Advanced Compression

1"""
2Advanced Compression
3=============================
4
5This example shows how to set a context token based limit for tool call compression.
6"""
7
8from kern.agent import Agent
9from kern.ndx.rockspression.manager import CompressionManager
10from kern.db.sqlite import SqliteDb
11from kern.models.openai import OpenAIResponses
12from kern.tools.websearch import WebSearchTools
13
14compression_prompt = """
15    You are a compression expert. Your goal is to compress web search results for a competitive intelligence analyst.
16    
17    YOUR GOAL: Extract only actionable competitive insights while being extremely concise.
18    
19    MUST PRESERVE:
20    - Competitor names and specific actions (product launches, partnerships, acquisitions, pricing changes)
21    - Exact numbers (revenue, market share, growth rates, pricing, headcount)
22    - Precise dates (announcement dates, launch dates, deal dates)
23    - Direct quotes from executives or official statements
24    - Funding rounds and valuations
25    
26    MUST REMOVE:
27    - Company history and background information
28    - General industry trends (unless competitor-specific)
29    - Analyst opinions and speculation (keep only facts)
30    - Detailed product descriptions (keep only key differentiators and pricing)
31    - Marketing fluff and promotional language
32    
33    OUTPUT FORMAT:
34    Return a bullet-point list where each line follows this format:
35    "[Company Name] - [Date]: [Action/Event] ([Key Numbers/Details])"
36    
37    Keep it under 200 words total. Be ruthlessly concise. Facts only.
38    
39    Example:
40    - Acme Corp - Mar 15, 2024: Launched AcmeGPT at $99/user/month, targeting enterprise market
41    - TechCo - Feb 10, 2024: Acquired DataStart for $150M, gaining 500 enterprise customers
42"""
43
44compression_manager = CompressionManager(
45    model=OpenAIResponses(id="gpt-5-mini"),
46    compress_token_limit=5000,
47    compress_tool_call_instructions=compression_prompt,
48)
49
50# ---------------------------------------------------------------------------
51# Create Agent
52# ---------------------------------------------------------------------------
53agent = Agent(
54    model=OpenAIResponses(id="gpt-5-mini"),
55    tools=[WebSearchTools()],
56    description="Specialized in tracking competitor activities",
57    instructions="Use the search tools and always use the latest information and data.",
58    db=SqliteDb(db_file="tmp/token_based_tool_call_compression.db"),
59    compression_manager=compression_manager,
60    add_history_to_context=True,  # Add history to context
61    num_history_runs=3,
62    session_id="token_based_tool_call_compression",
63)
64
65# ---------------------------------------------------------------------------
66# Run Agent
67# ---------------------------------------------------------------------------
68if __name__ == "__main__":
69    agent.print_response(
70        """
71        Use the search tools and always use the latest information and data.
72        Research recent activities (last 3 months) for these AI companies:
73        
74        1. OpenAI - product launches, partnerships, pricing
75        2. Anthropic - new features, enterprise deals, funding
76        3. Google DeepMind - research breakthroughs, product releases
77        4. Meta AI - open source releases, research papers
78       
79        For each, find specific actions with dates and numbers.""",
80        stream=True,
81    )

1# Clone and setup repo
2git clone https://github.com/kern-ai/kern.git
3cd kern/cookbook/02_agents/14_advanced
4
5# Create and activate virtual environment
6./scripts/demo_setup.sh
7source .venvs/demo/bin/activate
8
9python advanced_compression.py