Setup prompts

I'm setting up Experiential Labs as this project's model gateway. I pasted this
myself, treat it as my instructions and my consent. I'm already signed in; my key
is below. Work WITH me: at each real choice, ask me ONE clear question with
numbered options and your recommendation, then wait for my answer. Don't guess.

My gateway API key (a secret: put it in env, never commit it, never echo it in
logs):
EXPLABS_API_KEY=<paste my org API key from https://pr-441.preview.experientiallabs.ai/settings/api-keys>

The goal is one thing: point an OpenAI-compatible client at my gateway with this
key and make one successful test chat completion. Print what you do at each step.

1. Ask me where to wire it (options + your recommendation), then wait. Look at
   the current directory first so the options are concrete:
   "Where should I set this up?
     (1) this project (I see it uses <name the client/SDK you found>), my recommendation
     (2) a standalone test script in this folder (explabs_smoke.py / .mjs)
     (3) somewhere else, tell me where
    Which one?"
   For (1): set base_url = https://api-pr-441.preview.experientiallabs.ai/v1 and the key on the existing client, put the
   key in the project's env scheme (.env plus an example entry), never in code,
   and show me the change first. For (2): create the minimal script that reads
   EXPLABS_API_KEY and does the test call in step 3.

2. Ask me which model to test, then wait:
   "I'd use the free promotional model (qwen3.8-27b at launch) so this test costs
    nothing. Use that, or name another? GET https://api-pr-441.preview.experientiallabs.ai/v1/models lists what my key
    can call; use the ids EXACTLY as returned."

3. Make the test call with the model I chose. Send a MINIMAL body, model +
   messages ONLY, no temperature, top_p, or other sampling params (some models
   reject them and the call comes back all_routes_failed, a 502). One header:
   Authorization: Bearer $EXPLABS_API_KEY. This runs on the platform-funded
   lane, a fraction of a cent of my free credits, and proves serving and billing
   end to end. Do NOT use any of my own provider keys for this test.

4. Report back what you changed and the test call (the model, that it succeeded,
   and its cost). Then ask me which optional follow-ups I want, with your
   recommendation, and do only the ones I pick:
     (a) Connect my own provider keys (BYOK, Pro) so those calls bill my own accounts
     (b) Import my Claude Code / Codex usage history (metadata only)
     (c) Review my credits and spend
   BYOK and import are quick for me to do at https://pr-441.preview.experientiallabs.ai/settings; balances live at
   https://pr-441.preview.experientiallabs.ai/credits.

Read the contract if you build further: https://pr-441.preview.experientiallabs.ai/docs (human docs) and
https://pr-441.preview.experientiallabs.ai/llms.txt (machine-readable: honored and refused parameters, error codes,
streaming caveats). Follow it literally.

I pasted this into you myself, wire THIS coding agent up to my Experiential
Labs gateway, so my model calls route through https://api-pr-441.preview.experientiallabs.ai and show up in my usage.

Ground rules: never print my full key (first 8 characters at most), ask me
before you edit any config file or shell profile, and if you cannot do a step
(no file access, settings live in a GUI), print the exact manual steps for me
instead. Print what you're doing at each step.

1. Get my key. Use EXPLABS_API_KEY from my environment if it is set; otherwise
   ask me to paste one (I can mint it at https://pr-441.preview.experientiallabs.ai/settings/api-keys). It looks
   like xpl_ followed by 40 hex characters.

2. Prove the key works before touching any config:
   GET https://api-pr-441.preview.experientiallabs.ai/v1/models with header "Authorization: Bearer <key>" -> 200 and
   the model slugs I can call. Remember the list; I'll pick models from it.

3. Identify which agent you are and apply YOUR integration:

   - Claude Code:
     You cannot repoint yourself mid-session, and switching auth under a running
     session causes mixed-state errors. First prove the lane without touching any
     config, via one isolated subprocess run from this project (so my MCP servers
     and plugins load and their tool schemas ride the request. Tool schemas are
     where provider strictness bites, so a bare hello proves too little):
       ANTHROPIC_BASE_URL="https://api-pr-441.preview.experientiallabs.ai" ANTHROPIC_API_KEY="<my key>" \
         ANTHROPIC_MODEL="<slug I pick>" \
         claude -p "list your available tools, then read ./README.md and reply
         with its first heading"
     Pass only if that run completes with tools listed and a real tool use.
     Then offer me three placements and apply the one I pick:
     (a) append to my shell profile, or (b) print for one-off use:
       export ANTHROPIC_BASE_URL="https://api-pr-441.preview.experientiallabs.ai"        # no /v1 suffix
       export ANTHROPIC_API_KEY="<my key>"
       export ANTHROPIC_MODEL="<slug I pick>"
     or (c) a named profile beside my normal Claude Code (ask me for the name):
     its own config dir ~/.claude-<name> with these three in settings.json "env"
     plus "model": "<slug I pick>", and a shell alias
       alias claude-<name>='CLAUDE_CONFIG_DIR=~/.claude-<name> claude'
     A fresh config dir starts with the first-run onboarding, so skip it: copy
     .claude.json from an existing profile dir that uses a gateway key (theme,
     trusted projects), else write {"hasCompletedOnboarding": true}; then set
     customApiKeyResponses to {"approved": ["<last 20 chars of my key>"],
     "rejected": []} so the custom-API-key prompt never appears. Never copy
     .credentials.json or a claude.ai sign-in into it.
     Use ANTHROPIC_API_KEY, never ANTHROPIC_AUTH_TOKEN: an existing claude.ai
     sign-in can outrank AUTH_TOKEN and send the gateway an OAuth token instead.
     Then tell me to start a NEW session from a shell with those set (for (c),
     a new terminal or after re-sourcing my shell profile). Name the
     model by its gateway slug exactly as GET https://api-pr-441.preview.experientiallabs.ai/v1/models lists it (the DOT
     form, e.g. claude-fable-5.1; Anthropic's dashed ids like claude-fable-5-1
     answer 403 model_not_granted). If the slug is not an Anthropic model, also
     export CLAUDE_CODE_MAX_CONTEXT_TOKENS=<max_input_tokens from
     GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>, rounded down to the nearest 50k>. Claude Code
     assumes 200k for names it does not know and compacts early. NEVER use
     context_window for that pin: on most models (every maker but Anthropic and
     Gemini) it is a total window the provider counts the model's maximum output
     inside, so a session pinned to it never compacts before the provider refuses
     it and /compact then fails the same way. max_input_tokens already accounts for this per maker, so it is
     always the right pin. Warn me plainly: if a session ever shows
     "Not logged in · Please run /login", do NOT run /login (it starts Anthropic
     OAuth that overrides the gateway). Restart with --resume, and if it
     persists, my <config dir>/.claude.json remembers a "No" to the custom-API-key
     prompt under customApiKeyResponses.rejected: answer Yes or move the entry to
     approved. Images work on image-capable routes and are rejected by name on
     text-only ones; extended thinking passes through on all-Anthropic routes
     and is translated to reasoning effort elsewhere.

   - Conductor:
     Conductor launches Claude Code with the environment it captured from my
     login shell (interactive login shell, cached per workspace) merged with its
     own settings, and passes the per-chat picker choice as --model, so the
     model is routed by remapping picker aliases with ANTHROPIC_DEFAULT_*_MODEL
     variables (ANTHROPIC_MODEL does not apply to picker selections). Offer me
     two placements and apply the one I pick:
     (a) per-repo, recommended: merge into this repo's .claude/settings.json
     "env" block (committed; Claude Code applies it in every workspace, and it
     overrides anything Conductor captured from my shell), one
     ANTHROPIC_DEFAULT_<ALIAS>_MODEL entry per picker alias I use:
       { "env": { "ANTHROPIC_BASE_URL": "https://api-pr-441.preview.experientiallabs.ai",
                  "ANTHROPIC_DEFAULT_OPUS_MODEL": "<slug I pick>" } }
     then put ONLY the key machine-local in .conductor/settings.local.toml
     under [environment_variables] (make sure that file is
     git-ignored before writing my key into it):
       ANTHROPIC_API_KEY = "<my key>"
     (b) app-wide: print the same variables for me to add by hand in
     Conductor -> Settings -> Environment, and remind me to check that
     Settings -> Harnesses -> Claude Code uses my API key, not a claude.ai
     CLI login.
     Warn me either way: settings reach newly created workspaces, not running
     ones, and if my shell profile exports a direct Anthropic
     ANTHROPIC_API_KEY, Conductor's capture hands it to every agent;
     placement (a) overrides it; otherwise I should remove the export.
     The key rides ANTHROPIC_API_KEY, never ANTHROPIC_AUTH_TOKEN: an existing
     claude.ai sign-in can outrank AUTH_TOKEN, and a set API key also stops
     Claude Code from trying to authenticate with Anthropic directly.

   - OpenAI Codex CLI:
     First prove the lane in a throwaway home so my real ~/.codex (config,
     sessions, auth) stays untouched, and test with my REAL tool surface: you
     send every mcp_servers/plugin tool schema on each request, and tool schemas
     are where provider strictness bites, so a bare hello proves too little.
     Copy ~/.codex into a fresh "$(mktemp -d)", append ONLY the
     [model_providers.explabs] table below to that copy's config.toml, then run
       CODEX_HOME=<that dir> codex exec -m "<slug I pick>" \
         -c model_provider=explabs -c model_reasoning_effort=max \
         "list your available tools, then read ./README.md and reply with its
         first heading"
     Pass only if it completes with my MCP/plugin tools listed and a real tool
     use. Then add the whole block to ~/.codex/config.toml (create it if
     missing, show me the diff before writing) and tell me to start a new Codex
     session:
       model = "<slug I pick>"
       model_provider = "explabs"
       model_reasoning_effort = "max"   # reasoning models; "max" is the top tier
       [model_providers.explabs]
       name = "Experiential Labs"
       base_url = "https://api-pr-441.preview.experientiallabs.ai/v1"
       env_key = "EXPLABS_API_KEY"
       wire_api = "responses"
     Leave requires_openai_auth unset, and make sure EXPLABS_API_KEY is exported
     where I launch you (export KEY=..., a plain KEY=... line is invisible to
     child processes like me).

   - OpenCode:
     Write the provider block into this project's opencode.json (or
     ~/.config/opencode/opencode.json if I prefer global, ask):
       {"provider": {"explabs": {"npm": "@ai-sdk/openai-compatible",
        "name": "Experiential Labs",
        "options": {"baseURL": "https://api-pr-441.preview.experientiallabs.ai/v1", "apiKey": "{env:EXPLABS_API_KEY}"},
        "models": {"<slug>": {"name": "<slug>"}}}}
     Set each model's "name" to its slug exactly. OpenCode shows that name in its
     status bar, so never put prices, discounts or promotion notes in it: they go
     stale, and promotions are applied at billing, not by this config.
     Fill limit.context/limit.output and cost ({"input": $/1M, "output": $/1M},
     i.e. *_nano_usd_per_million / 1000000000 of the providers entry whose id is
     the position-0 default_waterfall model_provider_id) for each slug from
     GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> so my context window and spend display are right.

   - Hermes Agent (Nous Research):
     First prove the lane in a throwaway data dir so my real ~/.hermes (config,
     sessions, memories) stays untouched: create "$(mktemp -d)", write ONLY the
     config below plus a .env line EXPLABS_API_KEY=<my key> (chmod 600) into it,
     then run
       HERMES_HOME=<that dir> hermes -z "list your available tools, then read
       ./README.md and reply with its first heading"
     Pass only if that run completes with tools listed and a real tool
     round-trip. Then merge the same block into ~/.hermes/config.yaml (show me
     the diff before writing) and put the key line in ~/.hermes/.env:
       providers:
         explabs:
           base_url: "https://api-pr-441.preview.experientiallabs.ai/v1"
           key_env: "EXPLABS_API_KEY"
           api_mode: chat_completions
       model:
         provider: "custom:explabs"
         default: "<slug I pick>"
     Any catalog slug works as the default, and /model custom:explabs:<slug>
     switches mid-session. Warn me plainly: Hermes' session-title side call
     sends a temperature value, so on models that pin their sampling the title
     falls back to a default; the session itself is unaffected.

   - Pi:
     First prove the lane in a throwaway home so my real ~/.pi (auth, sessions,
     model store) stays untouched: pi resolves its agent dir from HOME, so create
     "$(mktemp -d)" and copy my real ~/.pi into it (extensions, skills and
     settings ride the test; tool schemas are where provider strictness bites,
     so a defaults-only run proves too little; if ~/.pi does not exist yet, just
     create <that dir>/.pi/agent). Then merge the block below into that copy's
     .pi/agent/models.json and run
       HOME=<that dir> EXPLABS_API_KEY=<my key> pi -p --provider explabs \
         --model "<slug I pick>" "list your available tools, then read
         ./README.md and reply with its first heading"
     Pass only if that run completes with tools listed and a real tool
     round-trip, then DELETE the throwaway home ("rm -rf <that dir>"). The copy
     carries my auth and session state and must not outlive the proof. Then
     merge the same provider into ~/.pi/agent/models.json
     (create it as {"providers": {}} if missing; show me the diff before
     writing):
       { "providers": { "explabs": {
           "baseUrl": "https://api-pr-441.preview.experientiallabs.ai/v1",
           "api": "openai-completions",
           "apiKey": "$EXPLABS_API_KEY",
           "models": [ { "id": "<slug I pick>", "name": "<slug I pick>",
             "reasoning": true, "input": ["text", "image"],
             "contextWindow": <from GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>>,
             "maxTokens": <from the same> } ] } } }
     The apiKey stays the literal string "$EXPLABS_API_KEY"; pi interpolates it
     from my environment, so make sure the variable is exported where I launch
     pi and never write the raw key into the file. Set reasoning/input to the
     slug's actual catalog capabilities. Any catalog slug works; I pick models
     with --model at launch or /model in-session (models.json reloads each time
     /model opens, no restart).

