easy-agent-sdk 0.6.3

A lightweight agent SDK built on LiteLLM, with single-agent (ReactAgent / SkillAgent / SandboxAgent) and Entity-World-Schedule multi-agent orchestration.

EasyAgent

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EasyAgent is a lightweight agent SDK organised as a small set of composable layers. The goal is to let you learn agent design step by step: start with a single model call, then add memory and context, build up to a ReAct loop with tools and skills, drop into a sandbox, and finally orchestrate multiple agents through the Entity-World-Schedule architecture.

Install

pip install easy-agent-sdk

From source:

git clone https://github.com/SNHuan/EasyAgent.git
cd EasyAgent
pip install -e ".[dev]"

The default install includes model adapters, sandbox helpers, web helpers, and MCP integration.

Quick Start

import asyncio
from easyagent import LiteLLMModel, ReactAgent


async def main():
    agent = ReactAgent(
        model=LiteLLMModel("gpt-4o-mini"),
        system_prompt="You are a concise assistant.",
        max_iterations=5,
    )
    result = await agent.run("What is 2 + 2?")
    print(result.final_output)


asyncio.run(main())

Local Dashboard

EasyAgent can persist agent and runtime traces to SQLite and open a local dashboard for logs, events, message history, and token usage:

easyagent dashboard

By default the CLI reads .easyagent/traces.db. You can point it at another trace store and open the browser automatically:

easyagent dashboard --db path/to/traces.db --open

The dashboard understands both standalone agent sessions and runtime traces, so runtime/world/entity/session trees appear automatically when your application writes runtime events into the selected trace store.

Create easyagent/config/config.yaml or configure LiteLLM through environment variables:

debug: false

models:
  gpt-4o-mini:
    api_type: openai
    base_url: https://api.openai.com/v1
    api_key: sk-xxx

Layered Design

EasyAgent is organised around three layers:

Single-agent:    Model + Memory + Context + Tool → Agent / ReactAgent / SkillAgent / SandboxAgent
Multi-agent:     Entity + World + Schedule → Runtime
Presets:         sequential / fanout / debate / chatroom / groupchat

• Model — provider adapter and message schema.
• Memory + Context — store conversation history and decide what reaches the model each turn.
• Agent — composes a model, memory, context, and any tools/skills/sandbox. Four built-in classes: Agent (single-turn) → ReactAgent (ReAct loop) → SkillAgent / SandboxAgent.
• Entity — wraps an Agent (or any async actor) for multi-agent participation. Protocol: id property + async act(Perception) -> Action | None.
• World — the environment entities perceive and act upon. Built-ins: ConversationWorld, PipelineWorld, SpatialWorld, StatefulWorld.
• Schedule — determines who acts next. Built-ins: TakeTurns, RoundRobin, AllParallel, Reactive, MaxTicks, UntilIdle.
• Runtime — the perceive-act-apply loop wiring Entity + World + Schedule. When connected to an EventBus, it emits runtime/tick/entity events and links child agent sessions back to the same runtime run.

See docs/architecture.md for the full design guide.

Public API

The root package exposes the common SDK surface:

from easyagent import (
    # single-agent
    Agent, ReactAgent, SkillAgent, SandboxAgent,
    AgentSession, AgentRunResult,
    LiteLLMModel, Message,
    EventBus, MessageEvent,
    ToolManager, SkillManager, register_tool,
    MCPToolset, load_mcp_tools, register_mcp_tools,
    # multi-agent protocols
    Entity, World, Schedule, Runtime, RuntimeResult,
    # perception & action types
    Perception, Speak, Silent, ChatMessage,
    # entities
    LLMEntity, TeamEntity, HumanEntity,
    # worlds
    ConversationWorld, PipelineWorld, SpatialWorld, StatefulWorld, SharedState,
    # schedules
    TakeTurns, RoundRobin, AllParallel, MaxTicks, UntilIdle, Reactive,
    # presets
    sequential, fanout, debate, chatroom, groupchat,
)

Learning Path

The examples are ordered by layer. Each one introduces one new idea:

# Single agent (00–06)
python examples/00_model_call.py             # Just call the model
python examples/01_single_turn_agent.py      # Compose a minimal Agent
python examples/02_memory_and_context.py     # Memory + Context
python examples/03_react_with_tools.py       # ReactAgent + tool calls
python examples/04_skills_lazy_loading.py    # SkillAgent (SKILL.md packages)
python examples/05_sandbox_agent.py          # SandboxAgent (bash, write/read file)
python examples/06_custom_tool.py            # Define your own tool

# Multi-agent: Entity-World-Schedule (07–14)
python examples/07_two_agents_talk.py        # LLMEntity + ConversationWorld + RoundRobin
python examples/08_sequential.py             # sequential() preset
python examples/09_chatroom.py               # Manual turn-taking + if/else
python examples/10_groupchat.py              # Reactive schedule, LLM picks next
python examples/11_debate_and_judge.py       # Third-party judge after debate
python examples/12_nested.py                 # TeamEntity: Runtime-as-Entity nesting
python examples/13_shared_state.py           # SharedState + StatefulWorld blackboard
python examples/14_advanced_runtime.py       # SpatialWorld: 2D grid + range-limited perception

