command Palette (hidden by default)        │
├─────────────────────────────────────────────┤
│  Chat Input (multi-line, history, autocomplete) │
├─────────────────────────────────────────────┤
│  Status Bar (profile, context, busy state)  │
└─────────────────────────────────────────────┘

Thinking Panels & Virtualized Transcript

• Collapsible thinking panels — reasoning models that emit Gemma-4-style thinking blocks (<|channel|>thought … <channel|>) get an interactive 💭 THINKING panel per block, folded by default so answers stay readable. Click the panel — or press t / Enter / Space while it is focused — to expand the full reasoning and fold it again. show_thinking controls whether panels render at all; thinking_default_expanded starts them expanded (see Configuration).
• Virtualized transcript — the chat view keeps the full conversation as lightweight records and only materializes the messages on screen (plus an overscan window) as widgets. Very long sessions stay responsive and memory-bounded, and the transcript is never truncated: scrolling back re-renders history on demand.
• Auto-follow — the view pins to the newest message while you're at the bottom and stops following the moment you scroll up; returning to the bottom re-pins it.

Tool Results & Inspector

• Collapsible long outputs — tool results over ~2,000 characters start folded to a preview. Click the panel — or press t / Enter / Space while it is focused — to expand the full output in place.
• Diff coloring & code highlighting — unified diffs render with green/red/hunk styling; outputs with fenced code blocks get syntax highlighting.
• Tool inspector (Ctrl+T) — a modal screen listing every tool call in the session with its arguments and result preview, plus fuzzy filtering.

Command Palette

Press / to open the interactive command palette with:

• Fuzzy filtering — type any part of a command name to narrow
• Arrow-key navigation — ↑/↓ to browse, Enter to select
• Tab autocomplete — fills in the matching command
• Category grouping — Chat, Skills, Sessions, Plugins, MCP, Workspace, System
• Escape to dismiss

Slash Commands

Chat

Command	Description
/state	Show active settings (profile, quality, tokens, model)
/tokens	Show context usage, estimate source, and confidence
/model [name]	Show current model, list available, or switch models (persistent)
/history	Show compact transcript preview of last messages
/clear	Clear all conversation history
/compact	Summarize conversation history and continue
/profile <name>	Force a router task profile: simple, coding, complex_reasoning, long_context_qa, tool_agent, creative, strict_json
/quality fast|balanced|best	Set quality hint metadata
/json [on|off]	Toggle JSON object response mode
/max <tokens>	Override max output tokens
/cache-prompt [on|off]	Enable llama.cpp prompt-prefix caching
/config reload	Reload supported config.json settings into the current session
/autocompact [on|off]	Toggle auto-compaction at 80% context threshold
/reasoning auto|off|low|medium|high	Set reasoning effort (sent to capable backends)
/permissions <posture>	Set tool-execution posture (full-access|restricted|sandboxed|off)
/approve execute_python	One-time approval for code execution (this session)
/connect <provider> [base-url]	Switch live to a local or cloud OpenAI-compatible provider
/provider <provider> [base-url]	Alias for /connect
/copy	Copy last assistant message to system clipboard

Skills

Command	Description
/skills	List available skill files in the skills directory
/skill use <name> [key=val...]	Activate a skill with optional parameters
/skill drop <name>	Deactivate one skill
/skill clear	Deactivate all skills
/skill show <name>	Print the raw content of a skill file

Sessions & Export

Command	Description
/session save <name>	Save current conversation to ~/.config/r105/sessions/
/session load <name>	Load and restore a previously saved session (shows a diff of unsaved changes first)
/session list	List all saved sessions with previews and timestamps
/session search <query>	Search message text across saved sessions
/session delete <name>	Delete a saved session
/export text	Export conversation as plain text
/export markdown	Export conversation as Markdown
/export json	Export conversation as JSON
/export html	Export conversation as a styled HTML page

