pguso/ai-agents-from-scratch

Features

• Agentic Reasoning Loops - Implements a ReAct-based reasoning loop that interleaves internal thought, external action, and observation to solve complex problems.
• Embedding-Based Tool Routing - Ships an embedding-based routing system to select the most relevant tools from a large catalog using cosine similarity.
• AI Agent Development - Provides the tools and environment for building specialized autonomous agents by defining personas, rules, and tool sets.
• Iterative Reasoning Workflows - Implements iterative reasoning workflows using the ReAct pattern to solve multi-step problems through self-correction.
• LLM Tooling Integrations - Connects language models to external functions and APIs to perform real-world actions beyond text generation.
• Autonomous Agent Frameworks - Ships a comprehensive framework for building autonomous agents that reason, use tools, and execute multi-step plans.
• Conversation Memory Managers - Implements a memory management system to store and retrieve long-term context and user preferences across sessions.
• Conversation State Management - Tracks interaction history and session state to maintain coherence across stateless language model calls.
• LLM Orchestrators - Provides an orchestration engine that manages multi-agent workflows, state machines, and dependency graphs.
• Memory Management Systems - Provides a persistence layer for maintaining historical context and user preferences across extended AI interaction sessions.
• Model Provider Abstractions - Provides a common interface to normalize API interactions and decouple application logic from specific AI service providers.
• AI Tool Schemas - Uses declarative function schemas to define the arguments and return types required for models to trigger external tools.
• ReAct Pattern Implementations - Implements the ReAct pattern to interleave reasoning and action cycles for iterative problem solving and self-correction.
• Execution Audits - Tracks and logs internal reasoning steps to create explainable and debuggable AI execution audits.
• Multi-Agent Orchestration - Coordinates multiple specialized agents to work in parallel or sequence to decompose and solve complex tasks.
• Prompt Templates - Provides systems for defining and managing reusable prompt structures to guide language model behavior.
• Directed Acyclic Graph Engines - Implements logic execution by chaining modular reasoning nodes in a dependency-ordered directed acyclic graph.

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