dvalincode

English · 中文 · 🌐 dvalincode.dev

Find the security holes in your repo, fix them, and prove the fix — in one command.
Every fix is diffed, tested, re-scanned, and recorded in a tamper-evident audit log before it can become a PR.

⏱️ 30 seconds, no install, no API key

npx dvalincode dvalin .

That is the whole thing. It runs the built-in rules for injection, hardcoded secrets, XSS, eval, and unsafe shell use against the current directory and prints what it found. No account, no model, no config, no code leaves your machine. Add --scanners builtin,semgrep,trivy,osv-scanner to pull in whichever of those engines you already have on PATH.

Or put it on every pull request — nothing to install at all

# .github/workflows/security.yml
permissions:
  contents: read
  security-events: write
steps:
  - uses: actions/checkout@v5
  - uses: arthurpanhku/dvalincode@v0.15.0
    with:
      fail-on: high

Findings land inline on the pull request diff and in your Security tab. No API key, no secrets, no model — the scan is deterministic and local to the runner. Full example →

Or let your agent call it

If an agent is writing the code, something other than that agent has to check it. DvalinCode is an MCP server, so any agent that speaks MCP can:

claude mcp add dvalin -- npx -y dvalincode mcp-serve --workspace .

dvalin_scan is read-only and deterministic — no model runs, no credentials are needed, no files are touched — so an agent can call it after every edit. A real call against a vulnerable fixture returns in ~170ms with a ~600 byte payload. The same server also exposes dvalin_run_task for delegating a whole governed task, plus the session and audit-evidence tools. Agent integrations →

Then let it fix what it found

dvalincode dvalin . --fix --verify --draft-pr

This step does use a model — your model, any OpenAI-compatible endpoint. It prepares focused repairs in an isolated worktree, runs your tests, and requires a clean re-scan before anything can proceed to a draft PR. It never auto-merges, and a clean scan is never treated as proof that the code is safe.

This animation is made from the real application, not a mock. The input is an Apache-2.0-licensed example adapted from OWASP NodeGoat, whose contribution route evaluated user-controlled text.

🛡️ What that run actually did

Dvalin turns open-source scanner evidence into a controlled scan → fix → test → re-scan → draft-PR workflow. Here is the run in the animation above, measured:

Real NodeGoat-derived run	Before	After Dvalin remediation
Security health (triage heuristic)	22 / 100 · F	100 / 100 · A
Findings	10 (eval across 3 rules, 2 engines)	0
Tests	2 passing	3 passing, including an injection regression test
Scanner fleet	4 / 4 completed	4 / 4 completed

The scanning and hardening control plane uses open-source components:

• Semgrep CE and its community rules for semantic SAST.
• Trivy for filesystem vulnerabilities, secrets, and misconfiguration.
• OSV-Scanner with the open OSV database for dependency vulnerabilities.
• DvalinCode's MIT-licensed built-in rules, remediation orchestration, test and re-scan gates, plus SARIF 2.1 import for other compatible scanners.

The scanners find and rank evidence. The configured model proposes source changes; DvalinCode constrains that work, records the diff, runs project tests, re-scans the changed tree, and keeps PR publication explicit. It does not auto-merge and it does not claim that a clean scan proves the absence of bugs. Choose an open-weight model through Ollama if the repair-proposal step must also stay fully local and open; hosted model licensing depends on the provider.

You can still prove what the agent did after the fact:

dvalincode report verify    # re-derive the hash chain of the last run's audit log

🏛️ And it survives a security review

That last command is the part that matters once more than one person depends on this. DvalinCode is a full coding agent — terminal, web GUI, and desktop app — built so that an organization, not the developer, bounds what it may do: a policy file constrains modes, commands, paths, tools, and models; every run is hash-chained into a tamper-evident audit log; nothing reaches a provider that the egress guard did not allow. A repo policy can only ever narrow the machine-level one.

If you are the person who has to approve this class of tool, start at APPROVABILITY-PLAN.md and the Evidence Pack that every release ships of itself.

