little-coder

little-coder

A coding agent tuned for small local models, built on top of pi.

The research story behind all this — why scaffold–model fit matters, how a 9.7 B Qwen beat frontier entries on Aider Polyglot, and what the load-bearing mechanisms actually do — is written up on Substack: Honey, I Shrunk the Coding Agent. Start there if you want the “why”; stay here for the “how”.

How it relates to pi

pi is the minimal substrate — agent loop, multi-provider API, TUI, session tree, compaction, extension model. Four built-in tools (read / write / edit / bash) and a ~1000-token system prompt.

little-coder is pi + 30-odd extensions + 30 skill markdown files + a Python benchmark harness. It doesn’t fork pi or shadow its CLI — pi is a plain dependency in package.json, and everything little-coder-specific lives under .pi/extensions/, skills/, and benchmarks/. It ships no npm install scripts; the launcher does everything at launch time.

The launcher runs pi with --no-extensions and wires in exactly the bundled set. That’s what keeps the cold-start context around 7k tokens and makes behavior predictable — the set that loads is the set that ships, and nothing in your working directory changes it mid-task. The consequence is that a globally pi install’d package won’t load inside little-coder by default, because pi install registers into pi’s settings and --no-extensions skips those.

You have three opt-in ways around that, none of which change the default: drop your own extensions in ~/.config/little-coder/extensions/, point LITTLE_CODER_EXTRA_EXTENSIONS at files anywhere, or relaunch with --with-pi-extensions to let pi discover its own. Run /extensions to see what’s loaded. Full guide: Extending little-coder. (Themes are unaffected — pi themes have always loaded.)

If you’ve never used pi, it’s useful to skim pi.dev first — the rest of this doc assumes pi’s model of --agent-import-path, --mode rpc, and .pi/extensions/ auto-discovery.

Install

One-line install (Node.js 22.19+ required):

curl -fsSL https://raw.githubusercontent.com/itayinbarr/little-coder/main/install.sh | bash

Or with npm directly:

npm install -g little-coder

Or with bun:

bun add -g little-coder

That’s the whole install. No clone, no npm install in a workspace, no PATH fiddling. little-coder is now on your PATH and works from any directory.

Note for bun add -g users. The launcher (bin/little-coder.mjs) is a Node.js script with #!/usr/bin/env node at the top, so Node ≥ 22.19 still has to be on your PATH for the binary to start — bun is fine for installing/updating the package, but the runtime is Node. If you want a fully node-less setup, replace the shebang in $(bun pm bin -g)/little-coder with #!/usr/bin/env bun.

Run

cd ~/your-project
little-coder                                    # launches the default model (see below)
little-coder --model llamacpp/qwen3.6-35b-a3b   # or name one explicitly

This is the canonical setup little-coder is tuned for: a local llama.cpp server hosting Qwen3.6-35B-A3B. See Local model setup (optional) below for how to serve it.

Bare little-coder (no --model) launches the default model declared in models.json ("default": "llamacpp/qwen3.6-35b-a3b" out of the box), printing its friendly name at startup. This only kicks in on a first run — once you pick a model in-session, that choice sticks and the default never overrides it. Change the default with a default key in your user override file. See Configuring models.

Cloud models work the same way:

little-coder --model anthropic/claude-haiku-4-5
little-coder --model openai/gpt-4o-mini "What does this codebase do?"
little-coder --model ollama/qwen3.5             # local Ollama
little-coder --model lmstudio/local-model       # local LM Studio (whatever model you have loaded)
little-coder --list-models                      # see everything pi knows about

The agent uses the directory you launched it from as its working directory — Read / Write / Edit / Bash operate on your project, not on little-coder’s install path.

Interactive features

• Plan Mode — press ctrl+q to toggle (a ◆ PLAN MODE indicator shows below the input), or launch with --plan-mode (LITTLE_CODER_PLAN_MODE=1) to start there. Submit a request and little-coder researches it with sub-coders, asks you 1-3 clarifying questions (each with suggested answers and a free-text option), then writes a plan in the chat instead of editing anything. Approving it saves the plan to .pi/approved-plan.md and stops there; /implement is what switches to the action model, opens a fresh session seeded with the plan, and starts the work, so the research and Q&A that produced the plan do not eat the context the implementation needs (#98). Esc cancels a plan mid-run. (shift+tab stays pi’s thinking-level cycle.)
• Deep Research — press f2 (or run /deep-research <topic>) to scope a topic into a research brief, fan out read-only research sub-coders, and get back one cited markdown report, saved next to your working directory. The research children run in an ephemeral scratch directory, not your project, so f2 is for external and online research, not for exploring the code you are sitting in (#100); use a normal session or dispatch for that. Esc cancels mid-run.
• Keyboard shortcuts — press ctrl+h for a panel of the keys worth knowing; /hotkeys is the full reference. ctrl+o expands tool output (“more”), ctrl+t toggles thinking blocks, ctrl+p cycles models.
• Tool skill cards: little-coder injects a short usage card for the tools a turn is likely to need, chosen by error-recovery > recency > intent. /skills lists what is loaded and what each costs, /skills <tool> pins one when the selector keeps picking a different card, and /skills off returns to automatic selection (#118).
• Prompt history — from an empty input, ↑ recalls your recent prompts (most-recent first), ↓ walks forward. History persists across sessions, so a fresh session can recall prompts from earlier runs.
• Sub-coders (dispatch) — little-coder can spawn isolated child sessions to research a question (read the repo + browse online, read-only) and report back concisely, without cluttering the main conversation. A live panel above the input tracks them. Sub-coders run serially by default (two of them contend for the same local model server and finish slower than one); opt into parallelism with LITTLE_CODER_SUBCODER_CONCURRENCY=2 or more. They are read-only by design, which is what makes fanning them out safe, since their answers come back as text and two of them cannot race on the same file, but LITTLE_CODER_SUBCODER_ACCESS=write gives them edit/write when you want the work done rather than reported (#93). Neither level ever grants dispatch: a child that can spawn children is a fan-out bomb.
• Background jobs (ShellStart) — long commands (training, builds, servers, watchers) run in the background instead of blocking a turn, and little-coder wakes the model on events in the job rather than on a timer. A footer line shows what’s running. See Background jobs below.
• Per-phase models — plan on a big model, implement on a small one, with /plan-model and /action-model. See Per-phase model selection below.
• Sessions — each session is auto-named from your first prompt (rename with /name) and shown in the terminal tab title. Use /resume to list and reopen past sessions for the current directory.
• Read-before-edit — editing a file requires reading it first, so edits match the file’s exact current text.
• Your own extensions — drop them in ~/.config/little-coder/extensions/ and they load on the next launch. Run /extensions to see what’s loaded and where it came from. See Extending little-coder.

