LLM Agent
An autonomous system that uses a large-language-model to perceive its environment, reason about actions, and execute tasks via tools or plugins.
Core Concepts
- Perception: Ingesting context from Environment or external data sources.
- Reasoning: Using the LLM to plan steps and make decisions.
- Action: Executing commands, calling APIs, or manipulating files.
- Feedback Loop: Observing the result of actions to refine subsequent steps.
Implementation Patterns
- ReAct: Combining Reasoning and Acting in an interleaved manner.
- Tool Use: Integrating external functions or scripts as capabilities.
- Memory: Maintaining short-term (context window) and long-term (vector DB) state.
Local Agent Architectures
Recent developments focus on running agents locally to ensure privacy and reduce latency.
- DeepSeek Harness (DSH): An open-source agent harness by DeepSeek AI designed to give LLMs “hands” for local environment interaction.
- Provider Agnostic: Supports integration with ollama and other local LLM providers.
- Plugin Ecosystem: Extends capabilities through modular plugins for specific tasks.
- Environment Interaction: Allows the agent to directly manipulate local files, run code, and interact with the OS.
For detailed technical breakdown and setup instructions, see: DeepSeek Harness: Local LLM Agent with Environment Interaction & Plugins