OpenAI ChatGPT

Use OpenAI GPT models with PraisonAI Agents

Models

• Recommended: gpt-4o (latest, best quality)
• Fast/Cheap: gpt-4o-mini (fast, cost-effective)
• Reasoning: o1, o1-mini (advanced reasoning)
• Latest: gpt-4.1, gpt-4.5-preview (newest releases)

Python

# export OPENAI_API_KEY=your-api-key
from praisonaiagents import Agent

agent = Agent(
    instructions="You are a helpful assistant",
    llm="gpt-4o"
)
agent.start("What is artificial intelligence?")

With Tools

# export OPENAI_API_KEY=your-api-key
from praisonaiagents import Agent

def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"Weather in {city}: 72°F, Sunny"

agent = Agent(
    instructions="You are a weather assistant",
    llm="gpt-4o-mini",
    tools=[get_weather]
)
agent.start("What's the weather in Tokyo?")

Multi-Agent

# export OPENAI_API_KEY=your-api-key
from praisonaiagents import Agent, Task, AgentTeam

researcher = Agent(
    instructions="You research topics thoroughly",
    llm="gpt-4o"
)
writer = Agent(
    instructions="You write clear summaries",
    llm="gpt-4o-mini"
)

task1 = Task(description="Research AI trends", agent=researcher)
task2 = Task(description="Summarize findings", agent=writer)

agents = AgentTeam(agents=[researcher, writer], tasks=[task1, task2])
agents.start()

Transparent Responses API routing

For OpenAI models that support the Responses API (gpt-4o, gpt-4.1, gpt-4.5, gpt-5, o1, o3, o4, chatgpt-4o, and their azure/ / openai/ prefixes), PraisonAI uses the Responses endpoint automatically on the native client path — no code change, no flag. Chat Completions is only used when a custom base_url is set or the installed openai SDK does not expose responses.

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    llm="gpt-4o-mini",   # routes through the Responses API automatically
)
agent.start("Summarise today's news.")

On the Responses branch, sync callers (agent.chat / agent.start) use the same transparent routing — the client rebinds its async transport across asyncio.run loops, so a sync turn and the next sync turn stay on the Responses endpoint rather than silently swapping to Chat Completions.

Supplying history with tool calls

If you pass conversation history that includes an assistant turn with tool_calls, the content field may be any of:

• a plain string: "Checking"
• a list of text parts: [{"type": "text", "text": "Checking"}] (the Chat Completions multimodal shape)
• empty — "", None, [], or whitespace — in which case only the tool call is emitted

Multimodal list content was fixed in PraisonAI PR #5695 (merged 2026-10-08). On earlier versions the same history raised AttributeError and silently fell back to Chat Completions.

Fallback is quieter now

Two spurious Responses API failed, falling back to Chat Completions: … WARNING lines were fixed in PraisonAI PR #5682 (merged 2026-10-08):

• Orphan tool outputs no longer 400. If your history contains a role="tool" message whose tool_call_id has no matching assistant function_call in the same payload — e.g. history trimming dropped the assistant turn, or a user turn follows a tool round from an earlier session — the client now drops that function_call_output before sending. Before #5682 the Responses API rejected the payload with No tool call found for function call output with call_id … and the client logged a fallback WARNING for every such turn.
• Sync callers no longer see “Event loop is closed”. The cached AsyncOpenAI instance is rebuilt whenever the running loop differs from the one it was bound to (the common case for sync agent.chat() / agent.start() which bridge into the async path via a fresh asyncio.run per call). Before #5682 the stale httpx transport raised Event loop is closed on reuse and the client logged a fallback WARNING for every turn.

# export OPENAI_API_KEY=...
from praisonaiagents import Agent

def add(a: int, b: int) -> int:
    return a + b

agent = Agent(
    name="ToolProbe",
    role="Assistant",
    goal="Use tools",
    llm="gpt-4o-mini",
    tools=[add],
)
agent.chat("What is 19 + 23? Use the add tool.")
agent.chat("Say done.")   # second turn — no fallback WARNING on stderr

from praisonaiagents import Agent

agent = Agent(instructions="You are helpful", llm="gpt-4o-mini")
agent.chat("Hi")
agent.chat("Again")  # no asyncio ERROR lines on stderr

sequenceDiagram
    participant Sync as agent.chat() (sync)
    participant Loop as asyncio.run (new loop)
    participant GC as Python GC
    participant Stale as Stale AsyncOpenAI

    Sync->>Loop: start new sync turn
    Loop->>GC: previous client dropped
    GC->>Stale: __del__
    alt Before #5859
        Stale->>Loop: create_task(aclose())
        Loop--xStale: RuntimeError: Event loop is closed
    else After #5859
        Stale-->>GC: no cross-loop task scheduled
    end

Orphan function_call_output items are dropped while the Responses input is built — the client scans assistant tool_calls first, then keeps only the tool outputs whose call_id matches one it emitted.

The cached AsyncOpenAI is rebound whenever the running loop changes, so each sync turn gets a client bound to its own asyncio.run loop.

CLI

export OPENAI_API_KEY=your-api-key

# Basic prompt
python -m praisonai "What is AI?" --llm gpt-4o

# With temperature
python -m praisonai "Write a poem" --llm gpt-4o --temperature 0.9

# With tools
python -m praisonai "Search for AI news" --llm gpt-4o --tools web_search

# Run agents.yaml
python -m praisonai

YAML

framework: praisonai
topic: Research AI trends
agents:
  researcher:
    role: Research Analyst
    goal: Find latest AI developments
    instructions: You are an expert researcher who finds accurate information
    llm:
      model: gpt-4o
    tasks:
      research_task:
        description: Research the latest trends in artificial intelligence
        expected_output: A comprehensive report on AI trends

  writer:
    role: Content Writer
    goal: Create clear summaries
    instructions: You write engaging and clear content
    llm:
      model: gpt-4o-mini
    tasks:
      write_task:
        description: Write a summary of the research findings
        expected_output: A well-written summary article