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OpenAI client retries turn on the OpenAI SDK’s built-in retry engine so transient 429s and network blips on the native client path are retried automatically.

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Quick Start

1

Turn it on with one env var (zero code change)

# export OPENAI_MAX_RETRIES=5

from praisonaiagents import Agent

agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
)
agent.start("Summarise today's news.")
# Transient 429s and network blips are now retried automatically.
The user runs an agent; transient rate limits and network blips retry automatically on the native OpenAI client path.
2

Pass max_retries through the client factory

from praisonaiagents.llm import get_openai_client

client = get_openai_client(max_retries=5)
3

Construct the client directly

from praisonaiagents.llm import OpenAIClient

client = OpenAIClient(max_retries=5)

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How It Works

The value is forwarded into the underlying openai SDK, which honours Retry-After on 429s and applies exponential backoff for transient errors.
BehaviourDetail
Applies toThe native OpenAIClient / get_openai_client path (used internally by AutoAgents) and any direct callers.
Not affectedThe LiteLLM-backed LLM class and agent-loop resilience engine (see Agent Retry).
When unsetBehaviour is byte-identical to before — max_retries is only forwarded when it is not None.
Cacheget_openai_client() caches on max_retries, so changing it mid-process rebuilds the SDK client.

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Configuration Options

OptionTypeDefaultDescription
max_retries (arg on OpenAIClient / get_openai_client)Optional[int]NoneNumber of automatic retries for transient errors on the native OpenAI client path.
OPENAI_MAX_RETRIES (env var)int (string)unsetFallback used when max_retries is not passed. Non-integer values silently fall back to the SDK default.
Precedence: explicit arg > OPENAI_MAX_RETRIES env var > SDK default.
Non-integer OPENAI_MAX_RETRIES (e.g. "abc") is silently ignored and the SDK default takes over — no crash.

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Which Approach to Use

Match the retry surface to how broadly you want the policy to apply.

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Common Patterns

Turn it on globally with one env var — for production deployments where every call should retry the same way. 
# export OPENAI_MAX_RETRIES=5
from praisonaiagents import Agent

agent = Agent(name="Assistant", instructions="You are a helpful assistant.")
agent.start("Draft a weekly status report.")
Scope it to one call site — pass max_retries explicitly when you build the client for a hot path. 
from praisonaiagents.llm import get_openai_client

# Only this client retries; the rest of the process is unaffected.
client = get_openai_client(max_retries=5)
AutoAgents also accepts a max_retries argument, but that controls its own config-generation retries — it is separate from the SDK-level max_retries documented here. Use OPENAI_MAX_RETRIES for a process-wide native-client policy.
Local server (LM Studio / vLLM) — leave it off — local endpoints rarely emit Retry-After, so retries are wasted latency. 
from praisonaiagents.llm import get_openai_client

# No max_retries: keep local calls fast and fail fast.
client = get_openai_client(base_url="http://localhost:1234/v1")

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Best Practices

One place, no code change, easy to bump per environment. Set OPENAI_MAX_RETRIES once and every native call inherits it.
This matches the SDK’s typical rate-limit budget without inflating tail latency.
Combining OPENAI_MAX_RETRIES=5 with Agent(retry=True, max_retries=3) multiplies attempts (5 × 3 = 15). Pick one layer for the primary retry policy and use the other only as a safety net.
Retries help transient blips; for provider outages use LLMConfig(fallbacks=[...]) — see Model Fallback.

Agent Retry

Retry the LiteLLM-backed agent loop with jittered exponential backoff.

Model Fallback

Fail over to alternate models during provider outages.

LLM Error Classification

Understand which errors are retryable versus fatal.