The status line

The footer at the bottom of the screen looks like this:

↑26k ↓5.4k R447k CH99.8% 9.3%/262k (auto)          qwen3.6-35b-a3b • medium

Field	Meaning
↑26k	Cumulative input tokens billed as fresh across the whole session — not your current context size
↓5.4k	Cumulative output tokens generated
R447k	Cumulative tokens read from cache (the prefix your server didn’t have to reprocess)
W…	Cumulative cache-write tokens; only shown when non-zero
CH99.8%	Cache-hit rate of the latest response alone — cacheRead / (input + cacheRead + cacheWrite). Not a session average, so it moves turn to turn
9.3%/262k	Current context usage against the window size. Amber above 70%, red above 90%
(auto)	Automatic compaction is enabled
right side	Active model, and its thinking level if it’s a reasoning model

A low CH on a long conversation means your server is reprocessing history it should have been able to reuse — worth investigating.

For local providers (llama.cpp, Ollama, LM Studio) pi expects some value in the API-key env even though local servers ignore it:

export LLAMACPP_API_KEY=noop
export OLLAMA_API_KEY=noop
export LMSTUDIO_API_KEY=noop

LLAMACPP_BASE_URL, OLLAMA_BASE_URL, and LMSTUDIO_BASE_URL override the defaults (http://127.0.0.1:8888/v1, http://127.0.0.1:11434/v1, http://127.0.0.1:1234/v1).

For cloud providers, set the standard env (ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.) and pi will discover it.

Local model setup (optional)

Skip this section if you’re using a cloud model.

Option A — llama.cpp (fastest for local; supports Qwen3.6-35B-A3B MoE):

# One-time: build llama.cpp with CUDA (sm_XXX = your GPU arch; Blackwell = 120)
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=120 -DLLAMA_CURL=ON
cmake --build build --config Release -j

# Fetch the model GGUF and the matching vision projector.
# The mmproj (~900 MB) is what lets the model see attached screenshots.
pip install -U "huggingface_hub[cli]"
hf download unsloth/Qwen3.6-35B-A3B-GGUF Qwen3.6-35B-A3B-UD-Q4_K_M.gguf --local-dir ~/models
hf download unsloth/Qwen3.6-35B-A3B-GGUF mmproj-F16.gguf            --local-dir ~/models

# Serve it (MoE trick: experts in RAM, attention on GPU → 22 GB model on 8 GB VRAM)
build/bin/llama-server -m ~/models/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf \
   --mmproj ~/models/mmproj-F16.gguf \
   --host 127.0.0.1 --port 8888 --jinja \
   -c 16384 -ngl 99 --n-cpu-moe 999 --flash-attn on

If you only need text and want to skip the projector download, drop the second hf download line and the --mmproj flag — little-coder still works text-only, but the TUI’s image attachment will be rejected by the server with a 4xx.

Context window. -c sets the server’s context (-c 16384 = 16K above — a conservative default for 8 GB VRAM). little-coder auto-detects the live n_ctx from llama.cpp’s /props at startup and registers the model with it, so whatever you pass to -c is what the TUI shows and budgets against — no models.json edit needed. To run larger, relaunch the server with e.g. -c 131072 (128K) or -c 262144 (256K); the KV cache grows with it, so size it to your RAM/VRAM. (--list-models reflects the detected window.)

Option B — Ollama (simpler, but slower on MoE):

curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen3.5        # 9.7B — the paper's model
# or: ollama pull qwen3.6:35b-a3b

Option C — LM Studio (GUI; OpenAI-compatible server on port 1234):

1. Install LM Studio and download a model (e.g. Qwen3.6 35B A3B GGUF).
2. Open the Developer / Local Server tab, load the model, and click Start Server (default http://127.0.0.1:1234).
3. Run little-coder:

   export LMSTUDIO_API_KEY=noop
   little-coder --model lmstudio/local-model
   

   The shipped lmstudio/local-model id routes to whatever model LM Studio currently has loaded — no extra config needed for the single-model case. If you serve on a non-default port, set LMSTUDIO_BASE_URL=http://127.0.0.1:<port>/v1. To target a specific model when you have several loaded, add an entry to ~/.config/little-coder/models.json (see Configuring models below).

Serving from another machine on your LAN. Each provider’s *_BASE_URL env var accepts any host, not just 127.0.0.1, so you can run inference on a beefier box and connect from a laptop or another device on the same WiFi.