   - Cline:
     Your settings live in the VS Code UI, so print these for me to set by hand:
     API Provider "OpenAI Compatible"; Base URL https://api-pr-441.preview.experientiallabs.ai/v1; API Key = my key (no
     Bearer prefix); Model ID = a slug from step 2; and per-model context window /
     max output tokens from https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>.

   - VS Code (Copilot Chat):
     Copilot's bring-your-own-model support is VS Code core (1.122+); chat and
     agent mode need no Copilot plan. Find my VS Code user dir (macOS
     ~/Library/Application Support/Code/User, Linux ~/.config/Code/User, Windows
     %APPDATA%\Code\User) and merge this entry into chatLanguageModels.json
     there (create the file as a JSON array if missing), one models[] item per
     slug I pick:
       { "name": "Experiential Labs", "vendor": "customendpoint",
         "apiType": "chat-completions",
         "models": [ { "id": "<slug I pick>", "name": "<slug I pick>",
           "url": "https://api-pr-441.preview.experientiallabs.ai/v1/chat/completions",
           "toolCalling": true, "vision": true,
           "modelOptions": { "temperature": null, "top_p": null } } ] }
     Set toolCalling and vision to each slug's ACTUAL capabilities from
     GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> (tool support and image input). Declaring a
     capability the model lacks makes Copilot send tool calls or images the
     route rejects.
     The null modelOptions are required, not decoration: null deletes the field,
     and Copilot otherwise sends temperature 0.1 and top_p 1 on every request,
     which models that pin their sampling reject with a 400 naming the
     parameter. You cannot enter my key (it lives in VS Code's encrypted secret
     storage), so print the manual step: in the Chat model picker open Manage
     Models, right-click the Experiential Labs model, choose to enter an API
     key, and paste my key (<my key>). VS Code stores it encrypted and writes
     the ${input:chat.lm.secret.*} references into the entry itself; never
     hand-write a raw key into the file.

   - Cursor:
     Your gateway settings live in the Cursor Settings UI, so print these for me
     to set by hand. First tell me the prerequisite: a paid Cursor plan (the Free
     plan serves only Auto and refuses named models, which custom models are).
     Then: in Cursor Settings -> Models -> API Keys, put my key in
     "OpenAI API Key" (no Bearer prefix), enable "Override OpenAI Base URL" and
     set it to https://api-pr-441.preview.experientiallabs.ai/v1. Add each slug I pick as a custom model
     name in the models list; custom models ride the override in OpenAI format,
     so any catalog slug works. Warn me plainly: Cursor relays requests through
     its own servers, Tab autocomplete stays on Cursor's models, and if a chat
     answers 400 invalid_parameter naming temperature or top_p, that model pins
     its sampling (the Claude 5 family pins temperature to 1). Pick a
     different slug.

   - Any other OpenAI-compatible tool:
     Export OPENAI_BASE_URL="https://api-pr-441.preview.experientiallabs.ai/v1" and OPENAI_API_KEY="<my key>" wherever I
     launch you, and name models by slug. If your own config wants the values
     instead, it needs the same three: base URL https://api-pr-441.preview.experientiallabs.ai/v1, my key, and a slug.

4. Verify end to end. Make one tiny completion with the surface you configured
   ("reply with the single word: ok", small max output), via your own next
   model call if you now route through the gateway, otherwise via curl with my
   key. Then tell me it landed and that I can watch every call at
   https://pr-441.preview.experientiallabs.ai/telemetry.

5. Recap exactly what you changed (files and values, key shown as xpl_ prefix
   only) so I can undo it later.

Set up per-identity API keys for my Experiential Labs organization so
spend is bucketed per identity (for example "coding" vs "product") in usage.

Provisioning key: xpl_<paste-your-provisioning-key-here>
(I replace the placeholder above before sending. It is a PROVISIONING key:
it can manage this org's keys, identities, and budgets. Never print it back
to me, never write it into any file, and do not use it for inference. The
API does not stop you from doing those; these are my rules.)

Base URLs: API https://api-pr-441.preview.experientiallabs.ai (management under /api, inference under /v1),
dashboard https://pr-441.preview.experientiallabs.ai. Every call below sends
  Authorization: Bearer <the provisioning key>
plus Content-Type: application/json on writes. On a non-2xx read the JSON
"error" field; a body with "detail" instead is a request-shape failure and
detail[0].loc[-1] names the offending field. Then apply the Fallbacks.
What this key can call: GET /api/whoami; GET, POST /api/orgs/{org_id}/identities; PATCH /api/orgs/{org_id}/identities/{identity_id} (display_name and description only); GET, POST /api/v1/keys; GET, PATCH, DELETE /api/v1/keys/{hash}; GET, PUT /api/orgs/{org_id}/budgets; DELETE /api/orgs/{org_id}/budgets/{budget_id} (identity- and key-scope budgets this key created); GET /api/orgs/{org_id}/usage/by-identity.
Disabling an identity, model grants, org-wide budgets, the default identity's
budget, and any budget an admin set are the admin's, on https://pr-441.preview.experientiallabs.ai/api-keys; if a
step needs one of those, ask me instead.

1. Discover the organization (reads only).
   GET https://api-pr-441.preview.experientiallabs.ai/api/whoami -> {org_id, org_slug, org_name}. Keep org_id; every
   /api/orgs/{org_id}/... call below uses it. Then
   GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/{org_id}/identities -> {identities: [{identity_id,
   display_name, active, is_default, ...}]}. The is_default row is the org's
   own identity: leave it alone, mint no keys under it, cap nothing on it.

2. Intake, BEFORE any change. Do not send a single POST, PUT, PATCH, or
   DELETE until I have answered these and said yes to the plan. Ask me, in
   my words, skipping any question my message already answers:
   - What identities do I want? Identities are spend buckets, usually one per
     team, product, or agent. Ask: "Do you have more than one product?
     Which teams or agents will call the API?" Offer these as a starting
     point, and take my renames, drops, and additions:
       coding   - IDE and terminal coding agents
       product  - the product's own inference traffic
       research - experiments, evals, notebooks
   - Does each identity need its own inference key, and which tool or
     service will hold it (Claude Code, Codex, a server, ...)? One key per
     identity is the default; say so and let me change it.
   - Do I want a spend budget per identity? If yes: how much (USD) and for
     what period, monthly recurring or one calendar month, on the identity
     or on its key. If I have no number, propose none: no budget means no cap.
   Then show me the plan as ONE table: identity_id, display name, purpose,
   key name and the tool it goes to (or "no key"), budget (amount + period,
   or "none"). Wait for my explicit "yes". Anything else is an edit: apply it
   and show the table again. The first write happens only after that yes.

3. Create each identity (reuse one that exists).
   POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/{org_id}/identities
     {"display_name": "Coding", "identity_id": "coding", "description": "<one line>"}
   201 -> created. 409 (code identity_id_unavailable) -> the id already
   exists: if it is in MY list from step 1, reuse that identity; otherwise
   (ids are unique platform-wide, and only need to be unique to my tooling)
   retry ONCE with details.suggested_id from that response. identity_id
   starts with a letter, continues with lowercase letters or digits, groups
   joined by ".", "-" or "_", and never starts with "org-". If step 1 listed
   the identity with active=false, do not use it: an admin disabled it on
   purpose and only the dashboard re-enables it. Tell me and pick another.

4. Mint the inference keys the plan names (one per identity by default).
   POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys
     {"name": "<identity>-<tool>", "identity_id": "<identity_id>"}
   200 -> {data: {hash, name, identity_id, ...}, key: "xpl_..."}. The plaintext
   appears exactly once; hold it only until step 6. Never set "provisioning":
   true on these keys (the API would allow it; I do not). Check
   data.identity_id matches. An optional DAILY USD cap: "limit": 5 in the body.

5. Budgets, only where the plan has one, in nano-USD ($1 = 1000000000).
   "period": "*" recurs every month; "period": "YYYY-MM" caps that one
   calendar month only. 0 is a HARD $0 cap, never "no cap"; remove one with
   DELETE https://api-pr-441.preview.experientiallabs.ai/api/orgs/{org_id}/budgets/{budget_id}. Replace or delete only
   budgets this key created (an admin's row is 403). Never the default identity's.
   PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/{org_id}/budgets
     {"period": "*", "scope_kind": "identity", "identity_id": "coding",
      "limit_nano_usd": 50000000000}          # $50 every month for the identity
     {"period": "*", "scope_kind": "key", "api_key_id": "<data.hash>",
      "limit_nano_usd": 20000000000}          # $20 every month for one key
     {"period": "2026-10", "scope_kind": "identity", "identity_id": "coding",
      "limit_nano_usd": 5000000000}           # $5 for October 2026 only
   Only those two scope kinds are this key's to set. Read back with
   the calendar month, never "*": GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/{org_id}/budgets?period=YYYY-MM

6. Hand me one table: identity -> key name -> where the key goes. Print each
   new key exactly once, in that table, and nowhere else. Never commit a key;
   prefer the OS keychain or a .env file that is gitignored. Per tool:
     Claude Code:   export ANTHROPIC_BASE_URL="https://api-pr-441.preview.experientiallabs.ai"      (no /v1 suffix)
                    export ANTHROPIC_API_KEY="<key>"
                    export ANTHROPIC_MODEL="<slug from GET https://api-pr-441.preview.experientiallabs.ai/v1/models>"
     Codex, Cline, Cursor, the OpenAI SDK, any OpenAI-compatible tool:
                    export OPENAI_BASE_URL="https://api-pr-441.preview.experientiallabs.ai/v1"
                    export OPENAI_API_KEY="<key>"
     OpenCode:      a provider in opencode.json with
                    "npm": "@ai-sdk/openai-compatible" and
                    "options": {"baseURL": "https://api-pr-441.preview.experientiallabs.ai/v1", "apiKey": "{env:OPENAI_API_KEY}"}
     A server:      the same OPENAI_* pair in its secret store. Per-tool
                    steps: https://pr-441.preview.experientiallabs.ai/docs/coding-agents.

7. Verify every key with one request and show me where the spend lands.
   GET https://api-pr-441.preview.experientiallabs.ai/v1/models (bearer required) for a slug, then
   POST https://api-pr-441.preview.experientiallabs.ai/v1/chat/completions with Authorization: Bearer <that key>
     {"model": "<slug>", "max_tokens": 5,
      "messages": [{"role": "user", "content": "ping"}]}
   Then, with the provisioning key,
   GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/{org_id}/usage/by-identity?window=30d and confirm each
   identity row lists its key with a request_count (only keys that carried
   traffic appear; a deleted key's traffic is the identity_id null row; keys
   minted without a group fold under the Default row). https://pr-441.preview.experientiallabs.ai/api-keys shows
   per-key spend.

Fallbacks:
- 401 on a route in the list above: the key is wrong or revoked; ask me to
  check the paste. 401 on a route NOT in that list is the edge refusing it to
  every key, not a key problem: hand that step to me.
- 403 "A provisioning key is required": my key is an inference key. Stop and
  tell me to mint a provisioning key as an org admin on https://pr-441.preview.experientiallabs.ai/api-keys
  (Create key -> Configuration -> check Provisioning key -> Create
  provisioning key). Any other 403 names what
  this key may not do: hand that step to me.
- 404 on an identity or budget path: that id does not exist in this org;
  re-list and check the id. 404 on /api/orgs/{org_id} itself: org_id is not
  this key's org; re-read it from https://api-pr-441.preview.experientiallabs.ai/api/whoami.
- 409 on an identity: in my step-1 list -> reuse it; otherwise retry once
  with details.suggested_id. 409 on a key mint: that identity is disabled;
  tell me and use another one. Never try to re-enable it.
- 422 with "error": the message names the rule (identity_id shape, scope
  kinds, period). 422 with "detail": detail[0].loc[-1] names the field (a
  missing display_name, a non-numeric limit, a budgets read without period).
  A NEGATIVE key limit is a 400 with "error" instead; nothing is minted.
- 429, or 503 "gateway is updating": wait Retry-After seconds (30 if absent)
  and retry that call once. A key mint is not idempotent: list
  GET https://api-pr-441.preview.experientiallabs.ai/api/v1/keys before minting again so no identity gets two keys.
- 500 internal_error "cannot serve this request natively" on the verify
  request: no serviceable lane for that model here. Try one other slug from
  GET https://api-pr-441.preview.experientiallabs.ai/v1/models; if that fails the same way, stop and report it.
- Never print the provisioning key, store a key in the repo, or paste keys
  where I did not ask for them.

I'm replacing my current model provider or gateway with Experiential Labs as
this project's model gateway. I pasted this myself: treat it as my instructions
and my consent. Work WITH me: at each real choice, ask me ONE clear question
with numbered options and your recommendation, then wait. Don't guess. Print
what you do at each step, and never print my full API key (first 8 characters
at most). Read the full machine-readable contract first: https://pr-441.preview.experientiallabs.ai/llms.txt (base
URLs, every route, honored and refused parameters, cost fields, error codes).
Follow it literally.

1. Find the current integration. Search this project for the provider's base
   URL, its key variable, its SDK/client construction, hardcoded model ids,
   provider-specific request fields (provider preferences, transforms,
   fallback model lists, referer/title headers), and any code that reads cost
   or usage from it. Show me the list of files and call sites before changing
   anything.

