deepseek-local-server v0.2 — direct DeepSeek Web API gateway

Local DeepSeek Web gateway for agents and OpenAI-compatible clients. All chat traffic goes through the direct DeepSeek Web HTTP/SSE contract (SHA3 PoW executed locally via wasmtime). Chrome is used only once, for interactive auth capture.

What changed from v0.1

v0.1 drove the DeepSeek UI through Playwright for every request. v0.2 talks the internal Web Chat HTTP/SSE contract directly. The browser fallback and Playwright automation layer were removed entirely — Chrome over CDP is used only for the one-time auth capture from your own logged-in browser.

Request path:

Pi / Claude / OpenAI client
        |
        v
127.0.0.1:9874
        |
        v
CompletionService --> Direct DeepSeek Web API + PoW + true SSE

Related MCP server: DeepSeek MCP Server

Features

• OpenAI POST /v1/chat/completions (streaming and non-streaming)

• true upstream SSE streaming, reasoning_content for thinking modes

• image input (vision) via OpenAI image content parts

• per-agent remote DeepSeek sessions (x-agent-session / user)

• automatic session reset when the client history changes

• basic OpenAI tool-call adapter

• MCP ask_deepseek: always reasoning + web search; the result is the answer (markdown and LaTeX rendered for plain-text hosts) while reasoning streams as a one-line status ticker; images passed by local file path

• basic Anthropic /v1/messages shim

• basic OpenAI Responses /v1/responses shim

• local bearer token is always required

• loopback-only binding is enforced

Models

Compatibility aliases (DeepSeek merged Instant/Expert/Vision into one model, 2026-09): deepseek-web, deepseek-expert, deepseek-v4-pro -> deepseek-reasoner; deepseek-r1 -> deepseek-reasoner; deepseek-r1-search -> deepseek-reasoner-search; deepseek-v3, deepseek-default -> deepseek-chat.

Windows installation

cd G:\path\to\deepseek-local-server-v2
py -3.12 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -U pip
.\.venv\Scripts\pip.exe install -e ".[dev]"

No playwright install needed: auth drives your real, already-installed Google Chrome over the DevTools protocol instead of a Playwright-managed browser build. Set DEEPSEEK_LOCAL_SERVER_CHROME_PATH if Chrome isn't in one of the default install locations.

First setup

.\.venv\Scripts\deepseek-local-server.exe init
.\.venv\Scripts\deepseek-local-server.exe auth

auth opens Chrome. Log in to DeepSeek and send one tiny message, for example AUTH_OK. The program captures the auth token, cookie, x-hif-* headers and SHA3 PoW WASM URL from your own browser request, stores them in:

%LOCALAPPDATA%\deepseek-local-server\deepseek-auth.json

Do not commit or share that file.

Then:

.\.venv\Scripts\deepseek-local-server.exe doctor
.\.venv\Scripts\deepseek-local-server.exe serve

In another terminal:

.\.venv\Scripts\deepseek-local-server.exe chat "Reply exactly SERVICE_OK" --model deepseek-reasoner

OpenAI client

from openai import OpenAI
from pathlib import Path
import os

home = Path(os.environ["LOCALAPPDATA"]) / "deepseek-local-server"
token = (home / "token").read_text().strip()

client = OpenAI(base_url="http://127.0.0.1:9874/v1", api_key=token)

stream = client.chat.completions.create(
    model="deepseek-reasoner",
    messages=[{"role": "user", "content": "Analyze this design."}],
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.reasoning_content:
        print("[think]", delta.reasoning_content, end="", flush=True)
    else:
        print(delta.content or "", end="", flush=True)

Images: send OpenAI vision content parts with image_url entries whose url is a base64 data URL (data:image/png;base64,...) — bytes are uploaded to DeepSeek directly. In MCP, pass image_path with a local file path instead.

MCP / Pi

~/.pi/agent/mcp.json:

{
  "deepseek-web": {
    "command": "G:/path/to/deepseek-local-server-v2/.venv/Scripts/python.exe",
    "args": ["-m", "deepseek_local_server", "mcp"],
    "cwd": "G:/path/to/deepseek-local-server-v2",
    "lifecycle": "keep-alive",
    "directTools": false,
    "requestTimeoutMs": 600000
  }
}

MCP tool:

ask_deepseek(
  question,
  new_conversation=false,  # clear MCP-side history before this call
  image_path=None,         # local image file, e.g. "C:/pics/photo.jpg" (vision)
  timeout_seconds=<request timeout + 30>
)

Reasoning and web search are always on (deepseek-reasoner-search); there are no mode toggles.

