SimAgentPlg

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SimAgentPlg 0.2.2 is a lightweight framework for building stateful OpenAI-compatible agents with composable tool handlers, optional MCP tools, local skill routing, and simple role-based multi-agent workflows.

Features

• Stateful BaseAgent with conversation memory and explicit reset()
• Immutable, required agent_id owned by each agent
• OpenAI-compatible model configuration through .env or direct construction
• Opt-in tool mode with explicit handler registration
• Built-in BashHandler for bounded Bash execution
• Built-in FinishHandler for explicit task completion and Git change reports
• MethodToolHandler for small custom Python tools
• AgentManager with per-agent serialization and cross-agent concurrency
• Linear AgentWorkflow for planner, executor, reviewer, and similar roles
• Optional MCP integration through McpToolHandler and McpServerManager
• Optional local skill discovery and routing through SkillManager

Python 3.12 or newer is required.

Installation

Install the local project and dependencies with uv:

uv sync

Configuration

Copy .env_example to .env, then fill in your model credentials:

MODEL_API_KEY=sk-xxxxxxxx
MODEL_URL=https://api.deepseek.com
CHAT_MODEL=deepseek-v4-flash
SKILL_MODEL=deepseek-v4-flash
LLM_TIMEOUT=60
LLM_TEMPERATURE=0.7

ModelConfig.from_env() reads CHAT_MODEL, MODEL_API_KEY, MODEL_URL, LLM_TIMEOUT, and LLM_TEMPERATURE.

You can also construct a config directly:

from simagentplg import ModelConfig

config = ModelConfig(
    model="deepseek-v4-flash",
    api_key="sk-xxxxxxxx",
    base_url="https://api.deepseek.com",
)

Quick Start

Plain Chat

Tool execution is disabled by default. A plain agent keeps conversation history between runtime() calls:

from simagentplg import BaseAgent, ModelConfig

agent = BaseAgent(
    config=ModelConfig.from_env(),
    agent_id="tutor",
    system_prompt="You are a concise Python tutor.",
)

first = await agent.runtime(task="Remember that I prefer Python.")
second = await agent.runtime(task="Which language do I prefer?")

agent.reset()
await agent.shutdown()

Tool Mode

Set enable_tools=True and pass handlers explicitly:

import json

from simagentplg import BaseAgent, BashHandler, FinishHandler, ModelConfig

agent = BaseAgent(
    config=ModelConfig.from_env(),
    agent_id="developer",
    system_prompt="Complete coding tasks using the available tools.",
    handlers=[BashHandler(), FinishHandler()],
    enable_tools=True,
)

result = await agent.runtime(task="Create hello.py that prints 'hello'.")
report = json.loads(result)
print(report["summary"])
print(report["changes"])

await agent.shutdown()

Tool-enabled agents expose only the handlers passed to BaseAgent:

BaseAgent
  -> BashHandler
       -> bash_run
  -> FinishHandler
       -> run_finish
  -> MethodToolHandler subclasses
  -> McpToolHandler

In tool mode, ordinary text does not complete a task. The model must call a finishing tool, normally run_finish, or a custom tool must return StepOutcome(..., should_exit=True).

Tool mode stops with an error when:

• no finishing tool is called within max_steps
• the same tool and arguments are requested three consecutive times

Built-In Handlers

BashHandler exposes bash_run and executes a bounded Bash command. It has a working directory, timeout, output limit, and a small blacklist for obviously dangerous commands.

FinishHandler exposes run_finish. It returns a JSON result and exits the current runtime():

{
  "summary": "Created hello.py",
  "changes": {
    "available": true,
    "repository": "/repo/root",
    "added": ["hello.py"],
    "modified": [],
    "deleted": []
  }
}

The change report compares Git state at the beginning and end of the current task. run_finish does not commit, stage, or revert files. Outside a Git repository, the task can still finish with changes.available set to false.

Custom Tool Handlers

MethodToolHandler maps a tool named add to an async method named do_add:

from collections.abc import Mapping
from typing import Any

from simagentplg import BaseAgent, MethodToolHandler, ModelConfig, StepOutcome

ADD_TOOL = {
    "type": "function",
    "function": {
        "name": "add",
        "description": "Add two numbers.",
        "parameters": {
            "type": "object",
            "properties": {
                "left": {"type": "number"},
                "right": {"type": "number"},
            },
            "required": ["left", "right"],
        },
    },
}


class MathHandler(MethodToolHandler):
    def __init__(self) -> None:
        super().__init__((ADD_TOOL,))

    async def do_add(self, arguments: Mapping[str, Any]) -> StepOutcome:
        return StepOutcome(
            {"value": arguments["left"] + arguments["right"]}
        )


agent = BaseAgent(
    config=ModelConfig.from_env(),
    agent_id="calculator",
    handlers=[MathHandler()],
    enable_tools=True,
)

Handler startup builds one routing table. Duplicate tool names are rejected instead of silently overriding another handler.

