Compare / Amp vs Hermes
Head-to-head
Amp vs Hermes.
Side-by-side on ratings, pricing, pros, cons, and the honest take on which to pick. Cross-category comparison: Amp is a coding agent and Hermes is a open-source harness.
| Amp | Hermes | |
|---|---|---|
| Rating | 4.0 / 5 | 4.0 / 5 |
| Category | Coding Agent | Open-source harness |
| Tech level | developer | developer |
| Open source | No | Yes (MIT) |
| Pricing | Free tier with meaningful usage allowance. Paid tiers $19–$49/month for individuals. Enterprise pricing for teams bundled with Sourcegraph Code Search. Token-based usage on top of subscription tiers. | Free and open-source under MIT. You pay only for model API tokens (200+ models accessible through its marketplace integration — Claude, GPT, Gemini, DeepSeek, Kimi, GLM, local models) plus your own hosting. Hosting on a $5-$20/month VPS handles individual use; bare-metal or homelab handles team use. Typical individual model spend lands at $20-$200/month depending on workflow intensity. Heavy multi-agent users with goal-driven loops on Claude Sonnet can push past $300/month — budget caps and per-agent quotas are configurable. |
| Best for | Engineering teams already paying for Sourcegraph Code Search who want to add an AI agent that reuses the existing codebase index. Strong for large enterprise codebases (1M+ lines) where context retrieval is the bottleneck. Free tier is generous enough for individual evaluation. | Technical operators and developers who want a server-deployed agent that builds institutional memory across runs and improves from experience. Strong for sustained workflows: research synthesis, scheduled briefings, email triage, multi-agent orchestration, and any work where the agent should keep getting better at your specific job over weeks of use. |
| Not for | Teams not on Sourcegraph — the standalone story is less differentiated than Claude Code or Augment Code. Solo developers and small projects where the codebase-context advantage doesn't compound. Builders who want a simpler CLI or terminal-first experience. | Anyone wanting a quick setup with managed infrastructure. The self-improvement story requires consistent use to pay off; if you bounce between random tasks, the value compounding doesn't kick in. Teams without DevOps capacity should pick OpenClaw or Manus AI instead. Non-developers should pick Lindy. Developers wanting code-focused work should pair Hermes with Claude Code rather than expect Hermes to replace it. |
Our verdict on Amp
Sourcegraph's agentic coding tool built on years of code-search investment. The codebase-context story is genuinely differentiated for teams already running Sourcegraph at scale. As a standalone vs Cursor or Claude Code, it's solid but less obvious — the strategic moat is the Sourcegraph install base, not the agent itself.
Full Amp review →Our verdict on Hermes
The most technically sophisticated open-source agent harness in 2026. Server-deployed, model-agnostic, and the only platform with a genuine self-improvement loop that compounds over months of use. Right pick when you have technical capacity and want an agent that grows with you.
