LLM fallback strategy

A ready-to-run example is available here.

FallbackStrategy gives your agent automatic resilience: when the primary LLM fails with a transient error (rate limit, timeout, connection issue), the SDK tries alternate LLMs in order. Fallback is per-call — each new request always starts with the primary model.

Basic usage

Attach a FallbackStrategy to your primary LLM. The fallback LLMs are referenced by name from an LLM Profile Store:

from pydantic import SecretStr
from faheemcode.sdk import LLM, LLMProfileStore
from faheemcode.sdk.llm import FallbackStrategy

# Menage persisted LLM profiles
# default store directory: .faheem-code/profiles
store = LLMProfileStore()

fallback_llm = LLM(
    usage_id="fallback-1",
    model="openai/gpt-4o",
    api_key=SecretStr("your-openai-key"),
)
store.save("fallback-1", fallback_llm, include_secrets=True)

# Configure an LLM with a fallback strategy
primary_llm = LLM(
    usage_id="agent-primary",
    model="anthropic/claude-sonnet-4-5-20250929",
    api_key=SecretStr("your-api-key"),
    fallback_strategy=FallbackStrategy(
        fallback_llms=["fallback-1"],
    ),
)

How it works

1. The primary LLM handles the request as normal
2. If the call fails with a transient error, the FallbackStrategy kicks in and tries each fallback LLM in order
3. The first successful fallback response is returned to the caller
4. If all fallbacks fail, the original primary error is raised
5. Token usage and cost from fallback calls are merged into the primary LLM's metrics, so you get a unified view of total spend by model

Warning

Only transient errors trigger fallback. Non-transient errors (e.g., authentication failures, bad requests) are raised immediately without trying fallbacks. For a complete list of supported transient errors see the source code

Multiple fallback levels

Chain as many fallback LLMs as you need. They are tried in list order:

llm = LLM(
    usage_id="agent-primary",
    model="anthropic/claude-sonnet-4-5-20250929",
    api_key=SecretStr(api_key),
    fallback_strategy=FallbackStrategy(
        fallback_llms=["fallback-1", "fallback-2"],
    ),
)

If the primary fails, fallback-1 is tried. If that also fails, fallback-2 is tried. If all fail, the primary error is raised.

Custom profile store directory

By default, fallback profiles are loaded from .faheem-code/profiles. You can point to a different directory:

FallbackStrategy(
    fallback_llms=["fallback-1", "fallback-2"],
    profile_store_dir="/path/to/my/profiles",
)

Metrics

Fallback costs are automatically merged into the primary LLM's metrics. After a conversation, you can inspect exactly which models were used:

# After running a conversation
metrics = llm.metrics
print(f"Total cost (including fallbacks): ${metrics.accumulated_cost:.6f}")

for usage in metrics.token_usages:
    print(f"  model={usage.model}  prompt={usage.prompt_tokens}  completion={usage.completion_tokens}")

Individual token_usage records carry the fallback model name, so you can distinguish which LLM produced each usage record.

Use cases

• Rate limit handling — When one provider throttles you, seamlessly switch to another
• High availability — Keep your agent running during provider outages
• Cost optimization — Try a cheaper model first and fall back to a more capable one on failure
• Cross-provider redundancy — Spread risk across Anthropic, OpenAI, Google, etc.

Ready-to-run example

Note

This example is available on GitHub: examples/01_standalone_sdk/39_llm_fallback.py

"""Example: Using FallbackStrategy for LLM resilience.

When the primary LLM fails with a transient error (rate limit, timeout, etc.),
FallbackStrategy automatically tries alternate LLMs in order.  Fallback is
per-call: each new request starts with the primary model.  Token usage and
cost from fallback calls are merged into the primary LLM's metrics.

