Token efficient Claude Code full Python rebuild. AI Coding Agent in 270K LoC pure Python. Up to 200X Cost Saving!
# Add to your Claude Code skills
git clone https://github.com/agentforce314/clawcodexLast scanned: 5/30/2026
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}clawcodex is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by agentforce314. Token efficient Claude Code full Python rebuild. AI Coding Agent in 270K LoC pure Python. Up to 200X Cost Saving!. It has 820 GitHub stars.
Yes. clawcodex passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/agentforce314/clawcodex" and add it to your Claude Code skills directory (see the Installation section above).
clawcodex is primarily written in Python. It is open-source under agentforce314 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh clawcodex against similar tools.
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A production-oriented Python rebuild of Claude Code — real architecture, reliable CLI agent
Ported from the TypeScript reference implementation and extended with a Python-native runtime
🔥 Active Development • New Features Weekly 🔥

ClawCodex — the open-source Python rebuild of Claude Code — run headless on claude-opus-5 at effort=xhigh.
A single run (k=1) against the board's k=5 averages; benchmarked on main at #756. Fully reproducible from the
open-source Harbor adapter. Read the full breakdown ↓
tail away.One toggle, deterministic filters — failure-focused test summaries, git/pip/npm ceremony
stripping, log dedup, recoverable head-caps — guarded to be never worse than the raw rendering.
Stacks with the DeepSeek prefix cache: the cache makes your stable
prefix nearly free, /eco shrinks the fresh suffix every turn actually pays for.
See the measured benchmark ↓
~$0.0435 / 1M tokens — about 230× cheaper than Claude Fable 5 ($10 / 1M).ClawCodex keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your whole
system + tools + history span across turns. The longer you code, the more you save.
One line — installs uv, Python 3.10+, and puts clawcodex on your PATH:
curl -fsSL https://clawcodex.app/install.sh | bash
Then configure a provider and start coding:
clawcodex login # interactive provider + API key setup → ~/.clawcodex/config.json
clawcodex # start it in any project — Full Access by default, /permissions to change
The installer also ships clawcodex lifecycle helpers — doctor (diagnose your
environment), verify (health-check the install), update, and uninstall. It is
re-run-safe and works on macOS, Linux, and WSL. To pass flags through the pipe, use
curl -fsSL https://clawcodex.app/install.sh | bash -s -- --dry-run.
git clone https://github.com/agentforce314/clawcodex.git
cd clawcodex
python3 -m venv .venv && source .venv/bin/activate # Python 3.10+
pip install -r requirements.txt
python -m src.cli login # writes config to ~/.clawcodex/config.json
python -m src.cli # start it (Full Access by default; /permissions to change)
The configuration file is saved at ~/.clawcodex/config.json. Minimal example:
{
"default_provider": "deepseek",
"providers": {
"deepseek": {
"api_key": "xxx-xxx",
"base_url": "https://api.deepseek.com",
"default_model": "deepseek-v4-pro"
}
},
"env": {
"TAVILY_API_KEY": "tvly-YOUR-TAVILY-API-KEY"
}
}
Note:
TAVILY_API_KEYis required for the WebSearch tool — get a key at tavily.com.
The session, settings, and env blocks are optional — sensible defaults apply when they're omitted. See Configure for the full structure.
claude-opus-5 at effort=xhigh, ClawCodex solved 72 of 89 Terminal-Bench 2.1 tasks: 80.9% pass@1 on a single run. On the public 2.1 leaderboard (k=5 averages) that would slot around third — behind Claude Code / Fable 5 (83.8%) and Codex / GPT-5.5 (83.1%), statistically level with the 79–80% cluster, and ahead of Claude Code on Opus 4.8 (78.9%) and Sonnet 5 (74.6%). Getting there was open, unglamorous parity work: a Harbor eval adapter (eval/harbor/) for three-way ClawCodex-vs-openclaude-vs-Claude-Code runs (#720, #724, #725), then a run of prompt- and reliability-parity fixes — restored task-tool skip conditions and parallel-tool guidance, deferred nonessential initial tools, and recovery of trials lost to empty turns and transport drops (#747–#754). Also in v1.3.0: claude-opus-5 support with an interactive /effort fix (#746), bounded persistent memory with a background self-improvement review (#731), a VS Code extension driving the agent-server over stdio (#727), image-paste input with an [Image #N] un-attach chip (#761, #762), the CLAUDE.md → CLAWCODEX.md context-file rebrand (#732), and transport-retry hardening (#757, #760). Stated plainly: this is a single k=1 pass (binomial 1σ ±4.2pp) against the board's k=5 ± ~1.2pp averages, benchmarked on main at #756 (before the v1.3.0 tag), so read it as directional rather than a ranked submission./eco token compression — -80% Bash-output tokens, measured, now a headline (#708, #712) — a new session toggle compresses the model-bound rendering of every Bash result with deterministic filters ported from RTK's method set: failure-focused test summaries (kept error lines are never rewritten), git/pip/npm ceremony stripping, log dedup with [×N] counts, and a recoverable head-cap — all behind a never-worse guard, with every lossy compression teeing the full output to disk behind a runnable recovery hint (#708). A reproducible benchmark (eval/eco/) replays 27 real command outputs through the exact production pipeline and counts real tokenizer tokens: 92,989 → 17,767 (-80%) corpus-wide, -88% on filter hits, plus an honestly conservative recompute of RTK's own 30-minute-session model (-19% under their averaged assumptions — real sessions are fat-tailed) (#712). Full tables: the /eco section and eval/eco/results/.clawcodex login → openai → subscription (browser, device-code, or import from an existing Codex CLI login) routes requests through the ChatGPT Codex backend's Responses API — gpt-5.5, gpt-5.4, gpt-5.4-mini, and gpt-5.3-codex-spark on your Plus/Pro allowance, with encrypted-reasoning replay across turns and $0 metered cost. Claude Pro/Max (#697): clawcodex login → anthropic → subscription connects a Claude subscription via OAuth (PKCE) with automatic token refresh and the same $0 accounting; follow-ups repaired the login after Anthropic moved its OAuth endpoints to platform.claude.com (#702) and stopped sending adaptive thinking to models that don't support it (#699). A configured API key always wins, and subscription usage reports billing_mode: subscription. More models: a Meta (api.meta.ai) provider with the 1M-context muse-spark-1.1 reasoning model (#692) and refreshed MiniMax parameters (#696). Workflow & TUI: /plan mode with implicit plan-mode entry/exit (#676), --worktree/-w session isolation for parallel runs in separate git worktrees (#672), the /memory picker + $EDITOR spawn (#693), config/state directories rebranded .claude → .clawcodex with a one-time migration (#678), /logo startup color schemes (#677), plus TUI polish — Tab accepts the suggested placeholder (#690), past inputs get the Claude-Code highlight band (#691), clickable agent URLs (#694), and a per-terminal link-open affordance (#701). Quality: semantic tool-input coercion with parity validation errors (#700) and looser, Claude-Code-faithful permission granting (#673)./loop scheduled tasks now actually fire — full port of Claude Code's session-scoped scheduler (#680) — the bundled /loop skill finally has a real engine behind it: a new src/scheduled_tasks module parses standard 5-field cron expressions and fires due prompts between turns from the agent-server's idle poll. CronCreate/CronList/CronDelete register real firing jobs (8-char IDs, 50-job cap, deterministic jitter, 7-day recurring expiry with one fin