drydock-cli 2.6.99

Local-first CLI coding agent — tested with Gemma 4 26B via vLLM

DryDock

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Local-first CLI coding agent. Chart your course. Execute with precision.

DryDock is a TUI coding assistant designed to work with local LLMs. It provides a conversational interface to your codebase — explore, modify, build, and test projects through natural language and a powerful set of tools.

[!IMPORTANT] DryDock is tested and optimized for Gemma 4 26B-A4B (26B MoE, 4B active parameters) served via vLLM. Other models and providers are supported (Mistral, OpenAI, Anthropic, Ollama) but are not as thoroughly tested. If you use a different model, expect to tune prompts and tool settings.

Tested Hardware + Model

Component	Spec
GPUs	2x NVIDIA RTX 4060 Ti 16GB
Model	Gemma-4-26B-A4B-it-AWQ-4bit
Serving	vLLM (Docker image vllm/vllm-openai:gemma4)
Performance	~70 tok/s, 131K context, 0% timeouts
Active params	4B per token (MoE architecture — fast inference)

Install the model

# Download the model weights (requires HuggingFace access)
pip install huggingface-hub
huggingface-cli download casperhansen/gemma-4-26b-a4b-it-AWQ-4bit \
    --local-dir /path/to/models/Gemma-4-26B-A4B-it-AWQ-4bit

Start vLLM

docker run -d \
    --gpus all \
    --name gemma4 \
    -p 8000:8000 \
    -v /path/to/models:/models \
    --ipc=host \
    vllm/vllm-openai:gemma4 \
    --model /models/Gemma-4-26B-A4B-it-AWQ-4bit \
    --quantization compressed-tensors \
    --tensor-parallel-size 2 \
    --max-model-len 131072 \
    --max-num-seqs 2 \
    --gpu-memory-utilization 0.95 \
    --kv-cache-dtype fp8 \
    --served-model-name gemma4 \
    --trust-remote-code \
    --tool-call-parser gemma4 \
    --enable-auto-tool-choice \
    --attention-backend TRITON_ATTN

Key flags:

• --tensor-parallel-size 2 — split across 2 GPUs
• --kv-cache-dtype fp8 — reduce KV cache memory for longer contexts
• --tool-call-parser gemma4 + --enable-auto-tool-choice — required for Gemma 4 tool calling
• --max-num-seqs 2 — limit concurrent requests (prevents OOM on 16GB GPUs)

Verify the model is running:

curl http://localhost:8000/v1/models

Configure DryDock

# ~/.drydock/config.toml
[models.gemma4]
name = "gemma4"
provider = "generic-openai"
alias = "gemma4"
temperature = 0.2
thinking = "high"

[providers.generic-openai]
api_base = "http://localhost:8000/v1"
api_style = "openai"

Install

pip install drydock-cli

Or with uv:

uv tool install drydock-cli

Quick Start

cd your-project/
drydock

First run creates a config at ~/.drydock/config.toml and prompts for your provider setup.

> Review the PRD and build the package

Features

• TUI Interface: Full terminal UI with streaming output, tool approval, and session management.
• Adaptive Thinking: Automatically adjusts reasoning depth per turn — full thinking for planning, fast mode for file writes.
• Powerful Toolset: Read, write, and patch files. Execute shell commands. Search code with grep. Delegate to subagents.
• Project-Aware: Scans project structure, loads AGENTS.md / DRYDOCK.md for context.
• Subagent Delegation: Large tasks can be delegated to builder/planner/explorer subagents with isolated context.
• Loop Detection: Advisory-only detection that nudges the model away from repetitive actions without blocking.
• Conda/Pip Support: Auto-approves pip install, conda install, pytest, and other dev commands.
• Bundled Skills: Ships with skills like create-presentation for PowerPoint generation.
• MCP Support: Connect Model Context Protocol servers for extended capabilities.
• Safety First: Tool execution approval with --dangerously-skip-permissions for full auto-approve.

