DeepSeek

DeepSeek provides an OpenAI-compatible API for DeepSeek V4. Fabro includes direct access to V4 Flash and V4 Pro. The same Fabro model IDs also work through the optional Fireworks AI and OpenRouter providers.

Prerequisites

A DeepSeek Platform account
A DeepSeek API key from platform.deepseek.com/api_keys
A running Fabro server

Configure direct access

The direct deepseek provider is enabled in the built-in catalog. Store its key in the target Fabro server vault:

fabro provider login --provider deepseek

# For a non-default remote server:
fabro provider login --server https://your-fabro.example --provider deepseek

# Or set the vault token directly:
fabro secret set DEEPSEEK_API_KEY
fabro secret --server https://your-fabro.example set DEEPSEEK_API_KEY

Standalone SDK usage outside a Fabro server can use an env-backed credential source explicitly:

export DEEPSEEK_API_KEY=<api-key>

Fabro sends bearer-authenticated Chat Completions requests to https://api.deepseek.com.

Included models

Fabro model ID	DeepSeek API model ID	Context	Max output	Role
`deepseek-v4-flash`	`deepseek-v4-flash`	1,048,576	384,000	Provider default, small default, and connectivity probe; aliases `deepseek`, `deepseek-v4`, `deepseek-flash`
`deepseek-v4-pro`	`deepseek-v4-pro`	1,048,576	384,000	Higher-capability V4 model

Both models support text input, tool calling, native reasoning, streaming, JSON output, and automatic prompt caching. They do not support image input. Thinking mode is enabled by default.

Agent profile

DeepSeek V4 uses Fabro’s openai agent profile on every route. This profile is the closest match for DeepSeek’s general coding behavior: it supplies project AGENTS.md instructions, standard JSON function tools, and a JSON-compatible file editor on Chat Completions routes. The setting is model-specific, so it remains the same through direct DeepSeek, Fireworks AI, and OpenRouter. Fabro does not use the gpt56 profile for DeepSeek. That profile has a smaller Codex-specific tool set for GPT-5.6 models.

Use DeepSeek models

fabro model list --provider deepseek
fabro model test --provider deepseek --model deepseek-v4-flash --deep
fabro run workflow.fabro --provider deepseek --model deepseek

In workflow stylesheets:

workflow.fabro

digraph Example {
    graph [
        model_stylesheet="
            *          { model: deepseek-v4-flash; }
            .difficult { model: deepseek-v4-pro; }
        "
    ]

    start [shape=Mdiamond, label="Start"]
    work  [label="Implement", prompt="Implement and verify the requested change."]
    exit  [shape=Msquare, label="Exit"]

    start -> work -> exit
}

Thinking and reasoning effort

DeepSeek enables thinking by default at high effort. Fabro advertises only effort values that produce a distinct model behavior on each route:

Route	V4 Flash	V4 Pro
Direct DeepSeek	`low`, `high`, `max`	`high`, `max`
Fireworks AI	`high`, `max`	`high`, `max`
OpenRouter	`low`, `high`, `max`	`high`, `xhigh`

DeepSeek currently maps a V4 Pro request for low to high; its documentation says this mapping will change in early August 2026. Fireworks maps low and medium to high, and maps xhigh to max. OpenRouter names the Pro maximum tier xhigh. Fabro omits temperature and top_p for these models because DeepSeek ignores sampling parameters while thinking is enabled. To disable thinking on the direct provider, omit typed reasoning_effort and pass DeepSeek’s native toggle through provider options:

{
  "provider_options": {
    "deepseek": {
      "thinking": { "type": "disabled" }
    }
  }
}

DeepSeek requires reasoning_content from an assistant tool call to appear in every later request in that tool-use turn. Fabro captures this content and replays it with the assistant message. This keeps multi-step tool calls valid and prevents DeepSeek’s HTTP 400 response for missing reasoning history.

Prompt caching and pricing

DeepSeek applies prefix caching automatically. Fabro reads DeepSeek’s prompt_cache_hit_tokens usage field and reports cache-read tokens separately from uncached input tokens. The built-in catalog uses DeepSeek’s published prices per million tokens:

Model	Uncached input	Cache hit	Output
`deepseek-v4-flash`	$0.14	$0.0028	$0.28
`deepseek-v4-pro`	$0.435	$0.003625	$0.87

DeepSeek does not return an in-band dollar cost. Fabro calculates an estimated cost from these catalog rates and the reported token buckets.

Use a gateway

The deepseek-v4-flash, deepseek-v4-pro, deepseek, deepseek-v4, and deepseek-flash selectors are portable across direct DeepSeek, Fireworks AI, and OpenRouter routes. Use --provider or a provider-qualified model selector when you need a specific route. See the Fireworks AI integration and OpenRouter integration for gateway setup and provider-specific pricing.

Troubleshooting

“No API key configured” — Store DEEPSEEK_API_KEY on the target server with fabro provider login --provider deepseek. The server runtime resolves the key from its vault, not from process env. Unknown model — Use deepseek-v4-flash or deepseek-v4-pro. The retired deepseek-chat and deepseek-reasoner API IDs are not in the Fabro catalog. A tool continuation returns HTTP 400 — Keep the assistant thinking content in conversation history. Fabro does this automatically when it replays tool-call turns.

DeepSeek API

Official authentication, endpoints, and API reference.

Models and pricing

Official limits, features, and token prices.

Thinking mode

Official thinking toggles, effort mappings, and tool-call replay rules.

Getting Started

Core Concepts

Defining Workflows

Executing Workflows

Human Tools

Agents

Integrations

Reference

Prerequisites

Configure direct access

Included models

Agent profile

Use DeepSeek models

Thinking and reasoning effort

Prompt caching and pricing

Use a gateway

Troubleshooting

Further reading

DeepSeek API

Models and pricing

Thinking mode

​Prerequisites

​Configure direct access

​Included models

​Agent profile

​Use DeepSeek models

​Thinking and reasoning effort

​Prompt caching and pricing

​Use a gateway

​Troubleshooting

​Further reading

DeepSeek API

Models and pricing

Thinking mode

Prerequisites

Configure direct access

Included models

Agent profile

Use DeepSeek models

Thinking and reasoning effort

Prompt caching and pricing

Use a gateway

Troubleshooting

Further reading