kosha-discovery
MCP serverAI & modelsTells your agent which AI model to use and what it costs across many providers and local runtimes.
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About this server
Tells your agent which model to use and what it costs, across 46 providers and local runtimes.
Getting started
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From the project's README
As published by sriinnu/kosha-discovery in README.md.
Tells your agent — or your code — which model to use and what it costs.
kosha discovers models across 45 providers and local runtimes, finds your API keys wherever they already live (env vars, Claude CLI, Codex, gcloud ADC, AWS SSO), fills in pricing and context limits, and answers questions like the cheapest model with tool use and 128k context that I actually hold a key for. It ships as a TypeScript library, a CLI, an HTTP API, an OpenAI-compatible proxy with a spend ledger, and an MCP server.
It works with no API keys at all — discovery falls back to the public models.dev and
LiteLLM catalogs, so kosha list is useful on a fresh machine.
Install
npm install @sriinnu/kosha-discovery # library / server
npm install -g @sriinnu/kosha-discovery # global `kosha` CLI
Requires Node.js 22+.
Quick start
Library
import { createKosha } from "@sriinnu/kosha-discovery";
const kosha = await createKosha();
const models = kosha.models(); // ModelCard[] across every provider
const cheapest = kosha.cheapestModels({ role: "image" }); // ranked by price, with missingCredentials
const sonnet = kosha.model("sonnet"); // alias → canonical ID; undefined if unknown
console.log(sonnet?.pricing); // { inputPerMillion: 2, outputPerMillion: 10, cacheReadPerMillion: 0.2, ... }
CLI
kosha discover # query every provider; writes ~/.kosha/cache and the manifest
kosha list --provider anthropic # read from the local cache
kosha model sonnet # one model, alias-aware
kosha routes claude-opus-5 # every serving route for a model (direct, OpenRouter, Bedrock, …)
kosha cheapest --role embeddings # rank by price for a role
kosha doctor --ci # deprecations + provider health; non-zero exit for CI
kosha spend --since 2026-09-01 # roll up the proxy's spend ledger
kosha refresh # bypass the cache and re-discover
kosha serve --port 3000 # HTTP API + proxy; binds 127.0.0.1 (see Proxy below)
Every command takes --json. kosha --help lists the rest.
After each discovery, a stable v1 manifest lands at ~/.kosha/registry.json:
jq '.models[] | select(.pricing.inputPerMillion < 0.1) | .modelId' ~/.kosha/registry.json
Public snapshot
A weekly discovery run publishes a full snapshot — every provider, model, price and limit kosha can see without your keys — at a stable URL:
curl -sL https://github.com/sriinnu/kosha-discovery/releases/download/snapshot-latest/kosha-latest.json \
| jq '.modelCount, .providerCount'
It is a release asset rather than a file in the repository: at ~2.8 MB growing
with every provider added, committing it weekly would put roughly 150 MB of
already-stale data a year into a repo people are meant to clone. Dated
snapshot-YYYY-MM-DD pre-releases keep a short trail for diffing, pruned to the
two most recent, and an older one is only removed once a newer one exists.
HTTP API
GET /api/models?provider=&originProvider=&mode=&capability=
GET /api/models/:idOrAlias GET /api/models/:idOrAlias/routes
GET /api/models/cheapest?role=… GET /api/capabilities
GET /api/providers[/:id] GET /api/roles
GET /api/resolve/:alias GET /api/discovery-errors
GET /api/discovery[/delta|/watch|/cheapest|/binding] (stable v1 contract)
POST /api/refresh GET /health GET /metrics
GET /proxy/v1/models POST /proxy/v1/chat/completions
Parameters and response shapes: docs/api.md.
