disp8ch

Built During OpenAI Build Week

Before the Submission Period: Disp8ch was already a working, local-first workspace built and tested over two months with GPT-5.5. Its existing foundation connected WebChat, visual workflows, Boards, Hierarchy, Council, Data Sources, Memory, Skills, Design Studio, channels, approvals, and Activity.

Built for OpenAI Build Week with Codex and GPT-5.6: The submission adds the missing delivery loop around that foundation: consent-gated Live Sessions and timestamp-cited Delivery Briefs; real planner, implementer, and independent reviewer workers; acceptance contracts, bounded repair, executable proof, and Outcome Receipts; source-grounded Files Projects; governed preview, publishing, and feedback-to-revision; and outcome-first Cross-Tab Work Trails that keep the decision, owners, evidence, code, cost, approval, and public result connected.

Codex with GPT-5.6 was used throughout the Submission Period to inspect the existing architecture, implement and refactor these changes, test cross-tab behavior on Windows, diagnose live-model and UI failures, harden security and dependencies, and prepare the release and demo evidence. Inside Disp8ch, GPT-5.6 can also be selected through the operator's authenticated Codex session as the planner and independent reviewer for a Supervised Run, while a faster or lower-cost configured model performs implementation. Disp8ch does not copy or expose the operator's Codex OAuth token.

The final release pass also hardened evidence-bound capability reporting, generic external-project planning, exact requested response contracts, credential redaction across answers, logs, workflow traces, and exports, and the current dependency graph. Fresh npm and pnpm audits report zero known vulnerabilities.

The human operator chose the product scope, user problem, model policy, safety boundaries, acceptance criteria, and release decisions. External publishing, mutations, credentials, and untrusted feedback remain explicitly confirmation-gated. The included demo presents the Build Week work as an extension of the pre-existing product, not as a claim that one prompt created the full application.

⚡ Install in 30 seconds

Linux, macOS, or WSL

curl -fsSL https://raw.githubusercontent.com/aaronnat23/disp8ch/main/scripts/install.sh | bash -s -- --repo https://github.com/aaronnat23/disp8ch.git

Windows PowerShell

$env:DISP8CH_SOURCE_ZIP_URL = "https://github.com/aaronnat23/disp8ch/archive/refs/heads/main.zip"; iex (irm "https://raw.githubusercontent.com/aaronnat23/disp8ch/main/scripts/install-windows.ps1")

The installer sets up a managed Node runtime if needed, starts the app at http://localhost:3100, and opens onboarding. Connect a cloud key or a local model (Ollama, llama.cpp, LM Studio, vLLM, SGLang) and you're working. More options in Quick Start.

One-line install

The one-line installers are the easiest path for non-technical users. They download a managed Node.js 22 runtime if needed, use Corepack or npx pnpm, fetch the app source, install dependencies, create a clean local workspace, start disp8ch, and open onboarding.

First Model Setup

The easiest path is /onboarding. disp8ch supports four setup paths:

For API key or local setup:

1. Choose Online and add an API key, or choose Local.
2. For Local, select Check this PC to inspect installed models, available RAM and VRAM, and detected runtimes.
3. Run the recommended Ollama or llama-server command, then select Use this setup.
4. Run validation, then open WebChat and send a message.

For Claude account OAuth:

1. Install and sign in to Claude Code on the same Windows user that runs disp8ch.
2. Keep the Claude Code credential file private. Do not copy .claude, OAuth token files, or auth JSON into the repo.
3. In disp8ch, select or add an Anthropic model in Settings -> Models.
4. If the model form asks for a credential, use an environment or secret reference such as env:ANTHROPIC_TOKEN, env:ANTHROPIC_OAUTH_TOKEN, env:CLAUDE_CODE_OAUTH_TOKEN, or secret:CLAUDE_CODE_OAUTH_TOKEN.
5. Run the model test before using the model in WebChat or workflows.

For Codex account sign-in:

1. Install the Codex CLI and sign in locally with your Codex account.
2. Keep Codex auth files outside the repo and outside .env.local.
3. Leave normal WebChat on your selected provider or local model. Codex sign-in is only used when you explicitly choose the Codex coding-agent backend for delegated coding work.
4. Test with a harmless read-only delegation before granting write access.

You can also configure .env.local:

cp .env.example .env.local

Direct provider examples:

OPENAI_API_KEY=...
ANTHROPIC_API_KEY=...
ANTHROPIC_TOKEN=...
ANTHROPIC_OAUTH_TOKEN=...
CLAUDE_CODE_OAUTH_TOKEN=...
GOOGLE_API_KEY=...
DEEPSEEK_API_KEY=...

OpenRouter:

OPENROUTER_API_KEY=...

Local model endpoints:

OLLAMA_BASE_URL=http://127.0.0.1:11434
VLLM_BASE_URL=http://127.0.0.1:8000/v1
SGLANG_BASE_URL=http://127.0.0.1:30000/v1

Do not commit .env.local, .claude, .codex, auth JSON, OAuth token files, or any local credential store.

Quick Start

WebChat Examples

What can this app currently do? Separate implemented, configured, and callable.
List my automations and show which webhooks are enabled.
Create a webhook workflow that validates a GitHub-style JSON payload and summarizes it.
Compare three local model runtimes for an 8 GB VRAM laptop. Use current sources.
Audit this repo's API-key handling and cite exact files.
Create a board task for each blocker in this launch document.
Start a council session on whether we should prioritize reliability or new features.
Build a daily 9 AM research digest workflow, but ask before saving if anything is ambiguous.
Spin up a research team, put them in an org, and give them a board task to compare OCR models.
Generate a landing page concept for a local-first AI workspace and save it as a design.
Remember that I prefer concise technical answers. Reply only saved.
What is my preferred answer style?