Plugins & MCP

Command	Description
/plugin list	List loaded custom tool plugins
/plugin reload	Reload plugins from disk
/mcp list	List connected MCP servers and their tool counts
/mcp tools <server>	List tools exposed by a specific MCP server
/mcp reconnect <server>	Reconnect a server and rediscover its tools

Workspace & System

Command	Description
/workspace	Show workspace directory and list generated files
/preview <filename>	Preview a workspace file's contents
/theme <name>	Switch theme: r105, dracula, solarized-dark, high-contrast
/health	Check the selected backend health
/profiles	List available router task profiles
/help	Show the full command reference
/exit	Quit r105

Keybindings

Key	Context	Action
Enter	Normal mode	Submit message
Enter	Slash mode	Execute or select command
Shift+Enter	Any	Insert newline
↑ / ↓	Normal mode	Navigate input history (up to 200 entries)
↑ / ↓	Slash mode	Navigate command palette
Tab	Slash mode	Fuzzy-autocomplete command
Escape	Slash mode	Dismiss command palette, clear input
Ctrl+R	Any	Open history browser with fuzzy search
Ctrl+T	Any	Open tool-call inspector (names, args, result previews)
Ctrl+X	Any	Cancel the current local-tool batch
Ctrl+S	History screen	Quick-save current session
Ctrl+Y	Any	Copy last assistant response to clipboard
t / Enter / Space	Thinking panel focused	Expand / fold the thinking panel
t / Enter / Space	Long tool result focused	Expand / fold the full output
Ctrl+W	Input	Delete word backward
Ctrl+U	Input	Clear line
Ctrl+A	Input	Jump to line start
Ctrl+E	Input	Jump to line end
Ctrl+K	Input	Kill to end of line
F1 / Ctrl+H	Any	Show help screen
Ctrl+Q / Ctrl+C	Any	Quit

Core actions can be remapped in config.json by binding ID. The supported IDs are quit, show_help, show_history, copy_last_message, show_tools, cancel_tools, and cancel_request:

{
  "keybindings": {
    "show_tools": "ctrl+o",
    "copy_last_message": "ctrl+y"
  }
}

---

Built-in Tools

r105 provides 10 local tools the LLM can call. All tools are validated before execution with argument size caps, SSRF prevention, and path traversal hardening. Web fetches validate every redirect and revalidate DNS answers at the TCP connection boundary. execute_python runs in a sandboxed environment with no network or filesystem access — and needs a one-time approval per session (/approve execute_python, --yes, or the auto_approve_execute_python config key) before it runs at all. Web tools are rate-limited per session (30 searches / 60 fetches per minute).

Tool	Description	Sandbox Profile
execute_python	Sandboxed Python execution (256MB RAM, 25s CPU, seccomp)	No network, no filesystem
write_file	Write/update files in workspace — generates unified diffs on edit	Filesystem write
read_file	Read file contents (max 50MB, with <tool_output> tags)	Filesystem read
list_files	List directory contents	Filesystem read
web_search	Search via DuckDuckGo HTML (no API key)	Network access
web_fetch	Fetch URL content (HTML stripped to text)	Network access
get_time	Current system time in ISO 8601	Minimal isolation
calculate	Safe math evaluator (arithmetic, math functions, pi/e/tau)	Minimal isolation
convert	Unit conversion (length, mass, time, data, speed, volume, temperature)	Minimal isolation
system_info	OS, Python version, CPU count	Minimal isolation

Tool Execution Model

• Tools run synchronously in a thread pool (asyncio.to_thread) to avoid blocking the TUI event loop
• Multiple independent tool calls run in parallel via asyncio.gather
• The tool loop follows the OpenAI protocol: after tool results are appended to history, async_continue() sends the model the full conversation state without injecting an artificial user message
• Output is truncated at 8,000 characters to keep context lean

File Diffing

When write_file modifies an existing file, it generates a unified diff. The TUI displays this in a DiffView widget with:

• Syntax-colored additions (green) and deletions (red)
• Approve / Reject buttons — changes are not saved until approved
• New file detection — first writes show the file content directly

---

Sandbox & Security

r105 implements layered security for code execution. The backend is auto-detected: nsjail > bwrap > docker > rlimit > none (Windows fallback).