🏠 Home	One place for read-only Ask and approval-gated Collaborate workflows. Switch intent without leaving the project or conversation.
⚡ Code	Focused autonomous coding with full tool access and Ask / Plan / Auto / Bypass permission levels. Security and browser routines no longer compete with the core coding workflow.
🛡️ Dvalin	Dedicated white-box security engineering: orchestrate the built-in scanner plus installed Semgrep CE, Trivy, and OSV-Scanner; triage findings; create isolated fixes; run tests and re-scan; then explicitly publish a reviewable draft PR. Dvalin guide →
🏦 Regulated teams	Designed for finance, healthcare, security-sensitive SaaS, and internal platform teams that need AI coding under policy, audit, data minimization, and supply-chain review — not just developer convenience.
🛡️ Secure remediation	Run a multi-engine scan or import SARIF from CodeQL, GitHub Code Scanning, Semgrep, or compatible scanners, then create an isolated remediation worktree and turn findings into focused repair tasks with source context, verification evidence, and PR-ready reporting. Workflow →
📚 Skills	Upload, download, and inspect local skill bundles. DvalinCode ships built-in secure-code-scan and secure-code-remediation skills, plus agent tools for listing skills, reading skill instructions, scanning, listing cases, and preparing remediation worktrees. Format →
🛡️ Audit trail	Every run emits a tamper-evident, hash-chained JSONL log — every file read/written, every command, every approval. A Run Report renders it as Markdown; dvalincode report verify proves the chain is intact. Threat model →
🔒 Org policy & trust	A company — not the developer — bounds the agent. A dvalin.policy.json constrains modes, shell commands, file paths, tools, and models; a repo policy can only ever narrow the machine-level one, never widen it. Each run records the governing policy's hash. dvalincode trust prints the install's live security posture — active policy + hashes, audit status, runtime — so a reviewer can verify it directly. Policy reference → · Approvability plan →
🏛️ Governance evidence	OpenSSF Scorecard, CodeQL, Dependabot, pinned GitHub Actions, CODEOWNERS, and ISO/IEC 42001 AIMS alignment docs are maintained as reviewable project evidence, and every release ships an Evidence Pack the binary produced of itself. Scorecard map → · ISO 42001 alignment → · Release evidence →
📐 Open specs	PCP-1 — the provider-boundary contract (egress containment, credential containment, audit, policy binding) written as a vendor-neutral profile with test procedures, so any agent runtime can run it against its own adapters and publish the result. Not a DvalinCode test file; a checklist anyone can hold us to as well. Provider Conformance Profile →
🖥️ First-class GUI	Modern web UI with code highlighting, file @-references, / slash commands, Git branch indicator, live token + cost counter, multi-profile LLM config, and a dark / light / system theme switcher.
🖥️ Terminal or web — one binary	Run it bare for an interactive terminal agent with streaming output, inline approvals, and red/green diffs, or dvalincode serve to host the web GUI for browser/remote use. Both frontends drive the same agent core.
🖥️ Native desktop app	DvalinCode.app — a real dock application (OS-native webview, no Electron) over the same engine. On macOS the one-line installer puts it in /Applications automatically; launch it straight from Launchpad.
🪶 Zero-dependency binary	Single ~25MB executable per platform. No Node, no Python, no Docker.
🔐 Local-first	Sessions, config, profiles, and audit logs live in ~/.dvalincode/. .dvalincodeignore blocks the agent from reading sensitive files. AGENTS.md in your repo becomes persistent project instructions.
💾 Portable & exportable	Export all local data (memory, sessions, config, audit) to one file and import it on another machine — your setup moves with you. Any conversation downloads as a clean Markdown transcript.

🎯 Core Goal

Make AI coding approvable for regulated and security-sensitive teams.

DvalinCode is built as an approvable agent runtime, not just another coding agent app. The core product is not only "AI writes code"; it is the evidence a security, compliance, or platform team needs to safely allow AI coding in financial services, healthcare, internal enterprise platforms, and other confidential codebases.

• Any model — every OpenAI-compatible endpoint is a first-class citizen, local models included. Your workflow should never be hostage to one vendor's pricing, rate limits, or quality swings.
• Safe by default — three-tier approvals with diff preview, an undo stack, and sandboxed shell execution. An agent you can trust on full-auto.
• Small enough to audit — one ~25MB binary, a handful of runtime dependencies, a codebase you can read in a weekend. Trust through inspection, not promises. As of v0.5, every agent run is auditable too: a tamper-evident, hash-chained log of every action, verifiable after the fact.
• Open enough to embed — the agent core speaks a clean REST + WebSocket API, ready to be wired into your own product, CI, or internal tools.
• Approvable by any company — governance is built in, not bolted on. An org policy bounds the blast radius (controllable), dvalincode trust makes the posture self-verifiable (transparent), and the hash-chained log proves what every run did (auditable). Those three together are exactly what a security review needs to say yes — and what cloud, closed, mutable-log agents structurally struggle to provide. Approvability plan →

The bundled web GUI is the runtime's reference implementation and showcase — the first consumer of that public API, demonstrating everything the runtime can do.

✅ Why Teams Pick DvalinCode

DvalinCode is differentiated by approvability. It is built for teams that need AI coding to pass security, compliance, and data-governance review before it can touch production repositories.

• Closed-loop secure remediation — scan locally or import SARIF from CodeQL, GitHub Code Scanning, Semgrep, or compatible scanners; persist findings as local remediation cases; create an isolated dvalin/remediate/... worktree; then send a focused repair prompt with source context and verification instructions.
• Skills as governed operating procedures — upload, download, and inspect local skill bundles. Built-in secure scanning and remediation skills tell agents which tools to use and keep workflows portable across machines.
• Model freedom without policy drift — use DeepSeek, OpenAI, Claude via OpenRouter, Groq, Ollama, or any OpenAI-compatible endpoint while keeping tool permissions, audit, and workspace policy consistent.
• Security evidence, not just security claims — OpenSSF Scorecard support, CodeQL, Dependabot, pinned Actions, CODEOWNERS, ISO/IEC 42001 alignment docs, AI change-impact records, and hash-chained run logs are part of the project.
• Local-first by default — sessions, config, profiles, memory, and audit logs stay under ~/.dvalincode/; .dvalincodeignore and policy controls bound what the agent can read, write, or execute.