Full Hermes review →Amp
What works
- Built on Sourcegraph's mature code-search and indexing infrastructure
- Free tier with meaningful usage allowance for individual evaluation
- Strong codebase-context story without separate indexing setup
- Native integration with Sourcegraph Code Search
- Sourcegraph's enterprise compliance (SOC 2, on-prem options) carries over
- Cross-repo awareness that single-repo tools miss
- Active product velocity — feature gap vs Cursor is closing fast
What doesn't
- Standalone value less compelling than Claude Code or Augment Code for non-Sourcegraph teams
- Newer to agentic coding (launched mid-2025) than competitors with longer track records
- Smaller community vs Cursor or Copilot
- Locked into Sourcegraph as the indexing/context layer
- Best fit narrows to enterprise teams already paying for Sourcegraph
- Editor support skews VS Code-centric; JetBrains story less mature
Hermes
What works
- Genuine self-improvement loop — skills compound across runs over weeks of consistent use
- Built by Nous Research, one of the few independent AI labs with real frontier research credibility
- 200+ model support via its marketplace integration — Claude, GPT, Gemini, DeepSeek, Kimi, GLM, local models, no vendor lock-in
- Server-deployed — runs 24/7 without your machine being on, ideal for monitoring and background work
- Parallel subagent execution for complex multi-step workflows
- Atropos RL integration connects it to frontier agentic research methods
- Markdown-based memory works as a real 'second brain' with Obsidian/SyncThing integration
- MIT licensed and self-hostable — full data control for compliance-sensitive workflows
What doesn't
- Steeper setup than OpenClaw — Python-based server deployment with VPS or Modal hosting
- 119k stars vs OpenClaw's 365k — smaller community, less polished documentation
- The self-improvement story requires consistent use to pay off (bounces don't compound)
- No managed cloud option — you operate the server or pair it with a hosting provider
- Steeper learning curve than Lindy or Manus AI for first-time agent builders
- Marketplace dependency means you're trusting a model-routing layer alongside Hermes itself
Which to pick
These two are closely matched. Don't pick on overall rating — pick on use case. Amp for engineering teams already paying for sourcegraph code search who want to add an ai agent that reuses the existing codebase index. strong for large enterprise codebases (1m+ lines) where context retrieval is the bottleneck. free tier is generous enough for individual evaluation. Hermes for technical operators and developers who want a server-deployed agent that builds institutional memory across runs and improves from experience. strong for sustained workflows: research synthesis, scheduled briefings, email triage, multi-agent orchestration, and any work where the agent should keep getting better at your specific job over weeks of use.
Honest middle: most serious operators end up using more than one tool. If you're early in your AI agent journey, our five-question picker recommends a starting platform from your specific situation.
Common questions
Amp vs Hermes — which should I pick?
Amp and Hermes are closely matched (we rate them 4.0/5 and 4.0/5). Pick by use case rather than overall score: Amp for engineering teams already paying for sourcegraph code search who want to add an ai agent that reuses the existing codebase index. strong for large enterprise codebases (1m+ lines) where context retrieval is the bottleneck. free tier is generous enough for individual evaluation.; Hermes for technical operators and developers who want a server-deployed agent that builds institutional memory across runs and improves from experience. strong for sustained workflows: research synthesis, scheduled briefings, email triage, multi-agent orchestration, and any work where the agent should keep getting better at your specific job over weeks of use..
Is Amp or Hermes cheaper?
Amp's pricing: Free tier with meaningful usage allowance. Paid tiers $19–$49/month for individuals. Enterprise pricing for teams bundled with Sourcegraph Code Search. Token-based usage on top of subscription tiers. Hermes's pricing: Free and open-source under MIT. You pay only for model API tokens (200+ models accessible through its marketplace integration — Claude, GPT, Gemini, DeepSeek, Kimi, GLM, local models) plus your own hosting. Hosting on a $5-$20/month VPS handles individual use; bare-metal or homelab handles team use. Typical individual model spend lands at $20-$200/month depending on workflow intensity. Heavy multi-agent users with goal-driven loops on Claude Sonnet can push past $300/month — budget caps and per-agent quotas are configurable. The right "cheaper" pick depends on usage volume and what's included — see the pricing row in the table above.
What's Amp best for?
Engineering teams already paying for Sourcegraph Code Search who want to add an AI agent that reuses the existing codebase index. Strong for large enterprise codebases (1M+ lines) where context retrieval is the bottleneck. Free tier is generous enough for individual evaluation.
What's Hermes best for?
Technical operators and developers who want a server-deployed agent that builds institutional memory across runs and improves from experience. Strong for sustained workflows: research synthesis, scheduled briefings, email triage, multi-agent orchestration, and any work where the agent should keep getting better at your specific job over weeks of use.
Why compare Amp and Hermes if they're different categories?
Amp is a coding agent and Hermes is a open-source harness. The comparison still matters because builders evaluating one often consider the other for adjacent jobs. See the recommendation section above for how to think about the cross-category choice.
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