OmniRoute vs headroom
Two of the top projects in Gateways and observability, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
OmniRoute
LLM gateway routing one OpenAI-compatible endpoint across 372 providers
headroom
Compresses tool outputs, logs and history before they reach the LLM
Verdict
OmniRoute ranks higher, 96 to 92. Of the 4 score parts we measure for both, OmniRoute leads on 3, headroom on none and 1 is even.
- AdoptionOmniRoute leads by 1 point, 98 to 97: close to even.
- FreshnessEven at 100.
- MaintenanceOmniRoute leads by 2 points, 97 to 95: close to even.
- Agent-readyOmniRoute leads by 25 points, 70 to 45: a wide gap.
Pick OmniRoute if you are
Developers pooling free and paid LLM providers for coding tools
- Single /v1 endpoint covers 372 providers with automatic fallback to healthy targets
- Dashboard tracks free-tier pools and quota use at /dashboard/free-tiers
Pick headroom if you are
Developers cutting LLM input token costs in apps or coding agents
- Seeded, offline benchmark: 21–57% fewer input tokens across four agent scenarios
- One-command `headroom wrap` for many coding agents, reversible with `headroom unwrap`
| What we compare | OmniRoute | headroom |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 98, widely used | 97, widely used |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 95, healthy |
| Agent-ready | 70, partly | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 86.5k | 79.4k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | TypeScript | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
OmniRoute
OmniRoute is a TypeScript gateway that exposes one endpoint at localhost:20128/v1 and routes requests to 372 providers, including many free tiers, with automatic fallback between them. It accepts OpenAI, Claude, Gemini and Responses API formats, and a dashboard handles keys and free-tier quota tracking. Users bring their own provider accounts; the README's free-token figures are estimates, not a grant.
Who it is for: Developers pooling free and paid LLM providers for coding tools
Strengths
- Single /v1 endpoint covers 372 providers with automatic fallback to healthy targets
- Dashboard tracks free-tier pools and quota use at /dashboard/free-tiers
- Works with coding CLIs such as Claude Code, Codex, Cline and Cursor via one config
- Installable via npm, Docker or Electron; MIT licensed; includes MCP and A2A support
Weaknesses
- Free-token headline is an estimate; provider limits and terms change and can lower it
- Providers flagged tos: avoid are excluded from automatic routing by default
- Fresh install may have no eligible route until you connect a provider account
- Large surface (372 providers, 19 routing strategies) makes setup and review heavy
- no GPU
- Docker + Compose
- Needs Provider accounts or API keys, Node.js (npm) or Docker
- Models: OpenAI API, Claude API, Gemini API, Responses API
- port 20128
headroom
Headroom is a Python library, local proxy and CLI that shrinks agent context before it is sent to an LLM provider. It routes JSON, source code and text to separate compressors and keeps originals locally so the model can retrieve them. `headroom wrap` configures coding agents such as Claude Code, Codex, Aider and Cline to go through the proxy.
Who it is for: Developers cutting LLM input token costs in apps or coding agents
Strengths
- Seeded, offline benchmark: 21–57% fewer input tokens across four agent scenarios
- One-command `headroom wrap` for many coding agents, reversible with `headroom unwrap`
- Compression runs locally; reported 0.21 ms p50 on a 10K-token JSON result
- Originals cached locally and retrievable by the model via `headroom_retrieve`
Weaknesses
- Little savings on prose or already-dense output; gains depend on repetitive payloads
- Cursor and ZCode need manual base-URL setup; Cortex Code is library-only
- Third-party OpenAI-compatible providers need an extra flag or the key goes to OpenAI
- Output token reduction is off by default and reported as an estimate
- no GPU
- Docker + Compose
- Needs Python 3.13 (for uv tool install), Serena (installed by headroom wrap), Kompress-v2-base model from Hugging Face
- Models: OpenAI, Anthropic, Bedrock, Gemini, OpenAI-compatible providers
- port 8787
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