FlightDeck

AI Agent framework that manages state, session context, and takes care of the infrastructure.

📘 Docs

• Documentation
• Examples

🦄 Features

• Maintain session's context
• Language Agnostic
• Cost tracking and budget limiting
• Dashboard to track execution, decisions, and latency
• High performance using stream processing
• Scalable and fault tolerant (message replay)
• Previous message compaction

Coming Soon.

• User specific settings like sensitive API keys and user preference.
• Cancellation
• Dynamic prompt updates
• Error handling for APIs
• Multi-modal
• Live progress update to users

Getting Started

Prerequisites

• Docker and Docker Compose
• An Anthropic API key or a Google Gemini API key

Run

# 1. Clone the repo
git clone https://github.com/tsuz/ai-agent-orchestration-kafka-example.git
cd ai-agent-orchestration-kafka-example/examples/lead-followup-agent

# 2. Add Claude API key to your .env file
cp .env.example .env
# Edit .env and add your CLAUDE_API_KEY

# 3. Start all services
docker compose up --build

Once everything is up, open http://localhost in your browser.

Services

Service	Port
Frontend (UI)	localhost:80
Chat API (REST)	localhost:8000
Chat API (WebSocket)	localhost:8001
Kafka	localhost:9092

Project Structure

Folder	Description
api/	Chat API — REST endpoint and WebSocket server that accepts user messages and produces to Kafka
processor-apps/	Kafka Streams app — enriches messages with session history, aggregates tool results, and routes the pipeline
think/	Think consumer — calls the LLM API (Claude or Gemini) with tool definitions, produces LLM responses
tools/	Tool execution consumer — executes tool invocations (e.g. external APIs) dispatched by the LLM
memoir/	Memoir consumer — generates long-term session summaries using Claude
monitoring/	Logging consumer — tails all Kafka topics for observability
frontend/	React + TypeScript dashboard — chat UI, pipeline execution viewer, and logs

Configuration

All configuration is done via environment variables in the .env file. See .env.example for defaults.

Variable	Default	Description
AGENT_NAME	(required)	Unique name for the AI agent instance.
SYSTEM_PROMPT_FILE	(optional)	Path to a text file containing the system prompt. If not set, a default generic prompt is used. If set but file not found, startup fails.
TOOLS_JSON_FILE	(optional)	Path to a JSON file defining the tools available to the agent. If not set, the agent runs without tools. If set but file not found, startup fails. See prompts/tools.json for an example.
LLM_PROVIDER	claude	LLM provider to use — claude or gemini
CLAUDE_API_KEY	(required for Claude)	Your Anthropic API key
CLAUDE_MODEL	claude-sonnet-4-20250514	Claude model to use
CLAUDE_MAX_TOKENS	8096	Max tokens per Claude response
GEMINI_API_KEY	(required for Gemini)	Your Google Gemini API key
GEMINI_MODEL	gemini-2.5-flash	Gemini model to use
GEMINI_MAX_TOKENS	4096	Max tokens per Gemini response
MEMOIR_ENABLED	true	Enable per-user long-term memory across sessions. Set to false to disable.
MEMOIR_SESSION_INACTIVITY_THRESHOLD_SECONDS	20	Seconds of inactivity before a session ends and memoir is saved. Only applies when MEMOIR_ENABLED=true.
MEMOIR_SESSION_PUNCTUATE_INTERVAL_SECONDS	5	How often (in seconds) to check for inactive sessions. Only applies when MEMOIR_ENABLED=true.
INPUT_TOKEN_PRICE	(optional)	Price per 1M input tokens (e.g. 3 for $3/MTok). If not set, cost tracking is disabled.
OUTPUT_TOKEN_PRICE	(optional)	Price per 1M output tokens (e.g. 15 for $15/MTok). If not set, cost tracking is disabled.
BUDGET_PRICE_PER_SESSION	(optional)	Maximum dollar cost allowed per session. When the session cost goes over this limit, the agent stops processing on the next Think layer. Requires token prices to be set.

Tests

Requires Java 17+ and Maven.

# Run all tests (processing stream app)
cd processor-apps/processing
mvn test

Stop

docker compose down

🗾 Architecture

❤️ Support

If you like this project, please give it a ⭐️

⛔️ Warning

This library is not production ready. It is highly recommended to test very thoroughly if you're planning to release to a large group of users.