This example:
  1. Saves two fallback LLM profiles to a temporary store.
  2. Configures a primary LLM with a FallbackStrategy pointing at those profiles.
  3. Runs a conversation — if the primary model is unavailable, the agent
     transparently falls back to the next available model.
"""

import os
import tempfile

from pydantic import SecretStr

from faheemcode.sdk import LLM, Agent, Conversation, LLMProfileStore, Tool
from faheemcode.sdk.llm import FallbackStrategy
from faheemcode.tools.file_editor import FileEditorTool
from faheemcode.tools.terminal import TerminalTool

# Read configuration from environment
api_key = os.getenv("LLM_API_KEY", None)
assert api_key is not None, "LLM_API_KEY environment variable is not set."
base_url = os.getenv("LLM_BASE_URL")
primary_model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")

# Use a temporary directory so this example doesn't pollute your home folder.
# In real usage you can omit base_dir to use the default (~/.faheem-code/profiles).
profile_store_dir = tempfile.mkdtemp()
store = LLMProfileStore(base_dir=profile_store_dir)

fallback_1 = LLM(
    usage_id="fallback-1",
    model=os.getenv("LLM_FALLBACK_MODEL_1", "openai/gpt-4o"),
    api_key=SecretStr(os.getenv("LLM_FALLBACK_API_KEY_1", api_key)),
    base_url=os.getenv("LLM_FALLBACK_BASE_URL_1", base_url),
)
store.save("fallback-1", fallback_1, include_secrets=True)

fallback_2 = LLM(
    usage_id="fallback-2",
    model=os.getenv("LLM_FALLBACK_MODEL_2", "openai/gpt-4o-mini"),
    api_key=SecretStr(os.getenv("LLM_FALLBACK_API_KEY_2", api_key)),
    base_url=os.getenv("LLM_FALLBACK_BASE_URL_2", base_url),
)
store.save("fallback-2", fallback_2, include_secrets=True)

print(f"Saved fallback profiles: {store.list()}")

# Configure the primary LLM with a FallbackStrategy
primary_llm = LLM(
    usage_id="agent-primary",
    model=primary_model,
    api_key=SecretStr(api_key),
    base_url=base_url,
    fallback_strategy=FallbackStrategy(
        fallback_llms=["fallback-1", "fallback-2"],
        profile_store_dir=profile_store_dir,
    ),
)

# Run a conversation
agent = Agent(
    llm=primary_llm,
    tools=[
        Tool(name=TerminalTool.name),
        Tool(name=FileEditorTool.name),
    ],
)

conversation = Conversation(agent=agent, workspace=os.getcwd())
conversation.send_message("Write a haiku about resilience into HAIKU.txt.")
conversation.run()

# Inspect metrics (includes any fallback usage)
metrics = primary_llm.metrics
print(f"Total cost (including fallbacks): ${metrics.accumulated_cost:.6f}")
print(f"Token usage records: {len(metrics.token_usages)}")
for usage in metrics.token_usages:
    print(
        f"  model={usage.model}"
        f"  prompt={usage.prompt_tokens}"
        f"  completion={usage.completion_tokens}"
    )

print(f"EXAMPLE_COST: {metrics.accumulated_cost}")

You can run the example code as-is.

NoteThe model name follows the LiteLLM convention, provider/model_name (for example anthropic/claude-sonnet-4-5-20250929 or openai/gpt-4o). LLM_API_KEY is the API key for the provider you choose.

Bring your own provider key

export LLM_API_KEY="your-api-key"
export LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.
cd software-agent-sdk
uv run python examples/01_standalone_sdk/39_llm_fallback.py

Faheem Code Cloud key

# https://app.faheemcode.ai/settings/api-keys
export LLM_API_KEY="example-user-api-key"
export LLM_MODEL="faheemcode/claude-sonnet-4-5-20250929"
cd software-agent-sdk
uv run python examples/01_standalone_sdk/39_llm_fallback.py

Next steps

• LLM Profile Store — Save and load LLM configurations as reusable profiles
• Model Routing — Route requests based on content (e.g., multimodal vs text-only)
• Exception Handling — Handle LLM errors in your application
• LLM Metrics — Track token usage and costs across models