On the server (the box with the GPU):

• llama.cpp: start llama-server with --host 0.0.0.0 (or your specific LAN interface) instead of 127.0.0.1. Everything else from Option A unchanged.
• LM Studio: in the Server tab, enable Serve on local network so it binds 0.0.0.0:1234 instead of 127.0.0.1:1234.
• Ollama: OLLAMA_HOST=0.0.0.0:11434 ollama serve (or set OLLAMA_HOST=0.0.0.0 in the user systemd unit).
• If ufw / firewalld is active, allow your LAN subnet to the relevant port (e.g. sudo ufw allow from 192.168.0.0/16 to any port 8888 proto tcp).
• Find the LAN IP with hostname -I (Linux) or ipconfig getifaddr en0 (macOS).

On the client (the machine running little-coder):

# Pick the env vars matching whichever provider is running on the server
export LLAMACPP_API_KEY=noop
export LLAMACPP_BASE_URL=http://<server-lan-ip>:8888/v1

# Sanity check reachability before launching the agent
curl -s http://<server-lan-ip>:8888/v1/models | head

little-coder --model llamacpp/qwen3.6-35b-a3b

The streaming chat-completions adapter works over a local network the same way it does over loopback — no client code change, no proxy needed. The per-model profile in .pi/settings.json (context/thinking-budget/temperature) still applies because it’s keyed by <provider>/<model-id>, which the client picks regardless of where the server lives.

All small-model-specific extensions auto-disable for large/cloud models so they don’t interfere.

---

Configuring models

The shipped model list lives in models.json at the package root. The llama-cpp-provider extension reads it at startup and registers each provider via pi’s registerProvider(). Editing this file in your global install does take effect — but it’s overwritten on npm install -g little-coder@latest, so for anything you want to keep, use a user override file instead.

User override resolution (first match wins):

1. $LITTLE_CODER_MODELS_FILE — explicit path, useful for ad-hoc tests.
2. $XDG_CONFIG_HOME/little-coder/models.json
3. ~/.config/little-coder/models.json

Merge semantics: each top-level provider key in your override file fully replaces the same key in the shipped models.json. Providers only in your file are added; providers only in the shipped file are kept. (We don’t deep-merge per-model fields — you redeclare the whole provider entry, which avoids “your override silently inherited new fields from a future package release” surprises.)

Default model. A top-level "default": "provider/id" key names the model bare little-coder launches when you don’t pass --model and pi has no saved selection yet (shipped default: llamacpp/qwen3.6-35b-a3b). Your override file’s default wins over the shipped one, so {"default": "llamacpp/qwen3.6-27b"} in ~/.config/little-coder/models.json makes the dense 27B your first-run default. It’s first-run-only: once you switch models in-session, pi remembers that and the default stops applying.

Per-phase models. /plan-model and /action-model let planning and implementation run on different models — see Per-phase model selection below.

Qwen3.8-27B (dense + MTP). Added to the shipped registry in v1.17.0. It’s the quality option on a small card, not the fast one — measured on an RTX 5070 Laptop (8GB) with UD-Q4_K_XL:

context	-ngl	tok/s	VRAM
16k	20	6.72	7200 MB
32k	18	6.42	7042 MB
32k	20	—	loads, then generates nothing

Compare ~44 tok/s for Qwen3.6-35B-A3B (MoE) on the same box: the MoE is roughly 7× faster because its experts live in RAM (--n-cpu-moe), a trick a dense model has no equivalent for. Its NextN head is in the GGUF (qwen35.nextn_predict_layers=1, blk.64), so MTP speculative decoding works — measured draft acceptance ~0.87. The one caution worth repeating: at 32k, -ngl 20 passes /health and then produces zero tokens. It fits in VRAM but has no room left to compute, so “the server started” is not evidence the config works. Raise the context and you must lower -ngl.

Community-recommended models. The shipped models.json stays intentionally small and stable — it doesn’t track the fast-moving world of community fine-tunes (which get re-uploaded and disappear from Hugging Face constantly). If you want to try one that’s doing well in the community — e.g. Qwen3.6-35B-A3B-REAM-192, which topped both a community tournament and a little-coder pilot (#63) — add it to your own override file rather than waiting for it to ship. Load the GGUF on your llama.cpp server, then drop an entry in ~/.config/little-coder/models.json:

{
  "providers": {
    "llamacpp": {
      "api": "openai-completions",
      "baseUrl": "http://127.0.0.1:8888/v1",
      "apiKey": "LLAMACPP_API_KEY",
      "models": [
        { "id": "ream-192", "name": "Qwen3.6-35B-A3B REAM-192 (community)", "reasoning": true, "input": ["text"] }
      ]
    }
  }
}

Then pick it with little-coder --model llamacpp/ream-192. (llama.cpp serves whichever GGUF you loaded regardless of the id, so the id is just your handle for it.)

Example — switch the llama.cpp port and bump qwen3.6-35b-a3b to a 150K context, leave ollama untouched:

{
  "providers": {
    "llamacpp": {
      "api": "openai-completions",
      "baseUrl": "http://127.0.0.1:1234/v1",
      "apiKey": "LLAMACPP_API_KEY",
      "models": [
        {
          "id": "qwen3.6-35b-a3b",
          "name": "Qwen3.6-35B-A3B (local llama.cpp, 150K)",
          "reasoning": true,
          "input": ["text"],
          "contextWindow": 150000,
          "maxTokens": 4096,
          "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }
        }
      ]
    }
  }
}

Then verify with little-coder --list-models — you should see your overridden entry.

LLAMACPP_BASE_URL, OLLAMA_BASE_URL, and LMSTUDIO_BASE_URL env vars still beat both files for those three providers.