🛡️ Security & Governance

DvalinCode maintains project-level governance evidence for open-source and enterprise review. This is the differentiator for teams where AI coding must pass security approval before it can reach production repositories:

• Threat model — the full attack surface of an agentic coding runtime (malicious AGENTS.md, poisoned MCP servers, prompt-injection escalation, egress, audit tampering, supply chain, sandbox escape), each mapped to the control that defends it and the honest residual gap. Threat model →
• OpenSSF Scorecard support — scheduled Scorecard workflow, SARIF upload, CodeQL, Dependabot, CODEOWNERS, least-privilege workflow permissions, and SHA-pinned GitHub Actions. Control map →
• ISO/IEC 42001 alignment — an AI management system scope, AI policy, role map, risk register, AI change classification, required records, and review cadence. AIMS alignment →
• AI change impact assessment — a reusable template for changes that affect model/provider behavior, prompts, permissions, tools, audit logs, or release security. Template →
• Regulated-use posture — local-first data handling, policy-controlled autonomy, minimized audit records, and release supply-chain evidence for finance, healthcare, security-sensitive SaaS, and internal enterprise use.
• Dvalin security engineering — the dedicated Dvalin workspace combines the built-in scanner with installed Semgrep CE, Trivy, and OSV-Scanner, normalizes SARIF findings, drives isolated test-backed fixes, and explicitly prepares a reviewable draft PR without auto-merging it. Workflow →

These documents are implementation evidence and operating procedures; they do not claim third-party ISO certification.

⭐ What's New in v0.14.0 — Dvalin security engineering

• Home unifies Chat and Cowork — the GUI now has a single Home workspace with read-only Ask and approval-gated Collaborate intents, while keeping the same project and conversation context.
• Code is focused again — the old Security and Routines panels have been removed from Code so its sidebar is dedicated to projects and autonomous implementation.
• Dvalin is a first-class workspace — orchestrate the built-in scanner plus installed Semgrep CE, Trivy, and OSV-Scanner; import SARIF; score and triage findings; persist remediation cases; and create isolated repair worktrees.
• One flow from evidence to draft PR — selected findings can launch an evidence-backed Agent fix, run focused tests/typecheck/build and a fresh scan, review the diff, and explicitly publish a draft PR without automatic merge.
• Agent loops converge sooner and cost less — investigation-before-edit and stall detection reduce repeated failed actions, general tool output is bounded, prompts remain append-only for cache reuse, and provider usage now accounts for cache hits/misses.
• Provider and evaluation upgrades — native Anthropic prompt caching and cache accounting are supported, and the SWE-bench Docker harness reports official scores, policy violations, stalls, and token/cache metrics.

⭐ What's New in v0.12.4 — finish the task before stopping

• Process narration no longer ends a task — responses such as “let me verify the file” are recognized as pending work, and the agent immediately continues with the promised action instead of treating them as a final answer.
• Truncated responses automatically recover — provider finish reasons are preserved, so output cut off by a token limit triggers another model step.
• Normal coding turns get room to finish — the per-turn action limit is now an emergency 100-action guard rather than a routine 15-action stopping point; stricter organization policy limits still take precedence.
• Completion is explicit — Code mode is instructed to return a tool-free answer only after the requested work and focused validation are complete.

⭐ What's New in v0.12.3 — resilient long-running Code mode

• Long coding turns keep going — Code mode now compacts context during an active tool loop, accounts for the full provider request when estimating tokens, and raises the default iteration checkpoint from 10 to 40.
• Interruptions are resumable — completed tool state is persisted when a turn is interrupted or its connection closes, so a follow-up continue resumes from the actual workspace progress.
• Visible, quieter agent activity — running sessions show a sidebar loading state, each response reports elapsed work time, and its Action timeline is available on click while raw Tool Calls stay collapsed by default.
• GitHub workflows from Code mode — network-aware git and GitHub CLI (gh) operations now support pull, push, PR creation, and Actions/repository commands through the governed shell approval path.
• Safer releases — package and CLI versions are synchronized, and prepublishOnly runs the build, typecheck, and test suite before publishing.
• Simple tasks stay simple — the Action budget is enforced across the whole turn instead of resetting on every model iteration, and Code mode is prompted to take the shortest direct path and stop when focused validation passes.

⭐ What's New in v0.12.2 — 🖥️ Desktop app milestone: it just works

• 🖥️ The native desktop app now works out of the box on macOS — DvalinCode.app opens a real dock window (WKWebView, no Electron) over the embedded engine. Two threading bugs that shipped in every earlier desktop build are fixed: the blocking webview loop no longer starves the embedded server (blank window), and the webview runs on the main thread as macOS requires (no window at all) — the server now lives in a child process of the same binary.
• 📦 The one-line installer installs the app — on macOS, curl … install.sh | bash now also puts DvalinCode.app (with the DvalinCode icon) into /Applications, so the desktop window launches straight from Launchpad after a CLI install. Opt out with DVALINCODE_NO_APP=1; pin with DVALINCODE_GUI_VERSION.
• ✅ Desktop is no longer "experimental" on macOS — the window and the embedded server are verified working; Windows and Linux desktop builds are cross-compiled and remain a preview.

