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Supports OpenRouter, Ollama, LM Studio, or any local/remote endpoint
that implements the OpenAI Chat Completions API with tool calling.
"""
import asyncio
import json
import logging
import time
from collections import defaultdict
import httpx
from openra_env.config import LLMConfig
from openra_env.game_data import get_building_stats, get_faction_info, get_tech_tree, get_unit_stats
from openra_env.mcp_ws_client import OpenRAMCPClient
logger = logging.getLogger("llm_agent")
def _looks_like_tool_capability_error(error_text: str) -> bool:
"""Best-effort detection of provider errors indicating no tool support."""
text = error_text.lower()
# Only match phrases that unambiguously refer to tool-calling capability.
# "no endpoints found" is too generic on its own — guard it with "tool".
if "no endpoints found" in text and "tool" in text:
return True
markers = (
"support tool use",
"does not support tool",
"tool calling",
"tools are not supported",
)
return any(m in text for m in markers)
def _bench_export_policy(encountered_agent_error: bool) -> tuple[bool, bool, str]:
"""Decide whether bench export and upload should run for this match.
Returns:
(should_export, should_upload, reason)
Local export always happens (useful for debugging).
Upload is skipped when runtime errors occurred.
"""
if encountered_agent_error:
return True, False, "runtime [ERROR] occurred during the match"
return True, True, ""
def _format_llm_api_error(status_code: int, error_text: str, llm_config: LLMConfig) -> str:
"""Map raw provider errors to clear, actionable runtime messages."""
error_lower = error_text.lower()
if status_code in (401, 403):
return (
f"Authentication failed ({status_code}). "
"Check your API key: openra-rl config"
)
if status_code == 400 and "model" in error_lower:
return (
f"Invalid model ID '{llm_config.model}'. "
"Update with: openra-rl config"
)
if status_code == 429:
return "Rate limited by LLM provider. Wait a minute and retry."
if status_code == 404 and _looks_like_tool_capability_error(error_text):
is_openrouter = "openrouter.ai" in llm_config.base_url.lower()
if is_openrouter:
return (
f"Model '{llm_config.model}' has no OpenRouter route that supports tool calling. "
"OpenRA-RL requires tool-calling models. "
"Use a tool-capable model/route (often not ':free'), or use Ollama "
"with qwen3:32b or qwen3:4b."
)
return (
f"Model '{llm_config.model}' does not support tool calling on this endpoint. "
"OpenRA-RL requires tool-calling models."
)
return f"LLM API error {status_code}: {error_text}"
async def _preflight_tool_calling_support(llm_config: LLMConfig) -> tuple[bool, str]:
"""Check OpenRouter model route support for tool calling before game start.
Returns:
(True, "") when preflight passes or does not apply.
(False, reason) when preflight confirms tools are unsupported.
"""
if "openrouter.ai" not in llm_config.base_url.lower():
return True, ""
preflight_cfg = llm_config.model_copy(
update={
"max_tokens": 1,
"request_timeout_s": min(llm_config.request_timeout_s, 30.0),
}
)
preflight_messages = [
{"role": "user", "content": "Tool-calling preflight check. Reply briefly."},
]
preflight_tools = [
{
"type": "function",
"function": {
"name": "preflight_ping",
"description": "Preflight-only tool for capability check.",
"parameters": {"type": "object", "properties": {}},
},
}
]
try:
await chat_completion(preflight_messages, preflight_tools, preflight_cfg, verbose=False, prompts=None)
return True, ""
except RuntimeError as e:
msg = str(e)
if _looks_like_tool_capability_error(msg):
return False, msg
raise
def _load_default_prompt() -> str:
"""Load the default system prompt shipped with the package."""
from openra_env.prompts import load_default_prompt
return load_default_prompt()
# Public constant for backward compatibility (lazy-loaded on first access)
SYSTEM_PROMPT = _load_default_prompt()
def load_system_prompt(config) -> str:
"""Resolve system prompt from config: inline > file > default.
Priority:
1. config.prompts.system_prompt (inline string)
2. config.prompts.system_prompt_file (path to .txt file)
3. config.agent.system_prompt (deprecated, backward compat)
4. config.agent.system_prompt_file (deprecated, backward compat)
5. Built-in default (openra_env/prompts/default.txt)
"""
from pathlib import Path
# Check prompts.* first (canonical location)
prompts_cfg = getattr(config, "prompts", None)
if prompts_cfg:
if getattr(prompts_cfg, "system_prompt", ""):
return prompts_cfg.system_prompt
prompt_file = getattr(prompts_cfg, "system_prompt_file", "")
if prompt_file:
p = Path(prompt_file).expanduser()
if p.is_file():
return p.read_text(encoding="utf-8").strip()
raise FileNotFoundError(f"system_prompt_file not found: {p}")
# Backward compat: check agent.* (deprecated)
agent_cfg = config.agent if hasattr(config, "agent") else config
if getattr(agent_cfg, "system_prompt", ""):
return agent_cfg.system_prompt
prompt_file = getattr(agent_cfg, "system_prompt_file", "")
if prompt_file:
p = Path(prompt_file).expanduser()
if p.is_file():
return p.read_text(encoding="utf-8").strip()
raise FileNotFoundError(f"system_prompt_file not found: {p}")
# Default
return SYSTEM_PROMPT
def compose_pregame_briefing(state: dict) -> str:
"""Compose a strategic briefing from initial game state + static game data.
