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The Agent class includes built-in context management that automatically handles context window overflow during long conversations. When enabled, the middleware monitors token usage and applies reduction strategies before the context exceeds the model’s limit.

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How It Works

Context management applies three strategies in order when the context window is exceeded:
  1. Prune old tool calls — Removes old tool call/return pairs, keeping only the most recent ones.
  2. LLM summarization — Summarizes older messages into condensed, structured messages via the LLM while keeping recent messages verbatim.
  3. Context full response — If the context is still full after all strategies, returns a fixed message indicating the context limit has been reached.

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Usage

from upsonic import Agent, Task

agent = Agent(
    model="openai/gpt-4o-mini",
    context_management=True,           # Enabled by default
    context_management_keep_recent=5,   # Number of recent messages to always preserve
    context_management_model="anthropic/claude-sonnet-4-5"   # Model for context managing
)

# Task with potentially long context
long_text = "..." * 100000

task = Task(f"Summarize this text: {long_text}")
result = agent.do(task)
print(result)

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Parameters

ParameterTypeDefaultDescription
context_managementboolTrueEnable or disable automatic context management.
context_management_keep_recentint5Number of recent messages (and tool call events) to preserve during pruning and summarization.
Context management uses a 90% safety margin of the model’s maximum context window. Token estimation relies on actual usage data from model responses when available, falling back to a character-based heuristic otherwise.