2. Get my Experiential key. Ask me: "Do you have an xpl_ key already?
     (1) yes, I'll paste it
     (2) no, create my account"
   For (2): ask me for my email (never scavenge one from my files or git
   config), show me https://pr-441.preview.experientiallabs.ai/terms and https://pr-441.preview.experientiallabs.ai/privacy and wait for my
   agreement, then
   POST https://pr-441.preview.experientiallabs.ai/api/signup/instant   (no auth)
   Header: Content-Type: application/json
   Body: {"email": "<MY_EMAIL>", "agree": true}
   On 200 capture api_key. On 409 account_exists tell me to sign in at
   https://pr-441.preview.experientiallabs.ai/signin for a key and stop. On any other error show me the response's
   `error` text verbatim and stop.
   Store the key as EXPLABS_API_KEY in my env scheme (.env plus an example
   entry), never in code.

3. Switch the base URL. The gateway keeps the path shapes my current
   integration already uses:
     base URL:  https://api-pr-441.preview.experientiallabs.ai/api/v1
     auth:      Authorization: Bearer $EXPLABS_API_KEY   (the ONLY header needed)
   POST /chat/completions, /responses, /messages (Anthropic Messages API) and
   GET /models work unchanged. Plain OpenAI or Anthropic SDKs can also use the
   bare base https://api-pr-441.preview.experientiallabs.ai/v1. Remove the provider-specific headers and request
   fields from step 1: the gateway does provider routing (fallback, caching)
   itself and unknown parameters may be rejected. Send model + messages and
   only the parameters I actually rely on.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free; this does not
   spend credits) lists the slugs I can call. For each model id from step 1,
   propose the matching slug (e.g. "claude-fable-5.1") and ask me to confirm
   the mapping before replacing. Use slugs EXACTLY as returned.

5. Move cost and usage reads over.
   - Every chat/responses reply carries usage.cost (USD, what I was charged)
     and usage.is_byok; on a stream it rides the final usage chunk. No flag
     needed. Exception: a request sent with Idempotency-Key carries no
     usage.cost at all (original or replay), so read the field as optional.
   - One request: read the x-request-id response header and call
     GET https://api-pr-441.preview.experientiallabs.ai/api/v1/generation?id=<that id>.
   - Balance: GET https://api-pr-441.preview.experientiallabs.ai/api/v1/credits. Recent activity:
     GET https://api-pr-441.preview.experientiallabs.ai/api/v1/activity.
   - Billing feed: GET https://api-pr-441.preview.experientiallabs.ai/api/v1/usage is the settled per-request export
     (cost_usd, estimated_cost_usd, real_cost_usd, tokens, attribution_label,
     api_key_id), newest first inside a lookback window (24h|7d|30d); its
     next_cursor pages OLDER within one call and is not a resume checkpoint.
     Each run scans from the top and dedupes on the row id.
   - If I rebill my own customers, pass their stable id as safety_identifier
     (alias: user) on every request; it comes back as attribution_label.
   Replace my old provider's cost/usage code with these; show me the diff
   first.

6. Key management (only if my code creates or rotates keys programmatically).
   https://api-pr-441.preview.experientiallabs.ai/api/v1/keys (GET/POST/PATCH/DELETE) requires a PROVISIONING key: an
   xpl_ key minted with "provisioning": true. The first one is created from
   the dashboard at https://pr-441.preview.experientiallabs.ai/api-keys (org admin, "Provisioning key" option).
   Tell me to mint it and paste it as a separate server-side secret; never use
   it for inference traffic.

7. Verify. Make ONE minimal test call (model + messages only, no sampling
   parameters) with a free promotional model from the /models list, or one I
   name. Report the model, that it succeeded, and its usage.cost. If it fails
   with a credits or activation error, tell me to finish activation at https://pr-441.preview.experientiallabs.ai
   and stop; do not retry with another provider. Then run the project's
   existing tests.

8. Report back: every file changed, the model-id mapping, what happened to the
   old provider key (tell me to revoke it at the old provider; don't delete it
   yourself), and the test call result. Human docs if you need more:
   https://pr-441.preview.experientiallabs.ai/docs/integrate.

I'm integrating Experiential Labs into this product as its model gateway: my
product calls models through it and meters or rebills my own end-users. I
pasted this myself: treat it as my instructions and my consent. Work WITH me:
at each real choice, ask me ONE clear question with numbered options and your
recommendation, then wait. Don't guess. Print what you do at each step, and
never print my full API key (first 8 characters at most). Read the full
machine-readable contract first: https://pr-441.preview.experientiallabs.ai/llms.txt (base URLs, every route,
honored and refused parameters, cost fields, error codes). Follow it literally.

1. Get my Experiential key. Ask me: "Do you have an xpl_ key already?
     (1) yes, I'll paste it
     (2) no, create my account"
   For (2): ask me for my email (never scavenge one from my files or git
   config), show me https://pr-441.preview.experientiallabs.ai/terms and https://pr-441.preview.experientiallabs.ai/privacy and wait for my
   agreement, then
   POST https://pr-441.preview.experientiallabs.ai/api/signup/instant   (no auth)
   Header: Content-Type: application/json
   Body: {"email": "<MY_EMAIL>", "agree": true}
   On 200 capture api_key. On 409 account_exists tell me to sign in at
   https://pr-441.preview.experientiallabs.ai/signin for a key and stop. On any other error show me the response's
   `error` text verbatim and stop.
   Store the key as EXPLABS_API_KEY in my env scheme (.env plus an example
   entry), never in code.

2. Wire the client. Ask me where (options + your recommendation, based on what
   you see in this project), then wait. Configure an OpenAI-compatible or
   Anthropic client with:
     base URL:  https://api-pr-441.preview.experientiallabs.ai/api/v1   (inference, model discovery, cost, and
                account routes under one base; plain OpenAI / Anthropic SDKs
                may also use https://api-pr-441.preview.experientiallabs.ai/v1)
     auth:      Authorization: Bearer $EXPLABS_API_KEY   (the ONLY header needed)
   POST /chat/completions, /responses, or /messages (Anthropic Messages API).
   Name models by slug EXACTLY as GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models returns them
   (free; this does not spend credits). Send model + messages and only the
   parameters I actually rely on; unknown parameters may be rejected.

3. Attribute every request to my end-customer. Pass their stable id as
   safety_identifier (alias: user) on every request. It comes back as
   attribution_label on the usage export, so I can group cost and tokens by
   my own customer id with ONE key. Ask me what my customer id looks like and
   wire it through from where my code knows the customer.

4. Read cost.
   - Inline: every chat/responses reply carries usage.cost (USD, what I was
     charged) and usage.is_byok; on a stream it rides the final usage chunk.
     No flag needed, but a request sent with Idempotency-Key carries no
     usage.cost at all (original or replay), so read it as optional. Show it
     in my product where it helps; do not treat it as the billing record.
   - One request: read the x-request-id response header and call
     GET https://api-pr-441.preview.experientiallabs.ai/api/v1/generation?id=<that id>.
   - Balance: GET https://api-pr-441.preview.experientiallabs.ai/api/v1/credits (data.total_credits - data.total_usage).

5. Build the billing sync on the settled export. Ask me which billing system I
   use (Metronome, Orb, Stripe metered billing, my own tables, or none yet),
   then write a scheduled job that:
   - reads GET https://api-pr-441.preview.experientiallabs.ai/api/v1/usage?limit=1000 (settled rows, newest first
     inside a lookback window: window=24h|7d|30d, default 7d; filterable by
     attribution_label, model, api_key_id, status). next_cursor
     ({cursor_ts, cursor_id, cursor_after}, passed back as query params)
     pages OLDER within this one call and is NOT a checkpoint to resume from
     next run: each run starts at the top, follows next_cursor until a whole
     page is older than the last successful run's start time minus a grace
     period covering my longest request, and dedupes on the row id. Schedule
     it at least daily; rows older than 30 days are no longer exportable.
   - lands each row in a local outbox table keyed on the row id (insert, on
     conflict do nothing) in one local transaction, then in a separate
     delivery step pushes unsent rows to the billing system and marks them
     sent (an external HTTP write cannot share my database transaction; the
     outbox makes a crash between the two safe to replay),
   - sends one usage event per row: customer from attribution_label,
     transaction_id / idempotency key = the row id, timestamp = created_at,
     quantity = the cost column I choose (below) plus token counts if I meter
     on tokens; the margin is a rate in the billing system, not in this job.
   Cost columns: cost_usd is the platform credits charged; estimated_cost_usd
   is the attributed value of a BYOK row (served on my own provider key) at
   catalog list rates, an estimate and never the provider's invoice;
   real_cost_usd is the two added together. Ask me whether to bill
   real_cost_usd (accepting the BYOK estimate) or cost_usd plus tokens on
   BYOK rows. pricing_known false means unpriced (cost 0), not free.

6. Key-per-customer (optional; ask me). If I want each customer isolated to
   their own key with independent revocation and a daily USD cap, that needs
   a PROVISIONING key: an xpl_ key minted with "provisioning": true. The
   first one comes from the dashboard at https://pr-441.preview.experientiallabs.ai/api-keys (org admin,
   "Provisioning key" option); keep it server-side and never use it for
   inference. With it: POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {name, limit, provisioning}
   returns the plaintext key exactly once; GET/PATCH/DELETE
   https://api-pr-441.preview.experientiallabs.ai/api/v1/keys/{hash} list, update (disabled:true revokes), and revoke.
   A normal inference key gets 403 on every /keys route.

7. Verify. Make ONE minimal test call (model + messages only) with a free
   promotional model from the /models list, or one I name, carrying a test
   safety_identifier. Report the model, that it succeeded, and its usage.cost;
   then confirm the row shows up on GET https://api-pr-441.preview.experientiallabs.ai/api/v1/usage with that
   attribution_label. If the call fails with a credits or activation error,
   tell me to finish activation at https://pr-441.preview.experientiallabs.ai and stop. Then run the project's
   existing tests.

8. Report back: every file changed, where the key lives, how customer
   attribution flows, and the billing job's schedule and cursor storage.
   Human docs if you need more: https://pr-441.preview.experientiallabs.ai/docs/integrate, https://pr-441.preview.experientiallabs.ai/docs/cost-api,
   and https://pr-441.preview.experientiallabs.ai/docs/account-api.

I'm moving this project off OpenRouter and onto the Experiential Labs gateway.
I pasted this myself: treat it as my instructions and my consent. Work through
it end to end; stop and ask me ONE numbered question only where this prompt
says to. Print what you change at each step. Never print my full API key
(first 8 characters at most). My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).

1. Find every place this project talks to OpenRouter. Search for:
   - the base URL https://openrouter.ai/api/v1 (openrouter.ai anywhere), env
     vars OPENROUTER_API_KEY / OPENROUTER_BASE_URL, and sk-or-v1- keys in
     .env*, docker-compose, Makefiles, CI config, and secret stores,
   - clients pointed at it: openai or Anthropic SDKs (TS / Python / Go),
     ruby-openai (uri_base:), Go net/http, fetch / curl / any HTTP client,
     Vercel AI SDK @openrouter/ai-sdk-provider, LangChain, LiteLLM openrouter/ ids,
   - coding-agent configs: opencode.json provider blocks, Claude Code
     ANTHROPIC_BASE_URL in .claude/settings*.json or shell exports, Cline /
     Continue / Cursor "OpenAI Compatible" settings, Codex config.toml,
   - hardcoded vendor/model ids (openai/gpt-6-astra, openrouter/auto, ...),
     OpenRouter-only fields (provider, transforms, route, models, plugins,
     usage), HTTP-Referer / X-Title, previous_response_id, and reads of
     /generation, /credits, /auth/key, /models/user, /activity, usage.cost.
   List the files and call sites before changing anything.

2. Substitute the base URL and the key. The gateway keeps OpenRouter's paths:
     https://openrouter.ai/api/v1  ->  https://api-pr-441.preview.experientiallabs.ai/api/v1
     Authorization: Bearer $EXPLABS_API_KEY   (the ONLY header needed)
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code; point every OpenAI-compatible client at
   the new base (a plain OpenAI SDK may also use https://api-pr-441.preview.experientiallabs.ai/v1) and
   REPLACE URL variables such as OPENROUTER_BASE_URL with it. An Anthropic SDK
   appends /v1/messages itself: its base is the bare origin https://api-pr-441.preview.experientiallabs.ai
   (never .../v1, which doubles to /v1/v1/messages). Do NOT
   delete OPENROUTER_API_KEY or the OpenRouter key from .env, secret stores or
   CI: stop reading it, leave it in place; I revoke it at openrouter.ai/keys.
   A key COMMITTED in a config file (ANTHROPIC_AUTH_TOKEN in a checked-in
   .claude/settings.json) is already leaked: remove it there, never send it on.
   Coding agents: Claude Code uses ANTHROPIC_BASE_URL="https://api-pr-441.preview.experientiallabs.ai"
   (no /v1); the key rides ANTHROPIC_API_KEY (never ANTHROPIC_AUTH_TOKEN) via
   a shell export, the gitignored .claude/settings.local.json, or an
   apiKeyHelper reading .env, never the committed .claude/settings.json; remap
   EVERY model variable (ANTHROPIC_MODEL, ANTHROPIC_SMALL_FAST_MODEL,
   ANTHROPIC_DEFAULT_*_MODEL) to slugs or a background role keeps calling an
   expensive model. OpenCode, Cline, Continue, Cursor and Codex take the
   OpenAI base https://api-pr-441.preview.experientiallabs.ai/v1.

