19 KiB

Raw Blame History

jcode

A minimal, single-file coding agent (~1,200 lines of Python) for local LLMs — with optional cloud fallback.

jcode connects to a local LM Studio or Ollama server (or, via settings.json, to OpenAI, Anthropic, or Gemini), takes a task in plain language, and works autonomously inside a sandboxed ./code directory: it reads, writes, and edits files, runs commands, searches the web, looks at images/screenshots/PDF pages, writes its own smoke tests, and keeps fixing the code until the tests pass.

   _               _
  (_) ___ ___   __| |  ___
  | |/ __/ _ \ / _  | / _ \
  | | (_| (_) | (_| ||  __/
 _/ |\___\___/ \____| \___|
|__/

Features

Autonomous test-fix loop — the agent writes a test for every change, runs it, analyzes failures, fixes the code, and repeats until green (up to MAX_STEPS, default 200).
Sandboxed — the file tools read and write only inside ./code; the skills/ folder next to it is readable but read-only. Every path is resolved first (.., ~ and symlinks included) and then checked, so ../settings.json, /etc/passwd or a symlink pointing outside are refused with a clear error, and an absolute path into ./code is used as is instead of being re-rooted. search_files rejects globs with .. and skips symlinks that leave the project. run_command is not path-checked (a shell can reach anything your user can); it starts in ./code. Destructive shell commands (rm -rf /, sudo, dd, mkfs, shutdown, fork bombs, curl | sh, …) are refused.
Zero-config local backends — auto-detects LM Studio (port 1234) first, then Ollama (port 11434).
Cloud providers via settings.json — switch to OpenAI, Anthropic, or Gemini at runtime with /backend <name>; all are used through their OpenAI-compatible endpoints, so the single openai client dependency covers everything.
Project guidelines via AGENTS.md — project-specific rules (coding style, per-language test conventions) are loaded from code/AGENTS.md and injected as binding instructions.
Skills — reusable instruction packs in skills/<name>/SKILL.md (Claude-Code-compatible YAML frontmatter + Markdown, with links to further files in the skill folder). The catalog goes into the system prompt; the model loads a skill on demand with load_skill, or you preload one with /<skill> task. Five skills are included: code-review (diff-scoped review with verified findings), save-load-json (state persistence with a completeness check), browser-test (headless Puppeteer harness), single-file-webapp (patterns + static checker) and threejs-csg (three.js with boolean operations, loaders and exporters).
Vision — the agent can look at images, screenshots and PDF pages (see the view_image/take_screenshot/view_pdf_page tools below) to visually check its own work, e.g. a generated plot or a running GUI.
Session-wide context management — token usage is tracked (server-reported or estimated); when the context nears its limit, older history is compacted into an LLM-written summary so long sessions keep working. The threshold is configurable in settings.json (autoCompactWindow, Claude-Code-compatible).
Adjustable reasoning effort — defaults to low; /reasoning high|medium|low|max|none changes the model's thinking effort at runtime (backend-dependent; none turns it off).
Robust against flaky servers — visible retries with backoff for connection errors, timeouts and 5xx; if the backend goes down, the session is preserved and the task can simply be resubmitted. A 4xx rejection is not retried: if the request contained images, they are stripped and the task continues text-only (models without vision); if the context is too long, the history is compacted and the request repeated once; otherwise the server's message is shown.
Loop guard — an identical tool call repeated MAX_REPEATS times in a row first triggers a forced reflection step (expected / observed / hypotheses / one experiment); if the model loops again, the task is stopped with the session intact. A no-op edit_file (old = new) is rejected, so it can't feed a loop.
Transparent — every request, tool call, and result is logged to logFile.md; file changes are shown as colored unified diffs in the terminal. The stats line after each response shows time, generated tokens, tok/s and the prompt size the server actually received (context N) — compare it with the context length configured in LM Studio/Ollama: if the server's window is smaller than autoCompactWindow, the server silently truncates the oldest messages (including the system prompt and skills) long before jcode compacts.
Tools available to the model:
- list_files (skips node_modules, .git, caches and build folders — shown as one line with their entry count — and caps the output at 300 entries), read_file (optionally with line numbers and a start_line/end_line range, so large files aren't re-read whole), search_files (regex grep over the sandbox, hits as file:line: content)
- write_file, edit_file (search/replace), insert_lines, delete_lines
- run_command (runs in bash wherever it is installed — on Debian/Ubuntu /bin/sh is dash, which lacks pipefail, [[ ]], source, {a,b}, echo -e and misparses &>; pipes report the exit code of the first failing command; blocks known-destructive patterns; pip/pip3 calls are redirected to jcode's own Python interpreter; background processes must redirect their output — cmd > x.log 2>&1 & — or the call blocks until the timeout; on timeout the whole process tree is killed and the output collected so far is returned)
- web_search (DuckDuckGo) and fetch_url (fetches a page and extracts its text)
- view_image (png/jpg/gif/webp/bmp/svg), take_screenshot (whole screen, e.g. of a GUI app under test), view_pdf_page (renders one PDF page) — images are delivered to the model as an inline image right after the tool result
- load_skill (returns a skill's SKILL.md or one of its linked files)