v0.9.0 — 🛡️ Secure remediation · Skills · CodeQL hardening

• 🛡️ Secure remediation workflow — run a built-in local scan or import SARIF from CodeQL, GitHub Code Scanning, Semgrep, and compatible scanners; findings become local remediation cases with source context, verification guidance, and isolated worktree repair tasks.
• 📚 Skills — upload, download, inspect, and reuse local skill bundles. DvalinCode now ships built-in secure-code-scan and secure-code-remediation skills, plus agent tools for listing skills, reading instructions, scanning, listing remediation cases, and preparing remediation worktrees.
• 🔐 CodeQL path hardening — user-controlled workspace, remediation, and skill paths now go through explicit root-containment checks, with regression tests covering traversal-safe resolution and skill import boundaries.
• 🎨 App icons — dark and light theme application icons now ship with the web bundle and desktop build inputs.

v0.8.0 — 🔒 Governance: controllable · transparent · auditable

• 🔒 Org policy — a dvalin.policy.json lets a company, not the developer, bound the agent: which modes, shell commands, file paths, tools, and models are allowed. Two layers (machine ~/.dvalincode/policy.json + repo) resolve by narrowing — a repo policy can only ever make the machine policy stricter, never widen it. With no policy file, behavior is identical to before. Enforced at a single chokepoint; every denial is an inline ⛔ Blocked by policy plus a policy_violation audit event. Policy reference →
• 🔎 dvalincode trust — prints this install's live security posture in one command — active policy + source hashes, audit status, runtime, dependencies — so a reviewer can verify what the agent may and may not do directly, instead of taking claims on trust. --json for tooling.
• dvalincode policy check — validates dvalin.policy.json against the schema, prints the resolved policy + canonical hash (after narrowing with the machine layer), and exits non-zero on failure — for CI and policy authoring. Policy reference →
• 🧾 Policy-aware audit — every run records the hash of the governing policy (and which files contributed) in run_start, so the tamper-evident log proves which rules were in force.
• 📐 Approvability plan — the through-line is documented in docs/APPROVABILITY-PLAN.md: make DvalinCode trivially approvable by any company — controllable, transparent, auditable.

v0.7.0 — 🧪 Desktop app (beta)

• 🧠 Portable memory & full data export/import — the upgraded local memory mechanism, plus every session, config, profile, and audit log, can now be bundled into a single file and restored on another machine. Migrate your whole setup in one step: dvalincode export / dvalincode import, or the Export / Import buttons in the GUI Settings panel.
• 📝 Download any AI interaction as Markdown — every conversation can be saved as a clean Markdown transcript (user turns, assistant replies, tool calls + results, decisions — all inline). Use the download icon on any session in the sidebar, dvalincode session md <id>, or GET /api/sessions/:id/markdown.
• 🖥️ Native desktop app — a real application window (not a browser tab) over the same engine: DvalinCode.app on macOS, plus Windows/Linux builds. Built with webview-bun using the OS-native webview (WKWebView / WebView2 / WebKitGTK) — no Electron, stays a small self-contained binary.
• 🧩 A third frontend, one core — the desktop app, terminal UI, and web GUI all drive the same shared turn-runner. The current dvalincode binary is now positioned purely as the CLI (terminal + serve).
• Status: the desktop binaries are experimental / unverified — grab them from the latest pre-release and please report how the window behaves on your OS.

v0.6.0 — terminal agent · serve · shared turn-runner

• 🖥️ Terminal agent — run dvalincode bare for an interactive terminal coding agent, Claude-Code-style: streaming responses, inline [y/N] write approvals with red/green diffs, /mode · /clear · /git · /plan · /compact · /undo · /help, Ctrl-C to interrupt, and a guided first-run provider setup. Defaults to read-only Chat, switchable live.
• 🌐 dvalincode serve — the web GUI now lives behind a command, so the same binary deploys headless on a server: dvalincode serve --host 0.0.0.0 --no-open.
• 🧩 One engine, two frontends — the terminal UI and web GUI both drive a shared, transport-agnostic turn-runner (src/agent/session.ts), keeping them at feature parity.

v0.5.0 — security-grade audit trail · Run Report · theme switcher

• 🛡️ Security-grade audit trail — every Cowork/Code run writes a tamper-evident, hash-chained JSONL log to ~/.dvalincode/audit/ (run_start, every tool_call / file_* / shell_exec / approval, run_end). The hash chain makes any after-the-fact edit detectable. No local coding agent ships verifiable behavior logs. Format + threat model →
• 📋 Run Report + dvalincode report CLI — a Markdown summary of each run (files read/changed, commands, decisions, test result), rendered as a collapsible card in the GUI and from the CLI:

  dvalincode report --last           # render the most recent run
  dvalincode report <run-id> --format json
  dvalincode report verify <run-id>  # ✓ chain intact / ✗ broken at seq N
  

• 🎨 Theme switcher — choose dark / light / system in Settings. system follows your OS live; the choice persists across sessions.

