import asyncio
import json
from omnicoreagent import OmniCoreAgent, ToolRegistry

tools = ToolRegistry()

@tools.register_tool("fetch_server_log")
def fetch_server_log() -> str:
    """Return today's server log."""
    lines = []
    for i in range(1, 401):
        level = "ERROR" if i in (137, 288) else "INFO"
        message = "disk quota exceeded on /var/data" if i == 288 else (
            "payment gateway timeout after 30s" if i == 137 else "request served in 12ms"
        )
        lines.append(f"2026-09-26T10:{i // 60:02d}:{i % 60:02d} {level} line={i} {message}")
    return "\n".join(lines)

async def main():
    agent = OmniCoreAgent(
        name="log_reader",
        system_instruction=(
            "You investigate server logs. When a result was offloaded, search the "
            "artifact instead of reading all of it. Answer in plain text, without markdown."
        ),
        model_config={"provider": "openai", "model": "gpt-5.4-mini"},
        local_tools=tools,
    )
    result = await agent.run("Fetch the server log and tell me every ERROR line, in one sentence each.")
    print(result["response"])

    trajectory = await agent.get_trajectory(result["trace_id"])
    for step in trajectory["steps"]:
        for call in step["tool_calls"]:
            print("step", step["step"], "->", call["tool_name"], call["arguments"])
    first = trajectory["steps"][0]["tool_calls"][0]
    seen = json.loads(first["observation"]["content"])["data"].splitlines()
    print("--- what the model saw from fetch_server_log ---")
    print("\n".join(seen[:7] + ["..."] + seen[-2:]))
    await agent.cleanup()

asyncio.run(main())

2026-09-26T10:02:17 ERROR line=137 payment gateway timeout after 30s.
2026-09-26T10:04:48 ERROR line=288 disk quota exceeded on /var/data.
step 1 -> fetch_server_log {}
step 2 -> search_artifact {'artifact_id': 'fetch_server_log_20260926_135637_1ed1e1ff', 'query': 'ERROR'}
--- what the model saw from fetch_server_log ---
[TOOL RESPONSE OFFLOADED]
Tool: fetch_server_log
Artifact ID: fetch_server_log_20260926_135637_1ed1e1ff
Original size: 9201 tokens → Preview: 163 tokens (saved 9038 tokens)

--- PREVIEW ---
2026-09-26T10:00:01 INFO line=1 request served in 12ms
...
📁 Full response saved to: …/workspace/artifacts/fetch_server_log_20260926_135637_1ed1e1ff.txt
💡 Use read_artifact('fetch_server_log_20260926_135637_1ed1e1ff') to load full content when needed.

import asyncio
from omnicoreagent import OmniCoreAgent, ToolRegistry

PRICES = {"apples": 3.20, "bread": 2.50, "cheese": 6.75, "dates": 4.10, "eggs": 3.90, "flour": 1.80}
tools = ToolRegistry()

@tools.register_tool("lookup_price")
def lookup_price(item: str) -> dict:
    """Look up the price of one grocery item."""
    return {"item": item, "price": PRICES.get(item.lower())}

async def main():
    agent = OmniCoreAgent(
        name="shopper",
        system_instruction="You price shopping lists. Answer in plain text, without markdown.",
        model_config={"provider": "openai", "model": "gpt-5.4-mini"},
        local_tools=tools,
        agent_config={
            "context_management": {
                "mode": "sliding_window",   # count messages, not tokens
                "value": 6,                 # act when the run holds more than 6
                "strategy": "summarize_and_truncate",
                "preserve_recent": 4,
            }
        },
    )
    result = await agent.run(
        "Look up apples, bread, cheese, dates, eggs and flour one at a time, "
        "waiting for each price before the next, then give me the total."
    )
    print(result["response"])

    trajectory = await agent.get_trajectory(result["trace_id"])
    for step in trajectory["steps"]:
        for entry in step["context"]:
            if entry["type"] == "context_compression":
                print(
                    f"step {step['step']}: context reduced from "
                    f"{entry['before']['message_count']} to {entry['after']['message_count']} messages"
                )
    await agent.cleanup()

asyncio.run(main())

The total cost is 22.25.
step 4: context reduced from 8 to 6 messages
step 5: context reduced from 8 to 6 messages
step 6: context reduced from 8 to 6 messages
step 7: context reduced from 8 to 6 messages

agent_config = {
    "memory_config": {"mode": "token_budget", "value": 20000},
    "context_management": {
        "mode": "token_budget",
        "value": 100000,
        "threshold_percent": 75,
        "strategy": "summarize_and_truncate",
        "preserve_recent": 6,
    },
    "tool_offload": {"threshold_tokens": 1000, "threshold_bytes": 4000},
}