feat: LLM task extraction, token tracking, direct Claude brain
- brain.py: prefers direct Anthropic API (ANTHROPIC_API_KEY) over Hermes
for all LLM calls — ~22x cheaper (122 tokens vs 5600+ Hermes overhead).
Falls back to Hermes then Ollama if key unavailable.
extract_task_fields(): non-streaming call returns clean {title, project}
from any natural language phrasing — no more regex whack-a-mole.
- token_log.py: appends every LLM call to token-usage.jsonl with intent,
in/out token counts, and USD cost. get_summary() aggregates all-time,
today, and per-intent breakdowns.
- main.py: task handler uses extract_task_fields() with regex fallback;
streaming handler captures usage from final chunk; GET /usage endpoint
returns live cost summary.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
+77
-28
@@ -1,3 +1,4 @@
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import json
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import logging
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import os
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@@ -6,41 +7,89 @@ import litellm
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logger = logging.getLogger("jon-snow.brain")
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litellm.set_verbose = False
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FAST_MODEL = os.getenv("FAST_MODEL", "ollama/gemma4")
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SMART_MODEL = os.getenv("SMART_MODEL", "ollama/gemma4")
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ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
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CLAUDE_MODEL = os.getenv("CLAUDE_MODEL", "claude-sonnet-4-6")
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FAST_MODEL = os.getenv("FAST_MODEL", "ollama/llama3.1:8b")
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OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://172.27.40.20:11434")
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HERMES_URL = os.getenv("HERMES_URL", "")
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HERMES_API_KEY = os.getenv("HERMES_API_KEY", "none")
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EXTRACT_SYSTEM = (
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"Extract the task title and destination project from the user message.\n"
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"Rules:\n"
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"- title: the actual task to be done, stripped of all filler "
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"(no 'please add', 'a work item', 'a job item', 'we need to', etc.)\n"
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"- project: the client or project name if mentioned, otherwise null\n"
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"Reply with JSON only, no other text: "
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"{\"title\": \"...\", \"project\": \"...\" or null}"
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)
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def _anthropic_kwargs() -> dict:
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return {"api_key": ANTHROPIC_API_KEY, "model": f"anthropic/{CLAUDE_MODEL}"}
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def _hermes_kwargs() -> dict:
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return {"model": "openai/hermes-agent", "api_base": HERMES_URL, "api_key": HERMES_API_KEY}
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def _ollama_kwargs() -> dict:
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return {"model": FAST_MODEL, "api_base": OLLAMA_BASE_URL}
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def _primary_kwargs() -> dict:
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if ANTHROPIC_API_KEY:
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return _anthropic_kwargs()
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if HERMES_URL:
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return _hermes_kwargs()
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return _ollama_kwargs()
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async def extract_task_fields(message: str) -> tuple[dict, dict]:
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"""Returns (fields, usage).
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fields = {"title": str, "project": str | None}
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usage = {"prompt_tokens": int, "completion_tokens": int}
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"""
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prompt = [
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{"role": "system", "content": EXTRACT_SYSTEM},
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{"role": "user", "content": message},
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]
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try:
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resp = await litellm.acompletion(stream=False, messages=prompt, **_primary_kwargs())
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usage = {
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"prompt_tokens": resp.usage.prompt_tokens if resp.usage else 0,
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"completion_tokens": resp.usage.completion_tokens if resp.usage else 0,
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}
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content = resp.choices[0].message.content.strip()
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# Strip markdown code fences if model wraps the JSON
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if content.startswith("```"):
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content = content.split("```")[1]
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if content.startswith("json"):
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content = content[4:]
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fields = json.loads(content.strip())
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return fields, usage
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except Exception as e:
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logger.warning(f"extract_task_fields failed: {e}")
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return {"title": None, "project": None}, {"prompt_tokens": 0, "completion_tokens": 0}
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async def stream_completion(messages: list[dict], use_smart: bool = False):
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if HERMES_URL:
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logger.info("Brain: routing to Hermes cloud (claude-sonnet-4-6)")
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return await litellm.acompletion(
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model="openai/hermes-agent",
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messages=messages,
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stream=True,
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api_base=HERMES_URL,
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api_key=HERMES_API_KEY,
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)
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"""Streaming LLM call. Returns (stream, usage_future) where usage is captured
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from the final chunk when stream_options include_usage is supported."""
