ea77b2308b
- Three-tier routing: light (router answers directly ~3s), medium (qwen3:4b + tools ~60s), complex (/think prefix → qwen3:8b + subagents ~140s) - Router: qwen2.5:1.5b, temp=0, regex pre-classifier + raw-text LLM classify - VRAMManager: explicit flush/poll/prewarm to prevent Ollama CPU-spill bug - agent_factory: build_medium_agent and build_complex_agent using deepagents (TodoListMiddleware + SubAgentMiddleware with research/memory subagents) - Fix: split Telegram replies >4000 chars into multiple messages - Benchmark: 30 questions (easy/medium/hard) — 10/10/10 verified passing easy→light, medium→medium, hard→complex with VRAM flush confirmed Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
from deepagents import create_deep_agent, SubAgent
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def build_medium_agent(model, agent_tools: list, system_prompt: str):
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"""Medium agent: create_deep_agent with TodoList planning, no subagents."""
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return create_deep_agent(
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model=model,
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tools=agent_tools,
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system_prompt=system_prompt,
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)
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def build_complex_agent(model, agent_tools: list, system_prompt: str):
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"""Complex agent: create_deep_agent with TodoList planning + research/memory subagents."""
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web_tools = [t for t in agent_tools if getattr(t, "name", "") == "web_search"]
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memory_tools = [
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t for t in agent_tools
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if getattr(t, "name", "") in ("search_memory", "get_all_memories")
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]
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research_sub: SubAgent = {
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"name": "research",
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"description": (
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"Runs multiple web searches in parallel and synthesizes findings. "
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"Use for thorough research tasks requiring several queries."
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),
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"system_prompt": (
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"You are a research specialist. Search the web thoroughly using multiple queries. "
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"Cite sources and synthesize information into a clear summary."
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),
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"tools": web_tools,
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"model": model,
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}
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memory_sub: SubAgent = {
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"name": "memory",
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"description": (
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"Searches and retrieves all relevant memories about the user comprehensively. "
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"Use to gather full context from past conversations."
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),
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"system_prompt": (
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"You are a memory specialist. Search broadly using multiple queries. "
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"Return all relevant facts and context you find."
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),
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"tools": memory_tools,
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"model": model,
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}
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return create_deep_agent(
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model=model,
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tools=agent_tools,
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system_prompt=system_prompt,
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subagents=[research_sub, memory_sub],
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)
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