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Agentic Starter Kits

Agentic Starter Kits

Purpose

Production-ready agent templates to build and deploy LLM-powered agents. Run locally (e.g. with Ollama/Llama Stack) or deploy to Red Hat OpenShift. Each agent has step-by-step docs.

Agents

Agents are organized by framework. Pick one and follow its README:

Framework Agent Description
LangGraph ReAct Agent General-purpose agent using a ReAct loop: it reasons and calls tools (e.g. search, math) step by step. Built with LangGraph and LangChain.
LangGraph Agentic RAG RAG agent that indexes documents in a vector store (Milvus) and retrieves relevant chunks to augment the LLM's answers with your own data.
LangGraph ReAct + DB Memory ReAct agent with PostgreSQL-backed conversation memory. Full chat history is persisted in the database while a FIFO sliding window keeps only the last N messages in the LLM context.
LangGraph Human-in-the-Loop ReAct agent with a human approval step. The agent pauses before executing tool calls and waits for user confirmation, enabling oversight of critical actions.
LlamaIndex WebSearch Agent Agent built on LlamaIndex that uses a web search tool to query the internet and use the results in its answers.
CrewAI WebSearch Agent CrewAI-based agent with a web search tool to query the internet and answer user questions.
Vanilla Python OpenAI Responses Agent Minimal agent with no framework: only the OpenAI Python client and an Action/Observation loop with tools. Use with OpenAI or any compatible API.
AutoGen MCP Agent AutoGen AssistantAgent with MCP tools over SSE (e.g. churn prediction, math tools), FastAPI /chat/completions.
Google ADK ADK Agent General-purpose agent using Google ADK 2.0 with LiteLLM to route inference through a LlamaStack-compatible endpoint.
Langflow Simple Tool Calling Agent Tool-calling agent built with Langflow's visual flow builder. Calls external APIs as tools and reasons over results. Includes Langfuse v3 tracing. Runs locally via podman-compose.
A2A LangGraph + CrewAI Agent Multi-agent system using the Agent-to-Agent (A2A) protocol. A LangGraph orchestrator delegates tasks to a CrewAI worker agent. Uses a dedicated Helm chart.

Deployment Options

Agents in this repository can support two deployment modes:

🖥️ Local Development

  • Run agents on your local machine
  • Use Llama Stack server with Ollama for model serving
  • Ideal for development, testing, and experimentation
  • No cloud infrastructure required

☁️ Production Deployment

  • Deploy agents to Red Hat OpenShift Cluster
  • Containerized deployment with Kubernetes
  • Production-grade scaling and monitoring
  • CI/CD ready

Repository Structure

agentic-starter-kits/
├── agents/
│   ├── langgraph/
│   │   ├── react_agent/              # LangGraph ReAct agent
│   │   ├── agentic_rag/             # LangGraph RAG agent with Milvus
│   │   ├── react_with_database_memory/ # LangGraph ReAct + PostgreSQL memory
│   │   └── human_in_the_loop/       # LangGraph Human-in-the-Loop agent
│   ├── crewai/
│   │   └── websearch_agent/         # CrewAI web search agent
│   ├── llamaindex/
│   │   └── websearch_agent/         # LlamaIndex web search agent
│   ├── vanilla_python/
│   │   └── openai_responses_agent/  # OpenAI Responses API (no framework)
│   ├── autogen/
│   │   └── mcp_agent/               # AutoGen + MCP (SSE)
│   ├── google/
│   │   └── adk/                     # Google ADK 2.0 agent
│   ├── langflow/
│   │   └── simple_tool_calling_agent/ # Langflow tool-calling agent
│   └── a2a/
│       └── langgraph_crewai_agent/  # A2A multi-agent (LangGraph + CrewAI)
├── evals/
│   ├── harness/                     # Shared eval engine (runner, scorers, MLflow client)
│   └── evalhub_adapter/             # EvalHub on-cluster adapter (JobSpec → harness)
├── tests/
│   └── behavioral/                  # Behavioral eval suite (shared infra)
├── charts/
│   ├── agent/                       # Shared Helm chart for all standard agents
│   └── a2a-langgraph-crewai/        # Dedicated Helm chart for A2A agent
├── infrastructure/
│   └── llm-d/                       # llm-d deployment manifests and test tooling
├── docs/                            # Guides: local dev, deployment, contributing
├── pyproject.toml                   # Test deps & pytest config
└── README.md

How to Use This Repository

  1. Start Here: Read this README to understand the overall structure and install core dependencies
  2. Choose an Agent: Select an agent from the agents/ directory based on your use case
  3. Follow Agent README: Navigate to the agent's directory and follow its specific README for:
    • Agent-specific dependencies installation
    • Configuration and setup
    • Local development or OpenShift deployment
    • Usage examples and API endpoints

Pre-requisitions to run that repo

Run this script to set up repo stuff with a use of UV and python 3.12

Download repo

git clone https://github.com/red-hat-data-services/agentic-starter-kits

Get into root dir

cd agentic-starter-kits

Install UV

curl -LsSf https://astral.sh/uv/install.sh | sh

Behavioral Tests

Behavioral eval suite that tests agents over HTTP against their shared OpenAI-compatible API. Tests are organized by capability so they apply to any agent.

Tests require a running agent. Set the target URL via environment variables:

Env var Test scope
AGENT_URL Cross-agent tests (api_contract, adversarial)
REACT_AGENT_URL LangGraph ReAct agent tests
VANILLA_PYTHON_AGENT_URL Vanilla Python agent tests
AUTOGEN_MCP_AGENT_URL AutoGen MCP agent tests
CREWAI_WEBSEARCH_AGENT_URL CrewAI Websearch agent tests
AGENTIC_RAG_AGENT_URL LangGraph Agentic RAG agent tests 
uv pip install -e ".[test]"
AGENT_URL=https://my-agent.example.com pytest tests/behavioral/ -v

See tests/behavioral/ for full details.

Documentation

Additional Resources

Contributing

Contributions are welcome! Please see individual agent READMEs for specific guidelines.

License

MIT License

Copyright (c) 2026