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from datetime import datetime, timezone
from services.supabase_service import supabase
from services.audit_service import audit_service
from services.budget_service import BudgetExceededError, budget_service
from services.evidence_service import evidence_service
from agents.agent_factory import AgentFactory
from services.semantic_backprop import semantic_backprop
from services.output_quality import build_quality_instructions, validate_output
from services.memory_service import memory_service
logger = logging.getLogger("agent_runner_service")
def _update_task_run(run_id: str, payload: dict):
try:
return supabase.table("task_runs").update(payload).eq("id", run_id).execute()
except Exception as exc:
if "duration_seconds" in payload and "duration_seconds" in str(exc) and "schema cache" in str(exc):
fallback_payload = {key: value for key, value in payload.items() if key != "duration_seconds"}
logger.warning("task_runs.duration_seconds is missing in Supabase schema; retrying run update without duration.")
return supabase.table("task_runs").update(fallback_payload).eq("id", run_id).execute()
raise
class AgentRunnerService:
@staticmethod
async def run_agent_task(
task: dict,
agent_data: dict,
*,
include_semantic_context: bool = False,
start_action: str = "execution_start",
start_content: str | None = None,
complete_action: str = "execution_complete",
complete_content: str = "Agent successfully completed the task and produced output.",
update_task: bool = True
) -> tuple[dict, str]:
task_id = task["id"]
project_id = task["project_id"]
run_id = None
if update_task:
supabase.table("tasks").update({"status": "in_progress"}).eq("id", task_id).execute()
await audit_service.log_action(
user_id=None,
action="task_status_changed",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={"project_id": project_id, "status": "in_progress"},
)
try:
run_res = supabase.table("task_runs").insert({
"task_id": task_id,
"agent_id": agent_data["id"],
"status": "running"
}).execute()
run_id = run_res.data[0]["id"]
await audit_service.log_action(
user_id=None,
action="task_run_created",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={"project_id": project_id, "run_id": run_id, "status": "running"},
)
# Emergency Model Override for decommissioned Groq models
model_to_use = agent_data["model"]
if "llama3-70b-8192" in model_to_use:
model_to_use = "llama-3.3-70b-versatile"
logger.warning(f"Overriding decommissioned model {agent_data['model']} with {model_to_use}")
agent = AgentFactory.get_agent(
provider=agent_data["api_provider"],
name=agent_data["name"],
role=agent_data["role"],
model=model_to_use,
system_prompt=agent_data.get("system_prompt")
)
context_res = supabase.table("tasks").select("title, output_data") \
.eq("project_id", project_id) \
.eq("status", "done") \
.execute()
context = context_res.data if context_res.data else []
project_data = task.get("project")
if not isinstance(project_data, dict):
project_res = (
supabase.table("projects")
.select("name,description,context")
.eq("id", project_id)
.single()
.execute()
)
project_data = project_res.data if project_res and project_res.data else {}
quality_task = {**task, "project": project_data}
extra_context = ""
if include_semantic_context:
extra_context = await semantic_backprop.get_project_context(project_id, task_id)
# Fetch Long-Term Memory (Cross-project)
memories = await memory_service.search_memory(
query=task.get("description") or task["title"],
limit=3,
threshold=0.72
)
if memories:
memory_header = "\n\n### RELEVANT HISTORICAL CONTEXT (CROSS-PROJECT)\n"
memory_blocks = []
for m in memories:
memory_blocks.append(f"- Memory: {m['content']}")
extra_context += memory_header + "\n".join(memory_blocks)
# Fetch Self-Optimization Lessons for this specific task
lessons_res = supabase.table("project_memory") \
.select("content") \
.eq("task_id", task_id) \
.eq("memory_type", "self_optimization_lesson") \
.order("created_at", desc=True) \
.limit(1) \
.execute()
if lessons_res.data:
lesson = lessons_res.data[0]["content"]
extra_context += f"\n\n### CRITICAL LESSON FROM PREVIOUS ATTEMPT\n{lesson}\n"
import time
import hashlib
# Simple in-memory cache for the session (could be persistent later)
if not hasattr(AgentRunnerService, "_task_cache"):
AgentRunnerService._task_cache = {}
# 1. Create a cache key based on task, agent (model + system prompt), and context
cache_input = f"{task['id']}-{agent_data['model']}-{agent_data.get('system_prompt', '')}-{task.get('description')}-{str(context)}-{extra_context}"
cache_key = hashlib.md5(cache_input.encode()).hexdigest()
# 2. Check Cache
if cache_key in AgentRunnerService._task_cache:
logger.info(f"Cache hit for task {task_id}. Skipping LLM call.")
