drydock-cli 3.0.68

Drydock — a local, provider-agnostic terminal coding agent for local LLMs

⚓ Drydock

A local-first, provider-agnostic terminal coding agent for your own LLM. No accounts, no telemetry, no cloud — the only outbound calls are to the model endpoint you configure and (optionally) the web-search tools you invoke. Primary target: dense Gemma-4-31B (QAT, 64K) served by llama.cpp on a single workstation.

v3 — clean-room rebuild. Drydock is being rebuilt as an original, Apache-2.0 codebase owned end to end (no upstream fork). Every release is gated by a credential-exfiltration scanner that blocks anything reaching off-box. See HARNESS_DESIGN.md and docs/PRD.md.

Why

A coding agent should build real projects from your machine without sending your code or credentials anywhere. Drydock runs entirely against a local model, feels like a first-class terminal agent, and keeps its data plane on your box.

Status

Shipping. Published on PyPI as drydock-cli (v3.x). The Textual TUI is the default surface: a scrolling transcript with streamed assistant text, collapsible tool cards, collapsible reasoning ("thinking") cards, a live nautical activity line, and a multi-line prompt. The agent loop, OpenAI-compatible provider, two-tier compaction, and the full agentic toolset (below) are in, with Gemma reliability hardening verified hands-on.

Capabilities

A full agentic CLI harness — every tool below is clean-room and dependency-free (nothing beyond openai + textual), and the model calls them autonomously:

• Files & shell — Read (with a structure index for huge files), Write, Edit, Bash, Glob, Grep.
• Version control — GitStatus, GitDiff, GitLog, GitCommit (structured + truncated; commit is local and reversible).
• Internet — WebSearch + WebFetch (DuckDuckGo; offline-safe).
• Knowledge base (GraphRAG) — build a local entity-graph index from your docs/code with /graphrag build <path>; the agent retrieves from it via the read-only Knowledge tool.
• Multi-agent — Dispatch runs several read-only sub-agents in parallel; task runs one — each in a fresh context, for focused investigation.
• MCP — connect to Model Context Protocol servers (~/.drydock/mcp.json); their tools appear as mcp__<server>__<tool>. List them with /mcp.
• Skills — reusable /<name> commands authored as markdown in ~/.drydock/skills/ (or <project>/.drydock/skills/); $ARGS substitution.
• Loops — /loop <count> <prompt> runs a prompt iteratively (Esc stops).

Slash commands

Typed into the prompt. The agent also knows these, so you can just ask it ("how do I add my own docs?") and it'll point you to the right one.

Command	What it does
/graphrag build <path>	Build a knowledge base from a file or folder of docs/code
/graphrag add <path>	Incrementally add more documents to the base
/graphrag query <q>	Test what the base returns (no model)
/graphrag status · clear	List indexed sources · wipe the base
/skills	List your skills
/skills new <name> <prompt>	Create a reusable /<name> skill (use $ARGS for input)
/<name>	Run a skill
/loop <count> <prompt>	Repeat a prompt N times (Esc stops)
/mcp	List connected MCP servers + their tools
/rmf bootstrap [families]	Ingest the NIST SP 800-53 catalog (RMF automation)
/rmf-control · /rmf-categorize · /rmf-review · /rmf-poam	Bundled RMF skills
/model · /cwd	Show/set model & endpoint · working directory
/undo · /back	Revert the last write · rewind the last turn
/compact · /status · /clear	Shrink context · session stats · reset
/help · /quit	Help · exit

Knowledge base (GraphRAG) — ingesting your documents

/graphrag build ./docs        # index a file or a whole folder
/graphrag add ./more_docs     # add more later, incrementally
/graphrag query "how are refunds handled?"   # check retrieval
/graphrag status              # what's indexed

Once built, the agent automatically retrieves from it (read-only Knowledge tool) when a question touches your material. Ingests text formats (.md .txt .py .js .json .yaml .sql …) plus PDF and Word (.docx). .docx needs nothing extra; PDF uses the pdftotext binary (poppler) if present, else pip install drydock-cli[pdf] (pypdf). The index is a single JSON at <project>/.drydock/graphrag.json — clean-room, no embeddings.

Custom skills

/skills new commitmsg  Write a concise conventional-commit message for: $ARGS
/commitmsg the staged auth changes      # runs the skill with $ARGS substituted

Skills are markdown files in ~/.drydock/skills/ (personal) or <project>/.drydock/skills/ (project); /skills new writes one for you.

RMF automation (NIST SP 800-53)

For Risk Management Framework work, Drydock can ingest the NIST SP 800-53 Rev 5 control catalog into the knowledge base and ships four RMF skills — all 100% local for CUI/sensitive systems.

