fastmind 0.1.9

A lightweight, event-driven multi-agent framework for embodied AI systems

FastMind 🧠

A lightweight, event-driven multi-agent framework for embodied AI systems.

Features

• FastAPI-like Decorators: Familiar @app.agent, @app.tool, @app.perception syntax, easy to learn
• State Graph Architecture: Build agent workflows like flowcharts, not nested loops
• Event-Driven: Asyncio-based, zero polling, high-performance async execution
• Built-in Streaming: Real-time streaming output with backpressure control
• Human-in-the-Loop: Interrupt and resume sessions for human approval
• Perception Loops: Native support for sensors, timers, and external triggers
• Tool Calling: ReAct-style agent-tool-agent loops
• Session Isolation: Multi-user support with isolated session state
• Lightweight: ~8000 lines, no big dependencies

Installation

From PyPI (Recommended)

pip install fastmind

From GitHub

pip install git+https://github.com/kandada/fastmind.git

With examples:

pip install git+https://github.com/kandada/fastmind.git#egg=fastmind[examples]

For development:

git clone https://github.com/kandada/fastmind.git
cd fastmind
pip install -e ".[all]"

Quick Start

from fastmind import FastMind, Graph, Event
from fastmind.contrib import FastMindAPI

app = FastMind()

@app.agent(name="chat_agent")
async def chat_agent(state: dict, event: Event) -> dict:
    state.setdefault("messages", [])
    state["messages"].append({"role": "user", "content": event.payload.get("text", "")})
    # Your LLM call here
    state["messages"].append({"role": "assistant", "content": "Hello!"})
    return state

graph = Graph()
graph.add_node("chat", chat_agent)
graph.set_entry_point("chat")
app.register_graph("main", graph)

async def main():
    api = FastMindAPI(app)
    await api.start()
    await api.push_event("user_001", Event("user.message", {"text": "Hello!"}, "user_001"))
    await api.stop()

import asyncio
asyncio.run(main())

Core Concepts

State

A dict-like container for session data shared across nodes:

state["messages"].append({"role": "user", "content": "Hello"})

Node

An async function that processes events and returns updated state:

async def my_node(state: dict, event: Event) -> dict:
    state["processed"] = True
    return state

Graph

A collection of nodes and edges defining your workflow:

graph = Graph()
graph.add_node("agent", chat_agent)
graph.add_edge("agent", "tool_node")
graph.set_entry_point("agent")

Event

External or internal triggers that drive graph execution:

event = Event(type="user.message", payload={"text": "Hello"}, session_id="user_001")

Streaming Output

Real-time streaming with zero polling:

@app.agent(name="chat_agent", stream=True)
async def chat_agent(state: dict, event: Event) -> dict:
    output_queue = state["_output_queue"]
    session_id = state["_session_id"]
    
    async def stream_llm():
        for chunk in llm_stream():
            for char in chunk:
                output_queue.put_nowait(Event(
                    type="stream.chunk",
                    payload={"delta": char},
                    session_id=session_id
                ))
                await asyncio.sleep(0.03)
        output_queue.put_nowait(Event(type="stream.end", payload={}, session_id=session_id))
    
    asyncio.create_task(stream_llm())
    return state

Human-in-the-Loop

Interrupt and resume for human approval:

@app.agent(name="order_agent")
async def order_agent(state: dict, event: Event) -> dict:
    state.setdefault("orders", [])
    amount = event.payload.get("amount", 0)
    state["orders"].append({"amount": amount, "status": "pending"})
    if amount > 1000:
        state["need_approval"] = True
    return state

async def approve_node(state: dict, event: Event) -> tuple[dict, list[Event]]:
    return state, [Event(
        type="interrupt",
        payload={"prompt": "Approve this transaction?", "resume_node": "confirm"},
        session_id=event.session_id
    )]

async def confirm_node(state: dict, event: Event) -> dict:
    if state.get("orders"):
        state["orders"][-1]["status"] = "confirmed"
    return state

async def reject_node(state: dict, event: Event) -> dict:
    if state.get("orders"):
        state["orders"][-1]["status"] = "rejected"
    return state

graph = Graph()
graph.add_node("order", order_agent)
graph.add_node("approve", approve_node)
graph.add_node("confirm", confirm_node)
graph.add_node("reject", reject_node)

graph.add_edge("order", "approve", condition=lambda s: s.get("need_approval"))
graph.add_edge("approve", "confirm")
graph.add_edge("approve", "reject")
graph.set_entry_point("order")

app.register_graph("main", graph)

