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usecases/multi-agent-team.md
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# Multi-Agent Specialized Team (Solo Founder Setup)
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Solo founders wear every hat — strategy, development, marketing, sales, operations. Context-switching between these roles destroys deep work. Hiring is expensive and slow. What if you could spin up a small, specialized team of AI agents, each with a distinct role and personality, all controllable from a single chat interface?
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This use case sets up multiple OpenClaw agents as a coordinated team, each specialized in a domain, communicating through shared memory and controlled via Telegram.
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## Pain Point
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- **One agent can't do everything well**: A single agent's context window fills up fast when juggling strategy, code, marketing research, and business analysis
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- **No specialization**: Generic prompts produce generic outputs — a coding agent shouldn't also be crafting marketing copy
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- **Solo founder burnout**: You need a team, not another tool to manage. The agents should work in the background and surface results, not require constant babysitting
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- **Knowledge silos**: Insights from marketing research don't automatically inform dev priorities unless you manually bridge them
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## What It Does
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- **Specialized agents**: Each agent has a distinct role, personality, and model optimized for its domain
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- **Shared memory**: Project docs, goals, and key decisions are accessible to all agents — nothing gets lost
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- **Private context**: Each agent also maintains its own conversation history and domain-specific notes
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- **Single control plane**: All agents are accessible through one Telegram group chat — tag the agent you need
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- **Scheduled daily tasks**: Agents proactively work without being asked — content prompts, competitor monitoring, metric tracking
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- **Parallel execution**: Multiple agents can work on independent tasks simultaneously
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## Example Team Configuration
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### Agent 1: Milo (Strategy Lead)
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```text
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## SOUL.md — Milo
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You are Milo, the team lead. Confident, big-picture, charismatic.
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Responsibilities:
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- Strategic planning and prioritization
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- Coordinating the other agents
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- Weekly goal setting and OKR tracking
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- Synthesizing insights from all agents into actionable decisions
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Model: Claude Opus
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Channel: Telegram (responds to @milo)
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Daily tasks:
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- 8:00 AM: Review overnight agent activity, post morning standup summary
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- 6:00 PM: End-of-day recap with progress toward weekly goals
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```
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### Agent 2: Josh (Business & Growth)
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```text
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## SOUL.md — Josh
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You are Josh, the business analyst. Pragmatic, straight to the point, numbers-driven.
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Responsibilities:
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- Pricing strategy and competitive analysis
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- Growth metrics and KPI tracking
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- Revenue modeling and unit economics
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- Customer feedback analysis
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Model: Claude Sonnet (fast, analytical)
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Channel: Telegram (responds to @josh)
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Daily tasks:
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- 9:00 AM: Pull and summarize key metrics
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- Track competitor pricing changes weekly
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```
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### Agent 3: Marketing Agent
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```text
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## SOUL.md — Marketing Agent
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You are the marketing researcher. Creative, curious, trend-aware.
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Responsibilities:
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- Content ideation and drafting
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- Competitor social media monitoring
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- Reddit/HN/X trend tracking for relevant topics
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- SEO keyword research
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Model: Gemini (strong at web research and long-context analysis)
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Channel: Telegram (responds to @marketing)
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Daily tasks:
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- 10:00 AM: Surface 3 content ideas based on trending topics
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- Monitor competitor Reddit/X mentions daily
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- Weekly content calendar draft
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```
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### Agent 4: Dev Agent
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```text
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## SOUL.md — Dev Agent
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You are the dev agent. Precise, thorough, security-conscious.
