Agents

Build AI agents with tool use, function calling, MCP servers, and streaming step output.

📄️ AI Agents — SynapseKit Python LLM Framework

Build async AI agents in Python with SynapseKit. ReAct, function calling, multi-agent, and tool use. LangChain agent alternative with 47+ built-in tools.

📄️ ReActAgent

ReActAgent implements the Reasoning + Acting pattern. It works with any BaseLLM — no native function calling required.

📄️ FunctionCallingAgent

FunctionCallingAgent uses native LLM function calling — OpenAI toolcalls or Anthropic tooluse. More reliable tool selection than ReAct, especially with multiple tools.

📄️ Agent Tools — Function Calling | SynapseKit Python

47+ built-in tools for Python AI agents. Use @tool decorator, function calling, or custom tools. Web search, code execution, file I/O, and more.

📄️ AgentExecutor

AgentExecutor is the recommended high-level entry point. It wraps ReActAgent or FunctionCallingAgent behind a consistent interface.

📄️ MCP (Model Context Protocol)

SynapseKit supports the Model Context Protocol for connecting to external tool servers, wrapping MCP tools for use with agents, and exposing your own tools as an MCP server.

📄️ Agent Cookbook

A collection of common agent patterns with full working code examples. Copy-paste and adapt these recipes for your use case.

📄️ Tool Authoring Guide

Write custom tools for SynapseKit agents using the @tool decorator or BaseTool class.

📄️ Streaming Agent Steps

Both ReActAgent and FunctionCallingAgent support stream_steps(), an async generator that yields structured step events as the agent reasons through a task. This enables real-time UIs, logging, and debugging.

📄️ Reasoning Agent

Routes queries to either a fast LLM or a reasoning-capable LLM based on query complexity. Simple queries use the fast path; complex queries (math, multi-step reasoning, architecture, debugging) are sent to the reasoning LLM with a token budget. Falls back to the fast LLM on timeout or budget exhaustion.

📄️ Agent Federation

Distributed agent routing across a registry of named agents. AgentFederation selects an agent based on tags, tools, and a routing strategy, then dispatches the prompt to that agent's client.