Hermes vs Implexa
Hermes is a model-agnostic, self-improving agent you operate and supply inference for; Implexa builds agents from a sentence and runs them inside the Claude or Codex you already pay for, improving from your feedback rather than a self-scored loop. Pick Hermes for open-ended autonomy with a model of your choosing; pick Implexa to get recurring work done free on your existing plan, with no second inference bill and no credentials held.
Both improve over time. The difference is who pays for the model, who holds the access, and whether the improvement is self-scored or checkable.
Side by side
| Hermes | Implexa | |
|---|---|---|
| Model & cost | You supply and meter the inference on every run. | Runs on your existing Claude or Codex plan; no separate inference bill from us. |
| Improvement | Self-improves and rewrites its own skills — self-scored. | Improves from your approved feedback and a private per-agent memory; draws on skills graded across an index you can inspect. |
| Hosting | A persistent process you operate. | Nothing to host — it runs inside the AI you already use. |
| Credentials | Holds the access you grant the standing agent. | Never receives credentials; approval-gated writes. |
| Setup | Developer-operated. | One sentence to a scheduled agent. |
When to pick Hermes over Implexa
If you want a fully autonomous, always-on agent that self-directs across many tools with a model you choose — and you can run and pay for that — Hermes is the more open-ended tool. Implexa trades that autonomy for something a non-developer can run free on an existing plan, with approval gates and no credentials held.
See the agents
Questions
- Which is cheaper, Hermes or Implexa?
- Implexa runs on the Claude or Codex plan you already pay for, with no separate inference bill from us. With Hermes you supply and meter the model yourself, so cost scales with every run.
- Is Implexa's self-improvement real or marketing?
- It is the honest version: Implexa improves each run from your approved feedback and a private per-agent memory, and the skills it uses are graded across a cross-vendor index you can inspect. It does not claim to silently re-score itself; you can check what changed and why.