Ec2 connection/non static ports

.dockerignore

@@ -0,0 +1,21 @@
+.git
+.omx
+.pytest_cache
+.venv
+.env
+.env.*
+!.env.example
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+*.swp
+*.swo
+*~
+._*
+Thumbs.db
+.DS_Store
+AWSCLIV2.pkg
+docker_container
+grpc_stubs
+stubs

.github/workflows/ci.yml

@@ -0,0 +1,42 @@
+name: CI
+
+on:
+  pull_request:
+    branches: [ main ]
+  workflow_dispatch:
+
+
+permissions:
+  contents: read
+
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
+      - name: "Run Ruff: Lint"
+        run: uvx ruff check .
+
+      - name: "Run Ruff: Format Check"
+        run: uvx ruff format --check .
+
+      - name: "Install dependencies for Ty"
+        run: |
+            uv venv 
+            uv pip install -e .
+
+      - name: "Run Ty: Type Check"
+        run: uvx ty check
+
+# If you want to run this locally, install act and run "act pull_request"
+# For fixing Ruff lint errors: uvx ruff check --fix .
+# For fixing Ruff formatting errors: uvx ruff format .

.gitignore

@@ -24,6 +24,7 @@ env/
 # OS files
 .DS_Store
 Thumbs.db
+._*
 
 # Generated stubs
 stubs/
@@ -32,3 +33,12 @@ docker_container/
 
 # Logs
 *.log
+
+# Local env / machine artifacts
+.env
+.env.*
+!.env.example
+AWSCLIV2.pkg
+.python-version
+
+sandbox/

README.md

@@ -20,7 +20,7 @@ git clone https://github.com/your-repo/ventis.git
 cd ventis
 pip install -e .
 ```
-Note: Installation of ventis only needs to be done on the machine where you are running the deploy command. It does not need to be installed on the remote hosts where the agents are deployed. Ventis will automatically push code and environment to the remote hosts.
+Note: Installation of ventis only needs to be done on the machine where you are running the deploy command. It does not need to be installed on the remote hosts where the agents are deployed. Ventis runs the built container images on the target hosts; for remote EC2 deployments, make sure the image is already available on the host.
 
 ### 2. Prerequisites
 
@@ -68,7 +68,7 @@ cp -r ../examples/* ./
 ## Deployment Guide
 
 #### Step 1: Configure the Global Controller
-Edit `examples/config/global_controller.yaml` to list the agents you want to deploy, their hosts, ports, and resource limits.
+Edit `config/global_controller.yaml` in your project directory to list the agents you want to deploy, their `provider`, `replicas`, and resource limits.
 
 #### Step 2: Build the project
 ```bash
@@ -89,10 +89,10 @@ Upon running the deploy command, ventis automatically generates a REST API endpo
 Users can send requests to this endpoint to trigger the workflow. For this example, workflow to send a request - 
 
 ```bash
-curl -X POST http://localhost:8080/finance_workflow/run \
+curl -X POST http://localhost:8080/main \
   -H "Content-Type: application/json" \
   -d '{
-    "query": "What is the current stock price of Apple?"
+    "ticker": "AAPL"
   }'
 ```
 The request is asynchronous. To get the result, you use the following URL-
@@ -105,7 +105,7 @@ curl http://localhost:8080/status/<request_id>
 Remove all generated stub and gRPC files:
 
 ```bash
-make clean
+ventis clean
 ```
 
 ### Harnessing the power of Ventis

examples/agents/finance_agent.py

@@ -1,4 +1,6 @@
 from vllm_agent_stub import VllmAgentStub
+
+
 # Example of a simple finance agent
 class FinanceAgent(object):
     def __init__(self):
@@ -19,15 +21,16 @@ def get_company_name(self, ticker: str) -> str:
     def run(self, query: str) -> str:
         # company = self.get_company_name(query)
         # price = self.get_stock_price(company)
-        
+
         prompt = f"The company is {query} and the stock price is . Please write a short, professional response."
-        
+
         # Call the VLLM agent remotely and wait for the result
         # .value() blocks until the future completes via Redis
         response = self.vllm.generate(prompt).value()
         # print(response.value())
         return response
 
