Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/Evincere/klisk/llms.txt

Use this file to discover all available pages before exploring further.

Klisk supports OpenAI models natively and any LiteLLM-compatible provider, giving you access to hundreds of language models from different providers.

​
Model string format

The model parameter in define_agent() uses a convention to determine whether to use OpenAI directly or route through LiteLLM:
​
OpenAI (no slash)
string
No / character → native OpenAI model
define_agent(name="Agent", model="gpt-5.2")         # OpenAI native
define_agent(name="Agent", model="gpt-4.1")        # OpenAI native
define_agent(name="Agent", model="o3")             # OpenAI native
​
OpenAI (with prefix)
string
openai/ prefix → strips prefix, uses native OpenAI
define_agent(name="Agent", model="openai/gpt-5.2")  # OpenAI native (prefix stripped)
​
LiteLLM (provider prefix)
string
provider/ format → routes through LiteLLM
define_agent(name="Agent", model="anthropic/claude-sonnet-4-5-20250929")
define_agent(name="Agent", model="gemini/gemini-2.5-flash")
define_agent(name="Agent", model="mistral/mistral-large-latest")

​
OpenAI models

OpenAI models work out of the box. Set your API key:
.env
OPENAI_API_KEY=sk-...
Then reference the model directly: 
from klisk import define_agent

agent = define_agent(
    name="Assistant",
    model="gpt-5.2",  # Default if omitted
)
Available OpenAI models:
  • gpt-5.2, gpt-5.1 — Latest GPT-5 series
  • gpt-4.1, gpt-4o, gpt-4o-mini — GPT-4 series
  • o3, o4-mini, o1 — Reasoning models
The default model is gpt-5.2 if you don’t specify the model parameter.

​
LiteLLM providers

For non-OpenAI models, Klisk uses LiteLLM to provide a unified interface to 100+ LLM providers.

​
Installation

LiteLLM is automatically installed when you use a non-OpenAI model string: 
# First time this runs, Klisk will prompt to install litellm
agent = define_agent(
    name="Agent",
    model="anthropic/claude-sonnet-4-5-20250929",
)
Or install it manually: 
pip install litellm

​
API keys

Klisk auto-detects API keys from environment variables based on the provider prefix:
.env
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
MISTRAL_API_KEY=...
COHERE_API_KEY=...
The pattern is {PROVIDER}_API_KEY where PROVIDER is the uppercase version of the prefix:
Model StringEnvironment Variable
anthropic/...ANTHROPIC_API_KEY
gemini/...GEMINI_API_KEY
mistral/...MISTRAL_API_KEY
cohere/...COHERE_API_KEY
azure/...AZURE_API_KEY
If you’re using a non-OpenAI model and OPENAI_API_KEY is not set, Klisk automatically disables OpenAI tracing to prevent errors.

​
Model resolution

When you specify a LiteLLM model, Klisk:
  1. Enables LiteLLM serializer compatibility patch to suppress Pydantic warnings
  2. Imports LitellmModel from the OpenAI Agents SDK
  3. Extracts the provider prefix (e.g., anthropic from anthropic/claude-sonnet-4-5)
  4. Looks up {PROVIDER}_API_KEY from environment variables
  5. Creates a LitellmModel(model=model_str, api_key=api_key) instance
This happens automatically in define_agent() via the _resolve_model() function in primitives.py:19.

​
Anthropic (Claude)

define_agent(
    name="Agent",
    model="anthropic/claude-sonnet-4-5-20250929",
)
Available models:
  • claude-sonnet-4-5-20250929 — Latest Claude Sonnet
  • claude-opus-4 — Most capable
  • claude-haiku-4 — Fast and efficient

​
Google (Gemini)

define_agent(
    name="Agent",
    model="gemini/gemini-2.5-flash",
)
Available models:
  • gemini-2.5-flash — Fast, cost-effective
  • gemini-2.5-pro — Advanced reasoning
  • gemini-1.5-pro — Previous generation

​
Mistral

define_agent(
    name="Agent",
    model="mistral/mistral-large-latest",
)
Available models:
  • mistral-large-latest — Most capable
  • mistral-medium-latest — Balanced
  • mistral-small-latest — Fast

​
Azure OpenAI

define_agent(
    name="Agent",
    model="azure/gpt-4o",
)
Requires additional Azure-specific environment variables. See LiteLLM Azure docs.

​
Reasoning effort

For reasoning models (o-series and gpt-5+), you can control how much reasoning the model applies: 
define_agent(
    name="Analyst",
    model="o3",
    reasoning_effort="high",
)
Valid values: "none", "minimal", "low", "medium", "high", "xhigh"
LiteLLM automatically translates reasoning_effort to each provider’s equivalent parameter. For example, Anthropic’s thinking parameter.
Only set reasoning_effort for reasoning models (o1, o3, o4-mini, gpt-5.1, gpt-5.2). It has no effect on standard models like gpt-4o or claude-sonnet-4-5.

​
When to use reasoning effort

ValueUse CaseCost
"none" or "minimal"Simple, routine tasksLower
"low" or "medium"Standard problem-solvingModerate
"high" or "xhigh"Complex analysis, math, codeHigher
# Simple data extraction
define_agent(
    name="Extractor",
    model="gpt-5.2",
    reasoning_effort="minimal",
)

# Complex mathematical analysis
define_agent(
    name="MathSolver",
    model="o3",
    reasoning_effort="xhigh",
)

​
Temperature

Control response randomness with the temperature parameter: 
define_agent(
    name="CreativeWriter",
    model="gpt-5.2",
    temperature=1.5,  # More creative, diverse outputs
)

define_agent(
    name="DataAnalyst",
    model="gpt-5.2",
    temperature=0.2,  # More focused, deterministic
)
Range: 0.0 (deterministic) to 2.0 (very random) Defaults: Each model has its own default (usually around 0.7 to 1.0)

​
Builtin tools compatibility

Builtin tools (web_search, code_interpreter, file_search, image_generation) only work with OpenAI models: 
# ✅ Works
define_agent(
    name="Researcher",
    model="gpt-5.2",
    builtin_tools=["web_search"],
)

# ❌ Warns and disables builtin tools
define_agent(
    name="Researcher",
    model="anthropic/claude-sonnet-4-5-20250929",
    builtin_tools=["web_search"],  # Will be disabled with a warning
)
This is checked at agent creation time in primitives.py:104. If you specify builtin tools with a non-OpenAI model, you’ll see: 
Warning: Builtin tools (web_search) are only supported with OpenAI models
and will be disabled for 'anthropic/claude-sonnet-4-5-20250929'.

​
Complete example

from klisk import define_agent, get_tools

# OpenAI with reasoning
analyst = define_agent(
    name="DataAnalyst",
    model="o3",
    reasoning_effort="high",
    temperature=0.3,
    tools=get_tools("analyze"),
)

# Anthropic with custom tools
researcher = define_agent(
    name="Researcher",
    model="anthropic/claude-sonnet-4-5-20250929",
    temperature=0.7,
    tools=get_tools("search", "summarize"),
)

# Google with high creativity
writer = define_agent(
    name="Writer",
    model="gemini/gemini-2.5-flash",
    temperature=1.5,
    tools=get_tools("draft", "revise"),
)

# OpenAI with builtin tools
assistant = define_agent(
    name="Assistant",
    model="gpt-5.2",
    builtin_tools=["web_search", "code_interpreter"],
)

​
Next steps

Agents

Learn more about agent configuration

Builtin Tools

Explore provider-hosted tools for OpenAI models

Build docs developers (and LLMs) love

Get started for freeTalk to us