Sent once at game start so the LLM knows map, base position, faction, tech tree,
and available units/buildings without needing extra tool calls.
"""
map_info = state.get("map", {})
map_w = map_info.get("width", 0)
map_h = map_info.get("height", 0)
map_name = map_info.get("map_name", "?")
# Determine base position from buildings/units
buildings = state.get("buildings_summary", [])
units = state.get("units_summary", [])
all_positions = [(b["cell_x"], b["cell_y"]) for b in buildings] + \
[(u["cell_x"], u["cell_y"]) for u in units]
if all_positions:
base_x = sum(p[0] for p in all_positions) // len(all_positions)
base_y = sum(p[1] for p in all_positions) // len(all_positions)
else:
base_x, base_y = map_w // 2, map_h // 2
# Estimate enemy spawn — opposite side of map
enemy_x = max(2, min(map_w - 2, map_w - base_x))
enemy_y = max(2, min(map_h - 2, map_h - base_y))
# Determine faction and side
faction = state.get("faction", "")
allied_factions = {"england", "france", "germany"}
soviet_factions = {"russia", "ukraine"}
if faction in allied_factions:
side = "Allied"
barracks = "tent"
elif faction in soviet_factions:
side = "Soviet"
barracks = "barr"
else:
# Infer from available production or buildings
avail = state.get("available_production", [])
bldg_types = state.get("building_types", [])
if "tent" in avail or "tent" in bldg_types:
side, barracks = "Allied", "tent"
else:
side, barracks = "Soviet", "barr"
# Get tech tree — returns {side: [order]} dict
tech = get_tech_tree(side.lower())
tech_order = tech.get(side.lower(), tech.get("build_order", []))
# Get faction info for available units/buildings
faction_info = get_faction_info(faction) if faction else get_faction_info(side.lower())
avail_units = faction_info.get("available_units", []) if faction_info else []
avail_buildings = faction_info.get("available_buildings", []) if faction_info else []
# Format key units with costs
unit_lines = []
for utype in avail_units[:12]: # Cap at 12 to keep concise
stats = get_unit_stats(utype)
if stats:
unit_lines.append(f" {utype}: {stats['name']} — ${stats['cost']}, {stats.get('category', '?')}")
# Format key buildings with costs and power
bldg_lines = []
for btype in avail_buildings[:10]:
stats = get_building_stats(btype)
if stats:
power = stats.get("power", 0)
power_str = f", {power:+d} power" if power else ""
bldg_lines.append(f" {btype}: {stats['name']} — ${stats['cost']}{power_str}")
# Calculate defense direction
dx = enemy_x - base_x
dy = enemy_y - base_y
dir_parts = []
if dy < -map_h // 6:
dir_parts.append("North")
elif dy > map_h // 6:
dir_parts.append("South")
if dx > map_w // 6:
dir_parts.append("East")
elif dx < -map_w // 6:
dir_parts.append("West")
defense_direction = "".join(dir_parts) if dir_parts else "Center"
parts = [
"## Strategic Briefing",
f"Map: {map_name} ({map_w}x{map_h})",
f"Your faction: {faction or side} ({side})",
f"Your base: ({base_x}, {base_y})",
f"Enemy likely near: ({enemy_x}, {enemy_y})",
f"Enemy approach direction: {defense_direction}",
"",
f"Tech tree: {' → '.join(tech_order[:8])}{'...' if len(tech_order) > 8 else ''}",
f"Barracks type: {barracks}",
"",
"Available units:",
*unit_lines,
"",
"Available buildings:",
*bldg_lines,
]
return "\n".join(parts)
def format_state_briefing(state: dict) -> str:
"""Format game state (from get_game_state tool) into a compact turn briefing with positions."""
