awerouter: Smart LLM Router
Route cheap/fast tasks to Flash, hard decisions to Pro.
Transparent same-protocol proxy that routes coding-agent requests by structural signals — no keyword guessing, no LLM classifier. Speaks Anthropic Messages, OpenAI Chat Completions, and OpenAI Responses.
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Transparent proxy that splits coding-agent traffic across providers by cost and capability. Same-protocol passthrough — no translation.
Install
pip install awerouter
Quick Start
# 1. Init config (creates ~/.config/awerouter/{providers,routing}.json)
awerouter init
# 2. Interactively add a profile (writes both files, references stay consistent)
awerouter add
# or edit by hand: providers.json for keys (${ENV_VAR}), routing.json for flash/pro
# 3. Start the daemon (profile name optional when only one exists)
awerouter serve [cc-router-1] # shorthand: awerouter cc-router-1
# 4. Point CC at it — the serve banner prints both lines below
export ANTHROPIC_BASE_URL=http://127.0.0.1:20128
# aweswitch profile env: ANTHROPIC_MODEL=auto, _HAIKU_=flash, _OPUS_=pro
Config
Two files in ~/.config/awerouter/ (override with AWEROUTER_CONFIG_DIR):
providers.json — endpoints + keys, grouped by wire protocol (redacted in config show):
{
"anthropic": {
"stepfun": { "base_url": "https://api.stepfun.com/step_plan", "auth": "${STEPFUN_AUTH_TOKEN}" },
"anthropic": { "base_url": "https://api.anthropic.com", "auth": "${ANTHROPIC_KEY}" }
},
"openai-chat": {
"stepfun": { "base_url": "https://api.stepfun.com/step_plan/v1", "auth": "${STEPFUN_AUTH_TOKEN}" }
},
"openai-responses": {
"openai": { "base_url": "https://api.openai.com/v1", "auth": "${OPENAI_API_KEY}" }
}
}
Three protocols are supported. base_url uses each native client's convention — copy it verbatim from your client config; awerouter appends the endpoint path the same way the native client would:
| Protocol id | base_url style |
Endpoint |
|---|---|---|
anthropic |
ANTHROPIC_BASE_URL (no /v1) |
base_url + /v1/messages |
openai-chat |
OPENAI_BASE_URL (includes version segment) |
base_url + /chat/completions |
openai-responses |
OPENAI_BASE_URL (includes version segment) |
base_url + /responses |
The same provider often uses a different path per protocol — GLM for instance: https://open.bigmodel.cn/api/coding/paas/v4 for chat completions but https://open.bigmodel.cn/api/v1 for responses. That's why each protocol group carries its own base_url.
The auth header is auto-detected from base_url: anthropic.com → x-api-key (bare token); everyone else → Authorization (auto-prefixes Bearer ). No auth_header field needed unless the heuristic is wrong.
routing.json — strategy, no secrets (safe to commit):
{
"settings": {
"backgroundModel": "flash",
"thinkModel": "pro"
},
"cc-router-1": {
"protocol": "anthropic",
"longContextThreshold": 8000,
"destinations": {
"flash": "stepfun,step-3.7-flash",
"pro": "anthropic,claude-opus-5"
}
}
}
settings is optional (defaults: flash/pro). It defines the model ids CC sends for background (Haiku) and think (Opus) tiers. The main loop uses auto — routed by difficulty by L3. Set these in your aweswitch profile: ANTHROPIC_DEFAULT_HAIKU_MODEL=flash, ANTHROPIC_MODEL=auto, ANTHROPIC_DEFAULT_OPUS_MODEL=pro.
Keys reference ${ENV_VAR} syntax. Missing env vars die with a clear message at startup.
Profile-based routing:
routing.jsongroups configs under profile ids (like aweswitch).awerouter serve <profile>starts one; with a single profile it auto-selects.protocolmaps the profile to a providers.json group and decides which endpoint it serves — the serve banner prints the matching client env (ANTHROPIC_BASE_URLfor Claude Code,OPENAI_BASE_URL/ Codexwire_apifor the openai protocols). Note: openai clients are single-model, so L2 tier labels effectively never fire for them — openai traffic routes by L1 + L3 with a flash default.
How It Routes
Three-layer first-match-wins pipeline, evaluated per request:
| Layer | Signal | Decision |
|---|---|---|
| L1 Capability | web_search tool in body |
pro (flash can't run it) |
| L2 Tier label | model == c1/flash or c1/think |
flash / pro respectively |
| L3 Difficulty | token count > threshold, or has image | pro; else flash |
CC's /model picker sets the tier model id (c1/flash / c1/pro / c1/think). awerouter reads it and routes accordingly — no keyword parsing, no LLM classifier.
Commands
awerouter init # create default config (= config init)
awerouter add # interactively add a profile (and new providers)
awerouter list # list profiles (name, protocol, flash, pro, threshold)
awerouter show [PROFILE] # show one profile or all config (redacted)
awerouter serve [PROFILE] [--port 20128] [--host 127.0.0.1]
awerouter <PROFILE> # shorthand for serve PROFILE
awerouter config path | show | edit | init
awerouter usage [--since 7d] [--profile NAME] # stats summary (default view)
awerouter usage stats [--clean]
awerouter usage tail [--lines 20]
awerouter usage calibrate
awerouter usage savings
All usage subcommands read the same request log; window options sit between usage and the subcommand (awerouter usage --since today savings).
usage stats aggregates the log per profile: label/destination/provider/model breakdowns with percentages, error and fallback counts, latency percentiles (first byte and total) per destination/provider/model, and estimated message tokens. --since accepts today, yesterday, 7d, or YYYY-MM-DD (local time); --profile restricts to one profile; --clean deletes the saved logs after a confirmation prompt. usage tail shows recent entries verbatim.
usage calibrate shows the message-token distribution of L3 traffic (the threshold-sensitive layer; messages only — system prompt and tools are excluded) and suggests candidate longContextThreshold values at p90/p95/p99. Run it after some real traffic, then edit routing.json.
usage savings is the token accounting view: how many message-input tokens each tier consumed and how many pro input tokens routing offloaded to flash vs a pro-only baseline. A cache-sensitivity section brackets the offload between "all cache reads" and "all full price" (Anthropic-style ~0.1x read / 1.25x write / 5-min TTL) and shows your switch cadence vs the TTL — a cache-warm pro-only baseline would have billed those tokens at cache-read prices. The output ends with ready-to-fill formulas using the measured token counts — substitute your providers' input prices (per 1M tokens) and read off the saved amount (output tokens, flash-side caching, and capability-mismatch turns are not modeled).
Troubleshooting
CC shows 502 status code (no body) right after launch — a shell proxy (Clash etc.) is hijacking loopback traffic. Requests to 127.0.0.1:20128 go into the proxy, whose 127.0.0.1 is itself, so nothing is listening and the proxy returns an empty 502. serve prints a warning when it detects this; fix it by exempting loopback in your shell config:
export no_proxy=127.0.0.1,localhost NO_PROXY=127.0.0.1,localhost
Then open a new terminal and relaunch CC.
Development
git clone https://github.com/mugpeng/awerouter
cd awerouter
pip install -e ".[dev]"
pytest
See docs/CONTRIBUTING.md for architecture notes, config semantics, and the release process.
Support
If awerouter saves you money, consider supporting it:
- ⭐ Star the repo — it helps others find it.
- ☕ Ko-fi — buy me a coffee.
- 💬 WeChat — scan the QR code below.
awerouter is free and open source. Sponsors keep it maintained — thank you.
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