Any OpenAI-compatible server (e.g. MLX / omlx)

little-coder registers providers from models.json — it doesn’t pick up pi’s standalone “picker” extensions. So a server isn’t added by installing its pi picker; you add it by declaring a provider. Any OpenAI-compatible endpoint works this way, including Apple’s MLX server (mlx_lm.server, often surfaced as omlx). Drop this into ~/.config/little-coder/models.json and pick it with little-coder --model omlx/<id>:

{
  "providers": {
    "omlx": {
      "api": "openai-completions",
      "baseUrl": "http://127.0.0.1:8000/v1",
      "apiKey": "IGNORED",
      "models": [
        {
          "id": "Qwen3-32B-4bit",
          "name": "Qwen3.6-35B-A3B (local omlx, 150K)",
          "reasoning": true,
          "input": ["text"],
          "contextWindow": 150000,
          "maxTokens": 4096,
          "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }
        }
      ]
    }
  }
}

Set id to whatever model your server reports, and baseUrl to its /v1 endpoint. Verify with little-coder --list-models.

.pi/settings.json is a separate concern: it controls per-model profiles (context_limit, thinking_budget, temperature, benchmark_overrides) referenced by the <provider>/<id> key. Profiles don’t register or describe models — they only tune how little-coder runs against models that are already registered.

---

Background jobs

bash blocks the turn until the command exits, which makes it the wrong tool for anything long. The usual workaround is worse: background the job and then poll it, spending a turn, a slice of a small context window, and seconds of local inference to learn that training is still on epoch 3. A six-hour job checked every five minutes is 71 wasted turns.

So ShellStart inverts it. The model says up front what is worth being interrupted for, and the harness stays quiet until one of those things happens:

{"name": "ShellStart", "input": {
  "command": "python train.py --epochs 50", "label": "finetune",
  "wake_on": {"match": ["Traceback", "CUDA out of memory", "val_loss="],
              "every_n_matches": 10, "silence": "15m"}}}

wake_on	fires when
exit	the job exits (default on)
match	a line matches — regex, falling back to literal text
silence	it produced output, then went quiet this long (e.g. "10m")
every_n_matches	only every Nth match, to throttle a chatty pattern

Urgency decides how the news arrives: a crash or an error-ish match interrupts the current turn; a clean exit or a milestone waits for the tool calls already in flight; routine output rides along with the next turn. Six hours of progress bars cost nothing; a traceback at minute 40 costs one turn, immediately. What the model receives is a bounded excerpt plus the exit code — never the whole log — with ShellLog to page deeper on demand. ShellList, ShellSend (stdin, for a REPL or a prompting installer) and ShellStop round it out.

Lifetime. A job outlives a turn but never the session. Jobs run in their own process group and are signalled as a group, so python train.py under a shell dies with the shell rather than being orphaned holding your VRAM. Session shutdown and every catchable signal reap them — and because SIGKILL is catchable by nobody, each job also carries a watchdog that kills its own group the moment little-coder’s pid disappears.

Permissions. ShellStart goes through the same whitelist as bash, so build and test commands usually need LITTLE_CODER_BASH_ALLOW (see Permissions).

Your project’s AGENTS.md

little-coder launches pi with --no-context-files, so its AGENTS.md is the system prompt rather than whatever happens to sit in the current directory. That is why the small-model adaptations hold in any repo, and until v1.20.0 it also meant your project’s own AGENTS.md was ignored outright, so the model globbed the whole tree at the start of every run to work out where it was (#104).

Now the nearest AGENTS.md (or CLAUDE.md, if there is no AGENTS.md) is found by walking up from the launch directory, and injected as project instructions. Three things keep it from undoing the reason --no-context-files is there:

• It adds. little-coder’s own prompt still governs behaviour, and the injected block says so, so a project file cannot quietly re-specify the harness.
• It is capped at 4000 characters (~1k tokens) and truncation is reported, to you and to the model, rather than being silent. The lean cold start is the product.
• It is injected once, as a hidden message at the conversation tail rather than a system-prompt append, so the cached prefix survives (#73).

/project-context shows which file loaded, its size, and its hash.

Env var	Values	Effect
LITTLE_CODER_PROJECT_CONTEXT	0 / off / false	Don’t load a project file at all, i.e. the pre-v1.20.0 behaviour.
LITTLE_CODER_PROJECT_CONTEXT_MAX_CHARS	integer	Raise or lower the cap. 0 means no cap.

Per-phase model selection

Plan on a big model, implement on a small one, without retyping ctrl+P at every transition (#61).

command	does
/plan-model <name>	model for Plan Mode (autocompletes; /plan-model 35b resolves)
/action-model <name>	model for implementation
/phase-models	show both, plus the active model and handover mode
/model-handover auto|manual	whether little-coder switches models for you
/implement	run the last approved plan in a fresh session on the action model

Defaults come from models.json ("planModel" / "actionModel" / "handover") or the environment (LITTLE_CODER_PLAN_MODEL, LITTLE_CODER_ACTION_MODEL, LITTLE_CODER_MODEL_HANDOVER), but the tags are session state — settable mid-session, because the case that motivated this is A/B-ing two planners against the same brief.

With auto (default), entering Plan Mode switches to the plan model and /implement hands over to the action model. The handover happens when implementation actually begins rather than at approval, so approving a plan you then decide to rewrite costs you nothing. With manual, the tags are shown but nothing switches on your behalf. That toggle matters more locally than it would on a hosted provider: on a single llama.cpp backend a handover evicts and reloads weights, so an automatic switch can mean a 15-second stall mid-thought. A handover where both phases resolve to the same model is a no-op rather than a reload, and a switch that fails (model unavailable, no key) degrades to staying put and says so. Leave both unset and nothing changes — each phase uses the active model.

