Filter Tool Calls From History

1"""
2Filter Tool Calls From History
3=============================
4
5Demonstrates `max_tool_calls_from_history` by showing that tool-call filtering only
6affects model input history while full run history remains in storage.
7"""
8
9import random
10
11from kern.agent import Agent
12from kern.db.sqlite import SqliteDb
13from kern.models.openai import OpenAIResponses
14
15
16def get_weather_for_city(city: str) -> str:
17    conditions = ["Sunny", "Cloudy", "Rainy", "Snowy", "Foggy", "Windy"]
18    temperature = random.randint(-10, 35)
19    condition = random.choice(conditions)
20
21    return f"{city}: {temperature}°C, {condition}"
22
23
24# ---------------------------------------------------------------------------
25# Setup
26# ---------------------------------------------------------------------------
27cities = [
28    "Tokyo",
29    "Delhi",
30    "Shanghai",
31    "São Paulo",
32    "Mumbai",
33    "Beijing",
34    "Cairo",
35    "London",
36]
37
38
39# ---------------------------------------------------------------------------
40# Create Agent
41# ---------------------------------------------------------------------------
42agent = Agent(
43    model=OpenAIResponses(id="gpt-5-mini"),
44    tools=[get_weather_for_city],
45    instructions="You are a weather assistant. Get the weather using the get_weather_for_city tool.",
46    # Only keep 3 most recent tool calls from history in context (reduces token costs)
47    max_tool_calls_from_history=3,
48    db=SqliteDb(db_file="tmp/weather_data.db"),
49    add_history_to_context=True,
50    markdown=True,
51    # debug_mode=True,
52)
53
54# ---------------------------------------------------------------------------
55# Run Agent
56# ---------------------------------------------------------------------------
57if __name__ == "__main__":
58    print("\n" + "=" * 90)
59    print("Tool Call Filtering Demo: max_tool_calls_from_history=3")
60    print("=" * 90)
61    print(
62        f"{'Run':<5} | {'City':<15} | {'History':<8} | {'Current':<8} | {'In Context':<11} | {'In DB':<8}"
63    )
64    print("-" * 90)
65
66    for i, city in enumerate(cities, 1):
67        run_response = agent.run(f"What's the weather in {city}?")
68
69        # Count tool calls from history (sent to model after filtering)
70        history_tool_calls = sum(
71            len(msg.tool_calls)
72            for msg in run_response.messages
73            if msg.role == "assistant"
74            and msg.tool_calls
75            and getattr(msg, "from_history", False)
76        )
77
78        # Count tool calls from current run
79        current_tool_calls = sum(
80            len(msg.tool_calls)
81            for msg in run_response.messages
82            if msg.role == "assistant"
83            and msg.tool_calls
84            and not getattr(msg, "from_history", False)
85        )
86
87        total_in_context = history_tool_calls + current_tool_calls
88
89        # Total tool calls stored in database (unfiltered)
90        saved_messages = agent.get_session_messages()
91        total_in_db = (
92            sum(
93                len(msg.tool_calls)
94                for msg in saved_messages
95                if msg.role == "assistant" and msg.tool_calls
96            )
97            if saved_messages
98            else 0
99        )
100
101        print(
102            f"{i:<5} | {city:<15} | {history_tool_calls:<8} | {current_tool_calls:<8} | {total_in_context:<11} | {total_in_db:<8}"
103        )

1# Clone and setup repo
2git clone https://github.com/kern-ai/kern.git
3cd kern/cookbook/02_agents/03_context_management
4
5# Create and activate virtual environment
6./scripts/demo_setup.sh
7source .venvs/demo/bin/activate
8
9python filter_tool_calls_from_history.py