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".

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 + 20 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/. You can mix little-coder with pi packages from anyone else, add your own extensions, or disable ours per-project via .pi/settings.json.

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.

One-line install (Node.js 20.6+ 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 ≥ 20.6 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.

cd ~/your-project
little-coder --model llamacpp/qwen3.6-35b-a3b

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.

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 --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.

For local providers (llama.cpp, Ollama) 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

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

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

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 a GGUF
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

# 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 \
   --host 127.0.0.1 --port 8888 --jinja \
   -c 16384 -ngl 99 --n-cpu-moe 999 --flash-attn on

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

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

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.)

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 and OLLAMA_BASE_URL env vars still beat both files for those two providers (legacy compat).

.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.

little-coder gates Bash tool calls against a built-in safe-prefix whitelist (ls, cat, git log/status/diff, find, grep, etc.) before pi's own confirmation flow ever sees them.

Two env vars control the gate:

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

Write/Edit confirmations are pi's responsibility; little-coder doesn't intercept those.

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.

The near-term focus is benchmarking, not new features. The paper established that scaffold–model fit moves a 9.7 B model from 19 % to 45 % on Aider Polyglot. The open question is: how wide is the impact radius? Does the same set of adaptations — Write-vs-Edit invariant, per-turn skill injection, thinking-budget cap, output-repair, quality monitor — help on tasks that aren't self-contained coding exercises? What breaks? What compounds?

The plan is to establish a wide baseline before any further scaffolding changes:

1. Aider Polyglot — done. 45.56 % (paper, Qwen3.5-9B) and 78.67 % (v0.0.5, Qwen3.6-35B-A3B).
2. Terminal-Bench-Core v0.1.1 — done. 40.0 % (v0.1.4).
3. Terminal-Bench 2.0 — done. Qwen3.6-35B-A3B at 23.82 % (PR #158) and Qwen3.5-9B at 9.21 % (PR #163), both awaiting maintainer merge. 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 done at v0.1.27: 40.00 % (66 / 165) on Qwen3.6-35B-A3B. Per-level L1 60.4 % / L2 37.2 % / L3 7.7 %. Test-split run (301 tasks) pending → leaderboard submission to follow.
5. SWE-bench Verified — after GAIA. Multi-file real-world patches; the longest-horizon test of whether the scaffolding generalizes past exercise-scale tasks.

After that baseline is in place, the next phase starts: improvement experiments targeted at the specific failure patterns we've seen (thinking-budget / quality-monitor behavior on long-horizon tasks, deliberate.py-style parallel branches on failure, better shell-session recovery for interactive-process traps). No scaffold changes until the data says which ones are worth running.

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.

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.

Extension load failures on startup — run little-coder --list-models --verbose; extension errors surface there. If the install looks corrupt: npm uninstall -g little-coder && npm install -g little-coder.

Node version too old — little-coder needs Node ≥ 20.6.0. Check with node --version. Easiest fix: nvm install 20 && nvm use 20.

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.

little-coder/
├── .pi/
│   ├── settings.json               # per-model profiles + benchmark_overrides (terminal_bench, gaia)
│   └── extensions/                 # 20 TypeScript extensions, auto-discovered by pi
│       ├── llama-cpp-provider/     # data-driven provider registration from models.json (+ user override file)
│       ├── write-guard/            # Write refuses on existing files — the whitepaper invariant
│       ├── 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 and can be enabled/disabled per deployment via .pi/settings.json. If you don't want one, delete its directory or disable it in settings; if you want to add another, drop it next to the existing ones.

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.

@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}
}

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 (@mariozechner/pi-coding-agent) by Mario Zechner — Apache 2.0 / MIT. 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.

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