Effective Context = Relevant Information / Total Tokens

def trim_context(messages: list, keep_last_n: int = 10) -> list:
    """Keep system message + last N turns."""
    system_msgs = [m for m in messages if m["role"] == "system"]
    other_msgs = [m for m in messages if m["role"] != "system"]
    return system_msgs + other_msgs[-keep_last_n:]

SUMMARIZATION_PROMPT = """Summarize the conversation so far, preserving:
1. Key decisions made
2. Important context established
3. Current task state and goals
4. Any constraints or preferences expressed

Be concise but complete. Output as structured markdown."""

async def summarize_context(messages: list, model) -> str:
    """Generate a summary of conversation history."""
    conversation_text = format_messages_for_summary(messages)
    response = await model.generate(
        system=SUMMARIZATION_PROMPT,
        user=conversation_text
    )
    return response.content

class HybridContextManager:
    def __init__(
        self,
        keep_recent: int = 5,      # Recent turns to keep verbatim
        summary_threshold: int = 20, # When to trigger summarization
    ):
        self.keep_recent = keep_recent
        self.summary_threshold = summary_threshold
        self.running_summary = ""

    def process(self, messages: list) -> list:
        if len(messages) < self.summary_threshold:
            return messages

        # Summarize older messages
        old_messages = messages[:-self.keep_recent]
        self.running_summary = summarize(old_messages, self.running_summary)

        # Return summary + recent messages
        return [
            {"role": "system", "content": f"Previous context:\n{self.running_summary}"},
            *messages[-self.keep_recent:]
        ]

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Args:
    query: Natural language description of what to find
    max_results: Maximum snippets to return (default 5)

Returns:
    Formatted code snippets with file paths and line numbers,
    or 'No results found' if nothing matches.
"""
results = perform_search(query, limit=max_results)
if not results:
    return "No results found for query."
return format_results(results)  # Concise, structured output

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async def compaction_loop(agent, messages, task):
    while not task.complete:
        # Process next step
        response = await agent.run(messages)
        messages.append(response)

        # Compact when approaching limit
        if estimate_tokens(messages) > TOKEN_LIMIT * 0.8:
            summary = await summarize_context(messages[:-3])
            messages = [
                {"role": "system", "content": agent.system_prompt},
                {"role": "assistant", "content": f"Summary of progress:\n{summary}"},
                *messages[-3:]  # Keep recent context
            ]

    return messages

class NoteTakingAgent:
    def __init__(self):
        self.notes = {}  # Key-value store outside context

    async def run(self, messages):
        tools = [
            Tool("save_note", self.save_note, "Save information for later"),
            Tool("get_note", self.get_note, "Retrieve saved information"),
            Tool("list_notes", self.list_notes, "List all saved note keys"),
        ]
        return await self.agent.run(messages, tools=tools)

    def save_note(self, key: str, content: str) -> str:
        self.notes[key] = content
        return f"Saved note: {key}"

    def get_note(self, key: str) -> str:
        return self.notes.get(key, f"No note found for key: {key}")

class OrchestratorAgent:
    def __init__(self):
        self.sub_agents = {
            "researcher": ResearchAgent(),
            "coder": CodingAgent(),
            "reviewer": ReviewAgent(),
        }

    async def delegate(self, task: str, agent_type: str) -> str:
        """Delegate to sub-agent, receive condensed summary."""
        agent = self.sub_agents[agent_type]

        # Sub-agent works with fresh context
        result = await agent.run(task)

        # Return only essential findings to main context
        return result.summary  # Not the full conversation

class SessionMemory:
    def __init__(
        self,
        keep_last_n_turns: int = 5,
        context_limit: int = 100_000,  # tokens
        summarizer = None,
    ):
        self.keep_last_n_turns = keep_last_n_turns
        self.context_limit = context_limit
        self.summarizer = summarizer
        self.messages = []
        self.summary = ""

    async def add_message(self, message: dict):
        self.messages.append(message)
        await self._maybe_compact()

    async def _maybe_compact(self):
        current_tokens = estimate_tokens(self.messages)

        if current_tokens > self.context_limit * 0.8:
            # Summarize all but recent messages
            old_messages = self.messages[:-self.keep_last_n_turns]
            new_summary = await self.summarizer.summarize(
                old_messages,
                previous_summary=self.summary
            )
            self.summary = new_summary
            self.messages = self.messages[-self.keep_last_n_turns:]

    def get_context(self) -> list:
        context = []
        if self.summary:
            context.append({
                "role": "system",
                "content": f"Conversation summary:\n{self.summary}"
            })
        context.extend(self.messages)
        return context

def estimate_tokens(messages: list) -> int:
    """Rough token estimation (4 chars ≈ 1 token for English)."""
    total_chars = sum(
        len(m.get("content", ""))
        for m in messages
    )
    return total_chars // 4

def estimate_tokens_accurate(messages: list, model: str) -> int:
    """Accurate token count using tiktoken."""
    import tiktoken
    encoding = tiktoken.encoding_for_model(model)
    return sum(
        len(encoding.encode(m.get("content", "")))
        for m in messages
    )