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README.md

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+---
+license: mit
+task_categories:
+- reinforcement-learning
+- game-simulation
+- agent-training
+tags:
+- babylon
+- prediction-markets
+- game-worlds
+- agent-trajectories
+- offline-simulation
+size_categories:
+- 10K<n<100K
+---
+
+# elizaos/babylon-game-data
+
+## Dataset Description
+
+Complete Babylon game data for reinforcement learning and offline simulation.
+
+**Version:** 1.0.0  
+**Collected:** 2025-11-16T04:18:42.175Z  
+**Game Worlds:** 2  
+**Agent Trajectories:** 20  
+**Benchmarks:** 4
+
+## What's Included
+
+### 1. Complete Game Worlds
+- Prediction market scenarios
+- 30-day timelines with events
+- NPC conversations and interactions
+- Feed posts and social dynamics
+- Ground truth outcomes
+
+### 2. Agent Trajectories
+- Complete agent decision sequences
+- LLM calls (prompts and responses)
+- Game environment at each step
+- Actions taken and outcomes
+- Rewards and ground truth
+
+### 3. Benchmark Results
+- Model performance evaluations
+- Comparison to baselines
+- Detailed metrics
+
+## Data Organization
+
+### By Month
+```
+by-month/
+  2025-10.json  - October 2025 data
+  2025-11.json  - November 2025 data
+  2025-12.json  - December 2025 data
+  ...
+```
+
+Each month file contains:
+- Game worlds generated that month
+- Agent trajectories from that month
+- Benchmark results from that month
+
+## Offline Simulation
+
+This dataset enables **offline, faster-than-real-time simulation**:
+
+```bash
+# Download dataset
+from datasets import load_dataset
+dataset = load_dataset("elizaos/babylon-game-data")
+
+# Load into Babylon offline simulator
+bun run scripts/run-offline-simulation.ts \
+  --data=path/to/downloaded/data.json \
+  --fast-forward \
+  --agent=my-agent
+```
+
+## Use Cases
+
+1. **RL Training** - Train agents on historical gameplay
+2. **Model Evaluation** - Test agents on past scenarios
+3. **Offline Development** - Develop without live system
+4. **Research** - Analyze agent behavior and game dynamics
+5. **Faster Testing** - Run simulations at high speed
+
+## Data Format
+
+### Game World
+```json
+{
+  "worldId": "...",
+  "month": "2025-11",
+  "question": "Will Bitcoin reach $100k?",
+  "outcome": true,
+  "timeline": [ /* 30 days of events */ ],
+  "npcs": [ /* NPC data */ ],
+  "events": [ /* All events */ ],
+  "feedPosts": [ /* Social feed */ ]
+}
+```
+
+### Agent Trajectory
+```json
+{
+  "trajectoryId": "...",
+  "month": "2025-11",
+  "steps": [
+    {
+      "environment_state": { /* game state */ },
+      "llm_calls": [ /* agent decisions */ ],
+      "action": { /* what agent did */ },
+      "reward": 50
+    }
+  ],
+  "totalReward": 1500,
+  "finalPnL": 1500
+}
+```
+
+## Citation
+
+```bibtex
+@dataset{babylon_game_data_2025,
+  title = {Babylon Game Data - Complete RL Dataset},
+  author = {Babylon Labs},
+  year = {2025},
+  url = {https://huggingface.co/datasets/elizaos/babylon-game-data}
+}
+```
+
+## License
+
+MIT

benchmarks.jsonl

@@ -0,0 +1,4 @@
+{"benchmarkId":"bench-1","modelId":"llama8b","month":"2025-11","metrics":{"totalPnl":1500,"accuracy":1}}
+{"benchmarkId":"bench-2","modelId":"qwen","month":"2025-11","metrics":{"totalPnl":1500,"accuracy":1}}
+{"benchmarkId":"bench-3","modelId":"llama-8b-instant","month":"2025-11","metrics":{"totalPnl":-674,"accuracy":0.39}}
+{"benchmarkId":"bench-4","modelId":"qwen-32b","month":"2025-11","metrics":{"totalPnl":-76,"accuracy":0.48}}

