modelchain
Cost-aware LLM router with streaming and tool calls
3 followers
Cost-aware LLM router with streaming and tool calls
3 followers
Drop-in, zero-dependency LLM router that routes prompts across OpenAI, Anthropic, Gemini and any HTTP endpoint by cost, latency and observed quality.
Free
Launch Team
Framer AI AgentsDesign and publish professional sites with AI
Promoted
I built takk/modelchain because picking the right LLM per request — and re-implementing streaming, tool calling and failover for every provider — kept leaking into every call site.
It sits between your app and any number of providers (OpenAI, Anthropic, Gemini, or any OpenAI-compatible HTTP endpoint). You declare a pool with cost and keys; it routes each request via 7 strategies (cost-first, quality-first, cost-then-quality, latency-first, weighted, round-robin, sequential-fallback), streams a normalised AsyncIterable, normalises tool calls across providers, and enforces hard budget ceilings.
The non-obvious part: it measures every response with pluggable scorers and feeds that score back into the next routing decision, so the pool adapts as providers ship new models — not a static rule table.
Proof: zero runtime dependencies, 182 tests across 12 suites passing under Vitest 4, 76% line coverage, 5.6 KB brotli core, published with SLSA provenance. A golden routing suite locks every strategy's decision as part of the SemVer contract.
Try the CLI proxy: npx takk/modelchain start --port 8788
Mental model is Prisma, not LangChain. Feedback and prior-art pointers welcome.