from deepagents import create_deep_agent
from compact_middleware import CompactionMiddleware, CompactionToolMiddleware

mw = CompactionMiddleware(
    model="anthropic:claude-sonnet-4-6",
    backend=backend,
)
tool_mw = CompactionToolMiddleware(mw)  # optional: lets the agent compact manually

agent = create_deep_agent(
    model="anthropic:claude-sonnet-4-6",
    tools=[read_file, edit_file, execute],
    system_prompt="You are a coding assistant.",
    backend=backend,
    middleware=[mw, tool_mw],
)

# That's it — compaction triggers automatically at ~85% context usage
result = agent.invoke({"messages": [("human", "Refactor the auth module")]})

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langchain.chat_models import init_chat_model

from compact_middleware import (
    CompactionConfig,
    CompactionMiddleware,
    CompactionToolMiddleware,
)

backend = FilesystemBackend(root_dir="/data/workspace")
model = init_chat_model("anthropic:claude-sonnet-4-6")

mw = CompactionMiddleware(model=model, backend=backend)
tool_mw = CompactionToolMiddleware(mw)

agent = create_deep_agent(
    model=model,
    tools=[search_tool, execute_tool, edit_file_tool],
    system_prompt="You are a senior engineer.",
    backend=backend,
    middleware=[mw, tool_mw],
    memory=["/memory/AGENTS.md"],
    interrupt_on={"edit_file": True},
)

# Async works too — all operations have async variants
result = await agent.ainvoke({"messages": [("human", "Add pagination to the API")]})

from compact_middleware import CompactionConfig, CompactionMiddleware
from compact_middleware.config import (
    CollapseConfig,
    MicrocompactConfig,
    RestorationConfig,
    TokenBudgetConfig,
    TruncateArgsConfig,
)

config = CompactionConfig(
    # --- When to compact ---
    trigger=("fraction", 0.80),          # at 80% of context window (default: 0.85)
    keep=("messages", 10),               # keep last 10 messages after compaction

    # --- Circuit breaker ---
    max_consecutive_failures=5,          # default: 3

    # --- Custom summary instructions ---
    custom_instructions="Focus on code diffs and test output. Include file paths verbatim.",
    suppress_follow_up_questions=True,   # resume without asking "where were we?"
)

mw = CompactionMiddleware(model=model, backend=backend, config=config)

config = CompactionConfig(
    microcompact=MicrocompactConfig(
        enabled=True,                       # default
        gap_threshold_minutes=30,           # clear after 30 min gap (default: 60)
        keep_recent=3,                      # always keep last 3 results (default: 5)
        compactable_tools={                 # which tools' results can be cleared
            "read_file", "execute", "grep", "glob",
            "web_search", "web_fetch", "edit_file", "write_file",
        },
    ),
)

config = CompactionConfig(
    truncate_args=TruncateArgsConfig(
        trigger=("fraction", 0.80),         # when to start truncating
        max_length=1_000,                   # chars per arg value (default: 2000)
        truncate_all_tools=True,            # all tools, not just write/edit (default)
    ),
)

config = CompactionConfig(
    collapse=CollapseConfig(
        enabled=True,
        min_group_size=3,                   # need 3+ consecutive reads to collapse (default: 2)
        collapse_tools={"read_file", "grep", "glob", "web_search"},
    ),
)

config = CompactionConfig(
    restoration=RestorationConfig(
        enabled=True,
        max_files=3,                        # re-read top 3 recent files (default: 5)
        file_budget_chars=30_000,           # total budget for restored content
        per_file_chars=10_000,              # max per file
        restore_plans=True,                 # re-attach active plan state
    ),
)

config = CompactionConfig(
    token_budget=TokenBudgetConfig(
        per_tool_chars=50_000,              # max chars per tool result
        per_message_chars=200_000,          # aggregate max per message turn
    ),
)

# Absolute token count
trigger=("tokens", 170_000)

# Fraction of context window (requires model with known context size)
trigger=("fraction", 0.85)

# Message count
trigger=("messages", 50)

# Multiple triggers (any fires)
trigger=[("fraction", 0.85), ("messages", 100)]