3. Map model ids. Call GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my
   key (free, spends no credits). EVERY entry carries canonical_slug, the
   plain slug the gateway serves; ids are usually vendor/slug (vendor/slug:free
   for a free lane), sometimes a bare slug (gpt-6-astra, grok-4); ~vendor/x-latest
   ids are rolling aliases, for projects that used OpenRouter's :latest ids.
   For each OpenRouter id from step 1: strip any :nitro, :floor, :online,
   :thinking, :extended, :beta or :exacto suffix (those variants do not exist
   here; only :free does), then match canonical_slug == the part after the
   vendor's slash (openai/gpt-6-astra -> gpt-6-astra, anthropic/claude-sonnet-5
   -> claude-sonnet-5, deepseek/deepseek-v4-flash -> deepseek-v4-flash).
   No match: look for the same model under a slightly different spelling and
   use ONE obvious candidate, noted in the report. openrouter/auto and every
   id still unmapped: ask me one question listing each with your best
   candidates from the list, then wait. Never guess a model I did not pick.
   Use canonical_slug EXACTLY as returned; a vendor/slug id works verbatim
   ONLY when it is in the list (x-ai/grok-4 for the bare grok-4 answers 403
   model_not_granted: that means "use the canonical_slug", not an entitlement
   problem). LiteLLM: model "openai/<slug>" (replace the "openrouter/" prefix)
   with api_base https://api-pr-441.preview.experientiallabs.ai/api/v1 and api_key from the new var.

4. Keep the request shapes (OpenAI Chat Completions and Responses bodies, SSE
   streaming, tool calling, json_schema, response shapes). Strip EVERY
   OpenRouter-only field BEFORE the first call: each one left in is either
   silently ignored or its own 400 unsupported_parameter naming it in "param".
   - usage: {"include": true} (a guaranteed 400 today); for a stream send
     stream_options: {"include_usage": true} instead (non-streams carry usage).
   - provider (order, allow_fallbacks, ignore, quantizations, sort, ...):
     accepted and silently ignored (not disclosed); the gateway routes and
     fails over itself. The one honored key is "provider": {"zdr": true}, a
     per-request zero-data-retention demand; keep exactly that if I used it.
   - transforms, route, models (fallback list: use its first model), plugins.
   - HTTP-Referer / X-Title headers: harmless, not shown; attribute via safety_identifier.
   Vercel AI SDK: replace @openrouter/ai-sdk-provider with @ai-sdk/openai
   (createOpenAI({ baseURL, apiKey })) or @ai-sdk/openai-compatible.
   "reasoning": {"effort"} is translated to reasoning_effort (rewriting it is optional).
   Unsupported efforts may only move down if policy permits and the request is admissible; else 400.
   "none" means off and can be refused; "minimal" still enables reasoning.
   On non-reasoning routes, effort may drop only when policy permits and the request is admissible.
   Translated, dropped or adjusted fields are usually listed in the reply's
   x-experiential-ignored-parameters JSON body field (not a header): informational.
   Anthropic-shaped traffic through OpenRouter (an Anthropic SDK, Claude Code)
   moves to the native Messages API, POST https://api-pr-441.preview.experientiallabs.ai/api/v1/messages
   (SDK base: the bare origin, step 2), same key; no Idempotency-Key there.
   Responses API previous_response_id: continuation resolves ONLY while my
   org's capture_prompt_content is on and within the retention window; with
   capture off the second turn 400s previous_response_not_found. If the
   project chains turns that way, read
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/telemetry-settings (org_id: /api/whoami);
   if off, ask me ONE question: turn it on (PUT the same route,
   {"capture_prompt_content": true}; it stores my prompts with Experiential)
   or rewrite the chain to resend the full conversation each turn.

5. Move cost reads. Every chat / responses reply carries usage.cost (USD, what
   I was charged) and usage.is_byok; on a stream it rides the final usage
   chunk (step 4's stream_options). A request sent with Idempotency-Key
   carries no usage.cost at all, so read the field as optional. Same key on
   all of these, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows (api_key_id, cost) for billing
     GET /credits   balance      GET /key   key info (OpenRouter's /auth/key)
     GET /activity  per-day rollup (not per request)
   OpenRouter's /models/user has no equivalent: use /models. /usage is paginated
   (50 rows + next_cursor) and, like /activity, ORG-wide: filter ?api_key_id=<my
   key's id, on every row> and match my x-request-id, or use /generation?id=.

6. My own provider keys (BYOK). If step 1 found provider keys I routed through
   OpenRouter (OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI, XAI, DEEPSEEK, AZURE,
   AWS/Bedrock, Fireworks, ...), ask whether to connect them with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections
   List existing accounts FIRST. For EACH new account, ask me for a unique
   setup_alias (1-63 lowercase letters, digits, hyphens or underscores), unused
   across all providers in my org; multiple keys need distinct account names.
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         Content-Type: application/json
         {"setup_alias": "<unique-account-name>", "secret": "<that provider key>"}
   Never overwrite an existing account. On 409 account_exists choose another
   name and retry POST, never bypass it with PUT. PUT is only for a deliberate
   rotation I request, with that saved account's exact setup_alias and config.
   Provider: openai, anthropic, gemini, xai, deepseek, or any other whose
   credential is ONE string; never my OpenRouter key: I am leaving OpenRouter.
   azure_openai, bedrock, fireworks, vertex and modal need structured config
   or a token pair: connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections instead.
   Use INFERENCE keys, never admin keys, as secret; OpenAI/Anthropic admin keys
   belong only in optional spend_secret. If spend_error is present,
   the serving key WAS saved: report partial success, never repeat POST; retry
   only the SAME returned connection.setup_alias with PUT, retaining its serving
   secret and config. Read check.status; if not "valid", tell me
   check.status_detail.remediation and stop. A 402 pro_required means a FIRST connection needs Pro
   (https://pr-441.preview.experientiallabs.ai/credits); an org already holding one keeps connecting.
   Saving keys adds no arbitrary models: let me select each account on the
   chosen catalog model's waterfall with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the CHEAPEST mapped slug: a reasoning model can cost cents even for a
   smoke, and an empty pricing object in the listing means unknown: read
   GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>, and if that 404s (an alias)
   price the model it resolves to (its canonical_slug) or skip it and smoke a
   priced slug. Stream if the project streams (add stream_options:
   {"include_usage": true} so the final chunk carries usage.cost); if the
   project chains previous_response_id, verify a TWO-turn continuation, not a
   single call. Report the model, that it succeeded, and its usage.cost, or
   read the cost from GET /api/v1/generation?id=<x-request-id>, which works
   either way; then confirm the request's row on GET /api/v1/usage by that
   x-request-id (I can also see it at https://pr-441.preview.experientiallabs.ai/logs). On a credits
   or activation error, tell me to finish activation at https://pr-441.preview.experientiallabs.ai
   and stop; do not retry against OpenRouter. Then run the project's tests.

8. Report back: every file changed; the model-id mapping and the ids I still
   need to decide; the OpenRouter-only fields removed; where EXPLABS_API_KEY
   lives; that the OpenRouter key stays in place for me to revoke; the
   verification result. Full contract: https://pr-441.preview.experientiallabs.ai/llms.txt; human
   docs: https://pr-441.preview.experientiallabs.ai/docs/integrate.

Help me assess and then migrate this project's Cloudflare AI Gateway traffic
onto the Experiential Labs gateway. Start with a read-only inventory and a
proposed mapping. This prompt is NOT blanket consent: ask for my explicit
approval before any file or policy writes, credential storage or rotation,
paid probes or other spend, traffic cutover, privacy/retention changes, or
secret revocation. Explain each proposed change and its rollback first.
My Experiential key is: xpl_...
If it is still "xpl_...", ask me for a key or direct me to
https://pr-441.preview.experientiallabs.ai/api-keys. Do not assume an inference key has admin authority.
Never print secrets, put them in committed files, or send an Experiential key
to Cloudflare or an upstream provider. Redact secret values in every report.

1. Inventory before editing. Find Cloudflare AI Gateway URLs (including
   gateway.ai.cloudflare.com, provider-specific paths, /compat and custom-*
   providers), SDKs, fetch/curl clients, Workers bindings, env variables, CI
   and secret-store references. List call sites, endpoints and model ids.
   Identify Chat Completions, Responses, Messages, streaming, tool calls,
   structured output, continuation, batches and any other used API surface.
   Record Cloudflare headers such as cf-aig-authorization, cf-aig-metadata,
   cf-aig-cache-ttl, cf-aig-skip-cache, cf-aig-custom-cost and cf-aig-log-level,
   plus all other cf-aig-* headers, retry/fallback settings and timeouts.
   Separate Cloudflare gateway/admin tokens from direct OpenAI/Anthropic or
   other provider inference credentials. A Cloudflare token is not BYOK for
   those providers. Inspect references without exposing secret values.

2. Ask for the actual Cloudflare rule export and customer subscription rules.
   Record dynamic-routing conditions, rule order, rate/spend limits, metadata
   filters/split dimensions and provider/model overrides. Identify the trusted
   customer id and the server-side subscription source. Cloudflare custom
   metadata labels are NOT authentication or authorization. Neither request
   metadata.plan, user nor safety_identifier may select a customer's plan.
   The application server must map its authenticated customer to an Experiential
   identity; all that identity's keys, including rotated keys, share policy.
   Inventory each tier's allowed models, Basic/Frontier (or other) model groups,
   exact memberships, separate allowance amounts, currency and cost basis.
   These are my authored groups, not universal model classes or platform
   Free/Pro subscriptions. Never turn illustrative amounts into defaults.
   Ask about reset timezone and window (fixed, rolling, billing-anniversary or
   UTC calendar-month), carryover, current-period usage and pending requests,
   carry-in at cutover, upgrades/downgrades and over-limit behavior. A zero
   allowance means hard zero, not unlimited. Also inventory the shared total
   across all groups and the exact action on group exhaustion. An absent/null
   aggregate_limit_nano_usd means no shared cap; zero denies. on_exhausted
   defaults to deny; next_model requires explicit policy and a certified
   server-authored model chain, never an arbitrary client retry. Aggregate
   exhaustion and forbidden models still stop. Unknown plans must block mapping,
   never become unassigned identities. Do not silently reset usage.

3. Read THIS deployment's live contract and catalog before proposing APIs:
     https://pr-441.preview.experientiallabs.ai/llms.txt
     https://pr-441.preview.experientiallabs.ai/docs/integrate
     https://pr-441.preview.experientiallabs.ai/docs/account-api
     https://pr-441.preview.experientiallabs.ai/docs/cost-api
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami
     GET https://api-pr-441.preview.experientiallabs.ai/v1/models
     GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models
   Use the catalog's canonical_slug and verified alias resolution to map every
   model, including provider-prefixed ids and custom models. Do not simply strip
   a prefix, assume a model exists, or replace one without asking me. Catalog
   discovery is not proof of this customer's access or successful serving.
   Build a requirement table: existing behavior, proposed mapping, live-doc
   evidence, test needed and blocker. Discover exact native model-group,
   customer-plan, assignment and allowance API routes from live documentation;
   do not invent routes, fields, or claim these controls are already deployed.
   Require native identity-bound access and separate per-group allowances,
   not application-side checks dressed up as equivalent gateway enforcement.
   Confirm admin/delegated assignment authority separately from provisioning
   keys and inference keys. Customers must not change plans, mint escape keys,
   or read another identity's usage; caller-supplied filters are not isolation.
   Verify allowed-model access across public models, aliases, retries/fallback
   and every used serving surface; unsupported lanes must refuse plan keys.
   Verify shared counters across keys, settled usage plus pending reservations,
   pricing revisions, plan-change effective times and preserved spent amounts.
   Read whoami's identity_id, customer_plan_identity_id and is_provisioning;
   restricted keys may self-read it, but that does not grant management rights.
   The implemented period contract supports UTC calendar-month or an explicit
   authoritative source interval with source_period_id and timezone-aware start/end.
   Explicit periods expire without automatic renewal; omitted budget_period on
   a plan change preserves it. Rolling windows are unsupported. Never rename,
   overlap or backdate a used source period to refill it. Treat unsupported
   reset/carryover rules as blockers, not approximations. Discover the documented
   allowance-cutover contract too, without assuming THIS deployment has it.
   If documented carry-in is unavailable, ask about a deliberate next-period cutover;
   never silently grant a fresh full allowance.

4. Map provider and cost behavior, not just a base URL. Existing Account API
   provisioning and Cost API settled exports are useful but do not by themselves
   prove subscription-policy parity. Keep wallet charges separate from customer
   allowance attribution. BYOK must not debit platform credits; attributed BYOK
   inference value uses a frozen rate schedule and is an estimate, NOT a provider
   invoice. Inventory input/output, cache-read/write, reasoning, long-context
   and custom rates, billable failures, missing usage and pending exposure.
   Unknown prices or missing usage must not turn a monetary allowance into free
   requests. Verify the allowance API reads the counters enforcement uses.
   Subscription events and invoicing stay in my application. For external
   billing, poll the documented settled Cost API export with pagination,
   durable checkpoints and deduplication; reconcile request ids, late settlement
   and retries. Do not invent webhooks or bill from a daily rollup or wallet
   balance. Treat inline usage.cost as optional; do not confuse it with a BYOK
   allowance estimate. Verify reporting scopes with two customer identities.
   List existing provider accounts before proposing BYOK. Ask before saving
   anything; use a distinct setup_alias for each new account and create-only
   POST from the live contract, never PUT over an existing account. Rotation
   needs separate approval. Respect the live plan entitlement for new BYOK
   connections; saving a provider key does not add arbitrary model support.

