Configuration¶

Settings live in ~/.config/lcode/config.toml (or $XDG_CONFIG_HOME/lcode/config.toml). lcode setup writes the model and context; change anything else with lcode config:

lcode config                           # show every setting, its value and where it comes from
lcode config set permission_mode auto-edit
lcode config set context 128k
lcode config unset context             # back to the default
lcode config path                      # print the file location

Settings¶

Setting	Default	Meaning
`model`	`qwen3.6-35b`	Catalog key (see `lcode models`) or any installed Ollama model tag
`context`	largest that fits	Context window in tokens; accepts `128k`, `1m`
`num_batch`	512	Prompt batch size. Larger reads prompts faster but needs more GPU memory
`keep_alive`	`30m`	How long Ollama keeps the model in memory after the last request
`ollama_host`	`http://localhost:11434`	Ollama server URL
`permission_mode`	`ask`	`ask`, `auto-edit` or `yolo`
`think`	`true`	Let the model reason before answering
`web`	`on`	Web search and page fetching: `on`, `ask` (before each search/website) or `off`
`search_backend`	`auto`	`auto`, `ollama`, `brave`, `tavily` or `searxng`; `auto` uses the first one configured
`searxng_url`		Your SearXNG instance, e.g. `http://localhost:8888`
`sandbox`	`off`	Run shell commands in a container: `docker` or `podman` (Sandbox)
`sandbox_image`	lcode's image	Container image for the sandbox; any image with bash and setsid
`sandbox_network`	`false`	Let commands in the sandbox use the network
`vision_model`	`auto`	The model that looks at images: `auto`, `off` or an Ollama model that can see
`mcp_tools`	`auto`	How MCP tool definitions reach the model: `auto`, `direct` or `search` (on demand)
`checkpoints`	`true`	Save a checkpoint before the model changes files, so `/undo` can restore them

Example file:

# ~/.config/lcode/config.toml
model = "qwen3.6-35b"
context = 262144
permission_mode = "ask"
keep_alive = "1h"

Environment variables¶

Environment variables override the file, which is useful for one-off runs and CI:

Variable	Setting
`LCODE_MODEL`	`model`
`LCODE_CONTEXT`	`context`
`LCODE_NUM_BATCH`	`num_batch`
`LCODE_KEEP_ALIVE`	`keep_alive`
`OLLAMA_HOST`	`ollama_host` (same variable the Ollama CLI uses)
`LCODE_WEB`	`web`
`LCODE_SANDBOX`	`sandbox`
`OLLAMA_API_KEY`, `BRAVE_API_KEY`, `TAVILY_API_KEY`	API key for that search provider (never stored in the config file)
`LCODE_HOME`	where sessions and prompt history are stored

Files lcode creates¶

Path	Contents
`~/.config/lcode/config.toml`	Settings
`~/.local/state/lcode/sessions/`	Saved conversations (for `lcode -c`)
`~/.local/state/lcode/history`	Prompt history (`Up` in the prompt)
`~/.config/lcode/mcp.json`	MCP servers (readable only by you; may contain tokens)
`~/.local/state/lcode/mcp-auth/`	Sign-in tokens for remote MCP servers (readable only by you)
`~/.local/state/lcode/mcp-logs/`	Error output of local MCP servers
`~/.local/state/lcode/checkpoints/`	Checkpoints for `/undo` (copies of your project's files; deleted after 14 days)
`~/.local/state/lcode/limits.json`	Context sizes that ran out of GPU memory on this machine (safe to delete)

Sessions contain everything the model read, including file contents. Delete the folder to clear them.

Tuning for speed and memory¶

Out-of-memory errors: lower the context (/ctx 128k), close other programs using the GPU, or pick a smaller model (/models).
Slow first answers on big files: a larger prompt batch reads prompts faster if your GPU has room: lcode config set num_batch 1024 makes qwen3.6-35b read prompts ~1.8x faster (~500 instead of ~280 tokens/s on a 12 GB GPU). On a 12 GB GPU at 256K context it only fits when little else uses VRAM; if the GPU runs out of memory before answering, lcode retries automatically with 512.
Faster answers, less accuracy: --no-think or /think off.
Keep the model warm: keep_alive = "2h" avoids reload delays between sessions but holds the memory.