Proxy
kosha serve also exposes an OpenAI-compatible endpoint at /proxy/v1. Point any OpenAI SDK at it; the proxy resolves the model or alias, picks a provider you hold credentials for, injects the upstream key, forwards the request, and writes a row to the spend ledger.
kosha serve # start on :3000
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "http://localhost:3000/proxy/v1",
apiKey: "not-used", // kosha resolves credentials from env
});
// Use any canonical model ID or alias
const res = await client.chat.completions.create({
model: "sonnet",
messages: [{ role: "user", content: "hello" }],
});
// Let kosha pick the cheapest model you have a key for
const cheap = await client.chat.completions.create({
model: "kosha:cheapest",
messages: [{ role: "user", content: "hello" }],
});
// Cheapest model with tool_use and at least 128k context
const routed = await client.chat.completions.create({
model: "kosha:cheapest[tool_use,128k]",
messages: [{ role: "user", content: "hello" }],
});
kosha:cheapest filter syntax (comma-separated, combinable):
| Filter | Example | Meaning |
|---|---|---|
| capability | tool_use, vision | model must have this tag |
<N>k | 128k, 200k | minimum context window |
provider:<id> | provider:groq | pin to a specific provider |
kosha:fastest, kosha:reliable, and kosha:balanced take the same filters and rank on observed latency and circuit-breaker state instead of price.
Every response carries x-kosha-model, x-kosha-provider, x-kosha-requested, x-kosha-attempt-chain, and x-kosha-estimated-cost-usd; non-streaming responses add x-kosha-actual-cost-usd when the upstream returned a usage block.
What the proxy can forward:
| Upstream wire format | Providers | Support |
|---|---|---|
| OpenAI-compatible | OpenAI, Ollama, OpenRouter, Vercel, Groq, Together, Fireworks, DeepInfra, … | passthrough, streaming included |
| Anthropic Messages | Anthropic | translated: streaming, tools, image_url, response_format, reasoning_effort; audio input and non-function tools fail over to an OpenAI-compatible route for the same model |
| Cloud SDKs | Google, Bedrock, Vertex | discovery only, not proxied yet |
| Non-chat / other wire | TypeSafe, Thinking Machines | discovery only — TypeSafe's System One endpoint is not a chat API, and Tinker's Anthropic-wire path differs from Anthropic's own |
Defaults that matter before you expose it: the server binds 127.0.0.1. Pass --host 0.0.0.0 (or KOSHA_HOST) to listen on a network interface, and set KOSHA_PROXY_TOKEN so /proxy/* and POST /api/refresh require Authorization: Bearer <token> or x-kosha-token. KOSHA_MONTHLY_BUDGET_USD caps spend per calendar month. Reference: docs/api.md, docs/operations.md.
MCP server
kosha-mcp serves the registry over the Model Context Protocol on stdio, so an agent can call kosha_query_models, kosha_cheapest_model, kosha_ranked_routes, kosha_model_detail, kosha_model_routes, kosha_resolve_alias, kosha_provider_health, and kosha_context_strategy without an HTTP server.
claude mcp add kosha -- kosha-mcp
It is published to the MCP registry
as io.github.sriinnu/kosha-discovery, so clients that read the registry can
install it without a manual command. The manifest is server.json.
Every provider key is optional. With no credentials at all the server still answers from the public models.dev and LiteLLM catalogs plus a curated offline list, so it is useful on a fresh machine.
Tools and protocol details: docs/mcp.md.
Supported providers
45 providers. Each has a descriptor in src/provider-catalog.ts; most OpenAI-compatible
ones are driven from GENERIC_OPENAI_PROVIDERS in src/discovery/generic-openai.ts
rather than a hand-written class.
