Chat completions
The OpenAI-compatible base isAPI_URL/api/v1. Authentication accepts an authorized Overmind API key.
model can pin a deployment’s model_id or use overmind/CAPABILITY_UUID for an active chat alias. Supported frontier models may route through the configured provider; catalogue presence and access depend on the deployment.
POST /api/v1/chat/completions supports message input, model generation parameters, tools, structured response configuration and streaming according to the selected runtime. Read the deployed schema and model contract for supported fields.
Input plus reserved output must fit the serving context. The default output reservation is 8,192 tokens; max_completion_tokens is an alternative to max_tokens and takes precedence when both are supplied. Requests do not silently shrink the output budget to fit. Serving context is separate from training sequence length.
finish_reason="length" means the generated answer is incomplete. A successful HTTP status alone does not establish a complete result.
Streaming and long requests
SSE and long completions retain 15-second idle keepalives. Streaming failures can carry an error frame. A non-streaming failure after keepalives committed headers can terminate without complete JSON and appear as a transport error. RetainX-Request-ID for failure investigation.
Durable MCP inference
run_inference requires deployment and request_key, with exactly one input contract:
The call returns an
inference_request job. Identical retries recover the receipt; changed inputs conflict. Unknown provider acknowledgement is reconciled without blind resubmission. Job reads do not wake a worker.
For chat results, truncated describes generation exhaustion. content_clipped separately describes the MCP response-size limit; follow the result resource to page the retained answer.
Typed decision API
POST /api/deployed-models/{deployment_id}/inference/ creates a durable deployment request; GET /api/inference-requests/{request_id}/ reads it. The system-one endpoint is POST /api/v1/systemone. Use its schema or the deployment’s generated snippet for the exact synchronous request contract.
Native decisions preserve supplied option order and return typed probabilities. They are not chat completions, and generation-temperature/token parameters do not apply.
Deployment and routing state
Deployment status, worker state, active routing and application connection are separate fields. Worker observations can be warm, warming, asleep or unknown; missing measurements are not zero containers. Compatible dense and explicitly supported MoE LoRA models can share a BF16 base. Other supported chat artifacts use their checkpoint preparation route. Native decision artifacts use their separately qualified runtime. Inspect the deployment’s actual profile instead of inferring precision or cold-start time from its model family.set_active_model starts a durable activation, verifies the target and only then switches a chat alias. set_benchmark_model changes future benchmark selection independently. A successful application API-key alias call confirms application connection; internal probes and pinned model calls do not.
Metrics and downloads
Deployment resources and REST metrics acceptperiod=1h|24h|7d|30d|all and source=application|all. Application traffic includes alias and pinned-model API-key calls; all traffic also includes internal and historical unknown-source calls.
Reported latency/speed distributions depend on measured successful requests; failed calls and missing timing remain separate. Cost coverage must accompany estimated totals.
overmind model download-checkpoint DEPLOYMENT_ID downloads the retained deployment archive. Deployment retries reuse successful training artifacts; they do not rerun training.