v0.4.0 — /compact · dvalin.json team playbook · self-contained binaries

• /compact — LLM-based context compaction: replaces conversation history with a structured five-section summary (Goal / Completed / Decisions / Current State / Pending). A divider in the chat thread shows the token reduction (e.g. 8,412 → 1,203 tokens −85%).
• dvalin.json team playbook — commit a shared set of automation prompts to your repo. The sidebar loads them automatically and lets teammates run the same one-click routines without any manual setup. Export button converts your personal routines to dvalin.json in one click.
• Self-contained binaries — single ~25 MB executable per platform; no Node, no Python, no Docker. Auto-opens your browser on launch. Built with bun --compile so the web UI is bundled alongside the server binary.

v0.3.0 — Mode-aware sidebar · one-line installer · multi-profile LLM config

• Mode-aware sidebar — Chat shows quick-prompt Templates, Cowork shows a Projects folder tree, Code shows custom Routines (one-click commands like "Run tests" / "Git status" / "Type check"). Add your own routines from the sidebar — they persist in localStorage.
• One-line installer — curl … | bash auto-detects your OS + arch, drops the binary into ~/.dvalincode/, and patches your PATH. No package manager dependencies.
• Multi-profile LLM config — save named (provider, model, API key) sets and switch in one click from the sidebar; live per-session cost counter in the topbar so you can compare providers on the fly.

📸 Preview

A real Dvalin scan of vulnerable code — Security health 22/100 · F, with the 10 findings the engines actually reported, located to the line:

The verified result — the three eval call sites replaced by a constrained numeric parser, one new injection regression test, all four open-source engines complete, 0 findings, 100/100 · A:

Home → Code → Dvalin — the current workspaces:

The scan images above are unedited captures of a real run against the documented NodeGoat-derived case: the scanners were run, the model repaired the source, the project's tests were run, and the tree was re-scanned. Nothing is staged, and a 100/A means the configured engines found nothing — not that the code is proven safe.

🆚 When to choose DvalinCode

If you need…	DvalinCode's answer
An agent your security team can approve	Policy-bound tools, explicit approval modes, dvalincode trust, audit logs, OpenSSF evidence, and ISO/IEC 42001 alignment docs.
AI coding for regulated repositories — finance, healthcare, enterprise data, customer-confidential code	Local-first runtime, bring-your-own-model, .dvalincodeignore, governed egress, and minimized audit records.
A safer alternative to generic autonomous coding agents	The product thesis is controllable / transparent / auditable, not only "the model can edit files".
IDE-centric AI workflows	Zero-dep binary (~25 MB). Runs anywhere, no IDE required. macOS shell is sandboxed by default — network denied, writes capped to cwd.
Terminal-first AI workflows	CLI start → auto-opens a modern Web UI with code highlighting and red/green diff approval. One install command, nothing else needed.
Cloud-only AI workflows	Every OpenAI-compatible endpoint is a first-class citizen. Run Ollama with Qwen2.5-Coder: no key, no internet, no per-token cost.
Single-machine AI setup	AGENTS.md committed to the repo ships AI context to every clone. dvalin.json ships the team's automation commands the same way — export from the sidebar, commit, done.

🚀 Quick Install

Homebrew (macOS / Linux)

brew tap arthurpanhku/dvalincode https://github.com/arthurpanhku/dvalincode
brew install arthurpanhku/dvalincode/dvalincode

Installs the same signed-by-checksum release archive the one-liner does, and brew upgrade keeps it current. Homebrew never applies the macOS quarantine attribute, so this path is not subject to Gatekeeper.

macOS / Linux (one-liner)

curl -fsSL https://raw.githubusercontent.com/arthurpanhku/dvalincode/main/scripts/install.sh | bash

Detects your OS + arch, downloads the right binary, installs to ~/.dvalincode/, and adds it to your PATH. On macOS it also installs the native DvalinCode.app into /Applications (skip with DVALINCODE_NO_APP=1), so the desktop window launches straight from Launchpad. After reload:

source ~/.zshrc    # or ~/.bashrc
dvalincode                       # interactive terminal agent
dvalincode dvalin .              # white-box security scan (GUI-independent)
dvalincode serve                 # start the web GUI, open the browser
dvalincode serve --host 0.0.0.0 --no-open   # host it on a server for remote/browser use
echo "inspect src and summarize" | dvalincode run - --output-format stream-json
dvalincode mcp-serve             # task-level stdio MCP server for external agents

Headless run and mcp-serve keep the same policy and audit chokepoint as the interactive clients. See the unattended recipes for cron, CI, and external-agent examples.

Windows

Download dvalincode-v*-windows-x64.zip from Releases, unzip, then double-click start.bat.

Manual download

Grab the archive for your platform from the Releases page:

Platform	Archive
macOS Apple Silicon (M1/M2/M3)	dvalincode-v*-macos-arm64.tar.gz
macOS Intel	dvalincode-v*-macos-x64.tar.gz
Windows x64	dvalincode-v*-windows-x64.zip
Linux ARM64	dvalincode-v*-linux-arm64.tar.gz
Linux x64	dvalincode-v*-linux-x64.tar.gz

Verify against SHA256SUMS.txt (included in each release).

