Text Generation
Safetensors
Rust
RWKV
English
oicio-rs
ternary
matmul-free
cpu-only
1.58-bit
bitnet
bonsai
infinite-context
em-llm
reattention
recursive-agent-harness
rlm
rah
edge-ai
needle
hadamard
mlgru
mamba
liquid-neural-networks
turbovec
turboquant
t-mac
vec-lut
axon
consumer-hardware
better-quality
intelligence-density
Instructions to use deeprcurs/OICIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RWKV
How to use deeprcurs/OICIO with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 4,883 Bytes
ce20bc6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | """
OICIO Real RAH: Actual Code-Execution Spawning
Credits: deepRcurs Labs @deeprcurs / Mzed Imamkh @mzedimamkh
Real implementation where parent writes executable Python script that spawns subagents via asyncio.gather
This bypasses per-turn tool-call limit (Anthropic dynamic workflows pattern)
"""
import os
import sys
import tempfile
import subprocess
import json
import asyncio
from typing import List, Dict
class RealRAH:
"""
Parent agent that WRITES CODE and EXECUTES it
"""
def __init__(self, parallel_limit=20):
self.parallel_limit = parallel_limit
def generate_spawning_script(self, entries: List[Dict], instruction: str) -> str:
"""
Generate executable Python script that spawns subagents
This is the core RAH innovation: code as action
"""
script = f'''
import asyncio
import json
import os
import sys
sys.path.insert(0, '/home/user')
from oicio.harness.rah import SubAgentHarness
async def run_subagent(agent_id, entry_id, content, instruction):
# Each subagent is full harness with tools
agent = SubAgentHarness(agent_id=agent_id)
result = agent.run(entry_id=entry_id, instruction=instruction, context_slice=content)
return {{
"agent_id": agent_id,
"entry_id": entry_id,
"answer": result.answer,
"confidence": result.confidence,
"reasoning": result.reasoning,
"success": result.success
}}
async def main():
entries = {json.dumps(entries)}
instruction = {json.dumps(instruction)}
# Create tasks for all entries (bypasses tool-call budget, scales to thousands)
tasks = []
for i, entry in enumerate(entries):
task = run_subagent(i, entry["id"], entry["content"], instruction)
tasks.append(task)
# Run in parallel with asyncio.gather (RAH pattern)
results = await asyncio.gather(*tasks)
# Write aggregated output to shared file (no IPC overhead)
with open("aggregated_results.json", "w") as f:
json.dump(results, f, indent=2)
# Print summary
entity_count = sum(1 for r in results if r["answer"] == "entity")
avg_conf = sum(r["confidence"] for r in results) / len(results) if results else 0
print(f"RAH Results: {{len(results)}} entries, {{entity_count}} entity, avg_conf {{avg_conf:.2f}}")
# Return via stdout
print(json.dumps({{"entity_count": entity_count, "total": len(results), "avg_confidence": avg_conf}}))
if __name__ == "__main__":
asyncio.run(main())
'''
return script
def execute_script(self, script_content: str) -> Dict:
"""Execute generated script via shell tool (like coding agent)"""
with tempfile.TemporaryDirectory() as tmpdir:
script_path = os.path.join(tmpdir, "spawn_subagents.py")
with open(script_path, 'w') as f:
f.write(script_content)
# Execute via shell (parent's execute tool)
result = subprocess.run(
[sys.executable, script_path],
cwd=tmpdir,
capture_output=True,
text=True,
timeout=30
)
print(f"[RealRAH] Script stdout:\n{result.stdout}")
if result.stderr:
print(f"[RealRAH] Script stderr:\n{result.stderr}")
# Read aggregated file
agg_path = os.path.join(tmpdir, "aggregated_results.json")
if os.path.exists(agg_path):
with open(agg_path, 'r') as f:
detailed = json.load(f)
else:
detailed = []
# Try parse last line as JSON summary
try:
lines = result.stdout.strip().split("\n")
summary = json.loads(lines[-1])
except:
summary = {"entity_count": 0, "total": 0}
return {"summary": summary, "detailed": detailed, "stdout": result.stdout}
def run(self, entries: List[Dict], instruction: str):
print(f"[RealRAH] Generating spawning script for {len(entries)} entries...")
script = self.generate_spawning_script(entries, instruction)
print(f"[RealRAH] Script generated ({len(script)} chars), executing via shell tool...")
# Save script for audit (snapshot-safe, small)
with open("/home/user/oicio/data/last_spawn_script.py", "w") as f:
f.write(script)
result = self.execute_script(script)
return result
if __name__ == "__main__":
print("=== Real RAH: Code-Execution Spawning POC ===")
entries = [{"id": i, "content": f"user_{i}: entity data" if i%3==0 else f"log {i}: system"} for i in range(20)]
instruction = "Count entity entries"
rah = RealRAH()
result = rah.run(entries, instruction)
print(f"\nFinal: {result['summary']}")
print(f"Detailed count: {len(result['detailed'])}")
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