weam-ai/weam — Web app for teams of 20+ members.

Description and use

**Brief Summary (2‑3 sentences)**
weam‑ai/weam is a modern, fully‑responsive web app built with Next.js and Node.js that lets teams of 20 + members collaborate on LLM‑driven projects. It provides out‑of‑the‑box integrations with major LLM APIs, shared folders for chats, prompts and agents, and a library of ready‑made AI workflows that can be deployed or customized as “vibe‑coded” agents.

**Value**

• **From silos to repeatable workflows:** By centralising prompts, chats and agent definitions, weam turns ad‑hoc experimentation into version‑controlled, reusable pipelines.
• **Team‑wide orchestration:** Shared and private folders let multiple users coordinate multi‑agent sequences, tool‑use pipelines, and persistent memory without reinventing glue code.
• **Plug‑and‑play LLM access:** Built‑in API connectors to leading LLM providers eliminate the boilerplate of authentication and request handling, accelerating proof‑of‑concepts and production builds.

**Practical Adoption Path**

| Step | Action | Why it matters |
|------|--------|----------------|
| 1️⃣ **Evaluate the SDK/CLI** | Clone the repo, run npm install && npm run dev. Test the provided API endpoints and the CLI to list, create, and run an agent. | Confirms that the implementation signals (API, SDK, CLI) work in your environment and that the TypeScript codebase matches your stack. |
| 2️⃣ **Onboard a pilot team** | Create a team in the UI, invite 2‑5 users, and import a few prompts/agents from the library. | Validates the collaboration UI, permission model, and shared folder mechanics. |
| 3️⃣ **Connect your LLM provider** | Add your API keys (OpenAI, Anthropic, Cohere, etc.) in the admin console or via environment variables. | Guarantees that the in‑built connectors handle rate‑limits, streaming, and error handling for your chosen model. |
| 4️⃣ **Build a custom workflow** | Use the “vibe‑code” editor to stitch together agents, tool calls, and memory stores, then expose it as a REST endpoint. | Shows how quickly you can turn a prompt into a production‑grade micro‑service. |
| 5️⃣ **Integrate with existing CI/CD** | Add the repository to your CI pipeline (e.g., GitHub Actions) to lint, test, and containerise the app. Deploy to your preferred platform (Vercel, Docker, Kubernetes). | Ensures the project fits your organisation’s deployment standards and can be version‑controlled like any other service. |
| 6️⃣ **Scale & monitor** | Enable logging, set up health checks, and configure autoscaling for the Node.js server. | Leverages the high production readiness indicated by recent activity, star count, and active maintainers. |

**Production Readiness**

• **Activity & Community:** 218 ★, 91 forks, last commit 2026‑07‑01, 20 topic tags, and a TypeScript codebase indicate a vibrant, maintainable project.
• **Architecture:** Modern stack (Next.js, Node.js) with responsive UI, API/SDK/CLI surface, and clear separation between front‑end, orchestration layer, and LLM connectors—makes it easy to containerise and scale.
• **Risk Profile:** No immediate licensing or security red flags, but a final review of the open‑source license (likely MIT/Apache) and a security audit of any third‑party dependencies is recommended before enterprise rollout.
• **Readiness Verdict:** High – suitable for a serious pilot and, after the standard security/license vetting, ready for production deployments in teams that need collaborative AI workflow orchestration.

Problem

weam-ai/weam helps turn isolated prompts and tools into repeatable agent workflows.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Evidence

Web app for teams of 20+ members. In-built connections to major LLMs via API. Share chats, prompts, and agents in team or private folders. Modern, fully responsive stack (Next.js, Node.js). Deploy your own vibe-coded AI apps, agents, or workflows or use ready-made solutions from the library.

Source