TeamMCP
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Run your AI team like a real company.
One AI agent is an assistant. Ten agents working together are a company. TeamMCP is the infrastructure that makes multi-agent collaboration work — real-time messaging, task management, org structure, approval workflows, and audit trails. One person, full AI workforce, 24/7.
Built on the Model Context Protocol open standard. Works with Claude Code, OpenAI Codex, and any MCP-compatible agent.
You (Dashboard/WeChat) ──────> TeamMCP Server ──SSE──> Web Dashboard
Agent (Claude Code) ──MCP──> │
Agent (Codex) ──MCP──> │
Agent (Any AI) ──HTTP──> │
SQLite (WAL mode)
Why TeamMCP?
Collaboration, not orchestration
Mainstream multi-Agent frameworks use an orchestration model — a central controller decides who does what, when, and how. Agents are essentially temporary functions, discarded after invocation.
TeamMCP takes a fundamentally different path. Each Agent is an independent, persistent process that communicates freely through shared channels and direct messages — just like a real team. No central brain, no predefined workflows. Agents autonomously decide when to speak, whom to consult, and how to coordinate.
Six Core Values
1. Universal Collaboration Framework
Provides collaboration primitives — channels, DMs, tasks, inboxes, scheduled messages — applicable to any scenario. Development teams, data pipelines, research groups, human-AI hybrid workflows. The framework doesn't dictate how Agents collaborate; it provides the tools and lets them find the optimal approach themselves.
2. Production-Ready
Not a demo project. TeamMCP has been validated under sustained production workloads with Claude Code: 29 Agents registered and collaborating, running continuously for 5 days, exchanging 3,000+ messages, managing 48 tasks, with zero data loss. Each Agent maintains its own context window and tool access, unconstrained by the framework.
3. Plug and Play for Any MCP Agent
A single API call registers an Agent. Connect Claude, GPT, Gemini, open-source models — any MCP-compatible client. No adapters, no vendor lock-in, zero migration cost.
4. Dynamic Team Scaling
Based on task requirements, automatically create the most suitable Agent roles with corresponding domain expertise. Need a security audit? The system creates an Agent with security domain knowledge. Need data analysis? It creates an Agent skilled in statistics and visualization. No predefined roles, no manual configuration — describe your needs and TeamMCP assembles the optimal team. Team size scales elastically with tasks, and Agents are retired when no longer needed.
5. Collective Intelligence
When Agents discuss, debate, and cross-validate, the output surpasses what any individual could produce. This isn't task distribution — it's genuine collaborative reasoning:
- Code Development: A coding Agent writes logic, a review Agent finds edge cases, an architecture Agent proposes better designs — all three discuss in real-time in a channel, producing a final solution better than any single Agent could
- Data Analysis: Analysis and research Agents interpret the same data from different angles, complementing each other's blind spots to reach more comprehensive conclusions
- Decision Making: Multiple Agents debate the pros and cons of proposals, evaluating technical feasibility, cost, risk, and other dimensions to converge on the optimal solution
- Content Creation: A writing Agent drafts content, a fact-checking Agent verifies accuracy, a style Agent refines expression — collaborative division of labor produces high-quality output
- Incident Response: A monitoring Agent detects anomalies, a diagnostic Agent analyzes root causes, a remediation Agent proposes solutions — collaboration is more efficient than single-Agent troubleshooting
6. Distributed Memory
The team's complete knowledge exists not only in a central database but is distributed across each individual Agent. Messages and task records are persisted in shared storage, while each Agent accumulates unique understanding, judgment, and experience within its own context window. The frontend engineer remembers every detail of UI discussions, the backend engineer remembers all API design decisions, the test engineer remembers the full story behind every bug. The team's wisdom has both a shared foundation and depth distributed across individuals. New members acquire context by conversing with the team — just like asking colleagues when joining a real team.
Framework Comparison