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Reflexion Actor

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The Reflexion Actor is a cognitive AI architecture that enhances an agent's ability to learn from its mistakes through self-reflection. It combines a base reasoning model (the Actor) with a memory mechanism and a critic loop that evaluates performance after each task attempt.

Reflexion Actor
Reflexion Actor

Why Reflexion?

Most AI agents operate in a trial-and-error fashion without adapting their reasoning strategy. Reflexion aims to overcome this by introducing self-awareness mechanisms. It takes inspiration from human cognitive behavior—particularly meta-cognition, the ability to think about one’s thinking.

Architecture Overview

The Reflexion Actor consists of:

Reflexion Flow

  1. 🔥The agent attempts a task using the Actor.
  2. 🔥It logs internal reasoning and outputs to Memory.
  3. 🔥The Critic reviews the attempt, highlighting mistakes.
  4. 🔥The Reflection step allows the Actor to revise its approach.
  5. 🔥The agent re-attempts the task with an improved strategy.

Comparison with Other Architectures

Here’s how Reflexion stacks up:

Applications

Sample LangChain Setup

In LangChain, you could implement a Reflexion-style agent using an LLM chain, a memory buffer, and a custom evaluator/critic:


const executor = new AgentExecutor({
  agent: baseAgent,
  memory: new BufferMemory(),
  tools: [searchTool, codeTool],
  reflectionEnabled: true
});

// Loop until task is solved or retry limit is reached

Challenges

Conclusion

Reflexion Actor architectures are paving the way for more robust, adaptive AI systems. By giving agents a mechanism to evaluate and improve their own reasoning, we move closer to autonomous systems that exhibit true learning behavior.

References

Reflexion
Reflexion: Agents in Action