if not isinstance(state, dict) or "tick" not in state:
return ""
eco = state.get("economy", {})
tick = state["tick"]
cash = eco.get("cash", 0)
ore = eco.get("ore", 0)
funds = cash + ore
parts = [
f"--- TURN BRIEFING (tick {tick}, ~{tick // 25}s game time) ---",
f"Funds: ${funds} (cash=${cash} + ore=${ore}) | Power: {state.get('power_balance', 0):+d} | Harvesters: {eco.get('harvester_count', 0)} | Explored: {state.get('explored_percent', 0)}%",
]
# Minimap (ASCII spatial overview)
minimap = state.get("minimap", "")
if minimap:
parts.append(minimap)
# Base center from buildings
buildings = state.get("buildings_summary", [])
if buildings:
base_x = sum(b["cell_x"] for b in buildings) // len(buildings)
base_y = sum(b["cell_y"] for b in buildings) // len(buildings)
parts.append(f"Base center: ({base_x},{base_y})")
# Compact unit summary grouped by type, with IDs, positions, and activity
units = state.get("units_summary", [])
if units:
by_type = defaultdict(list)
idle_ids = []
for u in units:
by_type[u["type"]].append(u)
if u.get("idle") and u.get("can_attack"):
idle_ids.append(u["id"])
unit_parts = []
for utype, us in by_type.items():
entries = []
for u in us:
pos = f"{u['id']}@({u['cell_x']},{u['cell_y']})"
if u.get("target_x") is not None:
pos += f"→({u['target_x']},{u['target_y']})"
elif not u.get("idle"):
# Show short activity tag for non-idle units without tracked target
act = u.get("activity", "")
if act and act not in ("Idle", "Unknown", "Wait"):
tag = act[:3].lower()
pos += f"→{tag}"
entries.append(pos)
unit_parts.append(f"{len(us)}x{utype}[{','.join(entries)}]")
line = f"Units: {' '.join(unit_parts)}"
if idle_ids:
line += f" | Idle: [{','.join(str(i) for i in idle_ids)}]"
parts.append(line)
else:
parts.append(f"Units: {state.get('own_units', '?')}")
# Compact building summary with IDs, positions, and production category
_BLDG_CATEGORY = {"tent": "infantry", "barr": "infantry", "weap": "vehicle",
"hpad": "aircraft", "afld": "aircraft", "syrd": "ship", "spen": "ship",
"gun": "defense", "ftur": "defense", "tsla": "defense",
"sam": "defense", "agun": "defense", "pbox": "defense", "hbox": "defense"}
if buildings:
bldg_parts = []
for b in buildings:
cat = _BLDG_CATEGORY.get(b["type"], "")
cat_str = f"[{cat}]" if cat else ""
bldg_parts.append(f"{b['type']}({b['id']})@({b['cell_x']},{b['cell_y']}){cat_str}")
parts.append(f"Buildings: {' '.join(bldg_parts)}")
else:
parts.append(f"Buildings: {state.get('own_buildings', '?')} ({', '.join(state.get('building_types', []))})")
# Enemy summary with IDs and positions (units + buildings)
enemies = state.get("enemy_summary", [])
enemy_bldgs = state.get("enemy_buildings_summary", [])
if enemies or enemy_bldgs:
enemy_parts = []
if enemies:
eby_type = defaultdict(list)
for e in enemies:
eby_type[e["type"]].append(e)
for etype, es in eby_type.items():
entries = ",".join(f"{e['id']}@({e['cell_x']},{e['cell_y']})" for e in es)
enemy_parts.append(f"{len(es)}x{etype}[{entries}]")
if enemy_bldgs:
ebby_type = defaultdict(list)
for b in enemy_bldgs:
ebby_type[b["type"]].append(b)
for btype, bs in ebby_type.items():
entries = ",".join(f"{b['id']}@({b['cell_x']},{b['cell_y']})" for b in bs)
enemy_parts.append(f"{len(bs)}x{btype}[{entries}]")
# Average position of all visible enemies
all_enemy_pos = (
[(e["cell_x"], e["cell_y"]) for e in enemies]
+ [(b["cell_x"], b["cell_y"]) for b in enemy_bldgs]
)
avg_x = sum(p[0] for p in all_enemy_pos) // len(all_enemy_pos)
avg_y = sum(p[1] for p in all_enemy_pos) // len(all_enemy_pos)
parts.append(f"Enemies: {' '.join(enemy_parts)} center ({avg_x},{avg_y})")
else:
n_enemy = state.get("visible_enemy_units", 0)
parts.append(f"Enemies: {'none visible' if n_enemy == 0 else f'{n_enemy} visible'}")
prod = state.get("production_items", [])
if prod:
active = [p for p in prod if "@100%" not in p]
ready = [p.split("@")[0] for p in prod if "@100%" in p]
parts_prod = []
if active:
parts_prod.append(", ".join(active))
if ready:
parts_prod.append(f"READY TO PLACE: {', '.join(ready)}")
parts.append(f"Production: {' | '.join(parts_prod)}")
else:
parts.append("Production: IDLE")
available = state.get("available_production", [])
if available:
parts.append(f"Can build: {', '.join(available)}")
alerts = state.get("alerts", [])
if alerts:
parts.append("ALERTS:")
for a in alerts:
parts.append(f" ** {a}")
parts.append("---")
if state.get("done"):
parts.append(f"GAME OVER: {state.get('result', '?')}")
return "\n".join(parts)
def mcp_tools_to_openai(tools: list) -> list[dict]:
"""Convert MCP Tool schemas to OpenAI function calling format."""
result = []
for tool in tools:
schema = tool.input_schema if hasattr(tool, 'input_schema') else {}
# Clean up schema — remove 'title' which confuses some models
params = dict(schema) if schema else {}
params.pop("title", None)
if "properties" not in params:
params["properties"] = {}
params["type"] = "object"
result.append({
"type": "function",
"function": {
"name": tool.name,
"description": tool.description or "",
"parameters": params,
},
})
return result
def _sanitize_messages(messages: list[dict], prompts=None) -> list[dict]:
"""Merge consecutive same-role messages for strict-alternation models (e.g. Mistral).