Option A: Ollama (easiest)

1. Install Ollama and start it. 2. In onboarding, select Check this PC and use the exact ollama run ... command shown for the recommended model. Ollama downloads that model only after you run the command yourself:

ollama serve
ollama run <recommended-model-tag>

1. Open onboarding at http://localhost:3100/onboarding, choose Local, select Check this PC, run the shown command, then select Use this setup, test, and save. No key required.

Memory search works without choosing a separate provider. New installs default to disp8ch's built-in local embedding model (Xenova/all-MiniLM-L6-v2) and fall back to keyword search if the model cache is unavailable. If you prefer Ollama embeddings instead, run ollama pull nomic-embed-text, set Settings -> Memory -> Embedding model to nomic-embed-text, then click Rebuild Index.

Or via .env.local:

OLLAMA_BASE_URL=http://127.0.0.1:11434

Option B: LM Studio, llama.cpp, vLLM, or SGLang (OpenAI-compatible)

1. Start your local server and load a model.
2. In onboarding choose the LM Studio / OpenAI-compatible preset and set the base URL. Leave the API key blank when the local server does not require one:

1. Run the test and save.

Or via .env.local:

VLLM_BASE_URL=http://127.0.0.1:8000/v1
SGLANG_BASE_URL=http://127.0.0.1:30000/v1

Tip: do not choose from parameter count alone. Context size, quantization, architecture, current free RAM/VRAM, and runtime support all affect whether a model is practical. Real AI image generation, live web search providers, external channels, and third-party APIs still need their own credentials, but the core local workspace runs without a model-provider key.

Run Fully Local (No API Key)

You do not need a cloud account or API key for core use. Run a local model server and point disp8ch at it: chat, local tools, memory, workflows, agents, boards, council, local document research, and local artifact work can run without a model-provider key. Live web search, external channels, cloud image generation, and third-party APIs still need network access and the credentials you choose to configure.

<p align="center"> <img src="docs/readme-assets/local-model-stack.svg" alt="disp8ch local model stack" width="100%" /> </p>

Useful CLI Commands

pnpm dpc status
pnpm dpc health
pnpm dpc doctor
pnpm dpc models list
pnpm dpc workflows list
pnpm dpc boards list
pnpm dpc orgs list
pnpm dpc skills list
pnpm dpc backup status
pnpm dpc learning status
pnpm dpc goals list

Developer checks:

pnpm install
pnpm exec tsc --noEmit
pnpm build

Desktop checks:

pnpm desktop:build

The maintainer release suite is run from the private development workspace before public exports. The public repository intentionally excludes generated runtime state and private release artifacts.

Visual Workflow Automation

• Drag-and-drop canvas covering triggers (message, webhook, cron, manual, GitHub), agents, HTTP, RSS, files, documents, memory, logic, boards, and channels.
• 57 templates plus a recipe pack for common jobs: issue triage, PR review, uptime watch, news digests, scheduled reports, docs crawling, and more.
• Test as you build: node testing, run-to-node, replay, execution traces, versions, and retry from the failed node.
• Guardrails for unattended runs: per-workflow concurrency (skip duplicates or queue them durably), daily run/cost budgets with optional auto-disable, and escalation policies with quiet hours.
• Notify only on change: a node can emit [SILENT] and downstream sends stay quiet until something actually needs attention.
• Webhook workflows can answer the HTTP caller directly with a response node, and workflow JSON from other visual automation tools imports with unsupported nodes kept as visible placeholders.

Connect Anything: MCP, Extensions, And Tools

• Connect any MCP server from a dedicated page: catalog, per-tool enablement, approval policy, connection diagnostics, and named-agent access.
• Install extension packs for channels, providers, and integrations, enabled per agent.
• Define custom tools and expose any workflow as an agent tool.

FAQ

Do I need an API key or a cloud account? No for core use. Run fully local with Ollama, LM Studio, llama.cpp, vLLM, or SGLang; see Run Fully Local. Cloud providers, web search, channels, and image generation only need the credentials you choose to configure.

How is this different from a chatbot or a terminal assistant? Those are one capability. disp8ch is the workspace around them: workflows, schedules, multi-agent operations, a decision council, memory, research, and design, all driven from plain-English chat.

Data Sources, Notebooks, or /learn? Data Sources for searchable files and cited answers. Notebooks to group related sources with a narrow boundary. /learn when the material should become a reusable skill; candidates stay pending until you approve them.

Can I run more than one organization? Yes. One deployment hosts multiple organizations with their own agents, goals, budgets, and governance.

Can I bring work from the app I already use? Yes. Import compatible skills, workflow JSON, and org templates. See Migration and Imports.

Does it work unattended? Yes. Cron, signed webhooks, heartbeats, and standing goals keep work moving; risky actions stay confirmation-gated.

Is my data private? Local-first. Your database, memories, documents, and chat history stay on your machine; only the model, tool, and channel calls you configure leave it.

Can I reach it from my phone? Yes. Run it on a machine or server and talk to it from WebChat, Telegram, Discord, Slack, WhatsApp, and more.