Each release also ships dvalincode-v*-evidence.json — an Evidence Pack the shipped binary produced of itself on the build machine: two real governed runs, one allowed and one blocked by policy, with their hash chains. You can check the claims on this page before installing anything:

dvalincode evidence verify dvalincode-v0.14.0-evidence.json   # offline, reads only the file

The pack's checksum is inside SHA256SUMS.txt, which is the subject of the release's build-provenance attestation. How it is produced →

macOS Gatekeeper: binaries are unsigned. On first run, either clear the quarantine flag with xattr -dr com.apple.quarantine ~/.dvalincode, or right-click the binary in Finder → Open → confirm.

Staying up to date

DvalinCode updates itself — no need to re-run the installer:

dvalincode update --check   # is a newer release out? (read-only)
dvalincode update           # download, verify, and install the latest

It finds the newest release on GitHub, and for a binary install downloads the matching archive, verifies it against the release's SHA256SUMS.txt before swapping anything in, then replaces ~/.dvalincode/ in place. npm installs are updated via npm i -g, and source checkouts are pointed at git pull. Add -y to skip the prompt, --prerelease to track pre-releases, or --json for scripting.

The macOS desktop app checks the separate gui-v* release track when it starts. When a newer GUI is available, it asks before downloading, verifies the archive against SHA256SUMS-gui.txt, validates the app version, then replaces and restarts DvalinCode.app. A failed replacement rolls back to the previous app.

🎬 First-time setup

Terminal (default): run dvalincode. On first launch it walks you through a one-time provider setup (pick a provider, paste your API key, choose a model) and saves it to ~/.dvalincode/config.json. Then you're at the prompt — type to chat, /mode to switch between Chat / Cowork / Code / Dvalin, /help for commands. In the GUI, Chat and Cowork are grouped under Home.

Web GUI: run dvalincode serve and:

• The server starts on http://localhost:3000 and your browser opens automatically.
• Click LLM Configuration in the sidebar (bottom-left).
• Pick a provider, paste your API key, choose a model, hit Save.
• Optional: save the current config as a named profile (e.g. fast, cheap, local-ollama) to switch quickly later.

Both share the same config and sessions in ~/.dvalincode/.

✨ Features

Category	Feature	Notes
Modes	Home / Code / Dvalin	Home contains read-only Ask and approval-gated Collaborate; Code is focused autonomous development; Dvalin is the scan-to-fix security workspace
Code permissions	Ask Permissions / Plan Mode / Auto Mode / Bypass permissions	Verified behavior: Ask requests approval before writes/commands, Plan is read-only and does not write files, Auto runs operations automatically, Bypass runs without confirmation prompts
Workspaces	Open folder / Import Git / Add worktree	Cowork and Code can switch to a local folder, clone a Git project, or create a Git worktree from the UI
Governance	OpenSSF Scorecard / ISO 42001 AIMS alignment	Scorecard, CodeQL, Dependabot, pinned Actions, AI impact assessment, risk register, and review cadence are documented under docs/security/ and docs/governance/
Secure remediation	Built-in + Semgrep CE + Trivy + OSV-Scanner / SARIF / cases / worktrees / tests / draft PR	Dvalin detects installed engines, normalizes SARIF, scores risk, persists cases, drives evidence-backed fixes, verifies changes, and publishes only after an explicit user action
Skills	Upload / download / built-in security skills	Skills live under ~/.dvalincode/skills; built-ins guide security scanning and remediation with dedicated agent tools. Format →
Composer	@ file references	Type @ for a fuzzy file search; selected files get inlined into the prompt
	/ slash commands	/clear /compact /git /plan /undo /help
	Multiline + interrupt	Shift+Enter for newline, stop button to abort mid-stream
Tool UI	Inline diffs	edit_file and write_file results render as red/green unified diff, default folded
	Approval dialog with diff	Cowork mode shows the diff before the change is applied
	Live tool counter + token + cost	Topbar shows session totals in real time
Agent	LLM-based context compaction	/compact summarises into Goal / Completed / Decisions / Pending
	Persistent undo stack	/undo [N] reverses the last N tool calls
	Run Report	Markdown summary per run (files, commands, decisions, test result) — GUI card + dvalincode report
	Git awareness	Branch name in topbar; git_status tool; git context auto-injected into prompt
	AGENTS.md project memory	Per-repo persistent instructions, auto-loaded each turn
Security	Tamper-evident audit trail	Hash-chained JSONL per run in ~/.dvalincode/audit/; dvalincode report verify detects edits
	macOS shell sandbox	sandbox-exec denies network; allows writes only inside cwd + /tmp
	.dvalincodeignore	gitignore-style exclusion; blocks read_file / list_files / search_text
	Per-action approval	Approve/deny each write / delete / shell call in Cowork mode
Appearance	Theme switcher	Dark / light / system, persisted; system follows the OS live
Providers	OpenAI-compatible endpoints	DeepSeek · OpenAI · Groq · OpenRouter · Ollama · custom
	Multi-profile config	Save and switch between named (provider, model, API key) sets
Sessions	Auto-save + restore	All sessions persisted to ~/.dvalincode/sessions/ as JSON
	LLM summary memory	Cross-session summary keeps the agent oriented after restart
Memory	Local user/project memory	Searchable facts, preferences, and decisions in ~/.dvalincode/memory/; import from Claude/Hermes/Markdown
Data portability	Export / import all data	One bundle of memory + sessions + config + audit — dvalincode export / import, or GUI Settings → Export / Import
	Markdown transcript	Download any conversation as Markdown — sidebar download icon, dvalincode session md <id>, or /api/sessions/:id/markdown

