ContextLengthExceededError
fireworks_ai.errors.ContextLengthExceededError
Stack trace
fireworks_ai.errors.ContextLengthExceededError: The input context length exceeds the maximum allowed tokens for this model (max 8192 tokens).
Why it happens
Fireworks AI models have a fixed maximum context length (token limit). When the sum of prompt tokens plus expected completion tokens exceeds this limit, the API rejects the request with this error. This often happens with long prompts, large conversation histories, or high max_tokens settings.
Detection
Monitor token usage before sending requests by tokenizing prompts and summing with max_tokens; log and alert if approaching or exceeding model limits.
Causes & fixes
Prompt plus max_tokens exceeds the model's maximum context length
Reduce prompt length by truncating conversation history or input text, or lower max_tokens to fit within the model's token limit.
Accumulating long conversation history without pruning
Implement conversation history windowing or summarization to keep prompt size within limits.
Using a model with a smaller context window than expected
Switch to a Fireworks AI model variant with a larger context length if available, or adjust prompt size accordingly.
Code: broken vs fixed
from fireworks_ai import FireworksClient
import os
client = FireworksClient(api_key=os.environ['FIREWORKS_API_KEY'])
prompt = """Very long prompt text exceeding model limits..."""
response = client.generate(
model="fireworks-large",
prompt=prompt,
max_tokens=5000 # This causes context length exceeded error
)
print(response.text) # This line triggers ContextLengthExceededErrorfrom fireworks_ai import FireworksClient
import os
client = FireworksClient(api_key=os.environ['FIREWORKS_API_KEY'])
prompt = """Very long prompt text trimmed to fit model limits..."""
response = client.generate(
model="fireworks-large",
prompt=prompt,
max_tokens=1000 # Reduced max_tokens to fit context length
)
print(response.text) # Fixed: no context length errorWorkaround
Catch ContextLengthExceededError and programmatically truncate or summarize the prompt before retrying the request.
Prevention
Implement token counting and prompt size checks before API calls; use conversation summarization or sliding windows to keep context within model limits.