Experia AI

The open-source experience learning layer for AI agents.

Experia enables agents to learn from past actions, failures, and outcomes. It provides experience capture, lesson extraction, behavioral improvement, and long-term cognitive memory without replacing existing agent frameworks.

Vision

Current AI agents start from zero on every interaction. Experia adds an experience learning loop around agents. It allows agents to remember what happened, understand why it worked or failed, and improve future decisions.

Observation → Action → Result → Experience → Lesson → Memory → Better Future Action

flowchart TD
    subgraph Agent Application
        Agent[AI Agent / LLM]
        Task[Execute Task]
        Agent -- Takes Action --> Task
    end

    subgraph Experia AI Cognitive Layer [Experia Async Cognitive Layer]
        Store[(MemoryStore Protocol)]
        Eval[Evaluator Protocol]
        Ctx[Context Builder]
        
        Task -- 1. await record() --> Store
        Store -- 2. Analyze Experience --> Eval
        Eval -- 3. Extract Lesson --> Store
        Store -- 4. await retrieve_context() --> Ctx
    end
    
    Ctx -- 5. Inject Knowledge --> Agent

Integrations

Experia acts as a cognitive plugin. It does not replace your agent frameworks (like LangChain, AutoGen, CrewAI), it enhances them by managing long-term memory, learned experiences, user knowledge, and behavioral patterns.

Getting Started

Experia uses an asynchronous and pluggable architecture.

pip install experia

import asyncio
from experia.core.learner import Learner
from experia.memory.store import SQLiteStore
from experia.experience.evaluator import SimpleHeuristicEvaluator


async def main():
    # 1. Dependency Injection setup
    store = SQLiteStore("my_agent.db")
    await store.initialize()

    evaluator = SimpleHeuristicEvaluator()

    # 2. Initialize Learner
    agent = Learner(store=store, evaluator=evaluator)

    # 3. Use the async API to record actions
    await agent.record(
        task="Deploy web app",
        action="Restart Nginx",
        result="failed with config syntax error",
    )

    # 4. Retrieve memory context for your LLM
    context = await agent.retrieve_context()
    print(context)


if __name__ == "__main__":
    asyncio.run(main())

License

This project is licensed under the MIT License. See the LICENSE file for details.