5. Custom providers need their own proof. Cloudflare custom providers can use
   arbitrary HTTP paths and native bodies; Experiential's supported adapters
   and model-bound custom endpoints are NOT an arbitrary HTTP proxy. Inventory
   the exact URL, wire protocol, auth headers, model ids, usage format and rate
   units. Use the documented Vault-backed connection/custom-endpoint flow and
   its validation, not a new proxy or a made-up Cloudflare provider connection.
   Custom endpoint rates are not inferred from public provider prices. Confirm
   the live rate_card contract, full schedule and frozen revision. Without an
   authored card, custom pricing is unknown, not free. New custom accounts need
   an explicit unique setup_alias and create-only POST. For key or rate changes,
   create a new custom account and deliberately select Use on the model; do not
   rotate it through the generic provider PUT or probe at the official origin.
   For Tinfoil (TinFoil), verify the endpoint and SDK's enclave-attestation and
   privacy requirements. OpenAI compatibility does NOT prove attestation survives
   an intermediary. If the required guarantee cannot be verified, stop that move.
   Keep Modal separate from the Cloudflare transfer. Determine whether it uses
   Modal proxy authentication, an application bearer, or another supported scheme,
   and whether it bills tokens or time/GPU usage. Do not claim token-cost parity
   for unsupported billing units. Name any adapter, auth, usage or pricing gap
   as a blocker and scope it separately; never silently reroute these services.

6. Preserve privacy and runtime semantics. Inventory prompt/response logging,
   retention, zero-data-retention/no-training needs, cache keys/TTL/bypass,
   cache hits and their accounting, retries, fallback order and timeout budgets.
   Cloudflare response caching is not the same as provider prompt caching.
   Map each cf-aig-* header to a documented control or report it unsupported;
   never forward Cloudflare auth to the new host or silently drop a required rule.
   Check continuation requirements such as previous_response_id against the live
   capture_prompt_content contract. Ask before enabling storage; offer resending
   the full conversation where supported instead. Never weaken privacy to make
   a smoke pass. Check retry/idempotency behavior per API surface, including
   ambiguous disconnects; do not assume every retry is free or safe to replay.

7. Present the mapping and blockers, then wait for approval of specific edits.
   For supported OpenAI clients the base is https://api-pr-441.preview.experientiallabs.ai/v1 with
   Authorization: Bearer $EXPLABS_API_KEY. An Anthropic SDK appends /v1/messages
   itself: use the bare origin https://api-pr-441.preview.experientiallabs.ai, not a doubled /v1 base.
   Store keys only in the approved local env/secret scheme, never source control.
   Preserve unrelated Cloudflare DNS, Workers, Turnstile and other services.
   Keep old untracked .env secrets, CI secrets and Cloudflare/provider tokens
   in place for rollback until deliberate revocation is approved. A committed
   secret is exposed: flag it and ask for removal/rotation, never echo it.
   Propose a reversible configuration diff and keep a redacted baseline of
   endpoint, model and policy settings. Do not switch production traffic yet.

8. Ask for an isolated test environment, bounded credentials and a request/spend
   budget before probes; connection checks may themselves make paid calls.
   Pin platform revision, deployed engine versions, schema, configuration and
   catalog/pricing revisions. Run local tests first; label mocks as mocks.
   With approval, test actual providers and the project's real request shapes,
   streams/tools/continuations, two customers, multiple models per group,
   independent allowance depletion, forbidden-model refusal before dispatch,
   key rotation, concurrent reservations, retries/fallback, interrupted streams,
   reset/plan changes, shared aggregate depletion, deny versus next_model,
   identity-scoped reports and BYOK without wallet debits. Reconcile request ids,
   settled costs and native plus imported pending allowance exposure. A single
   successful completion does not establish policy, billing or privacy parity.
   Stop before cutover on unsupported requirements or absent live verification.
   Do not autoactivate production policy or move traffic. Only after evidence and
   a separate cutover approval, use the supported administrator handoff:
   - Prepare at most 500 immutable, disjoint source-owned per-request receipts,
     each with external_ref, frozen group reference, settled and pending amounts.
     Never import cumulative snapshots or requests already counted by the destination.
     Pin exact period, source watermark, coverage and evidence digest. Identical
     prepare retries are idempotent; changed replays/overlapping exports refuse.
     More than 500 receipts is a blocker for this bounded path, not permission
     to truncate, aggregate them or invent pagination.
   - Prepare closes that identity's destination admission even while global
     enforcement is inactive. It does not stop Cloudflare. Explicitly stop source
     dispatch and reconcile in-flight ownership before activating with source_frozen
     and independent source-stop evidence. This is NOT a transaction across gateways.
     Require the documented trusted administrator transport; an ordinary or delegated
     provisioning key cannot perform cutover. Do not invent a browser proxy or
     supply actor headers if the live contract does not provide an admin entrypoint.
   - Rollback must account for usage on BOTH gateways. Freeze destination admission,
     drain native reservations and resolve native pending plus all imported pending
     while active or frozen, then obtain a complete fresh export before acknowledgment.
     Any unresolved native or imported exposure blocks acknowledgment.
     Include settled_periods across all historical periods, not just the current one.
     Preserve the original JSON and parse/serialize money losslessly with Python
     integers/exact decimals or a JavaScript lossless BigInt JSON library. Totals
     can exceed 2^53 - 1. Never use ordinary JSON.parse, response.json() or Number
     for rollback amounts; rounding cannot be repaired afterward. Send exact JSON
     integers, not numeric strings, and return the server-issued digest unchanged.
     The export bounds are 500 native outstanding rows, 500 external receipts and
     1,000 settled-period rows; overflow is never proof of a complete export.
     A changed export_sha256 or export_overflow blocks acknowledgment. Rolled-back
     destination admission stays closed; no step resets history or releases unknown
     usage. Unresolved prior exposure or destination use in the same period blocks
     a new prepare. Never grant extra allowance or replay uncertain paid requests.
   Keep old secrets until I deliberately approve revocation after verification.

9. Report pass/fail/blocked/untested for every requirement, changed files, model
   mappings, policy/reset/carry-in decisions, credential locations (no values),
   tests and their pinned cutoff, billing reconciliation and rollback steps.
   Distinguish existing Account/Cost APIs, new native controls and their actual
   deployment status, application billing work and unverified provider behavior.
   Never claim full Cloudflare parity before this workflow is tested.

I'm moving this project's model traffic from Vercel AI Gateway onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Vercel AI Gateway:
   - the base URL https://ai-gateway.vercel.sh (/v1 for OpenAI clients, the
     bare host for Anthropic clients and ANTHROPIC_BASE_URL), AI_GATEWAY_API_KEY,
     VERCEL_OIDC_TOKEN and .env.local files written by vercel env pull,
   - Vercel AI SDK calls ("ai" package) whose model is a PLAIN STRING such as
     generateText({ model: "anthropic/claude-sonnet-5" }): the AI SDK sends
     bare strings through the Vercel gateway by default, so these call Vercel
     even though no URL appears in the code; also gateway("...") from "ai" or
     "@ai-sdk/gateway", and createOpenAI / createOpenAICompatible clients
     pointed at ai-gateway.vercel.sh,
   - gateway extensions in request bodies: a top-level "models" fallback list,
     a "provider" object (sort and friends), and providerOptions.gateway
     (order, only, sort, models, byok, providerTimeouts, caching: "auto"),
   - provider keys configured as Vercel BYOK (in the Vercel dashboard or
     request-scoped under providerOptions.gateway.byok).
   Also list hardcoded model ids, every Vercel AI Gateway-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Vercel AI Gateway's features before moving anything.
   - order / only / sort / providerTimeouts: the gateway picks and fails over
     providers itself; drop them. "models" (fallback models): keep the primary
     model and tell me which fallbacks are lost.
   - caching: "auto" and cache_control markers: see prompt caching below.
   - BYOK (dashboard or per request): offered as saved accounts in step 6;
     request-scoped credentials have no equivalent and leave the body.
   - OIDC: there is no keyless equivalent. On Vercel the key becomes a project
     environment variable: ask me to add EXPLABS_API_KEY to the Vercel project
     (Production, Preview, Development) myself, or run vercel env add only
     after I say so.
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   AI SDK, two ways (ask me which; recommend (a) when many call sites use
   plain strings):
     (a) one provider for every plain string, set once at startup:
           import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
           globalThis.AI_SDK_DEFAULT_PROVIDER = createOpenAICompatible({
             name: "experiential", baseURL: "https://api-pr-441.preview.experientiallabs.ai/v1",
             apiKey: process.env.EXPLABS_API_KEY });
         then every model string must be an Experiential slug (step 4);
     (b) per call site: model: experiential("<slug>") with the same provider
         instance, or createAnthropic from "@ai-sdk/anthropic" with baseURL
         https://api-pr-441.preview.experientiallabs.ai/v1 for Claude call sites that need Anthropic-only options
         (that provider's default base already ends in /v1).
   Remove the gateway import once nothing uses it, and delete the
   AI_GATEWAY_API_KEY read (keep the variable itself for me to revoke).
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Vercel AI Gateway: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Vercel AI Gateway-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Vercel AI Gateway keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. Vercel ids are creator/model (anthropic/claude-sonnet-5,
   openai/gpt-6-astra): match canonical_slug against the part after the slash.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.
   AI SDK code that read providerMetadata.gateway (cost, routing) reads
   usage.cost or the Cost API instead.

6. Provider keys (bring your own key). Vercel may hold my provider keys (dashboard BYOK, or providerOptions.gateway.byok in requests). Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Vercel AI Gateway. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Vercel AI Gateway. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from LiteLLM onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to LiteLLM:
   - the proxy config (config.yaml / litellm_config.yaml: model_list,
     litellm_params, litellm_settings, router_settings, general_settings),
     LITELLM_MASTER_KEY, DATABASE_URL, and the proxy's Docker / Helm files;
     whether store_model_in_db is on (models added in the LiteLLM UI or via
     /model/new live in its database, not the YAML),
   - clients pointed at the proxy (http://localhost:4000 or its host) with sk-
     virtual keys, ANTHROPIC_BASE_URL / ANTHROPIC_AUTH_TOKEN pointed at it,
     and SDK calls to litellm.completion / acompletion / Router with
     provider-prefixed ids (openai/, anthropic/, azure/, bedrock/, vertex_ai/,
     openrouter/, litellm_proxy/, ...),
   - virtual keys and teams (models, max_budget, budget_duration, tpm_limit,
     rpm_limit), fallbacks and context_window_fallbacks, caching, the request
     "metadata" field, and success_callback / failure_callback integrations.
   Also list hardcoded model ids, every LiteLLM-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map LiteLLM's features before moving anything.
   - Virtual keys, teams and budgets: under (a) below they stay in LiteLLM;
     under (b) they become provisioning-key minted keys and budgets.
   - fallbacks across deployments of one model, retries, routing_strategy: the
     gateway does this itself; fallbacks across models and
     context_window_fallbacks: keep the primary and tell me.
   - drop_params: the gateway refuses or ignores unknown fields itself; the
     request fields the project sends still need stripping (step 3).
   - Request "metadata" (tags, trace ids): X-Explabs-Tags /
     X-Explabs-Session-Id. With overwrite_user_with_key_hash on, the "user" the
     provider saw was the key hash, not my end user.
   - Callbacks: Langfuse becomes the gateway's live export; list the others
     for me to decide.
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   First ask me ONE question, with your recommendation, then wait:
     (a) keep LiteLLM in front and make the gateway its upstream: clients and
         virtual keys stay unchanged; each model_list entry becomes
           - model_name: <the name my clients already send>
             litellm_params:
               model: openai/<slug>
               api_base: https://api-pr-441.preview.experientiallabs.ai/v1
               api_key: os.environ/EXPLABS_API_KEY
         (recommended when several teams hold LiteLLM virtual keys). With
         store_model_in_db on, change the models where they live (the LiteLLM
         UI or its model API), not only the YAML. LiteLLM does not know
         catalog prices, so its spend tracking reads 0 for these models
         unless I add custom pricing: read cost from the gateway (step 5);
     (b) remove LiteLLM from the request path and point the clients at the
         gateway directly (recommended for a single app or script).
   LiteLLM SDK without the proxy: litellm.completion(model="openai/<slug>",
   api_base="https://api-pr-441.preview.experientiallabs.ai/v1", api_key=os.environ["EXPLABS_API_KEY"]). A bare slug
   cannot route in LiteLLM; the openai/ prefix selects its OpenAI-compatible
   adapter.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at LiteLLM: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every LiteLLM-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old LiteLLM keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. LiteLLM ids carry a provider prefix (openai/gpt-6-astra,
   anthropic/claude-..., openrouter/<vendor>/<model>): strip the prefix (and an
   OpenRouter vendor) and match canonical_slug. Under (a) keep each model_name
   the clients send and change only litellm_params.model to openai/<slug>.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.
   Code that read LiteLLM spend (/key/info, /team/info, /user/info, the spend
   logs) reads the Cost API instead; under (a) both exist, and the gateway's
   is the one that matches the bill.