Fallback transparency: if a stronger backend is unavailable, r105 tells you why — a warning: line on startup, a ⚠ sandbox=<backend> (no isolation) segment in the TUI status bar, and a structured log entry. Falling back to rlimit/none means tool code runs without filesystem/network isolation.

Sandbox Backends

Backend	Isolation Level	Requirements
Nsjail (strongest)	Linux namespace isolation, seccomp-bpf syscall allowlist, no host filesystem	nsjail binary on PATH
Bwrap	User namespace isolation via bubblewrap, minimal /dev bind, conditional network/filesystem per profile	bwrap on PATH
Docker	Fresh container per execution, read-only root FS, dropped capabilities	docker + running daemon
RLimit	Resource limits via setrlimit() (256MB RAM, 25s CPU, no child procs)	Unix (not Windows)
Noop	No isolation — fallback for Windows or explicit config	None

Per-Tool Sandbox Profiles

Each tool gets a sandbox profile that specifies exactly what it needs:

Profile	Network	Filesystem	Write	seccomp
PROFILE_EXECUTE_PYTHON	No	No	No	Yes
PROFILE_FILE_TOOLS	No	Yes	Yes	Yes
PROFILE_WEB_TOOLS	Yes	No	No	Yes
PROFILE_SYSTEM_TOOLS	No	No	No	No

Security Features

• SSRF prevention — URL validation blocks private IPv4/IPv6 ranges, link-local addresses, and localhost aliases; DNS resolution is checked against reserved network blocks
• Path traversal hardening — symlink-aware path validation ensures all file operations stay within the workspace directory; skill loader blocks ../, \\, and dot-prefixed paths
• Environment sanitization — secrets, tokens, API keys, SSH keys, cloud credentials (AWS, GCP, Azure, OpenAI, Anthropic, etc.) are stripped from subprocess environments; only explicitly safe variables are forwarded
• Argument validation — code capped at 100KB, file writes at 10MB, file reads at 50MB, search queries at 500 characters

---

Skills

Skills are reusable Markdown prompt templates stored in ~/.config/r105/skills/. They support {param} placeholder substitution for dynamic content injection.

Skills appear as system messages in the LLM context, persisting across /clear but not across sessions (re-activate on each launch).

Built-in Skills

Skill	Description
code-check	Code review assistant — prefers executable snippets, checks syntax and imports
concise	Forces concise, direct responses with no preamble
deep-review	Comprehensive analysis covering deliverables, assumptions, edge cases, security

Creating a Skill

<!-- ~/.config/r105/skills/web-researcher.md -->
When answering questions, follow this process:
1. Break the question into search queries
2. Use web_search for each query
3. Use web_fetch for the top 3 results
4. Synthesize findings with citations (URL + snippet)
5. Note any gaps or uncertainties

Query: {query}

# Activate with parameter substitution
/skill use web-researcher query="how CPUs work"

The {query} placeholder is replaced with how CPUs work before injection.

Security

Skills are prompt templates, not executable code. The loader blocks path traversal:

if "/" in name or "\\" in name or name.startswith("."):
    return ""  # blocks ../../etc/passwd style attacks

Review skill content before activation if it comes from an untrusted source.

---

Plugins

Custom tools can be loaded from Python files in ~/.config/r105/plugins/. Each file exposes a register(registry) function that adds tools via registry.add_tool().

Example plugin (~/.config/r105/plugins/hello.py):

def register(registry):
    registry.add_tool(
        name="hello",
        description="Say hello to someone.",
        parameters={
            "name": {"type": "string", "description": "Name to greet."},
        },
        required=["name"],
        handler=lambda args, ws: f"Hello, {args.get('name', 'world')}!",
    )

Plugins are auto-discovered on startup. Use /plugin reload to reload without restarting.