Planning in a batch job

Plan Mode’s middle is interactive: research, then one to three clarifying questions, then the plan. Under -p there is nobody to ask, so headless runs used to refuse to plan at all, which is awkward for the workflow it matters most in, where a local model is a slow shared resource and jobs are queued and checked later (#95).

little-coder -p --plan-mode "add rate limiting to the ingest endpoint"

The questions are still generated; they are answered from the research instead of from you, and the plan opens with an Assumptions section saying what was decided for each and why, which is the part a batch user actually needs to review. The plan is printed and written to .pi/approved-plan.md, so the next job in the queue can run /implement against it.

Of the three policies that make sense here (skip the questions, pre-supply the answers from a file, or stop after generating them) this is the first. Pre-supplied answers need the question set to be stable across runs, and it is not; a two-phase run is by definition not unattended.

Permissions

little-coder gates shell tool calls — Bash and ShellSession alike — against a built-in safe-prefix whitelist (ls, cat, head, tail, git log/status/diff, find, grep, cp, mv, mkdir, touch, etc.) before pi’s own confirmation flow ever sees them. rm and sudo are intentionally not on the list — add them via LITTLE_CODER_BASH_ALLOW per deployment if you really need them.

Three rules beyond the prefix match:

• Every command in a chain is judged, not just the first (#70). ls && rm -rf / is refused on the rm, not allowed on the ls.
• A command that writes to a file through the shell is refused (#70), whatever it starts with. cat > main.py << 'EOF' is the same write as the Write tool and gets the same answer — use Write for a new file, Edit for an existing one. Redirects (>, >>), tee, and dd of= all count; 2>&1 and a > inside quotes don’t.
• Matching a prefix is not enough (#94). The interpreters are on the list so that scripts can be run, so python3 solution.py passes and python3 -c "..." does not. The same goes for node -e/-p, perl -e/-E, ruby -e, find -exec/-delete, sed -i, and env <command>: each is a way to reach an effect the whitelist just refused.

What the whitelist is and isn’t. It is a speed bump against a model that takes a wrong turn, not a sandbox. python3 script.py is one Write away from python3 -c, and no prefix list can close that. If you need a real boundary, use a container or a VM, or put a human in the loop with LITTLE_CODER_PERMISSION_MODE=manual.

In accept-all mode the whitelist is skipped, but the write guard still refuses a shell redirect that would clobber an existing file or a reserved device name — so the “small models don’t rewrite whole files” guarantee holds in benchmark runs too.

Two env vars control the gate:

Env var	Values	Effect
LITTLE_CODER_PERMISSION_MODE	auto (default) / accept-all / manual	auto: block any shell command not on the whitelist. accept-all: skip the gate entirely, every shell call passes (the benchmark runner sets this). manual: prompt before every shell command (execute y / cancel n) and before every write/edit — you choose Apply, Deny, or Apply all (this session) to stop re-prompting for the rest of the session. write-guard’s safety checks (reserved names, whole-file rewrites of existing files) still run even when you Apply.
LITTLE_CODER_BASH_ALLOW	comma-separated prefixes	Extra allow-prefixes merged with the built-in list. Trailing whitespace is meaningful: "make " allows make test but not makefoo; "make" allows both.

LITTLE_CODER_BASH_ALLOW only adds prefixes; it does not lift the inline-code rule. To run python3 -c you want accept-all (or manual, if you would rather see each one).

Examples:

# Add 'make' (with word-boundary) and 'docker compose ps' on top of the defaults
export LITTLE_CODER_BASH_ALLOW="make ,docker compose ps"

# Skip the gate entirely (use this only inside controlled environments)
export LITTLE_CODER_PERMISSION_MODE=accept-all

In auto and accept-all, write/edit confirmations are pi’s own; manual adds little-coder’s prompt in front of them.

---

Paper / benchmark results

Release	Model	Benchmark	Result
v0.0.2 (commit 1d62bde) — the paper	Qwen3.5-9B via Ollama	Aider Polyglot (225 exercises)	45.56 % mean of two runs; matched-model vanilla Aider baseline 19.11 %. Paper: Honey, I Shrunk the Coding Agent on Substack.
v0.0.5 — pre-pi Python	Qwen3.6-35B-A3B via llama.cpp	Aider Polyglot	78.67 %. Full narrative.
v0.1.4 — on pi	Qwen3.6-35B-A3B via llama.cpp	Terminal-Bench-Core v0.1.1 (80 tasks)	40.0 % in 6 h 50 min. Write-up.
v0.1.13 — on pi, TB 2.0 leaderboard	Qwen3.6-35B-A3B via llama.cpp	Terminal-Bench 2.0 (89 tasks × 5 trials = 445)	24.6 % ± 3.2 — accepted to the Terminal-Bench 2.0 leaderboard (rank 120).
v0.1.24 — on pi, TB 2.0 leaderboard, smaller model	Qwen3.5-9B (Q4_K_M) via llama.cpp (5.3 GB on GPU, 2× faster per-token than the 35B-A3B)	Terminal-Bench 2.0 (89 tasks × 5 trials = 445)	9.2 % ± 2.4 — accepted to the Terminal-Bench 2.0 leaderboard (rank 142).
v0.1.27 — on pi, GAIA validation	Qwen3.6-35B-A3B via llama.cpp	GAIA validation set (165 tasks)	40.00 % (66 / 165). L1 60.4 % / L2 37.2 % / L3 7.7 %. Test-split run pending.

All runs used a consumer laptop: i9-14900HX, 32 GB RAM, 8 GB VRAM on RTX 5070 Laptop (Blackwell). No cloud inference at any point.

