Experiential v0.7.113 修复缓存估算与结算逻辑

Official release notes (https://github.com/experientiallabs/experiential/releases/tag/v0.7.113) Corrects the disconnect estimate's cache handling held by platform #2197: an estimated cache-read leg is capped at input minus observed cache writes and every observed cache leg (reads, writes, one-hour writes) is kept as reported even when the input total itself is the gateway's count, so the read-first settlement clamps never displace reported write liability or price away an unknown-TTL write (experientiallabs/experiential#1092). Python only; the native crate stays at 0.3.102. Evidence: 192 estimator, accounting and ledger tests including partial reads-only and writes-only provider reports. Repository README (current repository documentation, not release changes) # Experiential [](https://github.com/experientiallabs/experiential/actions/workflows/gateway-latency.yml?query=branch%3Amain) Experiential is an open source gateway and router for agent workflows: 1. Use hosted, BYOK, and local models through one OpenAI-compatible API. 2. Control which users and agents can use which models, for which use cases, and how much they can spend. 3. Turn production traffic into a custom router or model optimized for quality, speed, and cost.  🌐 Platform | 📚 Docs | Discord ## Getting Started 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: ```bash pip install experiential exp ``` Choose a public alias such as `opus-5`, capture the issued key, and send a request: ```bash 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"}]}' ``` ## Setup / get started with the hosted gateway 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](./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. ## Using the API 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"}], ) ``` ## Optimize from Traffic 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): ```bash 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: ```bash # 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/). ```bash exp optimize model support-agent ``` ## Telemetry Anonymous aggregate PostHog product telemetry is enabled by default. It never includes prompts, traces, actions, observations, paths, model names, credentials, or raw customer content. ```bash exp config telemetry status exp config telemetry disable exp config telemetry enable ``` The preference is stored locally in `.exp/settings.toml`. ## Development ```bash 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](./AGENTS.md).