DeepSeek

Connector for DeepSeek's hosted models. The API is OpenAI-compatible end to end (same /v1/chat/completions shape, same SSE streaming, same Bearer auth) so the connector is a thin factory that wraps OpenAiClient with DeepSeek's default endpoint.

Source: crates/llm/src/deepseek.rs.

Configuration

# config/llm.yaml
providers:
  deepseek:
    api_key: ${DEEPSEEK_API_KEY}
    # base_url defaults to https://api.deepseek.com/v1 when blank.
    # Override only for self-hosted gateways or testing fixtures.
    base_url: ""
    rate_limit:
      requests_per_second: 2.0
      quota_alert_threshold: 100000

Pin the agent to it:

agents:
  - id: ana
    model:
      provider: deepseek
      model: deepseek-chat

Models

Model id	Use case
`deepseek-chat`	General-purpose. Supports tool calling.
`deepseek-reasoner`	Long-form reasoning. No tool calling in current API revision.

deepseek-reasoner agents must therefore leave allowed_tools empty (or list only tools the agent never plans to invoke). Tool calls fired against the reasoner endpoint return an error from upstream.

Streaming

Identical to OpenAI's SSE format, so OpenAiClient::chat_stream parses it without per-provider code. nexo_llm_stream_ttft_seconds and nexo_llm_stream_chunks_total Prometheus series labelled with provider="deepseek" show up automatically.

Tool calling

deepseek-chat follows OpenAI's tool-calling spec verbatim. JSON arguments deserialise the same way; parallel_tool_calls is honoured.

Rate limits

DeepSeek returns standard 429 with a retry-after header. The existing retry plumbing (crates/llm/src/retry.rs) consumes that header so 429s back off cleanly without touching the connector.

Quota / cost

DeepSeek's pricing is per-1M-tokens; the TokenUsage returned by each ChatResponse is forwarded to the standard agent_llm_tokens_total counter (labels: provider="deepseek", model, usage_kind).

Known limitations

No native embeddings client — DeepSeek does not currently publish an embeddings endpoint. Use a different provider for embedding_model if your agent needs vector search.
Reasoner tool-call gap — see Models. Validate at boot by leaving allowed_tools: [] on agents pinned to deepseek-reasoner.
Cache awareness — DeepSeek's KV-cache hit information is surfaced through the same cache_usage field as the OpenAI client reports it.

Nexo-rs