For live reasoning while the model thinks, the tool must be reachable through the proxy path, because pi's progress bridge only runs there — set "directTools": false for this server (pi then shows the running reasoning as a status ticker). With directTools: true pi drops progress notifications entirely, so nothing appears until the call finishes.

Result layout:

• the answer comes first, so a host that shows only the first lines of a collapsed tool block still shows the answer;

• while thinking, a compact one-line ticker (thinking: … + the newest ~140 chars) is pushed about every 1.5s — pi replaces the same status line in place;

• callers that cannot receive progress (direct-tool calls) get a bounded slice of the chain appended after the answer (4k head + 2k tail plus an … [N chars of reasoning omitted] … marker), dimmed with SGR 90.

Session history stores the clean answer only. Conversation history is kept process-wide and survives across calls; new_conversation=true resets it.

Rendering for plain-text hosts

MCP hosts print tool output literally (pi even wraps it in its theme's toolOutput colour), so the answer is normalised before it is returned:

• markdown tables become box-drawing tables (alignment taken from the ---: row, cells wrapped, width budget 98, CJK/emoji measured in terminal cells);

• markdown and LaTeX fences around the whole answer are unwrapped; ```python and other real code blocks stay verbatim;

• LaTeX is converted to readable Unicode: \frac{a}{b} → a/b, \sqrt{…} → √(…), \mu → μ, \cdot → ·, 10^{24} → 10²⁴, v_1 → v₁, \dot{m} → ṁ, \ln, \SI{a}{b}, \section{…}, tabular → box table, and so on;

• headings, **bold**, [links](url), > markers and math delimiters are dropped; inline $math$ is only unwrapped when it looks like math, so prices stay intact.

Testing

The renderer is pinned by snapshots of real DeepSeek answers:

.\.venv\Scripts\python.exe -m pytest          # unit + snapshot suite
.\.venv\Scripts\python.exe capture_fixtures.py  # capture new real answers (needs the gateway)
.\.venv\Scripts\python.exe refresh_fixtures.py  # re-render the saved fixtures offline
.\.venv\Scripts\python.exe probe_reasoning_live.py   # live ticker check
.\.venv\Scripts\python.exe probe_reasoning_block.py  # live no-ticker layout check

tests/fixtures/ keeps each answer as <name>.raw.txt (as the model returned it) next to <name>.expected.txt (what the renderer must produce). Review the diff before committing a refresh.

Rendering in pi

Every line of MCP tool output is painted with the theme's toolOutput colour (gray in the shipped dark theme); the reasoning block overrides that per line with SGR 90, so it stays grey, and the answer keeps the theme colour. To make the answer white, copy dark.json from the pi install into <agent-dir>/themes/, set colors.toolOutput to text, and pick that theme with /theme. A collapsed tool block shows only its first lines (collapsedResultLines, 1–3 in mcp.json settings); press Ctrl+O or start pi with --verbose to see the whole result.

Diagnostics

# validate files only
.\.venv\Scripts\deepseek-local-server.exe doctor --offline

# validate auth + call DeepSeek PoW challenge endpoint
.\.venv\Scripts\deepseek-local-server.exe doctor

Useful API endpoints:

GET  /health
GET  /v1/models
GET  /v1/model-capabilities
GET  /v1/sessions
POST /v1/sessions/reset?agent=<id|all>
POST /v1/chat/completions
POST /v1/messages
POST /v1/responses

Environment variables

Attribution

The direct Web API implementation was informed by the MIT-licensed ForgetMeAI/FreeDeepseekAPI project. See THIRD_PARTY_NOTICES.md.

Important limitations

This is an experimental adapter for DeepSeek Web, not the official DeepSeek API. Internal endpoints, PoW WASM exports, request headers, stream patches or auth headers can change without notice.

The direct backend is isolated under direct/ specifically so those changes do not spread through the OpenAI/MCP layers.

The implementation does not bypass CAPTCHA, 2FA or login challenges. Re-run auth and complete those checks normally in the browser.