Plugin loading is validated: the file must expose register(registry) taking exactly one argument, and every tool needs a non-empty name/description, a parameters schema object, and a callable handler — violations are reported as specific warnings instead of silently skipping. Plugins can declare __r105_min_version__ = "0.8.1" and PLUGIN_REQUIREMENTS = ["package_name"]; incompatible plugins are skipped with a warning. /plugin reload drains active plugin calls before replacing handlers. Plugins cannot shadow built-in tools unless you opt in via the allow_plugin_overrides config key or R105_ALLOW_PLUGIN_OVERRIDE=1.

See docs/TOOLS.md for the full API reference.

---

MCP — Model Context Protocol

r105 can connect to external MCP servers (stdio or SSE transport) to access community-built tools without changing r105's code.

Configuration

Configure servers in ~/.config/r105/config.json:

{
  "mcp_servers": [
    {
      "name": "github",
      "transport": "sse",
      "url": "http://127.0.0.1:3001/mcp"
    },
    {
      "name": "filesystem",
      "transport": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
    }
  ]
}

MCP tools are namespaced as mcp_<server>_<tool> in the LLM's tool definitions, keeping them distinct from built-in and plugin tools.

Transports

Transport	How it works
stdio	Spawns a subprocess, communicates via JSON-RPC over stdin/stdout
sse	Connects to an HTTP SSE endpoint (long-lived GET for events, POST for requests)

---

Sessions & Export

Conversations can be saved, loaded, and exported in multiple formats. r105 also auto-saves on exit as __autosave__.

Session Storage

Sessions are stored as JSON files in ~/.config/r105/sessions/. Each file contains:

• Conversation history (all user, assistant, and tool messages)
• Session state (profile, quality, skills, and parameters)
• Model settings (selected model, context capacity, and prompt-cache toggle)
• Message count and save timestamp

Export Formats

Format	What you get
markdown	Styled .md with role headers and message numbering
json	Raw conversation array, portable and machine-readable
html	Styled HTML page with role-colored messages

Export Dependencies

text, markdown, json, and html exports work out of the box. The pdf, docx, and pptx formats require the optional export extra:

pip install "r105[export]"

If the extra is missing, /export shows a helpful install hint. See docs/EXPORT.md for details.

---

Themes

Four themes are included. Switch at runtime with /theme <name>:

Theme	Accent	Style
r105 (default)	#cba6f7 mauve	Catppuccin Mocha
dracula	#bd93f9 purple	Dracula
solarized-dark	#268bd2 blue	Solarized Dark
high-contrast	#ffff00 yellow	Accessibility-focused

Themes are defined as Textual CSS files in r105/themes/. The selected theme persists in ~/.config/r105/config.json.

---

Auto-Compaction

When conversation context approaches 80% of the model's capacity, r105 can automatically summarize earlier messages to free space. This is controlled by:

• /autocompact on|off — toggle from within the TUI
• auto_compact field in config.json — persistent default

The context indicator reports its measurement source and confidence. After a backend returns standard usage metadata, r105 uses the provider's exact total. Before that, it uses tiktoken when available and labels generic or heuristic estimates with lower confidence.

Use /config reload after editing config.json to apply live session settings such as the theme, model, context overrides, prompt caching, keybindings, and permission posture. Workspace paths and MCP server lists remain startup-level settings; use /mcp reconnect <server> for an individual MCP connection.

Prompt Caching

cache_prompt is disabled by default. Enable it with /cache-prompt on or in config.json when the selected backend is llama.cpp-compatible. The flag is omitted when disabled because other OpenAI-compatible providers may reject it.

Compaction uses the complex_reasoning profile and keeps the most recent 30% of messages intact.