Requirements

Python 3.10+
pip install openai prompt_toolkit ddgs
One of:
- LM Studio with the local server running (default http://localhost:1234), or
- Ollama (http://localhost:11434), or
- an API key for OpenAI / Anthropic / Gemini (see settings.json below)
Optional, only needed for the vision tools (the agent will tell you what's missing and how to install it when it tries to use one):
- Pillow — view_image, take_screenshot, view_pdf_page
- cairosvg — view_image on .svg files
- pymupdf — view_pdf_page
- certifi — CA bundle for fetch_url; falls back to the system default if not installed. Set JCODE_INSECURE_SSL=1 to disable TLS verification (e.g. behind a re-signing corporate proxy) — use with care.

Usage

# One-shot task
python jcode.py "Write fib.py with fib(n) and a smoke test that checks fib(10)==55."

# Interactive mode (multiline input; history is kept across tasks)
python jcode.py

All generated files land in ./code, which is created automatically.

Interactive commands

Command	Effect
`/status`	Show context usage, message counts, model, sandbox
`/new`, `/neu`, `/reset`	Clear the session history
`/models`	List models available on the current backend
`/model <name>`	Switch model
`/backend <name>`	Switch to `lm-studio`, `ollama`, or a provider from `settings.json` (e.g. `openai`, `anthropic`, `gemini`)
`/reasoning <level>`	Set reasoning effort: `high`, `medium`, `low` (startup default), `max`, or `none` (off); no argument or `default` resets to the backend default
`/max-turns N`	Set the step limit per task
`/skills`	List the skills found in `skills/`
`/<skill> <task>`	Run a task with that skill's instructions preloaded (e.g. `/threejs-csg Make a plate with four holes`)
`Ctrl+C`	Abort the current input or running task (session is preserved)
`Ctrl+D`, or `Ctrl+C` on an empty prompt	Quit

Configuration

settings.json (optional)

Placed next to jcode.py. Defines cloud providers, optionally the backend to start on, and the context compaction threshold. API keys are read from environment variables — don't put literal keys in the file.

{
  "autoCompactEnabled": true,
  "autoCompactWindow": "256k",
  "default_provider": "",
  "providers": {
    "openai": {
      "base_url": "https://api.openai.com/v1",
      "api_key_env": "OPENAI_API_KEY",
      "model": "gpt-4.1"
    },
    "anthropic": {
      "base_url": "https://api.anthropic.com/v1/",
      "api_key_env": "ANTHROPIC_API_KEY",
      "model": "claude-sonnet-4-5"
    },
    "gemini": {
      "base_url": "https://generativelanguage.googleapis.com/v1beta/openai/",
      "api_key_env": "GEMINI_API_KEY",
      "model": "gemini-2.5-pro"
    }
  }
}

autoCompactEnabled / autoCompactWindow (same keys and notation as Claude Code): history compaction kicks in once the context exceeds autoCompactWindow tokens ("256k", "1m" or a plain number; default 250 000). false disables compaction entirely. /status shows the active threshold.
default_provider: name of a provider to activate at startup; leave "" to use the auto-detected local backend.
Each provider needs base_url (an OpenAI-compatible endpoint), api_key_env (environment variable holding the key), and optionally model.
If the file is missing or malformed, jcode prints a warning and runs purely locally.