cached_result = AgentRunnerService._task_cache[cache_key]
claims_count = await evidence_service.replace_task_claims(task, cached_result)
# Still log the "start" for UI consistency
agent_name = agent_data.get('name', 'Agent')
log_msg = start_content or f"Agent {agent_name} resuming task"
supabase.table("agent_logs").insert({
"task_id": task_id,
"run_id": run_id,
"action": start_action,
"content": f"[CACHE HIT] {log_msg}"
}).execute()
if update_task:
supabase.table("tasks").update({
"status": "awaiting_approval",
"output_data": cached_result
}).eq("id", task_id).execute()
await audit_service.log_action(
user_id=None,
action="task_status_changed",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={
"project_id": project_id,
"run_id": run_id,
"status": "awaiting_approval",
"cache_hit": True,
"claims_count": claims_count,
},
)
_update_task_run(run_id, {
"status": "completed",
"finished_at": datetime.now(timezone.utc).isoformat()
})
return cached_result, run_id
# 3. Log Start
supabase.table("agent_logs").insert({
"task_id": task_id,
"run_id": run_id,
"action": start_action,
"content": start_content or f"Agent {agent_data['name']} starting task: {task['title']}"
}).execute()
# 4. Execute Run with timing
start_time = time.time()
task_instructions = task.get("description") or task["title"]
task_instructions = f"{task_instructions}\n\n{build_quality_instructions(quality_task)}"
prompt_tokens = budget_service.estimate_prompt_tokens(
task_instructions=task_instructions,
context=context,
extra_context=extra_context,
system_prompt=agent_data.get("system_prompt"),
)
max_completion_tokens = int(getattr(agent, "max_tokens", 0) or 0)
estimated_preflight_cost = budget_service.estimate_cost(
agent_data.get("api_provider"),
agent_data.get("model"),
prompt_tokens,
max_completion_tokens,
)
budget_service.check_before_run(
project_id=project_id,
estimated_tokens=prompt_tokens + max_completion_tokens,
estimated_cost=estimated_preflight_cost,
)
result = await agent.run(task_instructions, context, extra_context=extra_context)
duration = time.time() - start_time
if result.get("status") == "error":
raise RuntimeError(result.get("error") or "Agent returned an error result.")
# 5. Security Sanitization (Defense in Depth)
raw_out = str(result.get("raw_output", ""))
suspicious_patterns = ["rm -rf", "mkfs", "dd if=", "curl", "wget", "chmod 777", "> /dev/sda"]
for pattern in suspicious_patterns:
if pattern in raw_out:
logger.warning(f"SECURITY: Suspicious pattern '{pattern}' detected in agent output for task {task_id}.")
result["security_warning"] = f"Output sanitized: suspicious pattern '{pattern}' detected."