---

Roadmap

Phase 1 — wide benchmark baseline: complete. The paper established that scaffold–model fit moves a 9.7 B model from 19 % to 45 % on Aider Polyglot, and the goal of Phase 1 was to find out how wide that impact radius is. We now have a four-benchmark baseline on a single laptop-class GPU:

1. Aider Polyglot — 45.56 % (paper, Qwen3.5-9B) and 78.67 % (v0.0.5, Qwen3.6-35B-A3B).
2. Terminal-Bench-Core v0.1.1 — 40.0 % (v0.1.4).
3. Terminal-Bench 2.0 — accepted to the official leaderboard: Qwen3.6-35B-A3B at 24.6 % ± 3.2 (rank 120) and Qwen3.5-9B at 9.2 % ± 2.4 (rank 142). The v0.1.24 prompt-repetition fix (re-add tool descriptions + concision guideline, validated by a 4 / 4 pilot on the previously-regressing prove-plus-comm task) was the prompt for both submissions.
4. GAIA — validation set at v0.1.27: 40.00 % (66 / 165) on Qwen3.6-35B-A3B. Per-level L1 60.4 % / L2 37.2 % / L3 7.7 %.

That spans short coding exercises (Polyglot), interactive shell-bound tasks (Terminal-Bench), and tool-using research (GAIA), all on the same scaffold. The data needed to choose what to fix next is now in hand.

Phase 2 — operating real knowledge bases as day-to-day work: the current focus. The motivating question shifts from how wide is the impact radius? to can a small local model reliably operate and traverse a large, messy knowledge base? little-coder’s day-to-day target is now real work over many markdown files at once — reading, cross-referencing, and updating sprawling note/log collections in the most token-efficient way a small local model can manage. Features are being implemented and tested across several real pipelines in parallel:

• Domains — medical, athletic, and educational knowledge bases, each with its own structure, vocabulary, and citation needs.
• Scale — 10+ years of logs, tens of thousands of entries of varied kinds, stressing retrieval, compaction, and the context-budgeting extensions on histories far longer than any single benchmark task.
• Messy real-world inputs — validation against conflicting OCR extractions of the same source, and multilingual content where the same fact recurs across languages.

This is where the scaffolding work now compounds: knowledge injection/selection, evidence handling, compaction fidelity, and the harness-intervention behaviors. Expect smaller, more frequent releases driven by what little-coder actually struggles with on this work rather than by a benchmark suite.

Benchmarks (deferred). The four-benchmark baseline above stands as the scaffold-fit reference point. Fresh runs — ProgramBench, SWE-bench Verified (multi-file real-world patches), a GAIA test split — come back into scope once the knowledge-base work has produced enough scaffolding signal to make a new measurement worth running.

---

Troubleshooting

--update flag — pass little-coder --update to force an immediate version check, bypassing the 12-hour cache. Useful right after a release. The flag is stripped before pi sees argv so it won’t produce an “Unknown option” error.

Auto-update fails on Windows (≤ v1.9.5): npm exit null — the updater in those versions can’t locate npm.cmd. Fixed in v1.9.6, but the broken updater can’t deliver its own fix — run npm install -g little-coder@latest once to get there, then auto-update works normally.

little-coder: command not found — npm’s global bin directory isn’t on your PATH. Run npm config get prefix to see where it installed; add <prefix>/bin to your PATH. Or reinstall with sudo if your prefix needs root.

ECONNREFUSED 127.0.0.1:8888 — llama.cpp isn’t running. Start llama-server first, or switch --model to an Ollama/cloud ID.

LAN client times out (no RST, just hangs) — the inference box’s firewall is dropping the SYN. The usual cause is ufw with a default-deny policy that allow-lists only SSH / a few dev ports. From the server: sudo ufw status verbose to confirm; sudo ufw allow from <your-lan-subnet>/24 to any port 8888 proto tcp to fix (scoped to the LAN so you’re not exposing the box). Docker-published ports bypass ufw via PREROUTING NAT, which is why a Docker container can be reachable while a plain llama-server on the same host isn’t.

Image attachment is accepted but the request returns 4xx — your llama-server is running without a vision projector. Re-launch it with --mmproj ~/models/mmproj-F16.gguf (or another mmproj variant from the same GGUF repo). The --list-models images column reflects what the client will attempt to send, not what the server can answer; the projector is what gives the model eyes.

Failed to parse input at pos N: SomeTool(arg='…')]<|tool_call_end|> (LFM2 / Liquid models) — the model is emitting its native Pythonic tool calls (<|tool_call_start|>[Read(path='…')]<|tool_call_end|>), but llama.cpp’s tool-call parser is choking on them — usually because the chat template doesn’t match the parser. The GGUF’s embedded template often renders tools as a plain List of tools: […] blob without the <|tool_list_start|> / <|tool_call_start|> special tokens the parser expects. Fix: serve with --jinja and the model’s proper chat template, e.g. llama-server -m LFM2.5-8B-A1B-Q4_K_M.gguf --jinja --chat-template-file LFM2-8B-A1B.jinja (templates ship under llama.cpp/models/templates/). With the matching template, llama.cpp parses the calls into native tool_calls and tools execute normally — verified end-to-end with LFM2.5-8B-A1B. If your build still leaks the calls as plain text, little-coder’s output-parser recognizes the format and surfaces this same diagnostic instead of a cryptic error (issue #42).