Some models require strict user/assistant alternation and reject sequences
like ``user → user`` or ``tool → user``. This helper:
1. Merges consecutive ``user`` messages by joining their content with newlines.
2. Inserts a bridge ``assistant`` message when a ``tool`` result is followed
by a ``user`` message (Mistral requires tool → assistant → user).
"""
if not messages:
return messages
bridge = prompts.sanitize_bridge if prompts else "Acknowledged. Continuing."
merged: list[dict] = [dict(messages[0])]
for msg in messages[1:]:
prev = merged[-1]
# Merge consecutive user messages
if msg["role"] == "user" and prev["role"] == "user":
merged[-1] = {**prev, "content": prev["content"] + "\n\n" + msg["content"]}
continue
# Bridge: tool → user needs an assistant message in between
if msg["role"] == "user" and prev["role"] == "tool":
merged.append({"role": "assistant", "content": bridge})
merged.append(msg)
return merged
async def chat_completion(
messages: list[dict],
tools: list[dict],
llm_config: LLMConfig,
verbose: bool = False,
prompts=None,
) -> dict:
"""Call an OpenAI-compatible chat completions API.
Works with OpenRouter, Ollama, LM Studio, or any endpoint
implementing the OpenAI Chat Completions spec with tool calling.
"""
clean_messages = _sanitize_messages(messages, prompts=prompts)
payload = {
"model": llm_config.model,
"messages": clean_messages,
"max_tokens": llm_config.max_tokens,
}
if tools:
payload["tools"] = tools
payload["tool_choice"] = "auto"
if llm_config.temperature is not None:
payload["temperature"] = llm_config.temperature
if llm_config.top_p is not None:
payload["top_p"] = llm_config.top_p
if llm_config.reasoning_effort is not None:
payload["reasoning"] = {"effort": llm_config.reasoning_effort}
headers = dict(llm_config.extra_headers)
if llm_config.api_key:
headers["Authorization"] = f"Bearer {llm_config.api_key}"
async with httpx.AsyncClient() as client:
if verbose:
n_msgs = len(clean_messages)
roles = [m.get("role", "?") for m in clean_messages]
print(f" [LLM] Sending {n_msgs} messages to {llm_config.model}...")
print(f" [LLM] Roles: {' → '.join(roles)}")
response = await client.post(
llm_config.base_url,
headers=headers,
json=payload,
timeout=llm_config.request_timeout_s,
)
if response.status_code != 200:
error_text = response.text[:2000]
raise RuntimeError(
_format_llm_api_error(response.status_code, error_text, llm_config)
)
try:
data = response.json()
except (json.JSONDecodeError, ValueError) as e:
raise RuntimeError(f"LLM API error 502: invalid JSON response ({e})")
if "error" in data:
raise RuntimeError(f"LLM API error 500: {data['error']}")
if verbose:
usage = data.get("usage", {})
print(
f" [LLM] Response: {usage.get('prompt_tokens', '?')} prompt + "
f"{usage.get('completion_tokens', '?')} completion tokens"
)
return data
def compress_history(messages: list[dict], keep_last: int = 40,
trigger: int = 0, prompts=None, compression=None) -> list[dict]:
"""Compress message history to stay within context limits.
Keeps the system prompt and the last ``keep_last`` messages, replacing
earlier messages with a state-aware summary that preserves critical
game context (buildings, economy, strategy, military, errors).
Args:
keep_last: Number of recent messages to keep after compression.
trigger: Compress when total messages exceed this threshold.
0 (default) means ``keep_last * 2``.
prompts: PromptsConfig for customizable text.
compression: CompressionConfig controlling what to include in summary.
