SoloFlow ⚡

The Brain Behind AI Workflow Orchestration

Turn chaotic multi-step AI tasks into structured, observable, retryable workflows — with cognitive memory and discipline-aware routing.

Why SoloFlow?

AI Agents fail in predictable ways:

Four Pillars

1. DAG + FSM Hybrid Architecture

expressiveness(DAG) + rigor(FSM) = reliability

• Kahn algorithm for topological sorting
• Parallel execution where possible, sequential where required
• Automatic retry with exponential backoff

2. Cognitive Memory System

R(t) = base × e^(-t / stability)

• Working Memory — LRU cache for current context
• Episodic Memory — SQLite + FTS5 for event history
• Semantic Memory — pattern extraction and template storage
• Ebbinghaus Forgetting Curve — automatic memory consolidation

3. Discipline-Aware Routing

quick (~2s) → deep (~30s) → visual (~30s) → ultrabrain (~120s)

• Auto-classify tasks by complexity
• Route to appropriate agent discipline
• Fallback to default when uncertain

4. Skill Auto-Evolution

pattern → detect → package → skill → MCP tool

• Detect repeated workflow patterns
• Package into versioned, reusable skills
• Expose as MCP tools for other agents

ETCLOVG Coverage

Coverage: 5/7 layers (71%)

Quick Start

git clone https://github.com/SonicBotMan/SoloFlow.git
cd SoloFlow
# Pure Python, zero dependencies

Create and Execute a Workflow

import asyncio
from pathlib import Path
from hermes_plugin.store.sqlite_store import SQLiteStore
from hermes_plugin.services.workflow_service import WorkflowService
from hermes_plugin.services.scheduler import Scheduler

async def main():
    # Initialize
    store = SQLiteStore(Path("soloflow.db"))
    store.initialize()
    ws = WorkflowService(store)
    ws.set_scheduler(Scheduler(store, ws))

    # Create workflow
    wf = await ws.create_workflow(
        name="research-report",
        description="行业调研报告",
        steps=[
            {"id": "topic", "name": "选题", "discipline": "deep", "prompt": "确定研究方向"},
            {"id": "search_a", "name": "学术搜索", "discipline": "quick", "prompt": "搜索学术资料"},
            {"id": "search_b", "name": "行业搜索", "discipline": "quick", "prompt": "搜索行业报告"},
            {"id": "outline", "name": "大纲", "discipline": "deep", "prompt": "整理大纲"},
            {"id": "write", "name": "撰写", "discipline": "deep", "prompt": "写正文"},
            {"id": "review", "name": "审校", "discipline": "quick", "prompt": "审校发布"},
        ],
        edges=[
            ("topic", "search_a"), ("topic", "search_b"),
            ("search_a", "outline"), ("search_b", "outline"),
            ("outline", "write"), ("write", "review"),
        ],
    )

    # Execute
    await ws.start_workflow(wf["id"])
    
    # Check status
    status = await ws.get_status(wf["id"])
    print(f"State: {status['state']}, Progress: {status['progress']}")

asyncio.run(main())

MCP Tools

SoloFlow exposes 5 MCP tools for integration with AI agents:

Usage with Hermes Agent

# config.yaml
tools:
  mcp:
    servers:
      soloflow:
        command: python
        args: ["-m", "mcp.server"]

Trace System

Track every workflow execution with nested spans:

from trace.collector import TraceCollector
from trace.exporter import TraceExporter
from trace.span import SpanStatus, TokenUsage

collector = TraceCollector(db_path=Path("traces.db"))
exporter = TraceExporter(collector)

# Start trace
span = collector.start_span(operation="workflow", node_name="research")

# Track steps
step = collector.start_span(
    operation="step", 
    node_name="search",
    parent_id=span.span_id,
    trace_id=span.trace_id,
)

# Finish with token usage
collector.finish_span(
    step.span_id,
    status=SpanStatus.SUCCESS,
    token_usage=TokenUsage(prompt_tokens=100, completion_tokens=200),
)

# Export
print(exporter.format_trace_tree(span.trace_id))

Ebbinghaus Memory

Memory system with automatic consolidation:

from memory.forgetting.consolidation import MemoryConsolidator

consolidator = MemoryConsolidator(db_path=Path("memory.db"))

# Add memories
await consolidator.add_memory(
    key="user_preference",
    content={"theme": "dark"},
    tier="episodic",
    stability=1.0,
)

# Access increases stability
entry = await consolidator.get_memory("user_preference")

# Run consolidation cycle
stats = await consolidator.consolidate_all()

Project Structure

SoloFlow/
├── hermes-plugin/          # Core engine
│   ├── core/               # DAG + FSM
│   ├── services/           # WorkflowService + Scheduler
│   ├── memory/             # Three-tier memory
│   └── store/              # SQLite persistence
├── mcp/                    # MCP Tool Layer
├── trace/                  # Observability
├── memory/forgetting/      # Ebbinghaus forgetting curve
├── routing/                # Discipline-aware routing
├── evolution/              # Skill auto-evolution
├── cli/                    # CLI tools
└── tests/                  # Test suite (64 tests)

Testing

# Run all tests
python -m pytest tests/ -v

# Run specific module
python -m pytest tests/mcp/ -v
python -m pytest tests/trace/ -v
python -m pytest tests/memory/ -v
python -m pytest tests/routing/ -v
python -m pytest tests/evolution/ -v

# Run end-to-end test
python -m pytest tests/e2e/ -v

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE

Acknowledgments

• Inspired by LangGraph, AutoGen, and the Agent Harness Engineering research
• Built with ❤️ for the AI Agent community