⌨️ Slash Commands

Command	Description
/clear	Clear the current conversation (client-side, starts a fresh session)
/compact	LLM-based context compaction — replaces history with a structured summary
/undo [N]	Reverse the last N tool calls (default 1)
/git	Run git_status and show branch, recent commits, changed files
/plan <task>	Ask the agent to plan the task step-by-step without executing
/help	Show all available slash commands

🛠️ Architecture

┌───────────────────────────┐   ┌─────────────────────────┐
│  Terminal UI (readline)   │   │  Browser GUI (React/Vite)│
│  streaming · approvals    │   │  ChatThread · DiffViewer │
└─────────────┬─────────────┘   └────────────┬────────────┘
              │ in-process          HTTP / WebSocket
              │                ┌───────────────▼─────────────┐
              │                │  Express + ws server         │
              │                │  /api/* · `dvalincode serve` │
              │                └───────────────┬─────────────┘
              └──────────────┬─────────────────┘
┌────────────────────────────▼────────────────────────────┐
│  runAgentTurn — shared turn-runner (src/agent/session)   │
│  provider · prompt (mode · git · AGENTS.md) · session    │
└────────────────────────────┬────────────────────────────┘
                             │
┌────────────────────────────▼────────────────────────────┐
│                    Agent Engine                          │
│  AgentLoop (8-state machine) → AgentRunner              │
│  Streaming · Interrupt · Undo stack · LLM compaction    │
│  run_start / run_end → AuditSink (hash-chained JSONL)   │
└──────────────────────────┬──────────────────────────────┘
                           │ run()
┌──────────────────────────▼──────────────────────────────┐
│  ToolRegistry — Zod schemas + permission gating         │
│  + audit taps: tool_call · file_* · shell_exec          │
│  read_file · list_files · search_text · git_status ·    │
│  write_file · edit_file · delete_file · shell           │
└─────────────────────────────────────────────────────────┘

Agent Loop — 8 States

RESTORE → COMPACT → COMMAND → BUILD → RUN → SAVE → RESPOND → DONE

• RESTORE — Load session from ~/.dvalincode/sessions/
• COMPACT — If context near the limit, compress history (LLM summary)
• COMMAND — Handle built-in slash commands
• BUILD — Assemble system prompt (mode prompt + project + git + AGENTS.md)
• RUN — Delegate to AgentRunner for the LLM tool-calling loop
• SAVE — Persist session
• RESPOND — Generate cross-session summary memory
• DONE

🧪 Tests

npm test

323 tests · 49 files · all green.

🏗️ Build from source

Requires Bun (curl -fsSL https://bun.sh/install | bash).

git clone https://github.com/arthurpanhku/dvalincode
cd dvalincode
npm install
npm run dev:all                 # start backend (3001) + Vite (5173)

Build release binaries for every platform:

bash scripts/build-release.sh   # → release/ with tar.gz / zip + SHA256SUMS.txt
bash scripts/build-release.sh darwin    # macOS only
bash scripts/build-release.sh windows   # Windows only

Before publishing a release:

(cd release && shasum -a 256 -c SHA256SUMS.txt)
unzip -l release/dvalincode-v*-windows-x64.zip | grep 'web/dist/index.html'
tar tzf release/dvalincode-v*-macos-arm64.tar.gz | grep 'DvalinCode.app/Contents/Resources/AppIcon.icns'

Windows smoke test: unzip dvalincode-v*-windows-x64.zip on Windows and run start.bat from the extracted folder. The server should open http://localhost:3000. If it reports an ENOENT path under B:\~BUN\root\web\dist, the compiled Bun virtual path detection has regressed; the packaged binary must resolve web/dist beside the extracted executable.

Note: Bun only allows Windows .exe icon/metadata injection when compiling on Windows. macOS/Linux cross-builds still produce a valid Windows archive, but without an embedded .exe icon.

🌐 Providers

DvalinCode supports any OpenAI-compatible endpoint. Built-in presets, sorted by cost:

Provider	Cheapest model	Input / Output	Notes
Groq	llama-3.1-8b-instant	Free tier	Fastest open models — Llama 3.3 70B, Mixtral
Ollama	qwen2.5-coder	$0 (local)	No API key needed, runs on your machine
DeepSeek	deepseek-chat	$0.14 / $0.28 per 1M	Cheap and strong; v3 nearly matches GPT-4 quality
OpenRouter	google/gemini-2.0-flash-001	$0.10 / $0.40 per 1M	200+ models including Claude, Gemini, Llama
OpenAI	gpt-4o-mini	$0.15 / $0.60 per 1M	Reliable; o1 available for deep reasoning
Custom	—	depends	Any OpenAI-compatible base URL

DvalinCode shows the per-session cost live in the topbar — flip between providers in the LLM Configuration modal, save named profiles, and compare on the fly.

