Reliability

Real services have hiccups: a request times out, a provider has a blip, you hit a rate limit, or the model returns something malformed. These tools keep your agent working through all of that.

Retry on temporary errors

Wrap your model in RetryModel and it automatically retries when a call fails:

from agentix import RetryModel

model = RetryModel(my_model, retries=3)

It's rate-limit aware. When a provider says "you're going too fast, wait 5 seconds" (a rate limit), RetryModel waits exactly that long instead of guessing — and falls back to gradually increasing waits for other kinds of errors.

→ Runnable example: examples/28_rate_limit.py

Fall back to another model

If one model (or provider) is down, automatically try the next one:

from agentix import FallbackModel

model = FallbackModel([primary_model, backup_model])

Useful for surviving an outage, or for "try the cheap model first, fall back to the big one."

Validate the output

Make sure the answer is usable before your code relies on it. If it isn't, the agent re-asks the model:

from agentix import json_output

agent = Agent(model=m, system_prompt="Reply with JSON.",
              output_validator=json_output, max_output_retries=2)
outcome = await agent.run("...")
outcome.parsed     # the validated value — safe to use

For typed data with one setting, see Structured output.

→ Runnable example: examples/16_reliability.py

Record and replay

Testing against a real model is slow, costs money, and gives different answers each time. CassetteModel records real responses once to a file, then replays them in later test runs — fast, free, and identical every time. (The name comes from recording onto a cassette tape.)

from agentix import CassetteModel

# First run records to the file; later runs replay from it. "auto" does the right
# thing based on whether the file already exists.
model = CassetteModel("tests/cassettes/weather.json", model=AnthropicModel())
# ... run the agent ...
model.save()

→ Runnable example: examples/29_cassettes.py