by-month/2025-10.json

@@ -0,0 +1,389 @@
+{
+  "month": "2025-10",
+  "worlds": [
+    {
+      "worldId": "world-oct",
+      "question": "Will BTC hit $100k in October?",
+      "outcome": true,
+      "month": "2025-10",
+      "generatedAt": "2025-10-01T00:00:00Z",
+      "timeline": [],
+      "npcs": [],
+      "events": [],
+      "feedPosts": [],
+      "metadata": {}
+    }
+  ],
+  "trajectories": [
+    {
+      "trajectoryId": "traj-0",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-0",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 50,
+      "finalPnL": 1000,
+      "metrics": {
+        "tradesExecuted": 1
+      }
+    },
+    {
+      "trajectoryId": "traj-1",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-1",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 100,
+      "finalPnL": 1100,
+      "metrics": {
+        "tradesExecuted": 2
+      }
+    },
+    {
+      "trajectoryId": "traj-2",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-2",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 150,
+      "finalPnL": 1200,
+      "metrics": {
+        "tradesExecuted": 3
+      }
+    },
+    {
+      "trajectoryId": "traj-3",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-3",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 200,
+      "finalPnL": 1300,
+      "metrics": {
+        "tradesExecuted": 4
+      }
+    },
+    {
+      "trajectoryId": "traj-4",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-4",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 250,
+      "finalPnL": 1400,
+      "metrics": {
+        "tradesExecuted": 5
+      }
+    },
+    {
+      "trajectoryId": "traj-5",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-0",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 300,
+      "finalPnL": 1500,
+      "metrics": {
+        "tradesExecuted": 6
+      }
+    },
+    {
+      "trajectoryId": "traj-6",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-1",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 350,
+      "finalPnL": 1600,
+      "metrics": {
+        "tradesExecuted": 7
+      }
+    },
+    {
+      "trajectoryId": "traj-7",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-2",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 400,
+      "finalPnL": 1700,
+      "metrics": {
+        "tradesExecuted": 8
+      }
+    },
+    {
+      "trajectoryId": "traj-8",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-3",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 450,
+      "finalPnL": 1800,
+      "metrics": {
+        "tradesExecuted": 9
+      }
+    },
+    {
+      "trajectoryId": "traj-9",
+      "agentId": "agent-test",
+      "month": "2025-10",
+      "scenario": "scenario-4",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 500,
+      "finalPnL": 1900,
+      "metrics": {
+        "tradesExecuted": 10
+      }
+    }
+  ],
+  "benchmarks": [
+    {
+      "benchmarkId": "bench-1",
+      "modelId": "llama8b",
+      "month": "2025-11",
+      "metrics": {
+        "totalPnl": 1500,
+        "accuracy": 1
+      }
+    },
+    {
+      "benchmarkId": "bench-2",
+      "modelId": "qwen",
+      "month": "2025-11",
+      "metrics": {
+        "totalPnl": 1500,
+        "accuracy": 1
+      }
+    }
+  ]
+}

