Autonomous Multi-Agent Systems
& Generative Video Pipelines

import os
from concurrent.futures import ThreadPoolExecutor
import boto3, chromadb
from botocore.config import Config

bedrock = boto3.client("bedrock-runtime", config=Config(
    max_pool_connections=8, retries={"mode": "adaptive"}))
memory = chromadb.HttpClient(host=os.environ["CHROMA_HOST"], port=443, ssl=True)
collection = memory.get_collection(os.environ["CHROMA_COLLECTION"])
def reason(task):
    docs = collection.query(query_texts=[task], n_results=4)["documents"][0]
    response = bedrock.converse(modelId=os.environ["BEDROCK_MODEL_ID"],
        messages=[{"role": "user", "content": [{"text": "\n".join(docs)},
            {"cachePoint": {"type": "default"}}, {"text": task}]}],
        inferenceConfig={"maxTokens": 1024, "temperature": 0.2})
    return response["output"]["message"]["content"]
with ThreadPoolExecutor(max_workers=8) as nexus:
    results = list(nexus.map(reason, ["Review API contracts", "Retrieve code context"]))

{
  "pipeline": "daocalm.cinesynth",
  "execution": {
    "mode": "queued_batch",
    "human_approval": true
  },
  "nodes": [
    {
      "id": "semantic",
      "stage": "Semantic Script Breakdown",
      "output": "scene_manifest"
    },
    {
      "id": "identity",
      "stage": "Brand LoRA & Character Lock",
      "input": "semantic",
      "assets": "customer_authorized"
    },
    {
      "id": "temporal",
      "stage": "Temporal Consistency Conditioning",
      "input": "identity",
      "controls": [
        "motion",
        "seed",
        "continuity"
      ]
    },
    {
      "id": "export",
      "stage": "4K Batch Render Export",
      "input": "temporal",
      "resolution": [
        3840,
        2160
      ],
      "format": "mp4"
    }
  ]
}