Agent Manager

Each agent owns its identity, so registration does not repeat the ID:

from simagentplg import AgentManager, BaseAgent, ModelConfig

config = ModelConfig.from_env()
manager = AgentManager()

manager.register(
    BaseAgent(
        config=config,
        agent_id="writer",
        system_prompt="You write concise release notes.",
    )
)
manager.register(
    BaseAgent(
        config=config,
        agent_id="reviewer",
        system_prompt="You review software changes for risk.",
    )
)

results = await manager.run_many(
    {
        "writer": "Write release notes for version 0.2.2.",
        "reviewer": "Review the release for compatibility risks.",
    }
)

await manager.shutdown()

Calls to the same agent are serialized because they share message history. Calls to different agents can run concurrently. run_many() returns failures as values so one failed agent does not cancel the others.

run_isolated(agent_id, task) resets and executes an agent while holding the same per-agent lock. Workflows use it to avoid implicit history leaks between roles or steps.

Role-Based Workflow

AgentWorkflow executes agent roles as a validated linear pipeline:

from simagentplg import (
    AgentManager,
    AgentWorkflow,
    BaseAgent,
    ModelConfig,
    WorkflowStep,
)

config = ModelConfig.from_env()
manager = AgentManager()
manager.register(
    BaseAgent(
        config=config,
        agent_id="planner",
        system_prompt="Create concise implementation plans.",
    )
)
manager.register(
    BaseAgent(
        config=config,
        agent_id="executor",
        system_prompt="Execute the plan using tools.",
        enable_tools=True,
    )
)
manager.register(
    BaseAgent(
        config=config,
        agent_id="reviewer",
        system_prompt="Review completed work for correctness and risk.",
    )
)

workflow = AgentWorkflow(
    manager,
    [
        WorkflowStep(
            name="plan",
            agent_id="planner",
            prompt="Plan this task:\n{input}",
        ),
        WorkflowStep(
            name="execute",
            agent_id="executor",
            prompt=(
                "Original task:\n{original_task}\n\n"
                "Execute this plan:\n{input}"
            ),
        ),
        WorkflowStep(
            name="review",
            agent_id="reviewer",
            prompt="Review the execution result:\n{execute}",
        ),
    ],
)

result = await workflow.run("Implement user login")
print(result.final_output)
await manager.shutdown()

Workflow templates support {input}, {original_task}, and outputs from completed named steps such as {plan} or {execute}. Unknown variables and forward references are rejected when the workflow is created. Version 0.2.2 supports linear steps only.

MCP Tools

MCP is opt-in and follows the same handler contract:

from simagentplg import BaseAgent, McpToolHandler, ModelConfig

agent = BaseAgent(
    config=ModelConfig.from_env(),
    agent_id="browser",
    handlers=[McpToolHandler("example/mcp_config.json")],
    enable_tools=True,
)

Example MCP configuration:

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest", "--headless"]
    }
  }
}

McpServerManager loads configured services, exposes tools with service-name prefixes, and lets one failed service avoid blocking the rest.

Skills

Skills are optional prompt extensions and remain separate from tool handlers:

from pathlib import Path

from simagentplg import BaseAgent, ModelConfig

agent = BaseAgent(
    config=ModelConfig.from_env(),
    agent_id="skilled-agent",
    skills_dir=Path("example/skills"),
    enable_tools=True,
)

SkillManager scans each child directory containing SKILL.md. The routing model selected by SKILL_MODEL chooses a skill from its YAML front matter. The selected SKILL.md, optional template.md, and optional examples/sample.md are injected into the agent context.

example/skills/
  release_notes/
    SKILL.md
    template.md
    examples/
      sample.md

Skills currently run through the tool-mode lifecycle, so set enable_tools=True and finish with run_finish.

Examples

Runnable examples are available in example/:

uv run python example/01_stateful_chat.py
uv run python example/02_custom_tool.py
uv run python example/03_multi_agent.py
uv run python example/04_mcp_tools.py
uv run python example/05_role_workflow.py
uv run python example/06_skill.py

Testing

Run the test suite from the repository root:

uv run python -m unittest

The current tests cover agents, custom handlers, finish behavior, manager locking/concurrency, workflows, and importable examples.

Public API

BaseAgent(
    config: ModelConfig | None = None,
    *,
    agent_id: str,
    system_prompt: str = REACT_LOOP_PROMPT,
    handlers: Iterable[BaseHandler] | None = None,
    enable_tools: bool = False,
    skills_dir: str | Path | None = None,
    max_steps: int = 20,
    client: Any | None = None,
)

await agent.runtime(*, task: str) -> str | None
agent.reset(history=None)
await agent.startup()
await agent.shutdown()

The top-level package exports BaseAgent, ModelConfig, StepOutcome, AgentManager, workflow types, handler base classes, MethodToolHandler, BashHandler, FinishHandler, McpToolHandler, handler errors, McpServerManager, SkillManager, and default resource paths.

Changes in 0.2.2

• Added the sibling FinishHandler and built-in run_finish tool
• Added per-task Git change reporting
• Required explicit finishing-tool completion in tool mode
• Added protection against three identical consecutive tool calls
• Raised a clear error when tool mode exhausts max_steps
• Kept BashHandler focused exclusively on bash_run

License

MIT