GeminiFleet: Autonomous Multi-Agent Intelligence Platform

Next-gen autonomous enterprise multi-agent fleet on Google Cloud & Gemini 3.5 for automated planning, deep research, execution, and validation.

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🌟 Inspiration

Modern enterprises and researchers grapple with fragmented workflows, information sprawl, and manual operational toil. While standalone LLMs are helpful chat assistants, they lack autonomy, persistent state, deterministic verification, and tool orchestration capabilities.

We created GeminiFleet: Autonomous Multi-Agent Intelligence Platform — a production-minded, decoupled multi-agent ecosystem engineered natively on Google Gemini 3.5 and Google Cloud. GeminiFleet takes high-level complex goals, autonomously formulates hierarchical Directed Acyclic Graph (DAG) execution plans, executes sub-tasks across specialized agent nodes, verifies outputs through a rigorous Critic & Hallucination Validator, and streams live results to an interactive dashboard.

🚀 What It Does

GeminiFleet delivers end-to-end task autonomy through 4 specialized agent roles operating under a centralized supervisor:

  1. 🎯 Planner Agent (Task Decomposition & Orchestration)

    • Deconstructs ambiguous objectives into discrete, parallelizable sub-tasks with strict dependencies and exit criteria.
    • Dynamically re-plans when sub-agent execution paths encounter roadblocks or incomplete data.

  2. 🔍 Researcher Agent (Deep Retrieval & Multi-Tier Extraction)

    • Performs domain-aware search querying via Tavily AI and Google Search integration.
    • Employs a robust 3-tier extraction engine (trafilatura -> BeautifulSoup4 -> raw fallback) to synthesize clean, citation-backed knowledge.

  3. ⚡ Executor Agent (Tool Calling & Action Engine)

    • Invokes external tools, runs python code in sandboxed environments, interfaces with Google Cloud APIs, and produces structured data payloads.

  4. 🛡️ Critic & Validator Agent (Objective Rubric & Self-Reflection)

    • Benchmarks every synthesized deliverable against factual consistency, completeness, and reasoning soundness.
    • Assigns a 10-point quality score and triggers recursive refinement loops if the output falls below strict quality thresholds.

  5. 📊 Real-time Streaming UI & Export Engine

    • Live WebSocket/Streamlit interface displaying real-time agent thought streams, DAG visualizer, score gauges, and 1-click A4 PDF / JSON bundle exports.

🛠️ How We Built It & Architecture Discipline

  • Core LLM Engine: Powered by Google Gemini 3.5 Pro & Gemini 3.5 Flash via the official google-genai SDK, leveraging structured JSON schemas, native tool calling, and multimodal understanding.
  • Orchestration & State Machine: Decoupled multi-agent architecture with persistent episodic memory and checkpointing.
  • Google Cloud Infrastructure:
    • Google Cloud Run: Containerized, serverless backend microservices supporting autoscaling and zero cold-start execution.
    • Google Cloud Firestore: Scalable NoSQL store managing agent execution states, persistent memory buffers, and audit trails.
    • Google Cloud Pub/Sub: Asynchronous event backbone handling inter-agent message buses and decoupled telemetry.
  • Resilience & Failure Recovery: Built-in exponential backoff retries (max_retries=5), fallback tool routes, and circuit-breaker patterns.

📋 Reproducible Testing Instructions

To run and test GeminiFleet locally or in the cloud:

# 1. Clone the repository
git clone https://github.com/Mahfujul-01726/LangChain-Multi-Agent-Research-System.git
cd LangChain-Multi-Agent-Research-System

# 2. Set up virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure environment variables (.env)
GEMINI_API_KEY=your_gemini_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here
GOOGLE_CLOUD_PROJECT=your_gcp_project_id

# 5. Launch the Streamlit Web Application
streamlit run app.py

Open http://localhost:8501, type a research or enterprise task (e.g., "Analyze breakthroughs in Agentic AI architectures for 2026"), and observe the 4-agent collaborative execution in real time!

🏆 Accomplishments that We're Proud of

  • True End-to-End Autonomy: Built a zero-handholding agentic loop capable of transforming a single sentence prompt into a fully verified, cited 10-page research dossier.
  • Dual-Model Optimization: Smart routing using Gemini 3.5 Flash for high-speed retrieval/parsing and Gemini 3.5 Pro for deep multi-step reasoning and critique.
  • Production-Grade Reliability: Fully decoupled state management preventing cascading failures and ensuring 100% deterministic runs.

🔮 What's Next

  • Integration of Google Cloud Vertex AI Search for private enterprise knowledge base grounding.
  • Voice-enabled agent collaboration using Gemini Live audio API.
  • Multi-tenant enterprise access control and SOC2-compliant audit logging.

Built With

  • agentic-ai
  • fastapi
  • google-cloud-firestore
  • google-cloud-pub/sub
  • google-cloud-run
  • google-gemini-3.5
  • google-genai-sdk
  • python
  • streamlit
  • tavily
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