One agent, two, or more?
The most common question after reading Tab 1 or Tab 2: “Do I really need more than one agent?” Here's the honest answer, including when each option makes sense and why the default recommendation is simpler than you'd think.
The short answer
Start with one agent. Add more only when you feel a specific gap.
Most people will be happy with a single agent indefinitely. Some will want a second (or third) instance within the first month. A small minority will want two different frameworks running side by side. This tab helps you figure out which camp you're in.
The three options
Option A: One agent (recommended default)
A single OpenClaw instance handling all your AI work — Telegram messages, research, drafting, scheduling. One agent, one persona, one point of contact.
Who it's for:
- Most people, most of the time
- Anyone just starting out
- Light-to-moderate daily usage
- People who value simplicity over capability
What you get:
- A single place to send any request
- Unified memory — everything the agent learns stays in one place
- Simpler maintenance, fewer moving parts
- Lower API costs
- No routing decisions (“which agent should handle this?”)
What you give up:
- Specialisation — the same agent that gives quick answers also tries to do deep research
- Context separation — long research projects share context with quick interactions
- Collaborative dynamics — only one perspective on any question
This is the right starting point for almost everyone. Build it, use it for a few weeks, and only move to more agents if you feel a specific limitation.
Option B: Multiple agents, same framework
Two or more OpenClaw instances on the same server, each with its own identity, persona, and purpose. Think of it as hiring different specialists who all speak the same language.
Examples of useful agent pairs:
- A fast responder + a deep thinker. One optimised for quick answers. The other for long-form research and analysis.
- A personal agent + a work agent. Clean separation prevents context bleed between personal and professional.
- A general agent + a domain specialist. One handles everything general. The other is loaded with deep domain knowledge for a specific engagement.
What you get:
- Specialised personalities for different kinds of work
- Separate context windows
- Same framework = same patterns, same troubleshooting
- Can share skills and configuration between instances
What you give up:
- Slightly more setup (second systemd service, second config, second identity)
- You have to decide which agent handles each task
- Higher API costs
- Cross-agent memory isn't automatic
The most common “upgrade path” from single-agent. If you're going to add a second agent, another OpenClaw is almost always the right choice — you already know the framework.
Option C: Multiple agents, different frameworks
One OpenClaw instance + one Hermes instance. Two different frameworks with different strengths running side by side.
What each framework is good at:
OpenClaw is stronger at multi-channel communication, structured identity via multi-file workspace templates, community skill ecosystem, scheduled tasks, and multi-device pairing.
Hermes is stronger at deep research and long-form analysis, simple configuration, built-in evolving memory, API-first access (built-in REST API), and lower-touch maintenance.
What you get:
- Best-of-both-worlds capability
- Framework diversity (if one has a bug, the other keeps working)
- Genuinely different “voices” because the frameworks shape output differently
What you give up:
- Significantly more complexity
- Two sets of configs, logs, and quirks
- The learning curve of a second framework
The “power user” option. Do it only after you've run single-agent or multi-instance for a while and have a specific reason to want framework diversity.
Framework comparison at a glance
A side-by-side comparison of OpenClaw and Hermes based on lived-experience use:
| Feature | OpenClaw | Hermes |
|---|---|---|
| Language | Node.js / TypeScript | Python |
| Identity system | Multi-file workspace (SOUL.md, USER.md, IDENTITY.md, AGENTS.md, HEARTBEAT.md, TOOLS.md, BOOTSTRAP.md) | Single SOUL.md + memories/USER.md |
| Configuration complexity | Higher — more knobs, more files | Lower — simpler setup, fewer decisions |
| Memory | Memory-wiki (claim/evidence) + Dreaming (nightly consolidation) | Built-in memories/ directory, agent-managed USER.md |
| Scheduled tasks | Heartbeat system + cron | Cron + home channel for outputs |
| Discord support | Native, granular guild/channel config | Native, env-var based config |
| Telegram support | Native, device pairing model | Native, user allowlist |
| API server | Not built in | Built-in OpenAI-compatible REST API |
| Bot-to-bot visibility | allowBots config option | ALLOW_BOTS environment variable |
| Community ecosystem | Larger plugin and skill ecosystem | Growing ecosystem, active community |
| Best for | Multi-channel operational work, structured workflows | Deep research, API-first access, simpler setups |
| Learning curve | Steeper — more to understand upfront | Gentler — get started faster |
| Troubleshooting | More moving parts = more potential issues | Simpler = fewer failure modes |
The recommendation
- Start with one OpenClaw agent. Follow Tab 2 (Full Build). Use it for at least two weeks before considering any additions.
- If you feel a gap, add a second OpenClaw instance. Give it a different persona and role. You already know the framework, so the second agent is mostly a copy-paste job with different identity content.
- Only consider Hermes if you have a specific reason — you want framework diversity, you need its built-in API server, or you want its simpler memory model for a specific use case. Don't add Hermes out of curiosity; add it because you have a concrete problem it solves.
The progression is: one → two same → two different. Most people stop at step 1. Some progress to step 2. A small minority reach step 3, and that's fine.
A note on model selection
This tab talks about how many agents you want, not which models power them. Model selection (Claude Opus vs Sonnet vs Haiku, OpenAI models, local models via Ollama, model routing via LiteLLM) is covered in Tab 6 (Models & Cost Optimisation). You can run any number of agents on any combination of models — the two decisions are independent.