Show HN: We built open OpenRouter that turns usage into a better model

Experiential Labs released Experiential, an open-source gateway and router for AI agent workflows that unifies hosted, BYOK, and local models behind an OpenAI-compatible API, with features for controlling model access, spend limits, and optimizing production traffic into custom routers or fine-tuned models. The tool, installable via pip and run locally or as a hosted platform, collects OpenTelemetry traces to build and optimize routers, and supports fine-tuning open-source models via Tinker. It includes anonymous aggregate telemetry that can be disabled, and is designed to work with coding agents like Claude Code, Cursor, and Codex.

Experiential is an open source gateway and router for agent workflows: - Use hosted, BYOK, and local models through one OpenAI-compatible API. - Control which users and agents can use which models, for which use cases, and how much they can spend. - Turn production traffic into a custom router or model optimized for quality, speed, and cost. Start a local OpenAI-compatible gateway. On first run, the setup wizard uses the shared provider, model, and reasoning-effort selectors, persists every selected provider connection, then shows defaults for the public alias, identity, and $50.00 command budget before printing a one-time key: pip install experiential exp Choose a public alias such as opus-5 , capture the issued key, and send a request: export EXP GATEWAY KEY=... curl http://127.0.0.1:8000/v1/chat/completions \ -H "Authorization: Bearer $EXP GATEWAY KEY" \ -H 'Content-Type: application/json' \ -d '{"model":"opus-5","messages": {"role":"user","content":"Help me"} }' Prefer a managed gateway to running one locally? The hosted platform at platform.experientiallabs.ai https://platform.experientiallabs.ai serves the same OpenAI-compatible and Anthropic Messages API at https://api.experientiallabs.ai/v1 . See SETUP.md /experientiallabs/experiential/blob/main/SETUP.md for copy-paste prompts you hand to your coding agent Claude Code, Cursor, Codex, and similar ; the agent runs the setup for you. It collects four prompts: - Upload your LLM traces as telemetry: create an account instantly from your email, then pull or upload your existing LLM traces onto the platform as telemetry. - Connect your inference provider keys BYOK : create an account, then connect your own OpenAI, Anthropic, Gemini, Azure, Bedrock, Fireworks, or OpenRouter keys for free pass-through. - Start calling models on the gateway: make your first /v1 call with the OpenAI and Anthropic SDKs using an xpl key, and optionally repoint your existing coding agents. - Full onboarding: create an account instantly from your email, connect your keys, import your spend, then repoint every coding agent Claude Code, Cursor, Codex, Aider, and similar or Conductor at the gateway. Start the local gateway with exp or exp run ; the compiled native data plane serves every route on loopback. From Python, load a fitted project router as an official OpenAI client backed by its own private gateway: python import exp with exp.load router "my-project" as client: response = client.chat.completions.create model="my-project", messages= {"role": "user", "content": "hello"} , First, collect OpenTelemetry traces from your current agent. If you just want to try it out, grab the public terminal-tasks OTLP dataset https://huggingface.co/datasets/experiential-labs/wmo-terminal-tasks-traces : curl -L -o traces.otel.jsonl \ https://huggingface.co/datasets/experiential-labs/wmo-terminal-tasks-traces/resolve/540883e451dc13d34fb50fdd36b143cb0f1fb0db/traces.otel.jsonl Then build a project. The build command walks you through providers, models, and budget, and asks for your trace file: Build simulation from your agent traces and optimize a router against it exp build support-agent After collecting traces from your router, fine-tune an open source model you own using Tinker https://tinker.thinkingmachines.ai/ . exp optimize model support-agent Anonymous aggregate PostHog product telemetry is enabled by default. It never includes prompts, traces, actions, observations, paths, model names, credentials, or raw customer content. exp config telemetry status exp config telemetry disable exp config telemetry enable The preference is stored locally in .exp/settings.toml . uv sync --extra dev uv run ruff format --check . uv run ruff check . uv run ty check uv run pytest -q Repository and documentation conventions live in AGENTS.md /experientiallabs/experiential/blob/main/AGENTS.md .