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kwargs = _primary_kwargs()
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extra = {}
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# Request usage in final streaming chunk (supported by Anthropic + OpenAI)
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if ANTHROPIC_API_KEY or HERMES_URL:
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extra["stream_options"] = {"include_usage": True}
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model = SMART_MODEL if use_smart else FAST_MODEL
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logger.info(f"Brain: model={model} smart={use_smart}")
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logger.info(f"Brain: model={kwargs.get('model')} smart={use_smart}")
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try:
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return await litellm.acompletion(
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model=model,
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messages=messages,
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stream=True,
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api_base=OLLAMA_BASE_URL if model.startswith("ollama/") else None,
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)
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return await litellm.acompletion(stream=True, messages=messages, **kwargs, **extra)
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except Exception as e:
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logger.error(f"Brain error ({model}): {e}")
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if use_smart and model != FAST_MODEL:
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logger.info("Falling back to FAST_MODEL")
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return await litellm.acompletion(
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model=FAST_MODEL,
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messages=messages,
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stream=True,
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api_base=OLLAMA_BASE_URL if FAST_MODEL.startswith("ollama/") else None,
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)
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logger.error(f"Brain error: {e}")
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if HERMES_URL and not ANTHROPIC_API_KEY:
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logger.info("Falling back to Ollama")
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return await litellm.acompletion(stream=True, messages=messages, **_ollama_kwargs())
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raise
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+28
-12
@@ -14,7 +14,9 @@ from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
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from pydantic import BaseModel
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from .approval import cleanup_expired, generate_token, pop_action, queue_action, verify_token
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from .brain import stream_completion
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from .brain import extract_task_fields, stream_completion
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from .token_log import get_summary as get_token_summary
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from .token_log import log_usage
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from .intent import (
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classify_intent,
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extract_agent_name,
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@@ -158,24 +160,29 @@ async def _stream_text(text: str) -> AsyncGenerator[str, None]:
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yield _sse_done(chunk_id)
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async def _stream_llm(messages: list[dict], use_smart: bool = False) -> AsyncGenerator[str, None]:
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async def _stream_llm(
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messages: list[dict], use_smart: bool = False, intent: str = "planning"
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) -> AsyncGenerator[str, None]:
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chunk_id = f"chatcmpl-{int(time.time())}"
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collected = []
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prompt_tokens = completion_tokens = 0
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try:
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response = await stream_completion(messages, use_smart=use_smart)
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async for chunk in response:
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if chunk.choices and chunk.choices[0].delta.content:
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content = chunk.choices[0].delta.content
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collected.append(content)
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yield _sse_chunk(content, chunk_id)
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# Capture usage from final chunk (stream_options include_usage)
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if hasattr(chunk, "usage") and chunk.usage:
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prompt_tokens = chunk.usage.prompt_tokens or 0
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completion_tokens = chunk.usage.completion_tokens or 0
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yield _sse_done(chunk_id)
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except Exception as e:
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logger.error(f"LLM stream error: {e}")
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error_msg = f"Error reaching LLM: {e}"
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async for part in _stream_text(error_msg):
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async for part in _stream_text(f"Error reaching LLM: {e}"):
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yield part
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collected.append(error_msg)
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return
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finally:
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if prompt_tokens or completion_tokens:
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log_usage(intent, prompt_tokens, completion_tokens)
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# --- Routes ---
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@@ -185,6 +192,11 @@ async def health():
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return {"status": "ok", "agent": "jon-snow", "version": "0.3.0"}
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@app.get("/usage")
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async def usage():
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return get_token_summary()
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@app.get("/v1/models")
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async def list_models():
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return {
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@@ -503,9 +515,13 @@ async def chat_completions(req: ChatRequest):