# MCP examples (external tool sources)
python examples/mcp/fastmcp_in_memory.py     # Wrap a FastMCP server as EasyAgent tools
python examples/mcp/config_load.py           # Load tools from mcp_config.example.json

Tools

from easyagent import LiteLLMModel, ReactAgent, register_tool


@register_tool
class GetWeather:
    name = "get_weather"
    type = "function"
    description = "Get weather for a city."
    parameters = {
        "type": "object",
        "properties": {"city": {"type": "string"}},
        "required": ["city"],
    }

    def init(self) -> None: ...

    def execute(self, city: str) -> str:
        return f"Sunny in {city}."


agent = ReactAgent(
    model=LiteLLMModel("gpt-4o-mini"),
    tools=[GetWeather],
)

Pass tool classes or instances directly via tools=[...]. The ReAct loop continues while the model returns tool calls; a plain assistant message with no tool calls is treated as the final answer.

MCP Tools

EasyAgent can consume MCP servers as external tool sources. MCP support is included in the default install.

Use a standard FastMCP/MCP config. The mcpServers keys act as natural tool categories:

{
  "mcpServers": {
    "literature": {
      "command": "python",
      "args": ["./examples/mcp/servers/literature_server.py"]
    }
  }
}

Register discovered MCP tools into a ToolManager, then decide per session which tools are visible to the model:

from easyagent import LiteLLMModel, ReactAgent, ToolManager, register_mcp_tools

tool_manager = ToolManager(discover_builtin=False)
literature_tools = await register_mcp_tools(
    tool_manager,
    mcp_config,
    servers=["literature"],
)

agent = ReactAgent(model=LiteLLMModel("gpt-4o-mini"), tool_manager=tool_manager)
session = agent.create_session()
session.enabled_tools.extend(literature_tools)

You can also filter FastMCP tools by tags:

await register_mcp_tools(tool_manager, mcp_config, tags=["demo"])

See examples/mcp/ for runnable examples.

Skills

Skills are Agent Skills compatible directory packages loaded on demand. SKILL.md is the required entry file and must include YAML frontmatter with at least name and description. The name must match the parent directory name.

.easyagent/skills/my-skill/
├── SKILL.md
├── references/
├── templates/
├── assets/
└── scripts/

from easyagent import LiteLLMModel, SkillAgent

agent = SkillAgent(
    model=LiteLLMModel("gpt-4o-mini"),
    skills=["my-skill"],
)

By default EasyAgent discovers skills from .easyagent/skills. Set EA_SKILLS_DIR to load skills from another Agent Skills-compatible directory such as .claude/skills or .codex/skills. Multiple directories can be separated with the platform path separator (: on macOS/Linux, ; on Windows).

Multi-agent

Wrap any Agent as an LLMEntity, then compose with presets:

from easyagent import LiteLLMModel, ReactAgent, LLMEntity, sequential

model = LiteLLMModel("gpt-4o-mini")
researcher = LLMEntity("researcher", ReactAgent(model=model, name="researcher", system_prompt="..."))
writer     = LLMEntity("writer",     ReactAgent(model=model, name="writer",     system_prompt="..."))
reviewer   = LLMEntity("reviewer",   ReactAgent(model=model, name="reviewer",   system_prompt="..."))

result = await sequential([researcher, writer, reviewer], "Write a product blurb.")
print(result.last_speech)

Available presets: sequential / fanout / chatroom / groupchat / debate. For recursive nesting, wrap an inner Runtime as a TeamEntity and drop it into any outer pipeline. See examples/07_* through examples/14_* for walkthroughs.

Custom World

The architecture is extensible beyond conversation. Swap the World to get entirely different behaviour with the same Entity and Schedule:

from easyagent import SpatialWorld, Grid2D, Runtime, RoundRobin, MaxTicks

grid = Grid2D()
grid.place("alice", (0, 0))
grid.place("bob", (5, 5))

world = SpatialWorld(grid=grid, listen_radius=3.0)
schedule = MaxTicks(inner=RoundRobin(ids=["alice", "bob"]), n=10)

rt = Runtime(world=world, entities={"alice": alice, "bob": bob}, schedule=schedule)
result = await rt.run("Start exploring")

Module Layout

easyagent/
├── agent/      # Agent, ReactAgent, SkillAgent, SandboxAgent, AgentSession
├── core/       # Entity, World, Schedule protocols + Runtime loop
├── entities/   # LLMEntity, TeamEntity, HumanEntity
├── worlds/     # ConversationWorld, PipelineWorld, SpatialWorld, StatefulWorld
├── presets.py  # sequential, fanout, debate, chatroom, groupchat
├── context/    # SlidingWindowContext, SummaryContext, MultiAgentFormatter
├── events/     # MessageEvent, EventBus, telemetry events
├── memory/     # InMemoryMemory
├── model/      # LiteLLMModel + Message schema
├── prompt/     # System-prompt builders
├── sandbox/    # Local / Docker sandboxes
├── skill/      # SKILL.md loading
├── tool/       # Tool registry + built-ins (bash, file, web, skill helpers)
├── config/     # Config loading
└── debug/      # Logging

License

MIT License © 2025 Yiran Peng