---

Configuration

r105 stores configuration in ~/.config/r105/:

~/.config/r105/
├── config.json          # theme, model, provider URL, sandbox backend, defaults
├── sessions/            # Saved conversation sessions (JSON)
│   ├── __autosave__.json
│   └── my-session.json
└── plugins/             # Custom tool Python files
    └── hello.py

Example config.json

{
  "model": "gemma-4-12b-it",
  "backend": "direct",
  "url": "http://127.0.0.1:8080/v1",
  "sandbox_backend": "bwrap",
  "auto_compact": true,
  "cache_prompt": false,
  "keybindings": {
    "show_tools": "ctrl+o"
  },
  "theme": "r105",
  "show_thinking": true,
  "thinking_default_expanded": false,
  "model_families": {
    "my-gemma4-finetune": "gemma-4",
    "gemma-4-12b": null
  },
  "mcp_servers": [
    {
      "name": "github",
      "transport": "sse",
      "url": "http://127.0.0.1:3001/mcp"
    }
  ]
}

Invalid configuration falls back to defaults during normal startup. Set R105_STRICT_CONFIG=1 when validating a deployment so unknown keys, invalid values, malformed JSON, and unreadable config files stop startup with an error. Run r105 config-schema to print the supported JSON Schema, or r105 config-schema --output /path/to/config.schema.json to write it.

Model Families (model_families)

r105 gates model-specific behavior (Gemma-4 channel-syntax handling: native <|tool_call|> parsing, <|channel|>thought capture, stray-token stripping) behind a built-in model-family catalog, so every other model's output is treated as opaque text. model_families lets you override that classification per model-name fragment — longest matching fragment wins:

• "<fragment>": "gemma-4" — force a model (e.g. a Gemma-4 fine-tune with a custom name) into the Gemma-4 family so channel-syntax handling applies.
• "<fragment>": null — force a model out of every family (opaque passthrough), e.g. to disable channel-syntax handling for a model the catalog would otherwise classify as Gemma-4.

Values are family names (currently gemma-4, gemma, qwen, llama, mistral, deepseek, glm, gpt, claude, phi) or null. Only the gemma-4 family currently gates behavior; other families are labels.

Config Merging

CLI arguments override config file values, which override built-in defaults. The config file only stores overrides — matching defaults are omitted to keep the file lean.

Config Validation

The config file is validated on load: unknown keys and invalid values fall back to defaults, and a clear error is raised on save.

Structured Logging

Debug logs are written to ~/.local/state/r105/log.jsonl in JSON Lines format. Set R105_LOG_LEVEL=DEBUG for verbose output.

---

Docker

Standalone

docker build -t r105 .
docker run -it --rm \
  -v ~/.config/r105:/root/.config/r105 \
  -v ~/r105-workspace:/root/r105-workspace \
  r105 chat

Quick-start stack with llama-router

The docker-compose.yml starts both r105 and llama-router on a shared network:

docker compose up -d llama-router
docker compose run r105 chat

The stack mounts ~/.config/r105 and ~/.config/llama-router for persistent configuration. R105_URL=http://llama-router:8010 is set automatically.

---

Updating & Uninstalling

pipx

pipx upgrade r105          # update (extras are preserved)
pipx uninstall r105        # remove

Homebrew

brew upgrade r105          # update
brew uninstall r105        # remove

Standalone binary

Download the latest binary from GitHub Releases and replace the old one.

For archive downloads, verify SHA256SUMS, extract the archive, and replace the executable inside it. Package-manager installations can be upgraded with the same command used for the initial install.

From source

cd /path/to/r105
git pull
.venv/bin/pip install -e .

# Uninstall
rm ~/.local/bin/r105
rm -rf /path/to/r105/.venv

Docker

git pull && docker build -t r105 .     # update
docker rmi r105                        # remove image

Config cleanup

None of these methods remove your user-level data. To wipe everything:

rm -rf ~/.config/r105
rm -rf ~/r105-workspace

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Testing

# Install dev dependencies
pip install -e ".[dev]"

# Run full test suite (440 tests)
python -m pytest tests/ -v

# With coverage
python -m pytest tests/ -v --cov=r105 --cov-report=term-missing

# Compare local token estimates with a tiktoken baseline
python benchmarks/token_estimation.py

# Lint and type-check
ruff check r105/ tests/
ruff format r105/ tests/
mypy r105/

Test Structure

File	Coverage
tests/test_client.py	Payload building, response parsing, tool call extraction
tests/test_client_chaos.py	Error handling, edge cases, malformed SSE, retry behavior
tests/test_integration.py	Full async flows with mocked HTTP (pytest-httpx), SSE streaming
tests/test_commands.py	Slash command handlers, state mutations, error paths
tests/test_tools.py	Tool dispatch, sandbox, SSRF checks, safe math evaluator, unit conversion
tests/test_state.py	ChatState defaults, TokenUsage math, token estimation
tests/test_skills.py	Skill listing, reading, parameter substitution, path traversal
tests/test_config.py	Config validation (manual + pydantic schema)
tests/test_architecture_layers.py	Client facade, command registry, tool protocol, trace IDs, atomic sessions, schema checks
tests/test_model_catalog.py	Model family/context resolution and overrides
tests/test_reasoning_permissions.py	Reasoning effort and permission posture handling
tests/test_sessions_export_guard.py	Optional export-dependency guard
tests/test_sandbox_fallback.py	Sandbox fallback reasons and weak-backend warnings
tests/test_plugin_validation.py	Plugin signature and tool-schema validation
tests/test_session_diff.py	Session diff summaries
tests/test_tools_screen.py	Tool-inspector collection and rendering
tests/test_tui.py	Command detection, palette data integrity, widget structure
tests/test_command_palette.py	Fuzzy scoring, palette filtering, navigation
tests/test_tui_features.py	TUI feature coverage (streaming, panels, markers)
tests/test_tui_virtualization.py	Virtualized transcript, collapsible panels and results

CI runs on GitHub Actions (ci.yml): ruff lint, mypy type-check, and pytest with coverage on Python 3.12 and 3.13. Coverage is uploaded to Codecov.

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Architecture

r105 is a layered Python application:

┌──────────────┐     HTTP/SSE      ┌──────────────┐     HTTP      ┌──────────────┐
│              │ ◄───────────────► │              │ ◄───────────► │              │
│   r105 TUI   │    (REST + SSE)   │ llama-router │   (OpenAI API) │ llama-server │
│  (Textual)   │                   │  (FastAPI)   │                │  (llama.cpp) │
│              │                   │              │                │              │
└──────┬───────┘                   └──────────────┘                └──────────────┘
       │
       │ Local execution
       ▼
┌──────────────┐
│  Tool Runner │
│  (subprocess │
│   sandbox)   │
└──────────────┘

Key Design Patterns

• Backend-agnostic client — BaseClient abstract class with RouterClient (profiles) and DirectClient (any OpenAI-compatible API) implementations, auto-detected based on URL and environment
• Stable client facade — r105.client.Client exposes chat(), stream_chat(), list_models(), health, and compaction without requiring callers to know which backend was selected
• @work(exclusive=True) cancellation — new messages cancel in-flight requests; finally blocks clean up UI state regardless of cancellation
• asyncio.to_thread() for tools — synchronous tool handlers run in the default thread pool, keeping the TUI responsive
• Parallel tool execution — independent tool calls run concurrently via asyncio.gather(return_exceptions=True), so one failure doesn't cancel the batch
• Shared httpx.AsyncClient — a single connection pool across the session lifetime for efficient HTTP reuse
• Debounced streaming — SSE tokens are buffered and rendered every ~50ms, preventing CPU thrashing from per-token Markdown re-renders
• Correlation and diagnostics — each ChatState carries a trace ID through request headers, structured logs, and tool execution; the TUI status bar periodically checks backend health and local runtime readiness

See ARCHITECTURE.md for the full system design, data flow, component tree, and implementation details.

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Documentation

Document	Description
ARCHITECTURE.md	System design, data flow, component tree, key patterns
CHANGELOG.md	Version history and release notes
CONTRIBUTING.md	Dev setup, testing, linting, PR and release process
docs/EXPORT.md	Optional export dependencies and formats
docs/TOOLS.md	Custom tool and plugin API with JSON Schema
docs/SKILLS.md	Skill authoring guide with parameterized examples
docs/homebrew.rb	Homebrew formula template

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License

MIT — see the LICENSE file for details.