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"""
Self-managed Context Management
This cookbook demonstrates Claude's context management feature for automatic tool result clearing.
This reduces token usage in long-running conversations with extensive tool use.
You can read more in Anthropic docs: https://docs.claude.com/en/docs/build-with-claude/context-editing
1. Install dependencies: `uv pip install -U agno anthropic ddgs sqlalchemy`
2. Set your `ANTHROPIC_API_KEY` in your environment variables.
3. Run the cookbook
"""
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.websearch import WebSearchTools
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
agent = Agent(
model=Claude(
id="claude-sonnet-4-5",
# Activate and configure the context management feature
betas=["context-management-2025-06-27"],
context_management={
"edits": [
{
"type": "clear_tool_uses_20250919",
"trigger": {"type": "tool_uses", "value": 2},
"keep": {"type": "tool_uses", "value": 1},
}
]
},
),
instructions="You are a helpful assistant with access to the web.",
tools=[WebSearchTools()],
session_id="context-editing",
add_history_to_context=True,
markdown=True,
)
agent.print_response(
"Search for AI regulation in US. Make multiple searches to find the latest information."
)
# Display context management metrics
print("\n" + "=" * 60)
print("CONTEXT MANAGEMENT SUMMARY")
print("=" * 60)
response = agent.get_last_run_output()
if response and response.metrics:
print(f"\nInput tokens: {response.metrics.input_tokens:,}")
# Print context management stats from the last message
if response and response.messages:
for message in reversed(response.messages):
if message.provider_data and "context_management" in message.provider_data:
edits = message.provider_data["context_management"].get("applied_edits", [])
if edits:
print(
f"\n✅ Saved: {edits[-1].get('cleared_input_tokens', 0):,} tokens"
)
print(f" Cleared: {edits[-1].get('cleared_tool_uses', 0)} tool uses")
break
print("\n" + "=" * 60)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
pass
Run the Example
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# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/90_models/anthropic
# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate
# Export relevant API keys
export ANTHROPIC_API_KEY="***"
python context_management.py