+
 if __name__ == "__main__":
     agent = FinanceAgent()
-    print(agent.run("What is the stock price of Apple?"))
+    print(agent.run("What is the stock price of Apple?"))

examples/agents/market_agent.py

@@ -1,5 +1,6 @@
 # Market Research Agent
 
+
 class MarketResearchAgent(object):
     def __init__(self):
         self.tools = [self.get_market_trend, self.get_sector_analysis]
@@ -19,4 +20,4 @@ def get_competitor_list(self, company: str) -> list:
     def run(self, query: str) -> str:
         trend = self.get_market_trend(query)
         analysis = self.get_sector_analysis(query)
-        return f"{analysis} Trend: {trend['trend']}"
+        return f"{analysis} Trend: {trend['trend']}"

examples/agents/vllm_agent.py

@@ -1,5 +1,6 @@
 # Simple VLLM Agent Example
 
+
 class VllmAgent(object):
     def __init__(self):
         self.tools = [self.generate]
@@ -10,11 +11,12 @@ def __init__(self):
     def generate(self, prompt: str) -> str:
         """Generates a response using an LLM model based on the given prompt."""
         print(f"VllmAgent: Received prompt: '{prompt}'")
-        
+
         # Simulated LLM generation
         synthetic_response = f"This is an LLM generated response to: '{prompt}'"
         return synthetic_response
 
+
 if __name__ == "__main__":
     agent = VllmAgent()
     print(agent.generate("What is the stock price?"))

examples/config/global_controller.ec2_smoke.yaml

@@ -0,0 +1,31 @@
+# EC2 variant of the main example config:
+# - exactly 2 agent instances
+# - generic agent image + bind-mounted project code
+# - t2.nano for lowest-cost MVP validation
+
+agents:
+  - name: MarketResearchAgent
+    replicas: 2
+    redis_port: 6379
+    instance_type: t2.nano
+    resources:
+      cpu: 1
+      memory: 256
+    entrypoint: agents/market_agent.py
+    provider: EC2
+
+
+poll_interval: 5
+
+redis:
+  host: localhost
+  port: 6379
+  db: 0
+
+ec2:
+  region: us-east-1
+  ami_id: ami-0123456789abcdef0
+  subnet_id: subnet-0123456789abcdef0
+  security_group_ids:
+    - sg-0123456789abcdef0
+  ssh_user: ubuntu

examples/config/global_controller.yaml

@@ -3,70 +3,63 @@
 
 agents:
   - name: FinanceAgent
-    # Replicas can be placed on different hosts with explicit host/port.
-    # Alternatively, use `replicas: 3` as shorthand to launch 3 instances
-    # on the same host with sequential ports starting from `port`.
-    replicas:
-      - host: localhost
-        port: 8051
-      - host: localhost
-        port: 8052
-    redis_port: 6379    # Redis port on this node
-    # user: sagarwal   # SSH user for remote hosts (omit for localhost)
-    # Stateful agents get session affinity: all calls within a single
-    # request_id are routed to the same instance.
+    replicas: 2
+    redis_port: 6379 # Redis port on this node
     stateful: true
-    # Resource limits per instance
     resources:
-      cpu: 1            # Number of CPU cores
-      memory: 512       # Memory in MB
-    # Path to the agent entrypoint script
+      cpu: 1
+      memory: 512
     entrypoint: agents/finance_agent.py
+    provider: local
 
   - name: MarketResearchAgent
-    host: localhost
-    port: 8053
-    redis_port: 6379
-    # user: sagarwal
     replicas: 1
+    redis_port: 6379
     resources:
       cpu: 1
       memory: 512
     entrypoint: agents/market_agent.py
+    provider: local
 
   - name: VllmAgent
-    host: localhost
-    port: 8054
-    redis_port: 6379
     replicas: 1
+    redis_port: 6379
     resources:
       cpu: 2
       memory: 2048
     entrypoint: agents/vllm_agent.py
+    provider: local
 
