⚡ Spark — AI Agent Memory & Intelligence Engine

Persistent memory, reasoning context, and self-awareness layer for AI agents.

Spark is a developer-first system that enables AI agents to:

• remember context across sessions
• evaluate their own capabilities
• improve over time

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🚀 Why Spark Exists

Most AI agents today are stateless:

• forget past interactions
• lack self-awareness
• cannot track improvement

Spark solves this by adding a memory + evaluation layer.

→ Your agent becomes stateful, adaptive, and evolving

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🧠 Core Capabilities

• 🧩 Persistent Memory Layer

  • store and retrieve structured agent knowledge

• 📊 Self-Evaluation Engine

  • score agent outputs (0–100)
  • detect weaknesses

• 🔍 Context Discovery

  • extract metadata from tasks and environments

• ⚙️ Agent Scaffolding

  • generate structured configs (spark.json)

• 🔌 Plugin Architecture

  • extend with custom reasoning modules

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⚡ Quick Start

git clone https://github.com/rudra496/spark.git
cd spark
pip install -e .

spark assess --root .

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🧪 Example (Agent Usage)

from spark import SparkProject

agent = SparkProject("./agent_workspace")

# Evaluate agent output
result = agent.assess()
print(result.score)
print(result.recommendations)

# Store memory-like metadata
info = agent.discover()

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🏗 Architecture

• core/ → evaluation + memory logic
• cli/ → developer tooling
• plugins/ → extensibility layer

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📈 Use Cases

• AI agents (Claude, GPT, GLM)
• autonomous coding systems
• research agents
• personal AI assistants

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🧭 Roadmap

• Long-term memory storage (vector DB integration)
• agent-to-agent communication layer
• real-time evaluation feedback loop
• dashboard for agent intelligence tracking

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👨‍💻 Author

Rudra Sarker
AI Systems Builder | SUST

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📄 License

MIT