6. Provider keys (bring your own key). litellm_params may reference my provider keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, AZURE_API_KEY, AWS credentials, ...). Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from LiteLLM. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through LiteLLM. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from Portkey onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Portkey:
   - the base URL https://api.portkey.ai/v1 (or a self-hosted Portkey gateway,
     often http://localhost:8787/v1), PORTKEY_API_KEY, PORTKEY_GATEWAY_URL,
   - the portkey-ai SDK (Python from portkey_ai import Portkey, Node
     "portkey-ai") and createHeaders(...) passed as default_headers /
     defaultHeaders to an OpenAI client,
   - how each call picks its provider: a Model Catalog string
     "@provider-slug/model" (@openai-prod/gpt-4o), the x-portkey-provider
     header ("@openai-prod"), or a legacy x-portkey-virtual-key,
   - every x-portkey-* header: x-portkey-config (a saved "pc-..." id or inline
     JSON), x-portkey-metadata, x-portkey-trace-id, x-portkey-cache-*,
     x-portkey-request-timeout, x-portkey-forward-headers and the provider
     credential headers (x-portkey-azure-*, -vertex-*, -aws-*),
   - prompt templates called by prompt id (portkey.prompts.completions.create).
   Also list hardcoded model ids, every Portkey-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Portkey's features before moving anything. Saved configs and AI
   Providers live in Portkey, not in this repo: ask me to export each config
   JSON the project uses, and read inline ones from the code.
   - strategy.mode "fallback" / "loadbalance" across providers of one model,
     and retry: the gateway does this itself. Targets that name DIFFERENT
     models, and mode "conditional" (routing on metadata or params): no
     equivalent; keep one model and raise it.
   - override_params / default_params: move the values into the request
     itself (show me each one); drop_params: strip those fields in code.
   - cache (simple or semantic): no equivalent; raise it if hit rates matter.
   - input_guardrails / output_guardrails: Experiential guardrails (below);
     list each check so I can recreate it.
   - x-portkey-metadata: X-Explabs-Tags, with _user as safety_identifier.
     x-portkey-trace-id: X-Explabs-Session-Id, or a W3C traceparent for the
     Langfuse export.
   - Prompt templates: no equivalent; ask me before inlining a template into
     code.
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   The Portkey client is OpenAI-shaped: replace Portkey(...), or an OpenAI
   client built with createHeaders / PORTKEY_GATEWAY_URL, by a plain OpenAI
   client (base URL https://api-pr-441.preview.experientiallabs.ai/v1, apiKey from EXPLABS_API_KEY) and keep the call
   sites. Remove every x-portkey-* header and the config attachment.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Portkey: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Portkey-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Portkey keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. Portkey sends the provider's own model id (gpt-4o,
   claude-sonnet-4-...) and picks the provider from the slug; a model may be
   written "@provider-slug/model". Take the model part, read the model a
   config's override_params sets when there is one, and match canonical_slug.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.
   Portkey analytics and log exports have no counterpart in code: the Cost API
   and https://pr-441.preview.experientiallabs.ai/spend replace them.

6. Provider keys (bring your own key). Portkey AI Providers (and legacy virtual keys) hold my provider credentials. Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Portkey. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Portkey. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from Helicone onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Helicone, and
   which of its two shapes each call uses:
   - the AI Gateway: base URL https://ai-gateway.helicone.ai with
     HELICONE_API_KEY as the bearer, and model strings that pin providers
     ("gpt-4o-mini/openai"), name a deployment ("gpt-4o-mini/azure/<id>"),
     chain fallbacks with commas, or exclude providers with "!",
   - the provider proxies: oai.helicone.ai, anthropic.helicone.ai,
     gateway.helicone.ai or any other *.helicone.ai base, where MY provider
     key (OPENAI_API_KEY, ANTHROPIC_API_KEY) is the bearer and a Helicone-Auth
     header carries the Helicone key,
   - every Helicone-* header: Helicone-Property-*, Helicone-User-Id,
     Helicone-Session-Id / -Path / -Name, Helicone-Cache-Enabled,
     Helicone-Retry-Enabled, Helicone-Fallbacks, Helicone-Prompt-Id,
     Helicone-RateLimit-Policy, Helicone-Target-Url,
   - async logging that records calls made elsewhere (@helicone/helpers,
     HeliconeManualLogger, OpenLLMetry exporters).
   Also list hardcoded model ids, every Helicone-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Helicone's features before moving anything.
   - Proxy calls ran on MY provider key and Helicone only observed: after the
     move they run on Experiential credits unless I connect that key in step 6.
     Say this plainly before I approve.
   - Provider pins, fallback chains and exclusions within one model: the
     gateway routes and fails over itself; a chain across DIFFERENT models:
     keep the first model and tell me. An exclusion I rely on for policy
     reasons is a data-control question: ask me.
   - Helicone-Property-*: X-Explabs-Tags. Helicone-User-Id: safety_identifier.
     Helicone-Session-Id: X-Explabs-Session-Id.
   - Helicone-Cache-Enabled: no equivalent; raise it if hit rates matter.
     Helicone-Retry-Enabled / Helicone-Fallbacks: the gateway does this
     itself. Helicone-RateLimit-Policy: keys and budgets (below).
   - Helicone-Prompt-Id and Helicone prompt management: no equivalent; raise it.
   - Async logging of calls that should stay where they are: do not move
     those; seeing them without moving inference is the "Capture traffic"
     prompt (https://pr-441.preview.experientiallabs.ai/docs/setup-prompts#capture-traffic).
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   Replace the Helicone base URL with https://api-pr-441.preview.experientiallabs.ai/v1 (Anthropic SDKs: https://api-pr-441.preview.experientiallabs.ai) and
   the bearer with EXPLABS_API_KEY; remove Helicone-Auth and every Helicone-*
   header. Do not chain the gateway behind a Helicone proxy.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Helicone: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Helicone-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Helicone keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. On the proxies the model is the provider's own id; on the AI
   Gateway take the part before the first "/" of the first entry in a chain
   (gpt-4o-mini/openai -> gpt-4o-mini) and match canonical_slug.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.

6. Provider keys (bring your own key). The proxies used my own provider keys, and the AI Gateway may hold BYOK keys in Helicone's Provider Settings. Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Helicone. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Helicone. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from Databricks AI Gateway onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Databricks
   model serving, and which gateway generation each call uses:
   - workspace hosts (*.cloud.databricks.com, *.azuredatabricks.net,
     *.gcp.databricks.com), DATABRICKS_HOST, DATABRICKS_TOKEN, OAuth
     service-principal settings and ~/.databrickscfg profiles (never print
     their values),
   - Unity AI Gateway calls: base URL <workspace>/ai-gateway/mlflow/v1 (model
     = a Unity Catalog name such as system.ai.<model>), and its native OpenAI,
     Anthropic and Gemini API paths,
   - legacy serving-endpoint calls: base URL <workspace>/serving-endpoints
     (model = the endpoint name), /serving-endpoints/<name>/invocations,
     WorkspaceClient().serving_endpoints.get_open_ai_client(),
     mlflow.deployments.get_deploy_client("databricks"), databricks-langchain
     ChatDatabricks,
   - ai_query(...) in SQL, notebooks and jobs, and the governance in use:
     budgets, rate limits, usage tracking, inference tables, guardrails,
     fallbacks, traffic splits, request tags (the
     Databricks-Ai-Gateway-Request-Tags header) and extra_body usage_context.
   Also list hardcoded model ids, every Databricks AI Gateway-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Databricks AI Gateway's features before moving anything.
   - Scope first: ask me which call sites move. Pay-per-token system.ai models
     and external-model endpoints can move; custom model serving endpoints
     (my own registered models and agents) and ai_query batch pipelines stay
     on Databricks unless I say otherwise. A custom endpoint I want served here
     is a custom model (https://pr-441.preview.experientiallabs.ai/docs/integrate#custom-endpoints).
   - Governance: if prompts and responses must stay inside the workspace
     (inference tables, Unity Catalog lineage, private networking or a
     compliance boundary), moving that traffic out is a blocker; ask before
     proposing it.
   - Budgets, rate limits and endpoint permissions: keys and budgets (below).
   - Usage tables (system.ai_gateway.usage, system.serving.endpoint_usage):
     the Cost API export (step 5); offer a scheduled job that lands
     /api/v1/usage rows in a Delta table if I want them in Databricks.
   - Inference tables (payload logging): the prompt and response storage
     switch at https://pr-441.preview.experientiallabs.ai/settings/data-controls plus the Langfuse export.
   - Request tags / usage_context: X-Explabs-Tags. Fallbacks and traffic
     splits across providers of one model: the gateway does this itself.
   - Guardrails: Experiential guardrails (below); list each one.
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway, only at the call sites I approved.
   An OpenAI client gets base URL https://api-pr-441.preview.experientiallabs.ai/v1 and EXPLABS_API_KEY instead of the
   workspace URL and DATABRICKS_TOKEN; ChatDatabricks becomes ChatOpenAI
   (langchain-openai) with the same base URL and key; mlflow deployments
   clients become a plain OpenAI client. A Unity AI Gateway Responses call
   stays a Responses call (the gateway serves /v1/responses). Leave every
   DATABRICKS_* setting in place for what stays.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Databricks AI Gateway: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Databricks AI Gateway-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Databricks AI Gateway keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. A Databricks model is a Unity Catalog name or an endpoint name,
   not a model id: for each one, find what it serves (system.ai.<model> names
   the model; for an endpoint read its served entities or external_model.name
   in the workspace UI or GET /api/2.0/serving-endpoints/<name>, with my
   approval and my own credentials), then match canonical_slug.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.

6. Provider keys (bring your own key). External-model endpoints hold my provider keys in Databricks secrets; never read a Databricks secret yourself. Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Databricks AI Gateway. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Databricks AI Gateway. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from Kong AI Gateway onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Kong AI Gateway:
   - Kong configuration: declarative kong.yaml / decK files, Helm values,
     KongPlugin / KongClusterPlugin resources, or Konnect, with the ai-proxy or
     ai-proxy-advanced plugin and its companions (ai-rate-limiting-advanced,
     ai-semantic-cache, ai-prompt-guard, ai-prompt-decorator,
     ai-prompt-template, ai-request-transformer, ai-response-transformer),
   - per plugin: config.route_type, config.model.provider and name,
     config.model.options (upstream_url and provider options),
     config.auth (header_name, header_value, allow_override), config.llm_format
     and config.logging,
   - the routes clients call (the Kong proxy host and route paths) and the
     consumer credentials they send (key-auth apikey header, JWT, OIDC),
   - provider keys referenced by the plugins and the vault references holding
     them.
   Also list hardcoded model ids, every Kong AI Gateway-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Kong AI Gateway's features before moving anything.
   - ai-rate-limiting-advanced, ai-prompt-guard, ai-semantic-cache, the
     decorators, templates and transformers: under (a) below they keep running
     in Kong; under (b) rate limits become keys and budgets, prompt guard
     becomes guardrails, and the cache, templates and transformers have no
     equivalent.
   - ai-proxy-advanced balancing and fallbacks across providers of one model:
     the gateway does this itself; across different models: raise it.
   - Kong consumers and their credentials: under (a) they stay; under (b)
     each consumer that needs its own spend becomes its own Experiential key.
   - logging.log_statistics / log_payloads: the Cost API and the Langfuse
     export (below).
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   First ask me ONE question, with your recommendation, then wait:
     (a) keep Kong as the edge and repoint each ai-proxy plugin (or each
         ai-proxy-advanced target) at the gateway: clients, consumers and Kong
         policies stay unchanged. Per plugin, after checking these names
         against the installed Kong version's ai-proxy schema:
           config.route_type: llm/v1/chat          (llm/v1/responses for Responses)
           config.model.provider: openai
           config.model.name: <slug>
           config.model.options.upstream_url: https://api-pr-441.preview.experientiallabs.ai/v1/chat/completions
             (https://api-pr-441.preview.experientiallabs.ai/v1/responses for a responses route)
           config.auth.header_name: Authorization
           config.auth.header_value: a Kong vault reference whose secret is the
             whole value "Bearer <my key>" (a reference replaces the field's
             entire value), never the key written into committed config
           config.auth.allow_override: false
         Ask me which vault (env, AWS / GCP / Azure secret manager, HashiCorp)
         holds it. Recommended when Kong enforces auth or policies for other
         teams;
     (b) move clients off Kong for model traffic and point them at the gateway
         directly; other Kong routes stay as they are.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Kong AI Gateway: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Kong AI Gateway-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Kong AI Gateway keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. Kong pins one model per plugin (config.model.name) or lets the
   client send it: map each to canonical_slug. Under (a) the route paths the
   clients call stay the same.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.

6. Provider keys (bring your own key). The plugins authenticate to my providers with my own keys (config.auth, often through a vault). Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Kong AI Gateway. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Kong AI Gateway. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from TrueFoundry AI Gateway onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to TrueFoundry AI Gateway:
   - the gateway base URL as the project spells it (TFY_GATEWAY_BASE_URL,
     GATEWAY_BASE_URL, or a literal such as https://<control-plane>/api/llm;
     the docs use more than one form, so record what the code actually uses),
     TFY_API_KEY or another TrueFoundry token,
   - model ids shaped <provider-account>/<model> (openai-main/gpt-4o-mini), and
     batch / fine-tuning bodies that use the bare model name,
   - the X-TFY-METADATA header (a JSON string, often application, environment
     and customer ids), x-tfy-provider-name (required there for the Responses,
     batch, files and fine-tuning APIs) and other x-tfy-* headers,
   - gateway configs in the repo or the TrueFoundry UI: routing and
     load-balancing (including virtual model names), fallbacks, rate limits,
     budgets and guardrails, and which metadata keys they match on.
   Also list hardcoded model ids, every TrueFoundry AI Gateway-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map TrueFoundry AI Gateway's features before moving anything.
   - Load balancing, fallbacks and retries across providers of one model: the
     gateway does this itself. A virtual model that routes to DIFFERENT models,
     or routing on X-TFY-METADATA: no equivalent; pick one model per call site
     with me.
   - X-TFY-METADATA: X-Explabs-Tags (a customer id goes to safety_identifier).
   - Rate limits and budgets per user, team or metadata value: keys and
     budgets (below); rules keyed on arbitrary metadata have no equivalent.
   - Guardrails: Experiential guardrails (below); list each one.
   - Batch jobs: the gateway serves /v1/batches; ask me before moving one.
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   Replace the TrueFoundry base URL with https://api-pr-441.preview.experientiallabs.ai/v1 and the token with
   EXPLABS_API_KEY; remove X-TFY-METADATA, x-tfy-provider-name and every
   x-tfy-* header once their content is mapped.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at TrueFoundry AI Gateway: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every TrueFoundry AI Gateway-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old TrueFoundry AI Gateway keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. Strip the provider-account prefix (openai-main/gpt-4o-mini ->
   gpt-4o-mini; a virtual model name: read what it routes to) and match
   canonical_slug.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.