"""
threshold = trigger if trigger > 0 else keep_last * 2
if len(messages) <= threshold:
return messages
system = messages[0]
# Find a clean cut point: recent must not start with tool role
cut = len(messages) - keep_last
while cut < len(messages) and messages[cut].get("role") == "tool":
cut += 1 # move cut forward to skip orphaned tool results
if cut >= len(messages) - 2:
return messages # can't compress safely
old_messages = messages[1:cut]
recent = messages[cut:]
# Compression config defaults
inc_strategy = compression.include_strategy if compression else True
inc_military = compression.include_military if compression else True
inc_production = compression.include_production if compression else True
# Extract game state context from old messages
last_state = {}
building_types = set()
unit_types_produced = set()
strategy_text = ""
errors = []
for msg in old_messages:
# Extract planning strategy from early user messages
if inc_strategy and msg.get("role") == "user" and not strategy_text:
content_str = msg.get("content", "")
if isinstance(content_str, str):
for line in content_str.split("\n"):
if line.strip().startswith("Strategy:"):
strategy_text = line.strip()
break
if msg.get("role") != "tool":
continue
try:
content = json.loads(msg["content"]) if isinstance(msg["content"], str) else msg["content"]
if not isinstance(content, dict):
continue
# Track latest state snapshot
if "tick" in content and "economy" in content:
last_state = content
# Track buildings built
for bt in content.get("building_types", []):
building_types.add(bt)
# Track units produced (from build_unit notes)
if inc_production and "note" in content:
note = content["note"]
if isinstance(note, str) and "queued" in note:
# Extract unit/building name from "'name' ... queued"
import re
m = re.search(r"'(\w+)'.*queued", note)
if m:
name = m.group(1)
# Distinguish units from buildings
if "per unit" in note or "each" in note:
unit_types_produced.add(name)
else:
building_types.add(name)
# Track placement failures and errors
if content.get("placement_failed"):
errors.append("placement failed")
elif "error" in content and isinstance(content["error"], str):
err = content["error"]
if len(err) < 80:
errors.append(err)
except (json.JSONDecodeError, TypeError):
pass
# Build summary
parts = [f"[History: {len(old_messages)} earlier messages removed]"]
if last_state:
eco = last_state.get("economy", {})
parts.append(
f"Last state at tick {last_state.get('tick', '?')}: "
f"${eco.get('cash', '?')} cash, "
f"{last_state.get('own_units', '?')} units, "
f"{last_state.get('own_buildings', '?')} buildings"
)
if inc_strategy and strategy_text:
parts.append(strategy_text)
if building_types:
parts.append(f"Buildings built: {', '.join(sorted(building_types))}")
if inc_production and unit_types_produced:
parts.append(f"Units produced: {', '.join(sorted(unit_types_produced))}")
if inc_military and last_state:
mil = last_state.get("military", {})
if mil:
parts.append(
f"Military: {mil.get('units_killed', 0)} kills, "
f"{mil.get('units_lost', 0)} losses"
)
if errors:
unique = list(dict.fromkeys(errors))[-3:]
parts.append(f"Recent issues: {'; '.join(unique)}")
suffix = prompts.compression_suffix if prompts else "Game continues from current state."
parts.append(suffix)
return [
system,
{"role": "user", "content": "\n".join(parts)},
*recent,
]
async def run_agent(config, verbose: bool = False):
"""Connect to OpenRA-RL and play a game using an LLM agent."""
url = config.agent.server_url
llm_config = config.llm
max_turns = config.agent.max_turns
max_time = config.agent.max_time_s
# Auto-increase timeout for local models (they're slower than cloud APIs)
is_local = any(h in llm_config.base_url for h in ("localhost", "127.0.0.1"))
if is_local and llm_config.request_timeout_s <= 120.0:
llm_config = llm_config.model_copy(update={"request_timeout_s": 300.0})
print(f"Connecting to {url}...")
print(f"Model: {llm_config.model} @ {llm_config.base_url}")
if is_local:
print(f"Timeout: {int(llm_config.request_timeout_s)}s (local model)")
if "openrouter.ai" in llm_config.base_url.lower():
print("Checking model route for tool-calling support...")
try:
preflight_ok, preflight_err = await _preflight_tool_calling_support(llm_config)
except Exception as e:
print(f" [ERROR] Preflight check failed: {e}")
print(" Aborting before game launch (no match started).")
return
if not preflight_ok:
print(f" [ERROR] Preflight check failed: {preflight_err}")
print(" Aborting before game launch (no match started).")
return
async with OpenRAMCPClient(base_url=url, message_timeout_s=300.0) as env:
print("Resetting environment (launching OpenRA)...")
await env.reset()
# Discover and convert tools
mcp_tools = await env.list_tools()
openai_tools = mcp_tools_to_openai(mcp_tools)
tool_names = {t["function"]["name"] for t in openai_tools}
print(f"Discovered {len(mcp_tools)} MCP tools")
if verbose:
for t in mcp_tools:
print(f" - {t.name}: {t.description[:60]}...")