| Provider | Discovery | Credential sources |
|---|---|---|
| Anthropic | GET /v1/models (context, output cap, capabilities read from the API) | ANTHROPIC_API_KEY, Claude CLI, Codex CLI |
| OpenAI | GET /v1/models | OPENAI_API_KEY, GitHub Copilot tokens |
GET /v1beta/models | GOOGLE_API_KEY, GEMINI_API_KEY, Gemini CLI, gcloud | |
| AWS Bedrock | SDK → CLI → static list | AWS_ACCESS_KEY_ID, ~/.aws/credentials, SSO, IAM |
| Vertex AI | API + gcloud | GOOGLE_APPLICATION_CREDENTIALS, ADC |
| Ollama, llama.cpp, LM Studio, vLLM | local HTTP API | none |
| OpenRouter | API | OPENROUTER_API_KEY (optional; unauthenticated is rate-limited) |
| Vercel AI Gateway | GET /v1/models | AI_GATEWAY_API_KEY, VERCEL_OIDC_TOKEN (discovery works without; execution needs one) |
| NVIDIA, Together, Fireworks, Groq, Cerebras, Cohere, DeepInfra, Perplexity | OpenAI-compatible API | <PROVIDER>_API_KEY |
| DeepSeek, Mistral, Moonshot (Kimi), GLM (Zhipu), Z.AI, MiniMax | OpenAI-compatible API | <PROVIDER>_API_KEY |
| xAI (Grok) | GET /v1/models; Grok Imagine split into image / video | XAI_API_KEY |
| TypeSafe (System One / Jev) | GET /v1/models — returns judgment models, not chat | TYPESAFE_API_KEY, JEV_API_KEY |
| Thinking Machines (Inkling) | Anthropic-wire endpoint, no model list — public catalog only | TINKER_API_KEY |
| Alibaba Model Studio (Qwen), Volcengine Ark (Doubao), Inception (Mercury), AI21 (Jamba), Upstage (Solar), StepFun | OpenAI-compatible API | DASHSCOPE_API_KEY, ARK_API_KEY, INCEPTION_API_KEY, AI21_API_KEY, UPSTAGE_API_KEY, STEPFUN_API_KEY |
| Baseten, Nebius Token Factory, Novita AI, SiliconFlow, Hugging Face, Ollama Cloud | OpenAI-compatible API | <PROVIDER>_API_KEY, HF_TOKEN |
Regional pairs
Several providers run separate hosts for international and mainland-China traffic, with separate keys and separate price sheets — Qwen 2.5 72B is $1.40/M input internationally against $0.574/M in China. Merging them would make a model's price depend on which host answered last, so each region is its own provider:
| International | China | Differs in |
|---|---|---|
moonshot (api.moonshot.ai) | moonshot-cn (api.moonshot.cn) | host, key |
minimax (api.minimax.io) | minimax-cn (api.minimaxi.com) | host, key |
alibaba (dashscope-intl) | alibaba-cn (dashscope) | host, key, pricing |
siliconflow (.com) | siliconflow-cn (.cn) | host, key, pricing |
stepfun (api.stepfun.ai) | stepfun-cn (api.stepfun.com) | host, key, pricing |
zai (api.z.ai) | glm (open.bigmodel.cn) | host, key, pricing |
kosha routes <model> lists every region a model is served from, so you can
compare prices across them directly.
Without a key, providers fall back to the public models.dev + LiteLLM catalog, then to a curated static list, so kosha list works on a fresh machine. Exact env var names: docs/credentials.md.
Non-chat modes
Most models are chat, but mode also covers embedding, image, video,
audio, moderation, rerank, and judgment. A judgment model — TypeSafe's
System One family — answers a question with a typed value (a choice and its
probability distribution, a probability, or a score on described levels) instead
of generating text. It carries no chat capability on purpose: routing a prompt
to one would be a category error.
kosha cheapest --role judgment # rank judgment models by price
kosha model jev # mode: judgment, $0.042/M in, $0 out
How it works
- Discovery — one discoverer per provider runs concurrently (
Promise.allSettled); each returns normalizedModelCards. A failing provider is recorded indiscoveryErrors()and doesn't block the others. - Enrichment — pricing, context window, and output cap are filled from models.dev and LiteLLM where the provider API doesn't publish them;
pricingSourceon each card says which. - Resilience — a per-provider circuit breaker with exponential cooldown, plus stale-cache fallback, so a provider outage degrades to cached data rather than an error.
- Cache and manifest — results are cached under
~/.kosha/cache/(24 h TTL) and exported as a versioned snapshot at~/.kosha/registry.jsonfor other tools to read. - Proxy — resolves the requested model or
kosha:<strategy>[filters]selector against the registry, ranks candidate routes, forwards with failover, and records estimated and reconciled cost in~/.kosha/ledger-YYYY-MM.jsonl.
Details: docs/architecture.md, docs/resilience.md.