Context overflows on a long task before compaction kicks in — fixed in v1.9.12. pi only re-checks compaction when the model goes idle at the end of a turn sequence, so a single long autonomous run (dozens of tool calls) could grow context all the way to an overflow error before that check ever ran (issue #59). little-coder now watches context usage at every turn boundary and triggers pi’s compaction mid-run once usage crosses 80 % of the window. Tune the trigger with LITTLE_CODER_COMPACT_AT_PERCENT=<n> (e.g. 70 to compact earlier; values ≤0 or ≥100, or LITTLE_CODER_NO_COMPACT_WATCHDOG=1, disable it and fall back to pi’s end-of-run behavior). This is independent of pi’s own reserveTokens / keepRecentTokens, which still govern how much is summarized vs. kept. As of v1.11.0 the watchdog also guards against a compaction loop (issue #68): if a mid-run compaction frees too little (usage stays near the threshold, e.g. a context window too small for the task), it pauses automatic compaction with a notice instead of firing a doomed second compaction that pi would reject with Nothing to compact — which previously left the session unrecoverable. It re-arms once usage drops back below the threshold (a /clear, /compact, or a smaller turn). If you hit the pause a lot, the real fix is a larger-context model or -c window.

No API key env var warning — pi expects some key even for local providers. Export LLAMACPP_API_KEY=noop (or OLLAMA_API_KEY=noop) before launching.

Update prompt hangs on launch / want it in the UI instead — when a new version is published the launcher asks Update now? [Y/n] before starting. As of v1.9.12 it auto-continues without updating after 10 s so an unattended terminal is never blocked; tune with LITTLE_CODER_UPDATE_PROMPT_TIMEOUT=<seconds> (0/off waits forever). If you dismiss or time out of the prompt, little-coder still shows a one-line “update available” notice inside the TUI, and you can run /update any time to install the latest and end the session for a clean restart (issue #64). As of v1.11.0, answering Y at the launcher prompt auto-relaunches into the new version with your original arguments (issue #66) — no manual re-run — printing Relaunching little-coder… (with a manual-relaunch fallback if the re-exec can’t start). The in-app /update still ends the session for a manual restart, since it runs inside the pi child process where an in-place re-exec isn’t safe.

No pi “Update Available” banner — that’s intentional. little-coder defaults PI_SKIP_VERSION_CHECK=1 so the bundled pi runtime doesn’t nag about updating itself; little-coder pins pi to a known-good version per release. If you actually want the banner back, export PI_SKIP_VERSION_CHECK=0 before launching.

Running little-coder from Zed’s agent panel — there’s no built-in ACP server, but a community pi-acp bridge works well; see docs/zed-acp.md for the full setup (issue #58).

Extension load failures on startup — run /extensions inside the TUI: it lists what loaded, where each one came from, and anything that failed. A user extension that can’t be resolved also raises a notification at session start. little-coder --list-models --verbose surfaces pi’s own load errors. If the install looks corrupt: npm uninstall -g little-coder && npm install -g little-coder.

My pi extensions / themes don’t load — themes do load; extensions don’t, by default. --no-extensions gates extensions only, so pi themes in ~/.pi/agent/themes work as normal. For extensions, relaunch with --with-pi-extensions, or put your own in ~/.config/little-coder/extensions/. See docs/extensions.md (issue #67).

A malware alert on npm install -g little-coder — Socket’s AI scanner flagged the postinstall script in v1.10.0/v1.11.0 as suspicious. It was a false positive on a visible, dependency-free patcher, but as of v1.12.0 little-coder ships no install scripts at all — the launcher does that work at launch time instead, which is also the only path that ever ran for upgrading users, since /update installs with --ignore-scripts (issues #75, #50).

ctrl+r does nothing — it isn’t bound at the prompt; pi binds “expand / more” to ctrl+o. Versions up to v1.11.0 advertised ctrl-r in the startup header, which was simply wrong (issue #74). Press ctrl+h for the current key list, or /hotkeys for the full reference. Note that during a Deep Research run there is nothing for ctrl+o to expand: research sub-coders are separate child processes, so their tool output never enters this session’s transcript — the progress bar is the view of that work. While a dialog is open (the max-agents or clarifying-question prompts), keys belong to the dialog.

llama.cpp reprocesses the whole conversation every turn — fixed in v1.12.0. little-coder’s per-turn skill and knowledge blocks used to be appended to the system prompt, which sits at the front of every request, so changing them invalidated the entire cached prefix and your server re-read the full history (issue #73). Those blocks now arrive as a message at the end of the conversation instead, leaving the prefix byte-identical. Watch the CH field in the status line to confirm cache reuse. LITTLE_CODER_INJECT_MODE=system restores the old placement, which is what the whitepaper scaffold numbers were measured against.

Node version too old — little-coder needs Node ≥ 22.19.0 (matching the minimum of the bundled @earendil-works/pi-coding-agent v0.75+). Check with node --version. Easiest fix: nvm install 22 && nvm use 22.

---

Developing little-coder locally

If you want to hack on the extensions or skills:

git clone https://github.com/itayinbarr/little-coder.git
cd little-coder
npm install
npm link            # makes the local checkout available as `little-coder`
little-coder --model llamacpp/qwen3.6-35b-a3b

To unlink: npm unlink -g little-coder.

The benchmarks harness (benchmarks/) is dev-only and not shipped with the npm package. Run it from a clone with python3 benchmarks/aider_polyglot.py … etc.