Handle the interrupt in your application:

async def main():
    api = FastMindAPI(app)
    await api.start()
    
    event = Event("user.message", {"amount": 2000}, "user_001")
    await api.push_event("user_001", event)
    
    async for ev in api.stream_events("user_001"):
        if ev.type == "interrupt":
            print(f"Interrupt: {ev.payload['prompt']}")
            await api.resume_session("user_001", "confirm")  # or "reject"

Perception Loop

React to sensors, timers, and external events:

@app.perception(interval=5.0, name="sensor_monitor")
async def sensor_monitor(app: FastMind):
    while True:
        data = await read_sensor()
        yield Event(type="sensor.data", payload=data, session_id="system")
        await asyncio.sleep(5.0)

Tool Calling (ReAct)

@app.tool(name="get_weather", description="Get weather")
async def get_weather(city: str) -> str:
    return f"{city} is sunny"

from fastmind import ToolNode

tool_node = ToolNode(app.get_tools())

def has_tool_calls(state: dict, event: Event) -> str:
    return "tools" if state.get("tool_calls") else None

graph.add_conditional_edges("agent", has_tool_calls, {None: "__end__"})
graph.add_edge("tools", "agent")

Examples

Example	Description
simple_chat.py	Basic chat agent
simple_chat_with_tool.py	Agent with tool calling (ReAct)
streaming_chat.py	Real-time streaming output
human_in_loop.py	Human approval workflow
perception_loop.py	Sensor data processing
drone.py	Timer-based perception
companion_bot.py	Multi-agent conversation
humanoid_robot.py	Multi-tool collaboration
sleep_assessment.py	Multi-state HITL flow
comprehensive_assistant.py	Full-featured assistant

Run an example:

python -m fastmind.examples.simple_chat

API Reference

FastMindAPI

api = FastMindAPI(app)

await api.start()                    # Start engine and perception loops
await api.push_event(session_id, event)  # Push event to session
async for ev in api.stream_events(session_id):  # Stream output events
    print(ev)
await api.stop()                     # Stop engine

Session

session = api.get_session(session_id)
state = session.state                 # Get session state
await session.wait_for_output(timeout=5.0)  # Wait for output event

Architecture

┌─────────────────────────────────────────────────────────┐
│                      FastMindAPI                         │
│  ┌─────────────────┐    ┌────────────────────────────┐  │
│  │ PerceptionLoop   │───▶│        Engine              │  │
│  │ Scheduler       │    │  ┌──────────────────────┐  │  │
│  └─────────────────┘    │  │ Session (per user)   │  │  │
│                         │  │  ├─ State           │  │  │
│                         │  │  ├─ Event Queue     │  │  │
│                         │  │  └─ Output Queue    │  │  │
│                         │  └──────────────────────┘  │  │
│                         └────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘

Testing

pip install fastmind[dev]
pytest tests/ -v

Changelog

v0.1.9

• Bug Fix: Fixed sync generator state preservation in PerceptionLoop (sync generators now maintain state across loops instead of restarting)
• Bug Fix: Fixed silent exception swallowing in perception handlers (exceptions are now properly logged)
• Bug Fix: Fixed hardcoded sensor.data event type filtering (all event types are now routed correctly)
• Improvement: Perception events auto-create sessions if they don't exist

v0.1.3

• Bug Fix: Fixed stream_events timeout issue when agent returns no output events
• Improvement: Enhanced debug logging in engine for better observability
• Improvement: Added _has_conditional_edges() helper method to Graph class
• Tests: Added comprehensive test suite for ReAct loops and node execution protection

v0.1.2

• Initial release

License

GPL-3.0 License - see LICENSE for details.

Acknowledgments

Inspired by LangGraph for the state graph architecture design.

Author

xiefujin email:[e-mail]

Links

• Documentation
• GitHub
• PyPI