6. Provider keys (bring your own key). TrueFoundry provider accounts hold my provider keys. Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from TrueFoundry AI Gateway. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through TrueFoundry AI Gateway. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from Azure API Management onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Azure API Management:
   - *.azure-api.net endpoints and the clients pointed at them: AzureOpenAI
     (azure_endpoint, api_version, azure_deployment; the model argument is a
     DEPLOYMENT name), OpenAI clients on an APIM unified model API, and
     Anthropic clients on an APIM Messages API,
   - AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, OPENAI_API_VERSION, the
     Ocp-Apim-Subscription-Key or api-key header, and azure-identity token
     providers (DefaultAzureCredential, get_bearer_token_provider),
   - the APIM policies on those APIs (llm-token-limit /
     azure-openai-token-limit, llm-emit-token-metric /
     azure-openai-emit-token-metric, llm-semantic-cache-lookup / -store,
     llm-content-safety), backend pools, load balancing and circuit breakers,
     and logging to Azure Monitor / Application Insights, in Bicep, Terraform,
     ARM or APIOps files.
   Also list hardcoded model ids, every Azure API Management-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Azure API Management's features before moving anything.
   - Token limits and quotas per subscription key or counter key: keys and
     budgets (below).
   - Token metrics and prompt logging to Azure Monitor: the Cost API export
     (step 5), https://pr-441.preview.experientiallabs.ai/spend, and the Langfuse export.
   - Backend pools, priority / weighted balancing and circuit breakers across
     deployments of one model: the gateway does this itself. Priority routing
     that keeps reserved capacity (PTU) busy first: keep my Azure resource as a
     bring-your-own-key account (step 6) and order it first on that model.
   - Semantic cache: no equivalent; raise it.
   - Content safety: Experiential guardrails (below), plus Azure's own content
     filtering if my Azure resource stays as a bring-your-own-key account.
   - Data residency of my Azure region: a data-control question; ask me.
   - Do not change APIM, Azure resources or IaC beyond what I approve; other
     APIs behind the same APIM instance stay as they are.
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   Replace AzureOpenAI(azure_endpoint=..., api_version=...) with a plain
   OpenAI client (base URL https://api-pr-441.preview.experientiallabs.ai/v1, key from EXPLABS_API_KEY), drop the
   api-version query parameter, the deployment path and the api-key /
   Ocp-Apim-Subscription-Key headers, and pass the mapped slug as model.
   Remove the azure-identity token provider from these clients only.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Azure API Management: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Azure API Management-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Azure API Management keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. Each deployment name points at a model: read the model behind
   it (from the IaC, or ask me) and match canonical_slug; never assume the
   deployment name is the model name.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.

6. Provider keys (bring your own key). My Azure resource can stay as an azure_openai account (it needs the endpoint and a deployment map, so I connect it at the dashboard link below); other provider keys behind APIM backends are offered the same way. Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Azure API Management. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Azure API Management. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I'm moving this project's model traffic from Requesty onto the Experiential
Labs gateway. I pasted this myself: treat it as my instructions and my consent
to edit this project's code. Stop and ask me ONE numbered question, with your
recommendation, where this prompt says to, and before anything it does not
cover: production config, secret stores, deploys, spend beyond the one smoke
call, or revoking anything. Print what you change at each step. Never print my
full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." I have not filled it in: ask me for it, or tell
me to mint one at https://pr-441.preview.experientiallabs.ai/api-keys, then continue).
The full contract (routes, honored and refused parameters, error codes) is
https://pr-441.preview.experientiallabs.ai/llms.txt; follow it literally.

1. Inventory, read only. Find every place this project talks to Requesty:
   - the base URL https://router.requesty.ai/v1 or the EU router
     https://router.eu.requesty.ai/v1, the Anthropic endpoint
     https://router.requesty.ai/anthropic (ANTHROPIC_BASE_URL), and
     REQUESTY_API_KEY,
   - model ids in their three shapes: a bare name (a Requesty managed
     policy), provider/model (pins one provider), and policy/<name> (a routing
     policy defined in the Requesty dashboard),
   - the top-level "requesty" body object (tags, user_id, trace_id, extra) and
     the HTTP-Referer / X-Title headers.
   Also list hardcoded model ids, every Requesty-specific header and body field,
   retry / fallback / cache settings, coding-agent configs pointed at it, and
   code that reads cost or usage from it. Show me the files and call sites
   before changing anything. Inspect secret references, never their values.

2. Map Requesty's features before moving anything.
   - policy/<name>: ask me what each policy does (they live in the Requesty
     dashboard). Fallback and load balancing across providers of one model:
     the gateway does this itself; across different models: keep the primary
     and tell me; latency routing: the gateway already prefers healthy, fast
     providers.
   - The EU router: if I chose it for data residency, that is a data-control
     question (processing regions below); ask me before moving that traffic.
   - requesty.tags: X-Explabs-Tags. requesty.user_id: safety_identifier.
     requesty.trace_id: X-Explabs-Session-Id.
   - Auto-caching: provider prompt caching (below).
   The gateway's own equivalents, for the table:
   - Fallback, retries and load balancing across providers of one model: the
     gateway routes and fails over itself (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug>/waterfall
     shows the order). A fallback list ACROSS models has no per-request
     equivalent.
   - Keys per team or customer, spend caps, budgets: a PROVISIONING key (an org
     admin mints the first at https://pr-441.preview.experientiallabs.ai/api-keys) mints inference keys with a
     daily USD cap (POST https://api-pr-441.preview.experientiallabs.ai/api/v1/keys {"name", "limit"}) and sets monthly
     caps per key or identity (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/budgets).
   - Metadata: the X-Explabs-Tags header (a JSON object, at most 16 labels, on
     chat, Responses and Messages calls; never secrets or personal data). End
     user: safety_identifier (alias user) in the body. Session or
     conversation: the X-Explabs-Session-Id header.
   - Logs and dashboards: https://pr-441.preview.experientiallabs.ai/logs and https://pr-441.preview.experientiallabs.ai/spend, the Cost API (step 5),
     and live export of every call to my Langfuse project
     (PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/observation-export).
   - Privacy: "provider": {"zdr": true} per request, or account-wide
     PUT https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/zero-data-retention (Pro); processing
     regions are in Settings, Data controls (https://pr-441.preview.experientiallabs.ai/settings/data-controls).
   - Content guardrails: per identity, on the Guardrails tab at https://pr-441.preview.experientiallabs.ai/api-keys.
   - Prompt caching: provider prompt caching applies where the route supports
     it; Anthropic cache_control markers are kept on supported routes and
     disclosed as not forwarded elsewhere (default five-minute markers; a
     one-hour marker is refused on platform credits).
   - No equivalent: gateway-side response or semantic caching, hosted prompt
     templates, metadata-based routing between models, and arbitrary
     request/response transforms.
   Show me a table: feature in use -> equivalent (or "none") -> what changes.
   Then ask me ONE question: proceed with this plan, adjust it, or stop. Wait.
   A feature I depend on with no equivalent is a blocker: say so, never drop it
   silently.

3. Point the clients at the gateway.
   Replace the Requesty base URL with https://api-pr-441.preview.experientiallabs.ai/v1 (the Anthropic endpoint with
   the bare origin https://api-pr-441.preview.experientiallabs.ai) and REQUESTY_API_KEY with EXPLABS_API_KEY. Remove the
   requesty body object (Python: from extra_body) and the HTTP-Referer /
   X-Title headers once their content is mapped.
   OpenAI-compatible clients: base URL https://api-pr-441.preview.experientiallabs.ai/v1 and
   Authorization: Bearer $EXPLABS_API_KEY (the ONLY header needed). An
   Anthropic SDK appends /v1/messages itself: its base is the bare origin
   https://api-pr-441.preview.experientiallabs.ai (never .../v1). A coding agent pointed at Requesty: follow that
   agent's section at https://pr-441.preview.experientiallabs.ai/docs/coding-agents instead of guessing.
   Store my key as EXPLABS_API_KEY in this project's env scheme (.env plus the
   example file entry), never in code. Strip every Requesty-only field before
   the first call: left in, each is ignored or refused with 400
   unsupported_parameter naming it. Do NOT delete the old Requesty keys from
   .env, secret stores or CI: stop reading them; I revoke them myself. A key
   committed in a tracked file is already leaked: flag it and remove it there.

4. Map model ids. GET https://api-pr-441.preview.experientiallabs.ai/api/v1/models with my key (free, spends no
   credits): every entry carries canonical_slug, the plain slug the gateway
   serves. provider/model: match canonical_slug against the part after the
   slash; a bare name: match it directly; policy/<name>: use the primary model
   of that policy, after I confirm it.
   One obvious candidate: use it and note it in the report. Anything else:
   ask me ONE question listing each id with your best candidates, then wait.
   Never guess a model I did not pick. Use canonical_slug EXACTLY as returned.

5. Move cost and usage reads. Every chat / Responses reply carries usage.cost
   (USD charged) and usage.is_byok; on a stream it rides the final usage chunk
   when the request sets stream_options: {"include_usage": true}. A request
   sent with Idempotency-Key carries no usage.cost, so read it as optional.
   Same key, under https://api-pr-441.preview.experientiallabs.ai/api/v1:
     GET /generation?id=<the x-request-id response header>   one request
     GET /usage     settled per-request rows for billing (org-wide; filter
                    ?api_key_id= or attribution_label)
     GET /credits   balance      GET /activity   per-day rollup
   Replace the old cost and usage code with these; show me the diff first.
   Code that already reads usage.cost from Requesty replies keeps working: the
   field has the same name here.

6. Provider keys (bring your own key). I may have added my own provider keys to Requesty. Ask whether to connect
   them so those calls bill my own provider accounts with no markup:
     GET https://api-pr-441.preview.experientiallabs.ai/api/whoami   -> org_id
     GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections   (list existing first)
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
         {"setup_alias": "<a new unique account name I approve>", "secret": "<key>"}
   setup_alias: 1-63 lowercase letters, digits, hyphens or underscores, unused
   across my accounts. Never overwrite an account; on 409 account_exists pick
   another name. Use inference keys, never admin keys, and ask me to paste
   each key: never export one from Requesty. A one-string key works for openai,
   anthropic, gemini, xai, deepseek and similar; azure_openai, bedrock,
   fireworks, vertex and modal need structured config: I connect those at
   https://pr-441.preview.experientiallabs.ai/settings/connections. Read check.status; if it is not "valid", show
   me check.status_detail.remediation. A 402 pro_required means a FIRST
   connection needs Pro (https://pr-441.preview.experientiallabs.ai/credits). Saving a key does not select it for
   every model: I pick it per model with Use this account. Never echo a key.

7. Verify. Make ONE minimal call (model + messages, a small max_tokens) with
   the cheapest mapped slug (GET https://api-pr-441.preview.experientiallabs.ai/api/models/<slug> shows its price).
   Stream if the project streams. Report the model, that it succeeded, and its
   usage.cost, or read it from GET /api/v1/generation?id=<x-request-id>; I can
   also see the call at https://pr-441.preview.experientiallabs.ai/logs. On a credits or activation error tell me
   to finish activation at https://pr-441.preview.experientiallabs.ai and stop; never retry through Requesty. Then
   run the project's tests.

8. Report back: every file changed; the feature table and blockers; the model
   mapping and the ids I still need to decide; where EXPLABS_API_KEY lives;
   which old credentials I still need to revoke; the verification result.
   Human docs: https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I pasted this into you myself, so connect my own AI provider accounts (OpenAI,
Anthropic, Azure and the rest) to my Experiential Labs gateway. Calls I route to them
bill my own provider accounts with no platform markup and still show in my usage.
This is my consent to wire them up. Connecting a first provider is part of the Pro
plan: a 402 with code pro_required means my org needs Pro at https://pr-441.preview.experientiallabs.ai/credits first
(free-tier accounts below work on every plan; an org that already holds a
connection keeps connecting).
Read my Experiential org key from the EXPLABS_API_KEY environment variable and never
print it in full. If it is not set, ask me for it (I can mint one at https://pr-441.preview.experientiallabs.ai/api-keys).
Use it as the bearer token (Authorization: Bearer $EXPLABS_API_KEY) on every call.

1. Find my org id, then list existing accounts BEFORE writing anything.
   GET https://api-pr-441.preview.experientiallabs.ai/api/whoami  -> org_id
   GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections

2. Ask me ONE question and wait: which providers should I connect?
     (1) OpenAI
     (2) Anthropic
     (3) Azure Foundry
     (4) Gemini
     (5) Vertex AI
     (6) Bedrock
     (7) xAI
     (8) DeepSeek
     (9) Mistral AI
     (10) Groq
     (11) Fireworks
     (12) OpenRouter
   (I can pick several.)