---

Architecture

little-coder/
├── .pi/
│   ├── settings.json               # per-model profiles + benchmark_overrides (terminal_bench, gaia)
│   └── extensions/                 # 27 TypeScript extensions, auto-discovered by pi
│       ├── branding/               # little-coder startup header + terminal title + session auto-naming
│       ├── plan-mode/              # alt+p "research → ask → plan" flow (sub-coders + clarifying questions → written plan)
│       ├── subagent/              # `dispatch` tool: isolated read/browse-only sub-coders + live tracker (spawn.ts engine)
│       ├── prompt-history/         # up-arrow recall of recent prompts (from an empty input)
│       ├── llama-cpp-provider/     # data-driven provider registration from models.json — ships llamacpp, ollama, lmstudio (+ user override file)
│       ├── write-guard/            # Write refuses on existing files; rewrites root-bare /foo.md paths to cwd
│       ├── read-guard/             # trims a Read that would overflow the context window to its first 30 lines + a search-instead directive
│       ├── read-guard-edit/        # Edit refuses until the file has been Read this session
│       ├── extra-tools/            # glob, webfetch, websearch (pi ships grep/find)
│       ├── skill-inject/           # per-turn tool-skill selection (error > recency > intent)
│       ├── knowledge-inject/       # algorithm cheat-sheet scoring (word=1.0, bigram=2.0, threshold=2.0)
│       ├── output-parser/          # repair malformed ```tool, <tool_call>, bare JSON
│       ├── quality-monitor/        # empty / hallucinated / loop detection + correction follow-up
│       ├── thinking-budget/        # cap thinking tokens per turn, retry with thinking off
│       ├── permission-gate/        # bash whitelist (ls, cat, git log/status/diff, etc.)
│       ├── checkpoint/             # snapshot files before Write/Edit
│       ├── tool-gating/            # enforces _allowed_tools at exec + schema levels
│       ├── turn-cap/               # max_turns abort (Polyglot unbounded, TB 40, GAIA 30)
│       ├── benchmark-profiles/     # reads settings.json → systemPromptOptions + sets temperature
│       ├── shell-session/          # ShellSession[Cwd|Reset] — tmux-proxy + subprocess backends
│       ├── browser/                # Playwright BrowserNavigate/Click/Type/Scroll/Extract/Back/History
│       ├── evidence/               # EvidenceAdd/Get/List — per-session store, 1 KB snippet cap
│       └── evidence-compact/       # preserves evidence across pi's auto-compaction
├── skills/                         # 30 markdown files the extensions inject on demand
│   ├── tools/*.md                  #   14 tool-usage cards
│   ├── knowledge/*.md              #   13 algorithm cheat sheets
│   └── protocols/*.md              #    3 research/cite/decomposition workflows
├── benchmarks/
│   ├── rpc_client.py               # PiRpc — spawns `pi --mode rpc`, demuxes events + UI requests
│   ├── aider_polyglot.py           # Polyglot driver with per-language transforms
│   ├── tb_adapter/                 # Terminal-Bench 1.0 BaseAgent (tmux-proxy)
│   ├── harbor_adapter/             # Terminal-Bench 2.0 BaseAgent (async env.exec proxy)
│   ├── tb_pilot.sh / harbor_pilot.sh
│   ├── tb_status.sh / harbor_status.sh
│   └── test_rpc_client.py
├── AGENTS.md                       # project system prompt (pi discovers it automatically)
├── models.json                     # canonical provider registration (loaded by llama-cpp-provider; user override at $XDG_CONFIG_HOME/little-coder/models.json)
└── docs/
    ├── benchmark-*.md              # per-benchmark narratives
    └── architecture.md             # v0.0.5-era Python architecture (historical)

Key invariant. pi is a minimal base by design. Every little-coder mechanism ships as a pi extension that hooks pi’s lifecycle events (before_agent_start, context, before_provider_request, tool_call, tool_result, turn_end, session_compact). Extensions are independent: the launcher discovers every .pi/extensions/*/index.ts and loads it explicitly with --extension, and pi runs with --no-extensions, so the bundled set is exactly what loads — no more, no less. If you don’t want one, delete its directory; if you want to add another, drop it next to the existing ones (or pass -e <path> at launch).

---

Reproducing the paper (v0.0.2)

git clone https://github.com/itayinbarr/little-coder.git
cd little-coder
git checkout v0.0.2
# Follow that version's README for its Python setup (pip install -e .)

The paper ran ollama/qwen3.5 through the Python little-coder at commit 1d62bde (tag v0.0.2). The 45.56 % mean figure is the average of two full 225-exercise runs on that exact codebase. For the 78.67 % headline, check out tag v0.0.5 — both are pre-pi Python and follow the pre-pi setup.

---

Citation

@misc{inbar2026littlecoder,
  title        = {little-coder: A Coding Agent Optimized for Small Local Language Models},
  subtitle     = {Architectural Adaptation Lets a 9.7B Model Outperform Frontier Models on Aider Polyglot},
  author       = {Inbar, Itay},
  year         = {2026},
  month        = apr,
  howpublished = {\url{https://open.substack.com/pub/itayinbarr/p/honey-i-shrunk-the-coding-agent}},
  note         = {White paper}
}

---

Attribution

little-coder v0.0.x was a derivative work of CheetahClaws / ClawSpring by SafeRL-Lab, Apache 2.0. That upstream provided the Python agent substrate, tool system, multi-provider support, and REPL.

little-coder v0.1.0+ replaces that substrate with pi by Mario Zechner — Apache 2.0 / MIT. The npm package was renamed from @mariozechner/pi-coding-agent to @earendil-works/pi-coding-agent in upstream’s 0.74 release; little-coder v1.4.2+ ships with the new package. pi provides the agent loop, provider abstraction, TUI, and extension model. little-coder rebuilds its small-model adaptations on top of pi as extensions.

All little-coder-specific mechanisms — Write-vs-Edit invariant, skill / knowledge injection, thinking-budget cap, output-parser, quality-monitor, per-model profiles, per-benchmark overrides, ShellSession / Browser / Evidence tool families, evidence-aware compaction — are preserved across versions.

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License

Apache 2.0 — see LICENSE for details. NOTICE tracks upstream attribution.