Hermes is steadier, but token cost can erase the benefit
Hands-on testing across AI agents and tools such as OpenClaw, Hermes, , Codex, Claude Code, Claude, and led to a simpler view of . The goal was to save time, but the setup became more work to maintain than the help it gave. OpenClaw used millions of tokens, needed repeated context, and sometimes gave the wrong answer with , apologized, and later repeated the same mistake.
Memory often felt less like a special feature and more like saving clear rules and notes into structured .md files. Hermes was more reliable and broke less often, but it still used too many tokens for relatively small tasks. That made it hard to justify financially.
Most may only need memory, such as MCP, skills or tools, basic logic, and a good LLM.
Key points
- Hermes was more reliable than some other agents and did not break as randomly.
- Token use was still too high for small tasks.
- Memory may work best as clear, structured .md files rather than a mysterious feature.
- A useful setup may only need memory, MCP, tools, logic, and a good LLM.
- Hermes users should reduce unnecessary context and keep tool connections lean.