/rmf bootstrap            # one-time: fetch + ingest the 800-53 catalog (offline after)
/graphrag build ./ssp     # ingest your own SSP/POA&M (PDF/Word/text)
/rmf-control AC-2         # look up a control
/rmf-categorize ...       # FIPS 199 categorization + tailored baseline
/rmf-review AC-2          # review an SSP implementation statement vs 800-53A
/rmf-poam <finding>       # generate a POA&M entry from a scan/STIG finding

Beyond text retrieval, /rmf bootstrap also builds a typed ontology graph (Control / Component / Vulnerability nodes; IMPLEMENTS / RESIDES_ON / ASSESSES edges). The agent records your system topology with GraphAdd and traces relationships with GraphQuery — including control inheritance ("which servers inherit physical controls from their enclave?"). Stdlib in-memory graph, no Neo4j.

Install

pip install drydock-cli
drydock

Requires Python 3.11+. From source instead: git clone https://github.com/fbobe321/drydock-v3.git && cd drydock-v3 && pip install -e .

On first launch with no config, Drydock probes localhost for a running local LLM (llama.cpp/vLLM :8000, Ollama :11434, LM Studio :1234) and wires up the first one it finds — no account or API-key prompt. Override anytime with --model / --provider / --base-url or ~/.drydock/config.toml.

Using it

Type a task and press Enter. Drydock reads/writes/edits files and runs commands to do the work, showing each as a collapsible tool card.

• Enter submits · Ctrl+J newline (multi-line prompts)
• ↑ / ↓ recall command history (persists across sessions)
• PgUp / PgDn (and Ctrl+Home/End) scroll the transcript
• Ctrl+O expand/collapse tool output · drag + Ctrl+C copy a selection
• Ctrl+C twice (or Ctrl+D, /quit) to exit
• A live activity line shows progress while it works: ◡ Keelhauling… (12s · ↓ 6.2k tokens · thinking with high effort)
• Submit while it's working and the prompt queues (drains in order)
• Slash commands: /model · /cwd · /undo (revert last write) · /back (rewind last turn) · /status · /compact (shrink context) · /graphrag (build/query a knowledge base) · /skills (list your /<name> skills) · /loop (repeat a prompt) · /mcp (list MCP servers) · /clear · /help · /quit

It honors AGENTS.md / DRYDOCK.md in the working directory for project conventions.

Safety

Two tiers, plus advisory guards — all designed so legitimate work is never blocked:

• Catastrophic denylist — commands like rm -rf /, mkfs, raw block-device writes, and fork bombs are refused outright (never run).
• Approval prompt — sensitive-but-legitimate commands (sudo, package installs, network fetches, git push) pause for Allow / Always / Deny.
• Advisory write guards — Drydock flags (never blocks) Python syntax errors, stub-only files, imports of sibling modules that don't exist yet, bare raise outside an except, and refuses to write git conflict-marker content.

Point it at a local OpenAI-compatible endpoint (e.g. llama.cpp's server-cuda serving Gemma-4-31B). The web tools (WebSearch/WebFetch) are read-only and degrade cleanly offline; the release scanner allowlists only the search backend.

Model server (reference setup)

Drydock is provider-agnostic, but it's tuned and measured against this rig:

• Model: dense Gemma-4-31B (QAT Q4_K_XL GGUF), served by ghcr.io/ggml-org/llama.cpp:server-cuda with --jinja. Swapped from the 26B-A4B MoE, whose ~4B active params caused fatal agentic tool-loops; the dense 31B is loop-free (slower, but it finishes).
• GPUs: 2× NVIDIA RTX 4060 Ti 16GB, tensor-split across both cards (--tensor-split 1,1) so the 31B weights fit.
• Context: 64k (-c 65536) with q8_0 KV-cache quantization (-ctk q8_0 -ctv q8_0); set context_limit in ~/.drydock/config.toml to match your server's -c.
• Throughput: ~15 tok/s decode (tensor-split 31B). Faster single-GPU options exist if you drop to a smaller model.
• Provider-agnostic: any OpenAI-compatible endpoint (llama.cpp, vLLM, Ollama, LM Studio) works — point --base-url at it.

Principles

• Clean provenance — original code only; nothing copied from any other project.
• Local-only data plane — no telemetry, no phone-home, no hardcoded third-party hosts, no credential transmission.
• Advisory, never blocking — loop/safety mechanisms inject better context; they never hard-stop legitimate work.
• The scanner is law — scripts/security_scan.py gates every release.

Security scan

python3 scripts/security_scan.py drydock/      # scan the source tree
python3 scripts/security_scan.py dist/*.whl    # scan a built wheel

Exit 2 (HIGH finding) blocks a release.

License

Apache-2.0, © 2026 Frank Bobe III. See LICENSE and NOTICE.