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Responsibilities:
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- Coding and architecture decisions
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- Code review and quality checks
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- Bug investigation and fixing
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- Technical documentation
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Model: Claude Opus / Codex (for implementation)
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Channel: Telegram (responds to @dev)
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Daily tasks:
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- Check CI/CD pipeline health
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- Review open PRs
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- Flag technical debt items
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```
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## Skills You Need
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- `telegram` skill for the shared control interface
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- `sessions_spawn` / `sessions_send` for multi-agent coordination
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- Shared file system or note-taking tool for team memory
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- Individual API keys for different model providers (if using mixed models)
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- A VPS or always-on machine to run the agents
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## How to Set It Up
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### 1. Shared Memory Structure
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```text
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team/
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├── GOALS.md # Current OKRs and priorities (all agents read)
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├── DECISIONS.md # Key decisions log (append-only)
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├── PROJECT_STATUS.md # Current project state (updated by all)
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├── agents/
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│ ├── milo/ # Milo's private context and notes
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│ ├── josh/ # Josh's private context
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│ ├── marketing/ # Marketing agent's research
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│ └── dev/ # Dev agent's technical notes
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```
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### 2. Telegram Routing
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Configure a single Telegram group where all agents listen, but each responds only when tagged:
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```text
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## AGENTS.md — Telegram Routing
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Telegram group: "Team"
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Routing:
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- @milo → Strategy agent (spawns/resumes milo session)
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- @josh → Business agent (spawns/resumes josh session)
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- @marketing → Marketing agent (spawns/resumes marketing session)
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- @dev → Dev agent (spawns/resumes dev session)
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- @all → Broadcast to all agents
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- No tag → Milo (team lead) handles by default
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Each agent:
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1. Reads shared GOALS.md and PROJECT_STATUS.md for context
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2. Reads its own private notes
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3. Processes the message
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4. Responds in Telegram
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5. Updates shared files if the response involves a decision or status change
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```
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### 3. Scheduled Tasks
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```text
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## HEARTBEAT.md — Team Schedule
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Daily:
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- 8:00 AM: Milo posts morning standup (aggregates overnight agent activity)
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- 9:00 AM: Josh pulls key metrics
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- 10:00 AM: Marketing surfaces content ideas from trending topics
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- 6:00 PM: Milo posts end-of-day recap
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Ongoing:
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- Dev: Monitor CI/CD health, review PRs as they come in
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- Marketing: Reddit/X keyword monitoring (every 2 hours)
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- Josh: Competitor pricing checks (weekly)
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Weekly:
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- Monday: Milo drafts weekly priorities (input from all agents)
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- Friday: Josh compiles weekly metrics report
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```
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## Key Insights
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- **Personality matters more than you'd think**: Giving agents distinct names and communication styles makes it natural to "talk to your team" rather than wrestle with a generic AI
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- **Shared memory + private context**: The combination is critical — agents need common ground (goals, decisions) but also their own space to accumulate domain expertise
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- **Right model for the right job**: Don't use an expensive reasoning model for keyword monitoring. Match model capability to task complexity
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- **Scheduled tasks are the flywheel**: The real value emerges when agents proactively surface insights, not just when you ask
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- **Start with 2, not 4**: Begin with a lead + one specialist, then add agents as you identify bottlenecks
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## Inspired By
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This pattern was described by [Trebuh on X](https://x.com/iamtrebuh/status/2011260468975771862), a solo founder who set up 4 OpenClaw agents — Milo (strategy lead), Josh (business), a marketing agent, and a dev agent — all controlled through a single Telegram chat on a VPS. Each agent has its own personality, model, and scheduled tasks, while sharing project memory. He described it as "a real small team available 24/7."
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The pattern was also confirmed on the [OpenClaw Showcase](https://openclaw.ai/showcase), where `@jdrhyne` reported running "15+ agents, 3 machines, 1 Discord server — IT built most of this, just by chatting," and `@nateliason` described a multi-model pipeline (prototype → summarize → optimize → implement → repeat) using different models at each stage. Another user, `@danpeguine`, runs two different OpenClaw instances collaborating in the same WhatsApp group.
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## Related Links
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- [OpenClaw Subagent Documentation](https://github.com/openclaw/openclaw)
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- [OpenClaw Telegram Skill](https://github.com/openclaw/openclaw)
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- [OpenClaw Showcase](https://openclaw.ai/showcase)
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- [Anthropic: Building Effective Agents](https://www.anthropic.com/research/building-effective-agents)
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