quality_review = validate_output(quality_task, result)
result["quality_review"] = quality_review
claims_count = await evidence_service.replace_task_claims(task, result)
# Use actual usage if provided by agent, otherwise fallback to estimation
usage = result.get("usage") or {}
actual_prompt_tokens = usage.get("prompt_tokens") or prompt_tokens
actual_completion_tokens = usage.get("completion_tokens") or budget_service.estimate_completion_tokens(result)
actual_cost = budget_service.estimate_cost(
agent_data.get("api_provider"),
agent_data.get("model"),
actual_prompt_tokens,
actual_completion_tokens,
)
budget_service.record_usage(
project_id=project_id,
task_id=task_id,
run_id=run_id,
agent_id=agent_data.get("id"),
provider=agent_data.get("api_provider"),
model=agent_data.get("model"),
prompt_tokens=actual_prompt_tokens,
completion_tokens=actual_completion_tokens,
estimated_cost=actual_cost,
metadata={"duration_seconds": round(duration, 2), "claims_count": claims_count, "usage_source": "api" if result.get("usage") else "estimation"},
)
# 6. Save to Cache
AgentRunnerService._task_cache[cache_key] = result
if update_task:
supabase.table("tasks").update({
"status": "awaiting_approval",
"output_data": result
}).eq("id", task_id).execute()
await audit_service.log_action(
user_id=None,
action="task_status_changed",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={
"project_id": project_id,
"run_id": run_id,
"status": "awaiting_approval",
"quality_approved": quality_review["approved"],
"claims_count": claims_count,
"estimated_tokens": prompt_tokens + completion_tokens,
"estimated_cost": float(estimated_cost),
},
)
# 7. Update Run Status
_update_task_run(run_id, {
"status": "completed",
"finished_at": datetime.now(timezone.utc).isoformat(),
"duration_seconds": round(duration, 2)
})
# 8. Log Completion with Metrics
supabase.table("agent_logs").insert({
"task_id": task_id,
"run_id": run_id,
"action": complete_action,
"content": f"{complete_content} (Execution time: {duration:.2f}s)"
}).execute()
if not quality_review["approved"]:
supabase.table("agent_logs").insert({
"task_id": task_id,
"run_id": run_id,
"action": "quality_review_failed",
"content": f"Quality review failed: {', '.join(quality_review['fail_reasons'])}"
}).execute()
await audit_service.log_action(
user_id=None,
action="task_quality_review_failed",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={
"project_id": project_id,
"run_id": run_id,
"fail_reasons": quality_review.get("fail_reasons", []),
},
)
return result, run_id
except BudgetExceededError as e:
logger.warning(f"Budget blocked task {task_id}: {str(e)}")
if run_id:
_update_task_run(run_id, {
"status": "cancelled",
"error_message": str(e),
"finished_at": datetime.now(timezone.utc).isoformat()
})
if update_task:
supabase.table("tasks").update({
"status": "failed",
"output_data": {"error": str(e), "budget_blocked": True}
}).eq("id", task_id).execute()
await audit_service.log_action(
user_id=None,
action="task_budget_blocked",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={"project_id": project_id, "run_id": run_id, "error": str(e)},
)
supabase.table("agent_logs").insert({
"task_id": task_id,
"run_id": run_id,
"action": "budget_blocked",
"content": f"Budget blocked execution: {str(e)}"
}).execute()
raise e
except Exception as e:
logger.error(f"Error executing task {task_id}: {str(e)}")
if run_id:
_update_task_run(run_id, {
"status": "failed",
"finished_at": datetime.now(timezone.utc).isoformat()
})
if update_task:
supabase.table("tasks").update({
"status": "failed",
"output_data": {"error": str(e)}
}).eq("id", task_id).execute()
await audit_service.log_action(
user_id=None,
action="task_status_changed",
agent_id=agent_data.get("id"),
task_id=task_id,
metadata={
"project_id": project_id,
"run_id": run_id,
"status": "failed",
"error": str(e),
},
)
# LOG ERROR TO AGENT CONSOLE
supabase.table("agent_logs").insert({
"task_id": task_id,
"run_id": run_id,
"action": "execution_failed",
"content": f"ERROR: {str(e)}"
}).execute()
raise e
@staticmethod
async def execute_agent_logic(task: dict, agent_data: dict):
task_id = task["id"]
try:
await AgentRunnerService.run_agent_task(
task,
agent_data,
include_semantic_context=True
)
await audit_service.log_action(
user_id=None,
action="agent_task_completed",
agent_id=agent_data["id"],
task_id=task_id,
metadata={"model": agent_data["model"]}
)
except Exception:
raise
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