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OpenAI-compatible chat completions endpoint. Any client that speaks the OpenAI Chat Completions API, official SDKs, Codex CLI, opencode, Cursor, Aider, a thousand internal scripts, works with zero code change by pointing its OPENAI_BASE_URL at the LangWatch AI Gateway and its OPENAI_API_KEY at a LangWatch virtual key.

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Request

POST /v1/chat/completions
Authorization: Bearer vk-lw-<ULID>
Content-Type: application/json
Body matches OpenAI’s Chat Completions schema verbatim. Example: 
{
  "model":       "gpt-5-mini",
  "messages":    [
    {"role": "system", "content": "You are concise."},
    {"role": "user",   "content": "Hi"}
  ],
  "temperature": 0.2,
  "max_tokens":  256,
  "stream":      false,
  "tools":       [],
  "tool_choice": "auto"
}

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LangWatch-specific headers

HeaderPurpose
X-LangWatch-Cache: respect|force|disable|ttl=<s>Override the VK’s cache mode for this request. See Caching Passthrough.
X-LangWatch-Trace-Metadata: {...}Attach arbitrary key/value metadata to the trace (e.g. deployment id, experiment tag).

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Model resolution

The model field can be:
  • A VK-defined alias (e.g. gpt-4o, claude) → routed via the VK’s model_aliases map.
  • An explicit <provider>/<model> form (e.g. openai/gpt-5-mini, azure/my-deployment), bypasses aliases.
If the alias/explicit form doesn’t resolve to a provider in the VK’s providers list, returns 403 model_not_allowed.

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Response (non-streaming)

OpenAI-shaped. Additional LangWatch headers: 
HTTP/1.1 200 OK
Content-Type: application/json
X-LangWatch-Request-Id: grq_01HZX9K3M...
X-LangWatch-Provider: openai
X-LangWatch-Model: gpt-5-mini
X-LangWatch-Cache: miss
X-LangWatch-Fallback-Count: 0

{
  "id":       "chatcmpl-...",
  "object":   "chat.completion",
  "created":  1734567890,
  "model":    "gpt-5-mini",
  "choices":  [...],
  "usage":    { ... }
}
usage.prompt_tokens_details.cached_tokens is populated when cache hits occur (used by the internal debit logic).

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Response (streaming)

Set "stream": true in the body. The gateway proxies upstream SSE events byte-for-byte after the first chunk, preserving tool-call delta ordering, this is important for coding CLIs like Codex that parse tool-call streams incrementally. 
data: {"id":"chatcmpl-…","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","content":"Hi"}}]}

data: {"id":"chatcmpl-…","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"!"}}]}

data: [DONE]
A response header X-LangWatch-Request-Id is emitted on the first frame. Mid-stream failures close the connection with a terminal event: error frame (see Errors).

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Error handling

All errors use the OpenAI-compatible envelope documented at Errors:
  • 401 invalid_api_key, missing/malformed/unknown VK.
  • 402 budget_exceeded, hard-cap budget breach.
  • 403 guardrail_blocked | tool_not_allowed | model_not_allowed | permission_denied | virtual_key_revoked, policy.
  • 429 rate_limit_exceeded, VK, project, org rate limit.
  • 502 provider_error, 504 upstream_timeout, upstream provider exhausted (possibly after fallback).
Every error carries X-LangWatch-Request-Id, use it for support tickets and to jump straight to the trace in the LangWatch UI.

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Example (Python)

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.langwatch.ai/v1",
    api_key="vk-lw-...",
)

resp = client.chat.completions.create(
    model="gpt-5-mini",
    messages=[{"role": "user", "content": "Hi"}],
    extra_headers={"X-LangWatch-Cache": "respect"},
)

print(resp.choices[0].message.content)
print("Request id:", resp.response.headers["X-LangWatch-Request-Id"])

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Example (curl)

curl https://gateway.langwatch.ai/v1/chat/completions \
  -H "Authorization: Bearer $LW_VK" \
  -H "Content-Type: application/json" \
  -d '{
        "model": "gpt-5-mini",
        "messages": [{"role":"user","content":"Hi"}]
      }' \
  -i

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Rate limits

Per-VK rate limits are configurable (rate_limits.rpm|tpm|rpd) and enforced at the gateway before the request leaves for the provider. Breaches return 429 rate_limit_exceeded with a Retry-After header in seconds. Upstream provider rate limits are opaque to the VK owner but can trigger fallback (if rate_limit is in the VK’s fallback.on). A Retry-After from upstream is surfaced to the client when no fallback remains.

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Observability

Every request emits a LangWatch trace with attributes:
  • langwatch.vk_id, langwatch.project_id, langwatch.team_id, langwatch.org_id, langwatch.principal_id.
  • langwatch.model_requested (what the client sent) vs langwatch.model_resolved (provider + model after alias).
  • gen_ai.usage.cache_read.input_tokens, gen_ai.usage.cache_creation.input_tokens (OTel GenAI semconv; cache economics).
  • langwatch.cost_usd (computed from tokens × price).
  • langwatch.fallback.attempt spans (one per attempt; attempt=0 is primary).
Filter in the LangWatch UI via attr.langwatch.model_resolved to see how many requests actually hit Azure vs OpenAI, for example.