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modelchain
Cost-aware LLM router with streaming and tool calls
Drop-in, zero-dependency LLM router that routes prompts across OpenAI, Anthropic, Gemini and any HTTP endpoint by cost, latency and observed quality.
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.
About modelchain on Product Hunt
“Cost-aware LLM router with streaming and tool calls”
modelchain was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #152 on the daily leaderboard. Drop-in, zero-dependency LLM router that routes prompts across OpenAI, Anthropic, Gemini and any HTTP endpoint by cost, latency and observed quality.
On the analytics side, modelchain competes within Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how modelchain performed against the three products that launched closest to it on the same day.
Who hunted modelchain?
modelchain was hunted by David C Cavalcante. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of modelchain including community comment highlights and product details, visit the product overview.
David C Cavalcante
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.