   - name: Workflow
-    host: localhost
-    port: 8050             # LC gRPC port (exposed to host)
+    replicas: 1
     type: workflow
-    api_port: 8080         # Flask REST API port (exposed to host)
     redis_port: 6379
-    replicas: 1
     workflow_file: workflows/example_workflow.py
+    provider: local
 
-# Polling interval in seconds
 poll_interval: 5
 
-# Redis connection
 redis:
   host: localhost
   port: 6379
   db: 0
 
-# Docker image registry (optional).
-# If set, `ventis deploy` will push images to this registry locally and
-# pull them on remote nodes before starting containers.
-# If omitted, images are shipped to remote nodes via `docker save | ssh ... docker load`.
+# EC2 defaults for `provider: EC2` replicas.
+# Security group must allow inbound TCP 22, 50051, and 6379 from the
+# global controller host's source IP.
+#
+# ec2:
+#   region: us-east-1
+#   ami_id: ami-0123456789abcdef0
+#   subnet_id: subnet-0123456789abcdef0
+#   security_group_ids:
+#     - sg-0123456789abcdef0
+#   ssh_user: ubuntu
+
+
+# Docker image registry (legacy; no longer used).
+# Remote nodes are expected to already have the image before `ventis deploy`.
 #
 # registry:
-#   url: myregistry.example.com:5000   # Registry host:port
-#   user: sagarwal                     # (optional) SSH user used when pulling on remote nodes
+#   url: myregistry.example.com:5000
+#   user: sagarwal

pyproject.toml

@@ -4,6 +4,7 @@ version = "0.1.0"
 description = "Distributed agent orchestration framework"
 requires-python = ">=3.10"
 dependencies = [
+    "boto3",
     "grpcio",
     "grpcio-tools",
     "redis",
@@ -26,3 +27,27 @@ ventis = [
     "templates/**/*",
     "controller/proto/*.proto",
 ]
+
+
+
+[tool.ty.environment]
+python = ".venv"
+
+[tool.ty.src]
+include = ["ventis"]
+exclude = [
+    "ventis/templates/**",
+    "ventis/stub_generator.py",
+]
+
+[tool.ty.analysis]
+allowed-unresolved-imports = [
+    "local_controler_pb2",
+    "local_controler_pb2_grpc",
+    "local_controller_frontend",
+    "redis_client",
+    "ventis_context",
+    "deploy",
+    "*_stub",
+    "*_agent_stub",
+]

requirements.txt

@@ -1,3 +1,4 @@
+boto3
 grpcio
 grpcio-tools
 redis

tests/README.md

@@ -4,10 +4,11 @@ This directory contains an automated end-to-end testing suite for Ventis. It is
 
 ## 1. Automated Test Runner (`run_tests.sh`)
 This script automates the entire testing lifecycle by interacting with the `ventis` CLI:
+0. Runs a small pytest suite from this `tests/` directory.
 1. Scaffolds a new temporary project using `ventis new-project`.
 2. Compiles the project using `ventis build`.
 3. Launches the project using `ventis deploy` in the background.
-4. Waits for the GlobalController and all agent sidecars to become healthy.
+4. Waits for the deployed workflow endpoint to become reachable, then gives the agents a few extra seconds to register.
 5. Runs the Python integration and performance scripts.
 6. **Cleanup:** Automatically terminates the deployment and cleans up the temporary directory upon success or failure.
 
@@ -19,7 +20,7 @@ To run the complete suite:
 ## 2. Functional Integration Validation (`test_integration.py`)
 Verifies that Ventis correctly passes data and dependencies between chained agents. 
 - Dispatches a single query to the deployed `/main` endpoint.
-- Polls the `/status` endpoint until completion.
+- Polls the `/status/<request_id>` endpoint until completion.
 - Validates the output payload structure and ensures that data successfully flowed through `FinanceAgent`, `MarketResearchAgent`, and `VllmAgent`.
 
 To run manually against an already-deployed Ventis instance: