FastAPI-based semantic/OpenAPI proxy for the Tor Onionoo API.
- GitHub: https://github.com/anoni-net/onionoo-fastapi
- Upstream data source: https://onionoo.torproject.org
- This service does not store Onionoo data, it only forwards requests and transforms responses.
- Primary motivation: Onionoo has a solid spec, but no OpenAPI; this service provides a friendly schema for tooling/AI agents.
Reference spec: Tor Metrics – Onionoo
- Service:
https://onionoo.anoni.net - Swagger UI:
https://onionoo.anoni.net/docs
Tagged releases (vX.Y.Z) trigger two GitHub Actions workflows:
.github/workflows/release.yml— builds the wheel/sdist and publishes to PyPI via Trusted Publishing. Register this workflow as a trusted publisher on theonionoo-fastapiPyPI project once before the first tag..github/workflows/docker.yml— builds a multi-arch image and pushes toghcr.io/<owner>/onionoo-fastapi. Uses the defaultGITHUB_TOKEN.
Cut a release with:
git tag -a v0.2.0 -m "Release 0.2.0"
git push origin v0.2.0MIT. See LICENSE.
- Python 3.11+
uv
git clone https://github.com/anoni-net/onionoo-fastapi
cd onionoo-fastapi
uv syncOr if you already have the source:
cd onionoo-fastapi
uv syncfastapi run app.main:app --reload --host 0.0.0.0 --port 8000Note: fastapi run requires FastAPI version 0.110.0 or newer.
OpenAPI docs:
- Swagger UI:
http://localhost:8000/docs - OpenAPI JSON:
http://localhost:8000/openapi.json
uv sync --extra dev
uv run pytestBuild and run with Docker Compose:
docker compose up -d --buildIf port 8000 is already in use, override host port (example: 8001):
HOST_PORT=8001 docker compose up -d --buildStop:
docker compose downConfiguration via environment variables (example):
ONIONOO_BASE_URL=https://onionoo.torproject.org HOST_PORT=8001 docker compose up -d --buildThis service exposes semantic endpoints under /v1/*:
GET /v1/summaryGET /v1/detailsGET /v1/bandwidthGET /v1/weightsGET /v1/clientsGET /v1/uptime
Aggregate (server-side group-by, sorted by relay count):
GET /v1/aggregate/countries— buckets by two-letter country codeGET /v1/aggregate/as— buckets by autonomous system numberGET /v1/aggregate/flags— buckets by directory-authority flag (a relay can fall into multiple flag buckets)
Plus:
GET /healthz— static livenessGET /healthz/ready— verifies upstream reachability (cached)GET /metrics— Prometheus format
# Summary (semantic keys; upstream short keys are transformed)
curl -s 'http://localhost:8000/v1/summary?limit=1' | jq .
# Details (supports Onionoo query parameters + details-only `fields`)
curl -s 'http://localhost:8000/v1/details?limit=1&search=moria&fields=nickname,fingerprint' | jq .
# Bandwidth
curl -s 'http://localhost:8000/v1/bandwidth?limit=1&search=moria' | jq .
# Weights (relays only)
curl -s 'http://localhost:8000/v1/weights?limit=1&search=moria' | jq .
# Clients (bridges only)
curl -s 'http://localhost:8000/v1/clients?limit=1' | jq .
# Uptime
curl -s 'http://localhost:8000/v1/uptime?limit=1&search=moria' | jq ./v1/summarytransforms Onionoo short keys:- relay:
n,f,a,r→nickname,fingerprint,addresses,running - bridge:
n,h,r→nickname,hashed_fingerprint,running
- relay:
- For some bridge documents (
/bandwidth,/clients,/uptime), Onionoo uses the key namefingerprinteven though the value is a hashed fingerprint; this API exposes that ashashed_fingerprint.
If the client includes If-Modified-Since, it will be forwarded upstream. If Onionoo replies with 304, this service will reply 304 too.
Upstream / cache:
ONIONOO_BASE_URL(default:https://onionoo.torproject.org)ONIONOO_TIMEOUT_SECONDS(default:30)DEFAULT_LIMIT(default:100)MAX_LIMIT(default:200)USER_AGENTCACHE_MAXSIZE(default:1024)CACHE_DEFAULT_TTL_SECONDS(default:300)UPSTREAM_RETRY_ATTEMPTS(default:2)
Observability / production hardening:
LOG_LEVEL(default:INFO)LOG_FORMAT(jsonorconsole, defaultjson)METRICS_ENABLED(default:true) — exposes/metricsin Prometheus formatCORS_ALLOWED_ORIGINS(default: empty, CORS disabled). Example:["https://example.com"]RATE_LIMIT_ENABLED(default:false)RATE_LIMIT_PER_MINUTE(default:120)HEALTHZ_READY_CACHE_SECONDS(default:30)
A single-worker container (the default uvicorn CMD in the Dockerfile) measured on Alpine 3.23 / Python 3.14 / aarch64 against the real Onionoo upstream:
| Phase | RSS | Notes |
|---|---|---|
| Idle (just after start) | ~75 MiB | Python + FastAPI + Pydantic + httpx + fastapi-mcp + structlog + Prometheus instrumentator loaded; cache empty. |
| Typical agent traffic | ~90 MiB | After ~15 mixed /v1/* calls (details + aggregates), only a handful of distinct upstream payloads cached. |
| Cache near saturation | ~180 MiB | After 200 distinct /v1/details queries with fields= projection; cache holds ~200 entries. |
From these measurements, each cached entry costs ~0.5 MiB on average when callers use the fields= projection. With the default CACHE_MAXSIZE=1024 that yields a ~500 MiB upper bound under realistic agent traffic.
