Quickstart — API key

Tokligence speaks the API formats your tools already use. The same account and key work across three drop-in compatible surfaces — point any client at the matching endpoint:

Format	Endpoint	Used by
OpenAI Chat Completions	POST https://llm-api.tokligence.ai/v1/chat/completions	OpenAI SDKs, most apps
OpenAI Responses	POST https://llm-api.tokligence.ai/v1/responses	Codex CLI
Anthropic Messages	POST https://llm-api.tokligence.ai/v1/messages	Claude Code, Anthropic SDKs

Base URL:  https://llm-api.tokligence.ai/v1
Auth:      Authorization: Bearer <your-api-key>

1. Create an account and get an API key from the dashboard.
2. Set the base URL and key in your client — nothing else changes.

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="tk-...",
    base_url="https://llm-api.tokligence.ai/v1",
)

resp = client.chat.completions.create(
    model="deepseek-v4-flash",          # see /models for the full list
    messages=[{"role": "user", "content": "Hello!"}],
)
print(resp.choices[0].message.content)

cURL

Every command below is copy-paste ready for your OS and shell. First, save your key to an environment variable so it isn't pasted into each command:

macOS / Linux (bash)

export TOKLIGENCE_API_KEY="tk-..."

Windows (PowerShell)

$env:TOKLIGENCE_API_KEY="tk-..."

Windows (cmd)

set TOKLIGENCE_API_KEY=tk-...

Then make your first call. Each tab is wrapped for its shell — select the whole block and paste it. The shells differ only in quoting and how the key variable is referenced.

macOS / Linux (bash)

curl https://llm-api.tokligence.ai/v1/chat/completions \
  -H "Authorization: Bearer $TOKLIGENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-v4-flash","messages":[{"role":"user","content":"Hello!"}]}'

Windows (PowerShell)

In PowerShell, use Invoke-RestMethod. Passing a JSON body to curl.exe through PowerShell's argument quoting is unreliable across versions (it can send the backslashes literally and produce invalid JSON). With Invoke-RestMethod the body is a plain single-quoted string handed straight to -Body, so there is nothing to escape:

$headers = @{ "Authorization" = "Bearer $env:TOKLIGENCE_API_KEY" }
$body = '{"model":"deepseek-v4-flash","messages":[{"role":"user","content":"Hello!"}]}'
Invoke-RestMethod `
  -Uri "https://llm-api.tokligence.ai/v1/chat/completions" `
  -Method Post `
  -Headers $headers `
  -ContentType "application/json" `
  -Body $body

Windows (cmd)

The JSON is double-quoted with the inner quotes backslash-escaped, and the key is read with %...%:

curl https://llm-api.tokligence.ai/v1/chat/completions ^
  -H "Authorization: Bearer %TOKLIGENCE_API_KEY%" ^
  -H "Content-Type: application/json" ^
  -d "{\"model\":\"deepseek-v4-flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello!\"}]}"

Coding agents (Claude Code, Codex & Cursor)

Because Tokligence serves the native Anthropic Messages and OpenAI Responses formats, coding agents work against it with no proxy or translation layer — just an environment variable or a config file.

Pick a model that speaks the format

The agent endpoints forward your request to the provider that serves the model. Use a model that natively supports the format you're calling — for example deepseek-v4-flash works for both Claude Code (/v1/messages) and Codex (/v1/responses). Browse the catalogue at llm.tokligence.ai/models.

Claude Code

Point Claude Code at Tokligence with three environment variables, then run it as usual:

macOS / Linux (bash)

export ANTHROPIC_BASE_URL="https://llm-api.tokligence.ai"
export ANTHROPIC_AUTH_TOKEN="tk-..."          # your Tokligence API key
export ANTHROPIC_MODEL="deepseek-v4-flash"    # see /models

claude

To make it stick, add the three export lines to your shell profile (~/.zshrc / ~/.bashrc).

Windows (PowerShell)

$env:ANTHROPIC_BASE_URL="https://llm-api.tokligence.ai"
$env:ANTHROPIC_AUTH_TOKEN="tk-..."          # your Tokligence API key
$env:ANTHROPIC_MODEL="deepseek-v4-flash"    # see /models

claude

These variables last for the current PowerShell session. To persist them, use setx (opens a new session to take effect) or set them under System → Environment Variables. Running Claude Code inside WSL is also fully supported — there, use the bash tab.

Windows (cmd)

set ANTHROPIC_BASE_URL=https://llm-api.tokligence.ai
set ANTHROPIC_AUTH_TOKEN=tk-...
set ANTHROPIC_MODEL=deepseek-v4-flash

claude

These set values last for the current cmd session. To persist them, use setx or set them under System → Environment Variables. Running Claude Code inside WSL is also fully supported — there, use the bash tab.

Claude Code sends Anthropic Messages requests to https://llm-api.tokligence.ai/v1/messages, with full tool use and streaming.

Codex

Codex uses the OpenAI Responses API. Add a provider to the Codex config file and select it. The file content is identical on every OS — only its location and how you set the key differ:

model = "deepseek-v4-flash"          # see /models
model_provider = "tokligence"

[model_providers.tokligence]
name = "tokligence"
base_url = "https://llm-api.tokligence.ai/v1"
wire_api = "responses"
env_key = "TOKLIGENCE_API_KEY"       # Codex reads the key from this env var

macOS / Linux (bash)

Config file: ~/.codex/config.toml

export TOKLIGENCE_API_KEY="tk-..."   # your Tokligence API key
codex "explain this repository"

Windows (PowerShell)

Config file: %USERPROFILE%\.codex\config.toml

$env:TOKLIGENCE_API_KEY="tk-..."     # your Tokligence API key
codex "explain this repository"

Windows (cmd)

Config file: %USERPROFILE%\.codex\config.toml

set TOKLIGENCE_API_KEY=tk-...
codex "explain this repository"

Codex sends Responses requests to https://llm-api.tokligence.ai/v1/responses, including streaming and reasoning.

Cursor

Cursor is cross-platform — the same setup works on macOS, Linux, and Windows. In Settings → Models, enable an OpenAI-compatible custom provider and fill in:

• Base URL — https://llm-api.tokligence.ai/v1
• API key — your Tokligence key (tk-...)
• Model — add a model from the catalogue, e.g. deepseek-v4-flash (see /models)

Cursor then talks to Tokligence over the OpenAI Chat Completions surface.

Discovering models

GET https://llm-api.tokligence.ai/v1/models

Or browse them with live prices at llm.tokligence.ai/models.

No account? Pay per request

Autonomous agents can skip signup and pay per call in USDC via x402.