# Initialize conversation
system_prompt = load_system_prompt(config)
messages = [{"role": "system", "content": system_prompt}]
# ─── Pre-Game Planning Phase ──────────────────────────────────
planning_strategy = ""
planning_status = await env.call_tool("get_planning_status")
if planning_status.get("planning_enabled", True) is not False:
print("Starting pre-game planning phase...")
planning_data = await env.call_tool("start_planning_phase")
if planning_data.get("planning_active"):
max_planning_turns = planning_data.get("max_turns", 10)
opponent_summary = planning_data.get("opponent_summary", "")
prompts = config.prompts
planning_prompt = prompts.planning_prompt.format(
max_turns=max_planning_turns,
map_name=planning_data.get("map", {}).get("map_name", "?"),
map_width=planning_data.get("map", {}).get("width", "?"),
map_height=planning_data.get("map", {}).get("height", "?"),
base_x=planning_data.get("base_position", {}).get("x", "?"),
base_y=planning_data.get("base_position", {}).get("y", "?"),
enemy_x=planning_data.get("enemy_estimated_position", {}).get("x", "?"),
enemy_y=planning_data.get("enemy_estimated_position", {}).get("y", "?"),
faction=planning_data.get("your_faction", "?"),
side=planning_data.get("your_side", "?"),
opponent_summary=opponent_summary,
planning_nudge=prompts.planning_nudge,
)
messages.append({"role": "user", "content": planning_prompt})
# Planning loop (bounded by max_planning_turns + margin)
planning_done = False
for planning_turn in range(max_planning_turns + 2):
try:
response = await chat_completion(messages, openai_tools, llm_config, verbose, prompts=config.prompts)
except (RuntimeError, httpx.ReadTimeout, httpx.ConnectTimeout) as e:
print(f" [Planning] API error: {e}")
print(" Skipping planning phase.")
break
if response is None:
break
choice = response["choices"][0]
assistant_msg = choice["message"]
messages.append(assistant_msg)
if verbose and assistant_msg.get("content"):
print(f" [Planning] {assistant_msg['content'][:200]}")
tool_calls = assistant_msg.get("tool_calls", [])
if not tool_calls:
messages.append({
"role": "user",
"content": prompts.planning_nudge,
})
continue
for tc in tool_calls:
fn_name = tc["function"]["name"]
try:
fn_args = json.loads(tc["function"].get("arguments", "{}"))
except (json.JSONDecodeError, TypeError):
fn_args = {}
if verbose:
args_str = json.dumps(fn_args)
if len(args_str) > 80:
args_str = args_str[:80] + "..."
print(f" [Planning Tool] {fn_name}({args_str})")
try:
result = await env.call_tool(fn_name, **fn_args)
except Exception as e:
result = {"error": str(e)}
messages.append({
"role": "tool",
"tool_call_id": tc["id"],
"content": json.dumps(result) if not isinstance(result, str) else result,
})
# Check if planning ended
if isinstance(result, dict):
if result.get("planning_complete"):
planning_strategy = result.get("strategy", "")
planning_done = True
if verbose:
print(f" [Planning] Strategy: {planning_strategy[:150]}...")
elif result.get("planning_expired"):
planning_strategy = result.get("strategy", "")
planning_done = True
print(f" [Planning] Expired: {result.get('reason', '?')}")
if planning_done:
break
if not planning_done:
# Force end planning
try:
result = await env.call_tool(
"end_planning_phase",
strategy="(planning timed out, no explicit strategy)"
)
planning_strategy = result.get("strategy", "")
except Exception:
pass
print(" Planning phase timed out, proceeding to gameplay.")
print(f"Planning phase complete. Strategy recorded: {bool(planning_strategy)}")
else:
if verbose:
print(f" Planning: {planning_data.get('message', 'skipped')}")
# ─── Game Start ───────────────────────────────────────────────
# Reset messages to just system prompt — planning context is captured
# in the strategy text below. This avoids tool/user role alternation
# issues with models that enforce strict message ordering (e.g. Mistral).
messages = [messages[0]] # keep only system prompt
state = await env.call_tool("get_game_state")
briefing = compose_pregame_briefing(state)
strategy_section = ""
if planning_strategy:
strategy_section = f"\n\n## Your Pre-Game Strategy\n{planning_strategy}\n"
# Find MCV unit ID and barracks type for context
mcv_id = None
for u in state.get("units_summary", []):
if u.get("type") == "mcv":
mcv_id = u["id"]
break
faction = state.get("faction", "")
barracks_type = "tent" if faction in {"england", "france", "germany"} else "barr"
mcv_note = f" Your MCV is unit {mcv_id}." if mcv_id else ""
game_start_prompts = config.prompts
messages.append({
"role": "user",
"content": game_start_prompts.game_start.format(
strategy_section=strategy_section,
briefing=briefing,
barracks_type=barracks_type,
mcv_note=mcv_note,
),
})
total_tool_calls = 0
total_api_calls = 0
start_time = time.time()
game_done = False
encountered_agent_error = False
consecutive_errors = 0
MAX_CONSECUTIVE_ERRORS = 3
turn = 0
while True:
# Check limits
elapsed = time.time() - start_time
if max_time and elapsed >= max_time:
print(f"\n TIME LIMIT reached ({max_time}s). Stopping.")