❓ FAQ

Does it send my code to a third party?

Only what the agent sends to the LLM you configured. Sessions, configs, and profiles all live on your machine in ~/.dvalincode/. To exclude sensitive files from the agent's view, drop a .dvalincodeignore in your repo root (gitignore-style patterns).

Can I run this without an API key?

Yes — use Ollama. Pull a model (ollama pull qwen2.5-coder), then in the LLM Configuration modal pick the Ollama provider. No key, no internet, no per-token cost.

Why Home, Code, and Dvalin?

They represent different outcomes and safety defaults. Home groups read-only Ask and approval-gated Collaborate. Code is the focused software-development agent. Dvalin is a security pipeline with scanner evidence, remediation cases, isolated worktrees, verification, and explicit draft-PR publication. You can switch at any time while keeping project context.

Is the shell tool sandboxed?

On macOS, commands use sandbox-exec; on Linux, restrictive network policies use Bubblewrap when installed. Windows has no supported subprocess network sandbox yet, so restrictive policies fail closed instead of silently running unrestricted. The native command runner itself works on all three platforms.

Which operating-system shells are supported?

Linux and macOS commands run through /bin/sh; Windows commands run through the system ComSpec (cmd.exe by default). Full native command lines support pipes, redirects, and conditional operators. The split command + args form quotes executable paths and arguments for the host shell.

How do I see what the agent actually did — and is the log trustworthy?

Every run writes a JSONL audit log to ~/.dvalincode/audit/run-<timestamp>-<id>.jsonl. Render it with dvalincode report --last (or see the collapsible Run Report card in the GUI). Each record is chained to the previous one with a SHA-256 hash, so any after-the-fact edit is detectable — dvalincode report verify <run-id> reports ✓ chain intact or the exact position of a break. It's tamper-evident, not tamper-proof: a local attacker who can rewrite the whole file could recompute the chain. The value is forensic/accountability. See docs/AUDIT-TRAIL.md for the full threat model.

Will it overwrite my files without asking?

Depends on the mode. Home → Ask never writes. Home → Collaborate requires approval per file (with inline red/green diff before you click Allow). Code and Dvalin honor their selected permission level; use Auto only for trusted workspaces or isolated branches.

The macOS binary won't open — "unverified developer"

The binary is unsigned. Run this once to clear the quarantine flag:

xattr -dr com.apple.quarantine ~/.dvalincode

Or right-click the binary in Finder → Open → confirm once.

Does AGENTS.md get sent every turn?

Yes — DvalinCode reads AGENTS.md from the project root before each turn and injects it under === PROJECT INSTRUCTIONS === in the system prompt. Keep it focused — it counts toward your token budget.

🤝 Contributing

Contributions welcome. The codebase is intentionally small and surgical — see CONTRIBUTING.md.

git clone https://github.com/arthurpanhku/dvalincode
cd dvalincode && npm install
npm test                # 323/323 ✅
npm run typecheck

📄 License

MIT — see LICENSE.

🔗 Independence & Attribution

DvalinCode is an independent implementation. It is not affiliated with, sponsored by, or endorsed by Anthropic, Claude, Claude Code, OpenAI, OpenAI Codex, GitHub, Cursor, Aider, opencode, Cline, HKUDS/nanobot, or any other project or vendor named here.

We gratefully acknowledge that DvalinCode's product direction and architecture were informed by public research, open-source projects, published papers, standards, release notes, and common workflow patterns across the agentic coding ecosystem:

• HKUDS/nanobot (MIT) helped validate the explicit turn-state approach used in DvalinCode's TurnState flow.
• The ReAct paper (Yao et al., 2022) provides the widely used "reason, act, observe" loop that informs many modern tool-using agents.
• OpenAI's tool_calls message format, along with the broader OpenAI-compatible provider ecosystem, gives DvalinCode a portable interface for model/tool interaction.
• OpenAI Codex / Codex CLI, Claude Code, Aider, opencode, Cursor, Cline, and similar coding agents clarified user expectations around terminal agents, plan/build modes, permission prompts, project-local context, sandboxing, session lifecycle, MCP integration, and diff-first editing workflows.
• The AGENTS.md project-instruction convention, common in coding-agent tools, informed DvalinCode's project-local instruction loading behavior.
• CodeQL, GitHub Code Scanning, Semgrep, SARIF, OpenSSF Scorecard, and ISO/IEC 42001 informed DvalinCode's security-remediation and approvability posture.
• Git worktree, MCP, and local-first developer tooling patterns influenced the product direction for isolated remediation, governed tool access, and auditable execution.

These references shaped our understanding of what users expect from coding agents. DvalinCode's source code, prompts, UI text, tool schemas, module layout, and product implementation remain original unless explicitly noted. No source code, prompts, or UI text from the projects above was copied.

Full source references: docs/REFERENCES.md

⭐ Star Growth

💛 Thanks to Our Contributors

Every issue, idea, documentation improvement, test, and code contribution helps make DvalinCode better.

_{Want to join them? Read the contribution guide and send your first pull request.}