iamai

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A cross-platform chatbot framework with native AI agent support. Rust core, Python plugins.

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Overview

iamai is a cross-platform chatbot framework built on Rust and Python. It combines a high-performance message engine (Rust, via PyO3) with a flexible plugin system (Python), and includes built-in primitives for building LLM-powered agents.

The architecture follows a three-layer pattern: Adapter (platform protocol), Runtime (plugin orchestration), and Plugin (business logic). Adapters normalize platform-specific payloads into unified Event, Message, and Context objects, so plugins can be written once and deployed across multiple platforms.

AI Agent Support

iamai ships with agent primitives in the core package — no additional SDKs or integrations required.

Component Description
LLMClient Async OpenAI-compatible client with chat_text() and chat_json() methods. Configurable via TOML or environment variables. Supports a mock mode (IAMAI_LLM_MOCK=1) that returns deterministic responses for testing.
ToolRegistry Named tool registry supporting input_schema, permission_name, requires_approval, and audit_fields. Includes an optional approval callback for human-in-the-loop workflows.
AgentTrace Append-only trace recording each model call, tool invocation, and observation. Serializable to JSON. Powers the built-in /trace management command.
Guardrail Token-level output filter. Blocks configured substrings and raises on match.
LLMConfig Unified configuration object, read from TOML config or OPENAI_API_KEY / OPENAI_BASE_URL / OPENAI_MODEL environment variables.

Agent Patterns (Examples)

The examples/ directory contains runnable agent projects demonstrating different orchestration patterns:

ReAct — examples/react-runtime

A think-act-observe loop with tool calling. The LLM decides at each turn whether to invoke a tool or return a final answer. Includes sandboxed tools (math via AST eval, keyword-search notes), a configurable turn limit, and trace inspection.

export OPENAI_API_KEY="sk-..."
uv run --package react-runtime python -m iamai --config examples/react-runtime/config.terminal.toml

Planner-Executor — examples/planner-executor-runtime

A two-stage pipeline: the planner (low temperature) produces a structured plan with steps and deliverables; the executor (higher temperature) executes each step and produces a summary. Run history is stored for context across sessions.

uv run --package planner-executor-runtime python -m iamai --config examples/planner-executor-runtime/config.terminal.toml

Supervisor-Team — examples/supervisor-team-runtime

A supervisor-worker pattern: the supervisor decomposes a goal, dispatches to role-specialized workers (strategist, builder, skeptic) with fixed persona prompts, then synthesizes the results.

uv run --package supervisor-team-runtime python -m iamai --config examples/supervisor-team-runtime/config.terminal.toml

More examples: group assistant, life simulator, persona role-play, and others in examples/.

Adapters

Adapters handle networking, authentication, and protocol conversion. Plugins interact only with normalized objects — write once, deploy on any supported platform.

Adapter Protocol Typical Use
TerminalAdapter stdin/stdout Local development and debugging
OneBot11Adapter WS / WS-Reverse / HTTP QQ, Lagrange, LLOneBot
TelegramAdapter Long polling Telegram bots
WebhookAdapter HTTP POST DingTalk, Feishu, WeCom, and generic webhooks
Custom Any Subclass Adapter and implement start() / send()

Quick Start

Installation

pip install iamai

Running the examples

git clone https://github.com/retrofor/iamai.git && cd iamai
uv sync

# Terminal echo bot (no API key required)
uv run python -m iamai --config examples/echo-runtime/config.terminal.toml

# ReAct agent (requires OPENAI_API_KEY)
export OPENAI_API_KEY="sk-..."
uv run --package react-runtime python -m iamai --config examples/react-runtime/config.terminal.toml

Writing a plugin

from iamai import Plugin, command

class MyPlugin(Plugin):
    @command("hello")
    async def handle(self, ctx):
        await ctx.reply("Hello from iamai!")

Plugins support @command, @message_handler, @event_handler, middleware (before / around / after / error), and dependency injection via type annotations or depends().

Comparison

iamai NoneBot Koishi LangChain
Agent runtime Built-in — — Core focus
Bot framework Built-in Core focus Core focus —
Plugin model Decorator + DI Decorator + DI Hook + Service —
Hot reload File-watch auto reload — Plugin-level —
Core language Rust (PyO3) + Python Python TypeScript Python
Package pip install iamai pip install nonebot2 npm install koishi pip install langchain

Documentation

  • iamai.readthedocs.io — installation, concepts, quickstart, tutorials, and API reference
  • examples/ — echo, arcade, ReAct, Planner-Executor, Supervisor-Team, group assistant, and more
  • docs/ — Sphinx documentation source

Community & Contributing

  • Issues: GitHub Issues
  • Pull Requests: fork → feature branch → PR to dev. Run ruff check, mypy, and pytest before submitting.
  • Discussions: GitHub Discussions

License

MIT

Acknowledgments

iamai is inspired by NoneBot (layered architecture), Koishi (adapter-as-plugin design), and AliceBot (simplicity-first approach). Agent patterns are informed by the ReAct paper and the broader LLM agent research community.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

Owner

PyPI data
Data sourced directly from PyPI's database.

License expression

MIT
View SPDX License List

Requires

Python >=3.11

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0.3.0 This release

16 files

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0.2.2a1.dev2

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0.1.6

15 files

0.1.4

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0.0.4

15 files

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0.0.3rc4

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0.0.3a3

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0.0.3a1

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0.0.2

2 files

Pre-release

0.0.1a1

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