File size: 3,088 Bytes
ad6248e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | name: meta-sre
version: "1.0.0"
description: >
OpenEnv environment for training LLM agents to act as Senior SREs.
Simulates real Meta production incidents across 3 interconnected services
with 5 difficulty levels, 10 engineering tools, and a self-improving
difficulty controller (Theme 4: Self-Improvement).
author: Meta-SRE Hackathon Team (Bhavya + Anvit)
license: MIT
endpoints:
base_url: http://localhost:8000
reset: POST /reset
step: POST /step
state: GET /state
grade: GET /grade
tools: GET /tools
observation_space:
type: object
fields:
- step: integer
- incident_id: string
- system_metrics: object # {service: ServiceMetrics}
- active_alerts: array # List[Alert]
- open_file: object # FileView | null
- terminal_output: string
- git_diff: string # null if no edits yet
- dependency_graph: object
- sre_memory: array # agent's working notes
- budget_remaining: integer # steps before SLA breach
action_space:
type: tool_call
tools:
- view_file
- edit_line
- run_tests
- check_dependency
- read_logs
- git_blame
- rollback
- query_metrics_history
- ask_senior_sre
- write_incident_report
reward:
step_penalty: -0.1
syntax_error_penalty: -0.5
rollback_penalty: -1.0
senior_sre_penalty: -0.2
terminal_tests_pass: +1.0
terminal_report_max: +0.5
terminal_sla_bonus: +0.3
terminal_no_regress: +0.2
security_patch_bonus: +0.5 # Task 5 only
max_possible: 3.0
tasks:
- id: 1
difficulty: easy
sla_budget: 15
description: Single service AttributeError — hallucinated dict method
- id: 2
difficulty: medium
sla_budget: 20
description: Silent timestamp corruption in CAPI → ROAS degradation
- id: 3
difficulty: medium-hard
sla_budget: 20
description: DB connection pool exhaustion under load
- id: 4
difficulty: hard
sla_budget: 25
description: Circular FK migration cascading to 3 services (red herrings)
- id: 5
difficulty: hard
sla_budget: 20
description: PII data exposure via DEBUG_MODE=True (security incident)
self_improvement:
enabled: true
controller: DifficultyController
description: >
After each episode the DifficultyController analyses which bug categories
the agent failed on and weights future task selection toward those weaknesses.
Bug categories: async_bugs, data_corruption, security_bugs,
cascading_failures, red_herrings.
usage_example: |
import requests
BASE = "http://localhost:8000"
obs = requests.post(f"{BASE}/reset", json={"task_id": 1}).json()
done = False
while not done:
action = your_agent.decide(obs) # returns {"tool": ..., "params": ...}
result = requests.post(f"{BASE}/step", json=action).json()
obs = result["observation"]
done = result["done"]
score = requests.get(f"{BASE}/grade").json()["normalized_score"]
print(f"Score: {score:.3f}")
|