Bay Run — find, prove, and serve the right task-specialist model
Agents don't need one giant model for everything — they need the right small specialistfor each narrow job (embeddings, reranking, classification, extraction, transcription),proven on their data, served instantly. Bay Run is that loop — OpenAI-compatible and MCP-native:
discover → eval → serve (over a catalog of 147K models, mirrored-first)
- Live:
https://bay-run-mvp-zfmlsu2yla-uc.a.run.app - Remote MCP:
https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp/(streamable-HTTP; add to any MCP client) - Public demo token (rate-limited, try instantly):
bayrun-demo-AS4XgfRmTHgNXRlpuP19zKeMxbcShyvP
⭐ Flagship showcase — the Semantic Intent-Router
flagship/ is the headline proof of the thesis. Every agent framework routes eachuser message to the right handler — usually with a $$ frontier-LLM call. The flagship does the samerouting with a 33M-param open embedder Bay Run picks + serves (thenlper/gte-small, proven onlabeled intents by a live bake-off): 95.8% routing accuracy on unseen messages, ~140 ms warm,~14× cheaper than a GPT-4o-mini intent call — every number captured from the live service.
git clone https://github.com/barneywohl/bay-run && cd bay-run/flagship
pip install -r requirements.txt && python router_demo.py
See flagship/README.md for the scorecard + cost table, andflagship/more-specialists.md for four more agent sub-tasks (RAGrerank, multilingual routing, dedup, semantic cache), each with a verified-servable tiny specialist.
Runnable demo — the whole loop in one command
demo/ is a self-contained killer demo: it routes support tickets with a 22M-param openembedder picked by a bake-off on labeled data — ~26× cheaper than a GPT-4o-mini classification baseline,at equal-or-lower latency, open weights, no lock-in. Every number is captured from the live service.
git clone https://github.com/barneywohl/bay-run && cd bay-run/demo
pip install -r requirements.txt
python demo.py # ships with the public demo token; runs discover → eval → serve → cost live
demo/README.md— the captured scorecard, latencies, and cost table.demo/mcp-quickstart.md— give your agent the tools in one command.demo/firecrawl-to-bay-run.md— scrape with Firecrawl, run the specialist here.demo/wrappers/— drop-in LangChain / LlamaIndex / OpenAI-Agents adapters.
30-second try
curl -s https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/v1/discover -H "authorization: Bearer bayrun-demo-AS4XgfRmTHgNXRlpuP19zKeMxbcShyvP" -H "content-type: application/json" \
-d '{"query":"multilingual sentence embeddings","kind":"embedding","limit":5}'
Point any OpenAI client at it
from openai import OpenAI
client = OpenAI(base_url="https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/v1", api_key="bayrun-demo-AS4XgfRmTHgNXRlpuP19zKeMxbcShyvP")
client.embeddings.create(model="BAAI/bge-small-en-v1.5", input=["hello"])
MCP (agent-callable)
Add the remote server https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp/ to your MCP client (Bearer auth). 9 tools:find_specialist_for_task (discover→eval→serve on your labeled data), request_specialist (serve-or-capture — returns a serve pointer if a specialist exists, else records your demand), route (runtime auto-router — no examples, picks a specialist per-request), discover_models, eval_models, embed, rerank, classify (guardrail/moderation/sentiment/intent, or zero-shot via candidate_labels + an NLI model), extract (HTML/text → schema-guided JSON).
Served from a content-addressed, quarantine-gated mirror. Neutral — it helps you pick themodel that wins on your data, not sell you one. Backend is closed; this repo is thepublic manifest + connector.