3. For EACH provider I picked, ask me for its credential (never search my
   files, shell history or other tools for one), pick a NEW setup_alias with me
   (1-63 lowercase letters, digits, hyphens or underscores, unused across ALL my
   accounts), then create the account:
   POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/<provider>
   Content-Type: application/json
   - OpenAI: POST .../provider-connections/openai
     Gather: My OpenAI INFERENCE key (starts sk-… or sk-proj-…). Do NOT use an admin key (sk-admin-…) here; that one cannot do inference. It is at platform.openai.com/api-keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my OpenAI inference key>"}
     Note: If I also hand you my OpenAI ADMIN key (sk-admin-…), add "spend_secret": "<my admin key>" to the same body so month-to-date spend shows.
   - Anthropic: POST .../provider-connections/anthropic
     Gather: My Anthropic INFERENCE key (starts sk-ant-api…). Do NOT use an admin key (sk-ant-admin…) here; that one cannot do inference. It is at console.anthropic.com/settings/keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my Anthropic inference key sk-ant-api…>"}
     Note: If I also hand you my Anthropic ADMIN key (sk-ant-admin…), add "spend_secret": "<my admin key>" to the same body so month-to-date spend shows.
   - Azure Foundry: POST .../provider-connections/azure_openai
     Gather: From my Azure AI Foundry resource: the API key; the resource endpoint (https://<my-resource>.openai.azure.com); optionally the API version ("v1" or a dated one like 2024-05-01-preview); and, for EACH catalog model I want served, the deployment name I created for it in that resource (Azure routes by deployment name, not model id, so the key alone cannot route).
     Body: {"setup_alias": "<unique-account-name>", 
       "secret": "<my Azure Foundry API key>",
       "config": {
         "endpoint": "https://<my-resource>.openai.azure.com",
         "api_version": "2024-05-01-preview",
         "deployments": { "gpt-5.5": "<my-gpt-5-5-deployment-name>" }
       }
     }
     Note: At least one deployment mapping is required or the connection routes nothing. api_version is optional; drop it to use the account default.
   - Gemini: POST .../provider-connections/gemini
     Gather: My Google AI Studio API key (starts AIza…), created at aistudio.google.com/apikey.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my AI Studio API key AIza…>"}
   - Vertex AI: POST .../provider-connections/vertex
     Gather: From Google Cloud: a service-account JSON key file for a service account with the Vertex AI User role (IAM & Admin → Service Accounts → Keys → Add key), plus the GCP project id it belongs to and the Vertex location to call (e.g. us-central1). The JSON key file contents are the credential; project and location are non-secret config.
     Body: {"setup_alias": "<unique-account-name>", 
       "secret": "<the full service-account JSON key file contents>",
       "config": {
         "project_id": "<my-gcp-project>",
         "region": "us-central1"
       }
     }
     Note: Enable the Vertex AI API (aiplatform.googleapis.com) on the project first.
   - Bedrock: POST .../provider-connections/bedrock
     Gather: From AWS: an access key id (starts AKIA…), its secret access key, and the region Bedrock should run in (e.g. us-east-1). The secret access key is the credential; the access key id and region are non-secret config.
     Body: {"setup_alias": "<unique-account-name>", 
       "secret": "<my AWS secret access key>",
       "config": {
         "access_key_id": "<my AWS access key id AKIA…>",
         "region": "us-east-1"
       }
     }
   - xAI: POST .../provider-connections/xai
     Gather: My xAI API key (starts xai-…), from console.x.ai → API keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my xAI API key xai-…>"}
     Note: This key serves the Grok family on api.x.ai; the key's models list shows which Grok models it can call.
   - DeepSeek: POST .../provider-connections/deepseek
     Gather: My DeepSeek API key (starts sk-…), from platform.deepseek.com → API keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my DeepSeek API key sk-…>"}
     Note: This key serves DeepSeek's own models on api.deepseek.com and reads the account balance; the key's models list shows which models it can call.
   - Mistral AI: POST .../provider-connections/mistral
     Gather: My Mistral AI API key, from console.mistral.ai → API keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my Mistral AI API key>"}
     Note: This key serves Mistral's own models (Large, Medium, Small, Ministral, Codestral, and the embedding models) on api.mistral.ai; the key's models list shows what it can call.
   - Groq (free account: works on every plan): POST .../provider-connections/groq
     Gather: An API key from my free Groq account: create the account at https://console.groq.com/login, then copy a key from https://console.groq.com/keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my Groq API key>"}
   - Fireworks: POST .../provider-connections/fireworks
     Gather: Two things from fireworks.ai: my API key (starts fw_…), and my account id, the account slug in my dashboard URL (fireworks.ai/account/<this-slug>), which the key alone does not reveal.
     Body: {"setup_alias": "<unique-account-name>", 
       "secret": "<my Fireworks key fw_…>",
       "config": { "account_id": "<my-fireworks-account-slug>" }
     }
   - OpenRouter: POST .../provider-connections/openrouter
     Gather: My OpenRouter API key (starts sk-or-v1-…), created at openrouter.ai/keys.
     Body: {"setup_alias": "<unique-account-name>", "secret": "<my OpenRouter key sk-or-v1-…>"}
   Never overwrite a saved account: a 409 account_exists means choose another
   name and retry POST, never PUT. Use INFERENCE keys, never admin keys.

4. Read each verdict, do not guess it: tell me check.status per account. If it
   is not "valid", tell me check.status_detail.remediation and move on to the
   next provider without retrying blindly. If spend_error is present, the
   serving key was saved; report the partial success and do not repeat POST.
   Never echo a key or secret.

5. Saving an account does not select it for every model. For each catalog model
   I want on my own account, let me choose it in that model's waterfall (Use this
   account) at https://pr-441.preview.experientiallabs.ai/models. Do not change other routing choices.

If you cannot do this, tell me what you gathered (by prefix only, never the full
value) and I will paste it at https://pr-441.preview.experientiallabs.ai/settings/connections myself; each key is
verified on save the same way.

I want Experiential Labs to see my model traffic WITHOUT moving any inference:
every request keeps going to the provider it goes to today, with the same keys
and the same code path, and a copy of each call (model, tokens, latency,
status) lands on my Experiential account as telemetry. I pasted this myself:
treat it as my instructions. Ask me before installing software, changing
system settings, or adding code, and never let capture slow down or break a
real model call. Never print my full API key (first 8 characters at most).
My Experiential key is: xpl_...
(if that still reads "xpl_..." ask me for it, or tell me to mint one at
https://pr-441.preview.experientiallabs.ai/api-keys). Keep it in env as EXPLABS_API_KEY, never in code.

1. Get my org id: GET https://api-pr-441.preview.experientiallabs.ai/api/whoami with
   Authorization: Bearer $EXPLABS_API_KEY; capture org_id.

2. Ask me ONE question, then follow only the matching step:
   "Which traffic should Experiential see?
     (1) apps and coding agents on this Mac (Claude Code, Codex, scripts)
         calling api.openai.com, api.anthropic.com or chatgpt.com
     (2) my product or service, wherever it runs
     (3) traces already in braintrust, langsmith, langfuse, posthog, mastra, postgres
    Which one?"

3. This Mac: exp capture (the Experiential CLI).
   What it does, which you tell me before I agree: it runs in the foreground
   until Ctrl+C, intercepts ONLY api.openai.com, chatgpt.com and
   api.anthropic.com, forwards every request unchanged to that provider, and
   uploads one trace span per exchange with authorization headers, cookies
   and key-shaped strings removed; message content IS included. macOS asks
   me to approve its network extension (System Settings) and to trust a local
   certificate restricted to those three hosts. It needs macOS and Python
   3.13 or newer. Then, only once I agree:
     uv tool install --python 3.13 experiential
     (or: pipx install --python python3.13 experiential)
   and give me these lines to run in a NEW terminal (they set the key for
   that terminal only):
     export EXPLABS_API_KEY=<my key>
     export EXP_GATEWAY_URL=https://api-pr-441.preview.experientiallabs.ai/v1
     export EXP_PLATFORM_URL=https://pr-441.preview.experientiallabs.ai
     exp capture
   Do not run exp capture yourself: it is a long-running process that waits
   for my approvals. When it says it is capturing, I use my tools as usual.
   Go to step 6.

4. My product: record each call after it returns, upload on a schedule.
   a. Find where the product calls models (OpenAI / Anthropic / other SDKs,
      HTTP clients, wrappers). Show me the call sites and ask where to hook.
   b. Add a recorder that runs AFTER each call returns (or fails) and appends
      one JSON line to a local file, in a try/except that never raises into
      the caller and never blocks the response. One line per call:
        {"trace_id": "<32 hex chars, reuse my request's trace id if I have one>",
         "span_id": "<16 hex chars>", "name": "chat <model>", "kind": "client",
         "startTimeUnixNano": <int>, "endTimeUnixNano": <int>,
         "status": "ok" | "error",
         "attributes": {"gen_ai.provider.name": "openai",
           "gen_ai.operation.name": "chat",
           "gen_ai.request.model": "<model sent>",
           "gen_ai.response.model": "<model returned>",
           "gen_ai.usage.input_tokens": <int>,
           "gen_ai.usage.output_tokens": <int>}}
      Read tokens from the provider's usage object; leave a count out when it
      is unknown (never write 0 for unknown). Metadata only by default: ask me
      before recording prompts or completions, and never record keys,
      headers, or personal data. Extra keys I want to group by (route, feature,
      customer id) go under "attributes" with my own prefix (app.*).
      If the product already exports OpenTelemetry GenAI spans to a collector,
      offer instead: add a collector file exporter and upload those OTLP JSON
      files with source_kind "otlp".
   c. Add a scheduled job (hourly unless I choose otherwise) that rotates the
      file and uploads each closed file (UTF-8 JSONL, at most 50 MB; split
      larger ones):
        POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/telemetry/traces/upload
          {"source_kind": "otel-genai", "source_label": "<short label, e.g. prod-api>"}
          -> 201 {"ingest_id", "signed_url", ...}
        PUT the exact file bytes to signed_url
          (Content-Type: application/octet-stream, no Authorization header)
        POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/telemetry/traces/<ingest_id>/finalize   -> 202
      Delete a file only after finalize answers 202; on any error keep it and
      retry next run with a NEW upload (a signed_url is single-use).
   d. Run the recorder once locally against one real call and upload that
      file now. Go to step 6.

5. Existing traces: pull them.
   Ask me for that tool's credential and the small config it needs (project
   name; for Langfuse the credential is "public_key:secret_key"; for Postgres a
   connection string and table), then
     POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/telemetry/traces/pull
       {"transport_kind": "<tool>", "source_kind": "<same tool>",
        "source_label": "<short label>", "credential": "<the secret>",
        "config": {"project": "<project>"}}
   The credential is used once and never stored, so a recurring pull is a job
   I schedule with the credential in my own secret store: ask before adding
   one. A 400 is a bad credential or config (show me the message); a 429 means
   the tool rate-limited the pull (wait and retry).

6. Verify. GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/telemetry/traces with my key until my ingest shows status "done" (a
   few minutes; "Local capture" is the exp capture label). Report its
   trace_count and the total_traces. Status "error" carries an error_code:
   show it to me and stop.

Report back: which source, what you installed or changed (files, schedule,
where the recorder writes), what the spans include (metadata only, or content
I approved), and the verification counts. No inference moved: my providers,
keys and model calls are exactly as before. When I want the gateway to serve
the traffic too, the switch prompts are at https://pr-441.preview.experientiallabs.ai/docs/switch-gateways.

I pasted this into you myself, so add my Claude plan to the Plans pool on my
Experiential Labs gateway. Every tool I point at the gateway then rotates across this plan and my API keys, and I can watch the limits at https://pr-441.preview.experientiallabs.ai/settings/connections#plans.
This is my consent to wire it up. Plans are part of the Pro plan: a 402 with code
pro_required means my org needs Pro at https://pr-441.preview.experientiallabs.ai/credits first.

Use my Experiential Labs org key as the bearer token on every call below. It is a
secret: read it from the EXPLABS_API_KEY environment variable and never print it in
full. If it is not set, ask me for it (I can mint one at https://pr-441.preview.experientiallabs.ai/api-keys).

First find my org id and the accounts it already has:
  GET https://api-pr-441.preview.experientiallabs.ai/api/whoami  with header Authorization: Bearer $EXPLABS_API_KEY
  Capture org_id, then GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections.
  Rows whose credential_kind is chatgpt_plan or claude_plan are plans. Pick a new
  setup_alias for each plan you add (chatgpt-1, chatgpt-2, claude-1, ...), unused
  across ALL of my accounts.

Add my Claude plan (the Pro, Max or Team seat I use Claude with):
0. First GET https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/anthropic/plans. If "available" is false, Claude plans are not
   enabled here: skip this and tell me so. Otherwise remember "completion" for below:
   "paste" means each sign-in ends on a page with a one-time code I paste back to you;
   "redirect" means it finishes on its own.
1. Ask me which way I prefer, then do exactly one:
   a. IMPORT (fastest, but it moves my sign-in, and ONLY when completion is "paste"): if
      this machine's Claude Code is signed in to that account, read
      ~/.claude/.credentials.json and POST its full contents to
      https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/anthropic/plans/import
      body {"auth_json": "<the file's contents as a string>", "setup_alias": "claude-<n>"}
      Tell me clearly: my local Claude Code is now signed out and must sign in again.
      On macOS the file may live in the Keychain instead; if it does, use the browser way.
   b. BROWSER (keeps both signed in): POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/anthropic/plans/sign-in
      body {"setup_alias": "claude-<n>"} and give me the authorize_url from the response.
      I open it in a browser where I am signed in to claude.ai and approve.
      - If completion is "redirect", it finishes on its own and lands on my Plans page at
        https://pr-441.preview.experientiallabs.ai/settings/connections#plans.
      - If completion is "paste", the page ends with a one-time code. I paste it to you,
        and you POST https://api-pr-441.preview.experientiallabs.ai/api/orgs/<org_id>/provider-connections/anthropic/plans/sign-in/complete
        body {"state": "<state from the sign-in response>", "callback": "<the code I pasted>",
              "setup_alias": "claude-<n>"}

After each add, report from the response: setup_alias, plan.label, plan.plan_type, and the
used_percent of plan.primary and plan.secondary if present. A 409 means that sign-in
expired or was already used: start it again, do not retry the completion. Never print a
token, the credentials file contents, or my org key.

Nothing else to configure: any harness (Claude Code, Codex, OpenCode, Cursor, ...) pointed
at https://api-pr-441.preview.experientiallabs.ai/v1 with my key uses the plan.

If you cannot do this, tell me and I will add the plan at https://pr-441.preview.experientiallabs.ai/settings/connections#plans.