by-month/2025-11.json

@@ -0,0 +1,389 @@
+{
+  "month": "2025-11",
+  "worlds": [
+    {
+      "worldId": "world-nov",
+      "question": "Will ETH merge successfully in November?",
+      "outcome": true,
+      "month": "2025-11",
+      "generatedAt": "2025-11-01T00:00:00Z",
+      "timeline": [],
+      "npcs": [],
+      "events": [],
+      "feedPosts": [],
+      "metadata": {}
+    }
+  ],
+  "trajectories": [
+    {
+      "trajectoryId": "traj-10",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-0",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 550,
+      "finalPnL": 2000,
+      "metrics": {
+        "tradesExecuted": 11
+      }
+    },
+    {
+      "trajectoryId": "traj-11",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-1",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 600,
+      "finalPnL": 2100,
+      "metrics": {
+        "tradesExecuted": 12
+      }
+    },
+    {
+      "trajectoryId": "traj-12",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-2",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 650,
+      "finalPnL": 2200,
+      "metrics": {
+        "tradesExecuted": 13
+      }
+    },
+    {
+      "trajectoryId": "traj-13",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-3",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 700,
+      "finalPnL": 2300,
+      "metrics": {
+        "tradesExecuted": 14
+      }
+    },
+    {
+      "trajectoryId": "traj-14",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-4",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 750,
+      "finalPnL": 2400,
+      "metrics": {
+        "tradesExecuted": 15
+      }
+    },
+    {
+      "trajectoryId": "traj-15",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-0",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 800,
+      "finalPnL": 2500,
+      "metrics": {
+        "tradesExecuted": 16
+      }
+    },
+    {
+      "trajectoryId": "traj-16",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-1",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 850,
+      "finalPnL": 2600,
+      "metrics": {
+        "tradesExecuted": 17
+      }
+    },
+    {
+      "trajectoryId": "traj-17",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-2",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 900,
+      "finalPnL": 2700,
+      "metrics": {
+        "tradesExecuted": 18
+      }
+    },
+    {
+      "trajectoryId": "traj-18",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-3",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 950,
+      "finalPnL": 2800,
+      "metrics": {
+        "tradesExecuted": 19
+      }
+    },
+    {
+      "trajectoryId": "traj-19",
+      "agentId": "agent-test",
+      "month": "2025-11",
+      "scenario": "scenario-4",
+      "steps": [
+        {
+          "stepNumber": 1,
+          "environmentState": {
+            "agentBalance": 10000,
+            "agentPnL": 0
+          },
+          "llm_calls": [
+            {
+              "model": "test-model",
+              "user_prompt": "What should I do?",
+              "response": "Buy shares"
+            }
+          ],
+          "action": {
+            "type": "BUY_SHARES",
+            "parameters": {
+              "amount": 100
+            },
+            "success": true
+          },
+          "reward": 50
+        }
+      ],
+      "totalReward": 1000,
+      "finalPnL": 2900,
+      "metrics": {
+        "tradesExecuted": 20
+      }
+    }
+  ],
+  "benchmarks": [
+    {
+      "benchmarkId": "bench-3",
+      "modelId": "llama-8b-instant",
+      "month": "2025-11",
+      "metrics": {
+        "totalPnl": -674,
+        "accuracy": 0.39
+      }
+    },
+    {
+      "benchmarkId": "bench-4",
+      "modelId": "qwen-32b",
+      "month": "2025-11",
+      "metrics": {
+        "totalPnl": -76,
+        "accuracy": 0.48
+      }
+    }
+  ]
+}

complete-data.json

@@ -0,0 +1,16 @@
+{
+  "metadata": {
+    "collectedAt": "2025-11-16T03:28:35.256Z",
+    "version": "1.0.0",
+    "totalWorlds": 0,
+    "totalTrajectories": 0,
+    "totalBenchmarks": 0,
+    "dateRange": {
+      "start": "2025-11",
+      "end": "2025-11"
+    }
+  },
+  "gameWorlds": [],
+  "trajectories": [],
+  "benchmarks": []
+}

index.json

@@ -0,0 +1,11 @@
+{
+  "collectedAt": "2025-11-16T04:18:42.175Z",
+  "version": "1.0.0",
+  "totalWorlds": 2,
+  "totalTrajectories": 20,
+  "totalBenchmarks": 4,
+  "dateRange": {
+    "start": "2025-10",
+    "end": "2025-11"
+  }
+}

summary.json

@@ -0,0 +1,11 @@
+{
+  "collectedAt": "2025-11-16T04:18:42.175Z",
+  "version": "1.0.0",
+  "totalWorlds": 2,
+  "totalTrajectories": 20,
+  "totalBenchmarks": 4,
+  "dateRange": {
+    "start": "2025-10",
+    "end": "2025-11"
+  }
+}

trajectories.jsonl

@@ -0,0 +1,20 @@
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