Built-in Agents

• default: Standard agent that requires approval for tool executions.
• plan: Read-only agent for exploration and planning.
• accept-edits: Auto-approves file edits only.
• auto-approve: Auto-approves all tool executions.

drydock --agent plan

Gemma 4 Optimizations

DryDock includes several optimizations specifically tuned for Gemma 4:

• Simplified prompt (gemma4.md): 20-line system prompt instead of 125 lines. Complex prompts cause Gemma 4 to plan instead of act.
• Non-streaming mode: Streaming breaks Gemma 4 tool call JSON parsing. DryDock automatically disables streaming for Gemma 4.
• Thinking token filtering: Gemma 4 leaks <|channel>thought<channel|> tokens into text output. DryDock strips these before storing in context.
• Adaptive thinking: Full thinking for planning (turn 1) and error recovery. Thinking OFF for routine file writes — eliminates 30-120s hangs between files.
• search_replace resilience: Auto-detects already-applied edits, infers missing file paths, fuzzy-matches whitespace differences.
• Reduced tool set: Disables tools that confuse Gemma 4 (ask_user_question, task_create, etc.).

Usage

Interactive Mode

drydock                        # Start interactive session
drydock "Fix the login bug"    # Start with a prompt
drydock --continue             # Resume last session
drydock --resume abc123        # Resume specific session

Keyboard shortcuts:

• Ctrl+C — Cancel current operation (double-tap to quit)
• Shift+Tab — Toggle auto-approve mode
• Ctrl+O — Toggle tool output
• Ctrl+G — Open external editor
• @ — File path autocompletion
• !command — Run shell command directly

Programmatic Mode

drydock --prompt "Analyze the codebase" --max-turns 5 --output json
drydock --dangerously-skip-permissions -p "Fix all lint errors"

Configuration

DryDock is configured via config.toml. It looks first in ./.drydock/config.toml, then ~/.drydock/config.toml.

API Key

drydock --setup                              # Interactive setup
export MISTRAL_API_KEY="your_key"            # Or set env var

Keys are saved to ~/.drydock/.env.

Consultant Model

Set a smarter model for the /consult command:

consultant_model = "gemini-2.5-pro"

The consultant provides read-only advice — it never calls tools. Use /consult <question> to ask it.

Custom Agents

Create agent configs in ~/.drydock/agents/:

# ~/.drydock/agents/redteam.toml
active_model = "devstral-2"
system_prompt_id = "redteam"
disabled_tools = ["search_replace", "write_file"]

Skills

DryDock discovers skills from:

1. Custom paths in config.toml via skill_paths
2. Project .drydock/skills/ or .agents/skills/
3. Global ~/.drydock/skills/
4. Bundled skills (shipped with the package)

MCP Servers

[[mcp_servers]]
name = "fetch_server"
transport = "stdio"
command = "uvx"
args = ["mcp-server-fetch"]

Testing

DryDock uses a shakedown harness (scripts/shakedown.py) that drives the real TUI via pexpect and judges on user-perceptible criteria — not tool-call counts.

# Single project test
python3 scripts/shakedown.py \
    --cwd /path/to/project \
    --prompt "review the PRD and build the package" \
    --pkg package_name

# Interactive back-and-forth test
python3 scripts/shakedown_interactive.py \
    --cwd /path/to/project \
    --pkg package_name

# Full regression suite (370 PRDs)
bash scripts/shakedown_suite.sh

Pass criteria: no write loops, no ignored interrupts, no search_replace cascades, package executes, session finishes within time budget.

Slash Commands

Type /help in the input for available commands. Create custom slash commands via the skills system.

Session Management

drydock --continue              # Continue last session
drydock --resume abc123         # Resume specific session
drydock --workdir /path/to/dir  # Set working directory

License

Copyright 2025 Mistral AI (original work) Copyright 2026 DryDock contributors (modifications)

Licensed under the Apache License, Version 2.0. See LICENSE for details.

DryDock is a fork of mistralai/mistral-vibe (Apache 2.0). See NOTICE for attribution.