break
if max_turns and turn >= max_turns:
break
turn += 1
# Compress history periodically (unless disabled)
if llm_config.compression_strategy != "none":
messages = compress_history(
messages, keep_last=llm_config.keep_last_messages,
trigger=llm_config.compression_trigger,
prompts=config.prompts,
compression=config.prompts.compression)
# Inject state briefing before LLM thinks (skip first turn — initial state already provided)
if total_api_calls > 0:
try:
briefing_state = await env.call_tool("get_game_state")
briefing = format_state_briefing(briefing_state)
if briefing:
messages.append({"role": "user", "content": briefing})
if verbose:
# Print just the alerts
for a in briefing_state.get("alerts", []):
print(f" [ALERT] {a}")
# Check game over from briefing
if isinstance(briefing_state, dict) and briefing_state.get("done"):
game_done = True
print(f"\n GAME OVER: {briefing_state.get('result', '?').upper()} at tick {briefing_state.get('tick', '?')}")
break
except Exception:
pass
# Call LLM with retry for rate limits
response = None
max_retries = llm_config.max_retries
is_local = any(h in llm_config.base_url for h in ("localhost", "127.0.0.1"))
for attempt in range(max_retries):
try:
response = await chat_completion(messages, openai_tools, llm_config, verbose, prompts=config.prompts)
break
except (httpx.ReadTimeout, httpx.ConnectTimeout):
timeout_s = int(llm_config.request_timeout_s)
print(f"\n [ERROR] Request timed out after {timeout_s}s.")
encountered_agent_error = True
if is_local:
print(" [HINT] Local models can be slow. Increase timeout in config.yaml:")
print(f" llm.request_timeout_s: {timeout_s * 2}")
break
except RuntimeError as e:
err_str = str(e)
retriable = any(code in err_str for code in ("429", "500", "502", "503", "504"))
if retriable and attempt < max_retries - 1:
wait = llm_config.retry_backoff_s * (attempt + 1)
print(f"\n [RETRY] Provider error, waiting {wait}s ({attempt + 1}/{max_retries})...")
print(f" {e}")
await asyncio.sleep(wait)
else:
print(f"\n [ERROR] API call failed: {e}")
encountered_agent_error = True
break
if response is None:
print(" [ERROR] Stopping agent.")
encountered_agent_error = True
break
total_api_calls += 1
choice = response["choices"][0]
assistant_msg = choice["message"]
# Add assistant response to history
messages.append(assistant_msg)
# Print assistant's reasoning
if assistant_msg.get("content") and verbose:
print(f"\n [LLM thinks] {assistant_msg['content'][:200]}")
# Handle tool calls
tool_calls = assistant_msg.get("tool_calls", [])
if not tool_calls:
# No tool calls — prompt to act
if verbose:
content = assistant_msg.get("content", "(no content)")
print(f" [LLM] No tool calls. Response: {content[:100]}")
messages.append({
"role": "user",
"content": config.prompts.no_tool_nudge,
})
continue
# Execute each tool call
for tc in tool_calls:
fn_name = tc["function"]["name"]
try:
fn_args = json.loads(tc["function"].get("arguments", "{}"))
except (json.JSONDecodeError, TypeError):
fn_args = {}
total_tool_calls += 1
if verbose:
args_str = json.dumps(fn_args)
if len(args_str) > 80:
args_str = args_str[:80] + "..."
print(f" [Tool] {fn_name}({args_str})")
try:
result = await env.call_tool(fn_name, **fn_args)
consecutive_errors = 0
except Exception as e:
result = {"error": str(e)}
# Suggest similar tools for unknown tool errors
if fn_name not in tool_names:
import difflib
close = difflib.get_close_matches(fn_name, tool_names, n=3, cutoff=0.4)
# Always include canonical build tools for build-related names
build_keywords = {"build", "place", "train", "produce", "construct"}
if any(kw in fn_name.lower() for kw in build_keywords):
for bt in ("build_unit", "build_structure", "build_and_place"):
if bt in tool_names and bt not in close:
close.append(bt)
if close:
result["suggested_tools"] = close
# Detect game connection lost
if isinstance(result, dict) and "connection lost" in str(result.get("error", "")).lower():
consecutive_errors += 1
if consecutive_errors >= MAX_CONSECUTIVE_ERRORS:
print(f"\n GAME CRASHED: {consecutive_errors} consecutive connection errors. Stopping.")
encountered_agent_error = True
game_done = True
# Format result for message
result_str = json.dumps(result) if not isinstance(result, str) else result
messages.append({
"role": "tool",
"tool_call_id": tc["id"],
"content": result_str,
})
# Check for game over
if isinstance(result, dict) and result.get("done"):
game_done = True
print(f"\n GAME OVER: {result.get('result', '?').upper()} at tick {result.get('tick', '?')}")
if verbose and isinstance(result, dict):
result_preview = json.dumps(result)
if len(result_preview) > 500:
result_preview = result_preview[:500] + "..."