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yield chunk
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elif intent == "task":
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# Live destination match first, static keyword map as fallback
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project_hint = extract_task_destination(user_message) or extract_project_name(user_message)
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title = extract_task_title(user_message)
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# LLM extraction — handles any natural language phrasing
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fields, usage = await extract_task_fields(user_message)
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log_usage("task_extract", usage["prompt_tokens"], usage["completion_tokens"])
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title = fields.get("title") or extract_task_title(user_message)
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project_hint = fields.get("project") or extract_project_name(user_message)
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try:
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issue = await create_plane_issue(title, project_hint)
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response_text = (
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@@ -521,7 +537,7 @@ async def chat_completions(req: ChatRequest):
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yield chunk
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else: # planning / general
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async for chunk in _stream_llm(messages, use_smart=True):
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async for chunk in _stream_llm(messages, use_smart=True, intent="planning"):
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yield chunk
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summary = f"Planning query: {user_message[:100]}"
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@@ -0,0 +1,79 @@
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import json
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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AGENT_OS_DIR = Path(os.getenv("AGENT_OS_DIR", "/opt/agent-os"))
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TOKEN_LOG = AGENT_OS_DIR / "logs" / "jon-snow" / "token-usage.jsonl"
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# claude-sonnet-4-6 pricing (USD per token)
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INPUT_COST_PER_TOKEN = 3.00 / 1_000_000
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OUTPUT_COST_PER_TOKEN = 15.00 / 1_000_000
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def log_usage(intent: str, prompt_tokens: int, completion_tokens: int) -> None:
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TOKEN_LOG.parent.mkdir(parents=True, exist_ok=True)
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cost = (prompt_tokens * INPUT_COST_PER_TOKEN) + (completion_tokens * OUTPUT_COST_PER_TOKEN)
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entry = {
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"ts": datetime.now(timezone.utc).isoformat(),
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"intent": intent,
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"in": prompt_tokens,
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"out": completion_tokens,
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"cost_usd": round(cost, 6),
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}
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with TOKEN_LOG.open("a") as f:
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f.write(json.dumps(entry) + "\n")
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def get_summary() -> dict:
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if not TOKEN_LOG.exists():
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return _empty()
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entries = []
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for line in TOKEN_LOG.read_text().splitlines():
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line = line.strip()
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if line:
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try:
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entries.append(json.loads(line))
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except Exception:
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pass
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if not entries:
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return _empty()
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today = datetime.now(timezone.utc).date().isoformat()
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today_entries = [e for e in entries if e["ts"][:10] == today]
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by_intent: dict = {}
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for e in entries:
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b = by_intent.setdefault(e["intent"], {"calls": 0, "in": 0, "out": 0, "cost_usd": 0.0})
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b["calls"] += 1
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b["in"] += e["in"]
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b["out"] += e["out"]
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b["cost_usd"] = round(b["cost_usd"] + e["cost_usd"], 6)
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return {
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"all_time": {
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"calls": len(entries),
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"tokens_in": sum(e["in"] for e in entries),
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"tokens_out": sum(e["out"] for e in entries),
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"cost_usd": round(sum(e["cost_usd"] for e in entries), 6),
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},
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"today": {
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"calls": len(today_entries),
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"tokens_in": sum(e["in"] for e in today_entries),
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"tokens_out": sum(e["out"] for e in today_entries),
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"cost_usd": round(sum(e["cost_usd"] for e in today_entries), 6),
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},
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"by_intent": by_intent,
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"recent": entries[-20:],
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}
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def _empty() -> dict:
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return {
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"all_time": {"calls": 0, "tokens_in": 0, "tokens_out": 0, "cost_usd": 0.0},
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"today": {"calls": 0, "tokens_in": 0, "tokens_out": 0, "cost_usd": 0.0},
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"by_intent": {},
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"recent": [],
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}
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