Development
pnpm install
pnpm run build # compile to dist/
pnpm run typecheck # tsc --noEmit
pnpm run lint # biome lint
pnpm test # vitest run
pnpm run check # lint + build + test
Project layout
src/
cli.ts # CLI entry point and arg parsing
cli-commands.ts # command implementations (+ cli-cmd-*.ts for the larger ones)
registry.ts # ModelRegistry public API
registry-runtime.ts # discovery orchestration, enrichment, cache, manifest export
registry-query.ts # model / role / capability queries
registry-selection.ts # cheapest candidates, binding hints
registry-routing.ts # cheapest / fastest / reliable / balanced ranking
discovery/ # one discoverer per provider + static and public-seed catalogs
enrichment/ # models.dev + LiteLLM pricing enrichment
credentials/ # credential resolution (env, CLI files, ADC, OAuth)
provider-catalog.ts # provider descriptors: base URLs, transport, env var names
aliases.ts # built-in short names → canonical model IDs
claude-generation.ts # which Claude generation accepts which API parameters
model-features.ts # tool dialect / structured-output inference per model
proxy.ts # OpenAI-compatible proxy: routing, failover, ledger
wire-anthropic.ts # OpenAI ↔ Anthropic request / response / SSE translation
cost.ts # spend ledger, budget gates, usage reconciliation
tally.ts # zero-dependency token usage + USD tally (also exported as ./tally)
server.ts # Hono HTTP API + operator token gate
mcp-server.ts # MCP stdio server
types.ts # shared types
Adding a provider
For an OpenAI-compatible provider — most of them — it is two table entries:
- Add a descriptor to
PROVIDER_CATALOGinsrc/provider-catalog.ts(id, base URL, transport, credential env vars). - Add a spec to
GENERIC_OPENAI_PROVIDERSinsrc/discovery/generic-openai.ts. It registers its own discoverer, resolves credentials from the descriptor'scredentialEnvVars, and falls back to the public catalog when there is no key. - Map the provider to its models.dev / LiteLLM slug in
src/discovery/modelsdev-seed.tsandlitellm-seed.tsso keyless discovery works. Check the slug — GLM is published aszhipuai, and a missing mapping means no keyless models at all. - Add a case to
test/discovery/generic-openai.test.tsand document the env vars indocs/credentials.md.
Write a discoverer class only when classification genuinely needs code — per-route pricing (OpenRouter), a non-OpenAI response envelope (TypeSafe), or origin remapping (Vercel):
- Create
src/discovery/<provider>.tsextendingBaseDiscovererorOpenAICompatibleDiscoverer;fetchJSONandmakeCardare provided. - Export it from
src/discovery/index.tsand add a factory entry toDISCOVERER_REGISTRY. - Add a resolver branch in
src/credentials/resolver.tsonly for a bespoke search order (CLI files, OAuth, ADC, SSO). A plain API key in env vars needs no branch — the descriptor covers it. - Add
test/discovery/<provider>.test.ts(mockfetch; seetypesafe.test.ts).
Docs
| Credentials | Env vars, CLI tools, and config files for every provider |
| CLI | Commands, flags, examples |
| HTTP API | Endpoints, parameters, response schemas |
| MCP server | Tools, protocol negotiation, client setup |
| Configuration | Aliases, routing, enrichment, programmatic config |
| Architecture | Discovery flow, module map, adding providers |
| Resilience | Circuit breakers, stale cache, health |
| Operations | Deployment sizing, metrics, spend ledger, recovery recipes |
| Security | Threat catalogue, runtime scanning, pre-commit hook |
| Discovery Plane v1 | Stable daemon contract (deltas, SSE watch, binding hints) |
Release
- Bump
versioninpackage.jsonand date the[Unreleased]section inCHANGELOG.md; merge that as a PR. - Tag and push:
git tag -s vX.Y.Z -m "vX.Y.Z" && git push origin vX.Y.Z
gh workflow run release-npm.yml -f tag=vX.Y.Z
The workflow checks that the tag matches package.json, runs lint / build / test, publishes to npm with provenance, and creates the GitHub Release. Publishing authenticates through npm trusted publishing (OIDC) when a trusted publisher is configured for this repo and workflow on npmjs.com, or through an NPM_TOKEN repository secret.
License
MIT
Signals
- GitHub stars
- 2
- Last commit
- Oct 2026
- Weekly_downloads
- 251 weekly_downloads
Advanced
- Delivery
- kosha-discovery MCP server → your ahel connector (mcp.ahel.ai) → your AI.
- Item type
- mcp-server
- Key
io-github-sriinnu-kosha-discovery- Source
- github.com/sriinnu/kosha-discovery
github.com/sriinnu/kosha-discovery