print(f" [Result] {result_preview}")
# Status update
if total_api_calls % 5 == 0 or game_done:
elapsed = time.time() - start_time
limit_str = f"/{max_turns}" if max_turns else ""
time_str = f"{elapsed:.0f}/{max_time}s" if max_time else f"{elapsed:.0f}s"
print(
f" Turn {turn}{limit_str} | "
f"API calls: {total_api_calls} | "
f"Tool calls: {total_tool_calls} | "
f"Time: {time_str}"
)
if game_done:
break
# Check finish reason
if choice.get("finish_reason") == "stop" and not tool_calls:
messages.append({
"role": "user",
"content": config.prompts.continue_nudge,
})
# Surrender so the replay has a proper ending
if not game_done:
try:
await env.call_tool("surrender")
print("\n Surrendered (replay will have proper ending)")
except Exception:
pass
# Final report
elapsed = time.time() - start_time
print()
print("=" * 70)
print(f"Agent finished after {total_api_calls} API calls, {total_tool_calls} tool calls")
print(f"Time: {elapsed:.1f}s ({elapsed / max(total_api_calls, 1):.1f}s per API call)")
# Get final state and scorecard
try:
final = await env.call_tool("get_game_state")
mil = final.get("military", {})
eco = final.get("economy", {})
print(f"Result: {final.get('result', 'ongoing').upper()}")
print()
print("--- SCORECARD ---")
print(f" Planning: {'ON — ' + planning_strategy[:100] if planning_strategy else 'OFF'}")
print(f" Ticks played: {final.get('tick', '?')}")
print(f" Units killed: {mil.get('units_killed', 0)} (value: ${mil.get('kills_cost', 0)})")
print(f" Units lost: {mil.get('units_lost', 0)} (value: ${mil.get('deaths_cost', 0)})")
print(f" Buildings killed: {mil.get('buildings_killed', 0)}")
print(f" Buildings lost: {mil.get('buildings_lost', 0)}")
print(f" Army value: ${mil.get('army_value', 0)}")
print(f" Assets value: ${mil.get('assets_value', 0)}")
print(f" Experience: {mil.get('experience', 0)}")
print(f" Orders issued: {mil.get('order_count', 0)}")
print(f" Cash remaining: ${eco.get('cash', 0)}")
print(f" K/D cost ratio: {mil.get('kills_cost', 0) / max(mil.get('deaths_cost', 1), 1):.2f}")
print(f" Own units: {final.get('own_units', '?')}")
print(f" Own buildings: {final.get('own_buildings', '?')}")
print(f" Explored: {final.get('explored_percent', 0)}%")
rv = final.get("reward_vector", {})
if rv:
print(" Reward vector:")
for dim, val in rv.items():
print(f" {dim:15s} {val:+.3f}")
print()
except Exception as e:
print(f" (could not get final state: {e})")
# Get replay
replay = {}
try:
replay = await env.call_tool("get_replay_path")
if replay.get("path"):
print(f"Replay: {replay['path']}")
except Exception:
pass
# Auto-export bench submission JSON (always local, upload gated on errors)
should_export, should_upload, skip_reason = _bench_export_policy(encountered_agent_error)
try:
from datetime import datetime, timezone
from pathlib import Path
resolved_name = config.agent.agent_name or llm_config.model
sub = {
"agent_name": resolved_name,
"agent_type": config.agent.agent_type or "LLM",
"agent_url": config.agent.agent_url,
"opponent": config.opponent.bot_type.capitalize(),
"games": 1,
"result": final.get("result", ""),
"win": final.get("result") == "win",
"ticks": final.get("tick", 0),
"kills_cost": mil.get("kills_cost", 0),
"deaths_cost": mil.get("deaths_cost", 0),
"kd_ratio": round(mil.get("kills_cost", 0) / max(mil.get("deaths_cost", 1), 1), 2),
"assets_value": mil.get("assets_value", 0),
"explored_percent": final.get("explored_percent", 0),
"reward_vector": final.get("reward_vector", {}),
"replay_path": replay.get("path", ""),
"timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
}
export_dir = Path.home() / ".openra-rl" / "bench-exports"
export_dir.mkdir(parents=True, exist_ok=True)
ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
slug = resolved_name.replace("/", "_")[:40]
export_path = export_dir / f"bench-{slug}-{ts}.json"
export_path.write_text(json.dumps(sub, indent=2))
print(f"Bench export: {export_path}")
# Auto-upload to bench if enabled (skip when agent errors occurred)
bench_url = config.agent.bench_url
if config.agent.bench_upload and bench_url:
if not should_upload:
print(f"Skipping bench upload: {skip_reason}")
else:
try:
from openra_env.bench_submit import gradio_submit
msg = gradio_submit(bench_url, sub, replay_path=replay.get("path", ""))
print(f"Uploaded to bench: {msg}")
except Exception as e:
print(f" (bench upload failed: {e})")
except Exception as e:
print(f" (bench export failed: {e})")
print("=" * 70)
|