Basic Chat Examples

Last updated: 2026-07-16 Audience: Beginner · Time to complete: 5 minutes

This page shows the simplest ways to chat with an AI model using LLM4Free. Every provider implements the OpenAI-compatible chat.completions.create(...) interface, so the code shape is identical across vendors.

Table of Contents

1. Using the unified Client
2. Simplest example (no API key)
3. Different providers
4. Customizing responses
5. Saving and reusing conversations
6. Using the unified Client (details)

---

Using the unified Client

Start here. The unified Client is the recommended way to chat — it mirrors the OpenAI SDK, picks a working provider for you, and auto-fails over when one is down. Use model="auto" to let it choose, or model="Provider/Model" to force a specific backend.

from llm4free.client import Client

# Let the client pick any working provider/model
client = Client(print_provider_info=True)
print(client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Tell me a fun fact about space."}],
).choices[0].message.content)

# Force a specific provider/model
print(client.chat.completions.create(
    model="HeckAI/google/gemini-2.5-flash-preview",
    messages=[{"role": "user", "content": "Hello!"}],
).choices[0].message.content)
print(client.chat.completions.last_provider)  # which provider was used

---

Simplest example (no API key)

HeckAI is a free provider — no key required.

from llm4free.llm.heckai import HeckAI

client = HeckAI()
response = client.chat.completions.create(
    model="google/gemini-2.5-flash-preview",
    messages=[{"role": "user", "content": "What is artificial intelligence?"}],
)
print(response.choices[0].message.content)

Output:

Artificial intelligence (AI) refers to computer systems designed to perform
tasks that typically require human intelligence — learning, reasoning,
problem-solving, language understanding, and visual perception...

---

Different providers

HeckAI (free)

from llm4free.llm.heckai import HeckAI

client = HeckAI()
print(client.chat.completions.create(
    model="google/gemini-2.5-flash-preview",
    messages=[{"role": "user", "content": "Hello!"}],
).choices[0].message.content)

ArtingAI (free)

from llm4free.llm.artingai import ArtingAI

client = ArtingAI()
print(client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
).choices[0].message.content)

FreeAI (free)

from llm4free.llm.freeai import FreeAI

client = FreeAI()
print(client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
).choices[0].message.content)

Groq (API key)

from llm4free.llm.Auth.groq import Groq

client = Groq(api_key="your-groq-key")
print(client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Explain machine learning simply"}],
).choices[0].message.content)

DeepInfra (API key)

from llm4free.llm.Auth.deepinfra import DeepInfra

client = DeepInfra(api_key="your-deepinfra-key")
print(client.chat.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "What is Python?"}],
).choices[0].message.content)

---

Customizing responses

Standard OpenAI parameters are supported: temperature, max_tokens, top_p, and tools.

from llm4free.llm.heckai import HeckAI

client = HeckAI()
response = client.chat.completions.create(
    model="google/gemini-2.5-flash-preview",
    messages=[
        {"role": "system", "content": "You are a concise tutor."},
        {"role": "user", "content": "Explain recursion in one sentence."},
    ],
    temperature=0.3,
    max_tokens=120,
)
print(response.choices[0].message.content)

---

Saving and reusing conversations

Pass the full message list each call to keep context:

from llm4free.llm.heckai import HeckAI

client = HeckAI()
messages = [{"role": "user", "content": "My name is Ada."}]

r1 = client.chat.completions.create(model="google/gemini-2.5-flash-preview", messages=messages)
print(r1.choices[0].message.content)

# Append the assistant reply, then ask a follow-up
messages.append({"role": "assistant", "content": r1.choices[0].message.content})
messages.append({"role": "user", "content": "What was my name?"})
r2 = client.chat.completions.create(model="google/gemini-2.5-flash-preview", messages=messages)
print(r2.choices[0].message.content)

---

Using the unified Client (details)

Forget provider names — let Client pick one and auto-fail over. Pass model="auto" for automatic selection, or model="Provider/Model" to force a specific backend.

from llm4free.client import Client

client = Client(print_provider_info=True)
print(client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Tell me a fun fact about space."}],
).choices[0].message.content)

# Force a specific provider/model
print(client.chat.completions.create(
    model="HeckAI/google/gemini-2.5-flash-preview",
    messages=[{"role": "user", "content": "Hello!"}],
).choices[0].message.content)

See client.md for the full reference and streaming-responses.md for streaming.