export ANTHROPIC_API_KEY=sk-ant-...
python jcode.py
Task> /backend anthropic

AGENTS.md (optional)

If code/AGENTS.md exists, it is loaded at session start and treated as binding project guidelines — e.g. "simplicity first", surgical changes only, and the per-language verification conventions (how to compile/test C, C++, Java, PHP, Python, HTML, CSS). Keeping language-specific rules here rather than in the system prompt keeps every request lean and lets you adapt conventions per project without touching code.

Skills (optional)

A skill is a folder skills/<name>/ next to jcode.py containing a SKILL.md — the same layout and frontmatter Claude Code uses, so skills can be shared between both:

skills/
├── browser-test/     # shot.js, actions-example.js
├── code-review/      # review_prep.py, checklist.md
├── save-load-json/   # js.md, python.md, check_roundtrip.py
├── single-file-webapp/   # patterns.md, check_html.py
└── threejs-csg/
    ├── SKILL.md        # frontmatter (name, description) + instructions
    ├── csg.md          # linked from SKILL.md as ./csg.md
    ├── template.html   # linked from SKILL.md as ./template.html
    └── loaders.md

---
name: threejs-csg
description: >
  Build 3D scenes with three.js and apply boolean operations (subtract, union,
  intersect) to meshes. Use for any three.js, WebGL or CSG task.
---
# Instructions ... see [csg.md](./csg.md) and [template.html](./template.html)

name and description are read from the frontmatter (name falls back to the folder name). The frontmatter parser is deliberately minimal: key: value lines, plus indented continuation lines for >/| block scalars — no PyYAML needed.
At session start, the names and descriptions of all skills are added to the system prompt, and the model can call load_skill(name) at any time; the body of SKILL.md (frontmatter stripped) is returned together with a list of the other files in the folder.
Automatic selection: at the start of every task, jcode asks the model in one tool-less request which skills match the task and injects their SKILL.md into the task message. Small models reliably ignore a "call load_skill first" instruction in the system prompt but reliably answer a direct question — one cheap request instead of a run that hallucinates an API. /<skill> task skips the selection and uses that skill.
Relative links in the Markdown (./forms.md, [x](forms.md)) refer to files in the skill folder; the model reads them with load_skill(name, "forms.md") (or read_file/list_files with skills/<name>/<file>, <name>/<file> or the absolute path — a garbled prefix in front of /skills/<name>/ is tolerated, since models mangle long paths; the skills folder is readable but read-only: run_command refuses sed -i, redirects, open(..., 'w') etc. on it, so the model copies a file into the project before changing it). Scripts can be run via their absolute path, which is included in the tool result.
/<skill> task injects the skill text directly into the task so no extra tool call is needed; /skills lists what was found.

Included skills:

Skill	Contents	Why
`browser-test`	`shot.js`: a Puppeteer harness that opens an HTML file via `file://` (no server), records JS exceptions / `console.error` / failed requests, runs an `actions.js` (click, shift-click, set input, key, eval, upload through the real file dialog, download through the real export button) and writes numbered screenshots; exit code 1 on any error. Probes WebGL and retries with SwiftShader flags (`--check` shows what works), traces every step and has its own watchdog, so a hang is reported with the step it stuck in instead of a silent timeout. Rules: test the control the user uses, not a stand-in; keep tests, fixtures and `node_modules` in the project. Needs `npm install puppeteer` in the project (or `CHROME_PATH` + `puppeteer-core`).	Without it the agent spends dozens of steps starting web servers and re-inventing a screenshot script, and finds runtime errors late.
`single-file-webapp`	Rules for one-file ES-module apps (no inline `onclick` with module scripts, one state object + `render()`, JSON serialization with nested groups, Blob download / FileReader import, `window.__app` test hook), `patterns.md` with snippets, `check_html.py` (syntax-checks every script block with `node --check`, validates the import map, flags inline handlers and missing element ids).	Targets the most common bug class in generated single-file apps: module scope vs. inline handlers, and exporters that silently miss nested data.
`code-review`	`review_prep.py` (stdlib only): scopes the review to the git diff (or given files), prints per file the changed line ranges, an outline of functions with line ranges, compiler/linter output restricted to bug-relevant rules (ruff/pyflakes, `node --check` incl. `<script>` blocks in HTML, gcc/clang `-Wall -Wextra`, `php -l`, `bash -n`/shellcheck) and high-signal hints; says `NOT CHECKED` when no tool is installed. `checklist.md` with questions per category and language. SKILL.md: understand intent → read one function at a time → every finding needs a concrete failure scenario, CONFIRMED by a tiny repro where cheap → ranked `REVIEW.md`, at most ~10 findings.	Small models produce long lists of plausible-sounding guesses and linter echoes. Forcing a scenario + repro per finding turns the review into something the author can act on.
`save-load-json`	Save/load of complete application state as JSON, for browser apps (download/upload) and Python/Node programs (atomic file write). The rule: serialize the whole state and subtract an explicit `RUNTIME` list, so a forgotten field is saved, not lost; one constructor path for add/paste/load; `version` + `migrate`; `assertPlain` (JS) / `allow_nan=False` (Python) turn a mesh, cycle or NaN in the data into an error at save time. `check_roundtrip.py` compares a saved file with the live state's field paths and lists what was lost. Snippets in `js.md` and `python.md`, all verified.	Three Tinkercad runs produced JSON without coordinates or nested groups because the serializer listed fields by hand.
`threejs-csg`	`template.html` (working scene: cube minus cylinder, import map for three / three-mesh-bvh / three-bvh-csg), `csg.md` (Brush, `updateMatrixWorld()`, operations, pitfalls), `loaders.md` (STL export/import, SVG extrusion, solid text, group = union of solids minus holes).	Pinned, verified CDN versions and the non-obvious rules (exporters want plain meshes with world matrices, not the scene or raw brushes).

Skills are plain folders — add your own next to them.

Constants in jcode.py

Constant	Default	Meaning
`MODEL`	(auto)	Model name; auto-corrected to the closest available model
`MAX_STEPS`	200	Tool-use steps per task
`MAX_REPEATS`	3	Identical tool calls in a row before the reflection step / stop
`CMD_TIMEOUT`	60 s	Default timeout per shell command (overridable per call via the `timeout` argument)
`CONTEXT_LIMIT`	256 000	Model context window (tokens)
`COMPACT_THRESHOLD`	250 000	History compaction kicks in above this; overridden by `autoCompactWindow` in `settings.json`
`AUTO_COMPACT`	`True`	Whether compaction happens at all; overridden by `autoCompactEnabled` in `settings.json`
`KEEP_RECENT`	8	Most recent messages kept uncompressed when compacting
`REQUEST_TIMEOUT`	11 600 s	Timeout per LLM request (local models are slow)
`REASONING_EFFORT`	`"low"`	Reasoning effort sent to the backend; change at runtime with `/reasoning` (`/reasoning default` = backend default)
`MAX_IMAGE_SIDE`	1280 px	Longest edge images are scaled to before being sent to the model
`MAX_IMAGES_IN_CONTEXT`	4	Older images beyond this are replaced by a text stub to save context
`SKILLS_DIR`	`skills/` next to `jcode.py`	Folder scanned for `<name>/SKILL.md`

How it works

The task is appended to the session history and sent to the model together with the tool schemas (derived automatically from the Python type annotations — no schema drift).
The model plans, calls tools (read → edit → run test), and gets each result back.
Failing tests loop back into analysis and fixes; only when the tests are green does the model answer with a summary and the turn ends.
Near the context limit, the older history is summarized by the model itself and replaced, so the session can continue indefinitely.

Safety notes

jcode blocks known-destructive command patterns and confines file access to the sandbox, but run_command still executes shell commands on your machine with your user's permissions. Review logFile.md when in doubt, and treat web content fetched by the agent as untrusted data.

License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

19 KiB Raw Blame History