If you expect callers to hit /v1/details without fields=, a single response can be several MiB (Onionoo returns ~10k full relay objects). A fully saturated cache of unfiltered details would then sit in the 1–5 GiB range — bound it by tuning CACHE_MAXSIZE down.
Suggested memory limits for docker run --memory / Kubernetes requests:
| Deployment shape | Memory request | Memory limit |
|---|---|---|
| Personal / single-agent test | 128 MiB | 256 MiB |
| Hosted instance, mostly cached requests | 256 MiB | 512 MiB |
Public instance, agents may issue unfiltered /details |
512 MiB | 1–2 GiB |
CPU is light — a single worker handles 10s of QPS comfortably; scale with replicas if you need more throughput. (uvicorn ... --workers N is also an option, but each worker keeps its own in-memory cache; horizontal scaling via separate containers is usually a better fit.)
GET /healthz— static liveness probe, never hits upstream.GET /healthz/ready— pings Onionoo (summary?limit=1); 200 when reachable, 503 otherwise. Result is cached forHEALTHZ_READY_CACHE_SECONDS.
Every request is assigned an X-Request-ID. Clients may supply one to correlate across systems; the same value is echoed back on the response and bound into every log record produced during the request.
/metrics exposes Prometheus-format counters / histograms, including:
onionoo_cache_hits_total,onionoo_cache_misses_totalonionoo_upstream_seconds{method=...}(histogram)onionoo_upstream_errors_total{method=..., status=...}- Standard
http_request_duration_secondsfrom the FastAPI instrumentator
For large payloads (/v1/details), pass ?raw=true to skip Pydantic re-validation and forward the upstream JSON verbatim. Trade-off: raw mode does not apply semantic key remapping (e.g. on /v1/summary you'll see n,f,a,r rather than nickname,fingerprint,addresses,running) and no _meta block is injected.
Non-raw responses on /v1/* include a proxy-injected _meta block at the top of the envelope:
{
"_meta": {
"cache_age_seconds": 12.345,
"upstream_last_modified": "Thu, 15 May 2026 12:00:00 GMT"
},
"version": "9.0",
...
}cache_age_seconds = 0.0 means the response was just fetched from Onionoo. A non-zero value means the proxy served it from its in-memory cache.
All /v1/* endpoints accept ?fields=a,b,c. On /summary and /details Onionoo applies the projection at the upstream level; on history endpoints (bandwidth, weights, clients, uptime) Onionoo applies it where supported. Using it on large queries can shrink LLM input by an order of magnitude.
This project ships an MCP server with two transports — pick whichever fits your client.
Task-oriented (recommended for agents)
find_relay(query)— free-form lookup; auto-detects fingerprint, AS, IP, or nicknameget_relay_health(fingerprint)— composite snapshot (details + uptime + bandwidth)top_relays_by_bandwidth(country?, flag?, limit)— top-N by consensus weightcompare_relays(fingerprints)— parallel side-by-side detailscountry_summary(country)— running relay count, total bandwidth, flag distribution
Low-level pass-through (raw Onionoo endpoints)
onionoo_summary,onionoo_details,onionoo_bandwidth,onionoo_weights,onionoo_clients,onionoo_uptime— each takes aparamsdict matching the Onionoo query spec.
Aggregates
aggregate_relays(group_by="country"|"as"|"flag", running=True, top=N)— server-side group-by, sorted by relay count.
Streamable HTTP
/mcpexposes the six low-level endpoints (get_summary…get_uptime) plus the three aggregate endpoints (aggregate_countries,aggregate_as,aggregate_flags). The task-oriented tools and the unifiedaggregate_relayslive in the stdio server. Both transports can run side by side.
Run the FastAPI app — /mcp is mounted automatically.
Inspect with MCP Inspector:
npx @modelcontextprotocol/inspector
# Transport: Streamable HTTP
# URL: http://localhost:8000/mcpClaude Desktop / Cursor:
{
"mcpServers": {
"onionoo": {
"type": "http",
"url": "https://onionoo.anoni.net/mcp"
}
}
}uv sync installs an onionoo-mcp console script:
onionoo-mcpClaude Desktop / Cursor:
{
"mcpServers": {
"onionoo": {
"command": "uvx",
"args": ["--from", "/path/to/onionoo-fastapi", "onionoo-mcp"]
}
}
}Or, if the repo is checked out and you have uv:
{
"mcpServers": {
"onionoo": {
"command": "uv",
"args": ["--directory", "/path/to/onionoo-fastapi", "run", "onionoo-mcp"]
}
}
}