SoloFlow ⚡

The Brain Behind AI Workflow Orchestration

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

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

observe → fingerprint → detect → package → install

• Passive observation via hermes.on("tool_call") event hooks
• Multi-step workflow aggregation — consecutive tool calls grouped automatically
• Rich step descriptions — extracts key args into human-readable steps
• 4-dimension quality scoring — reliability, efficiency, maturity, reusability
• Auto-generate SKILL.md + plugin.py and install to ~/.hermes/skills/

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():
    store = SQLiteStore(Path("soloflow.db"))
    store.initialize()
    ws = WorkflowService(store)
    ws.set_scheduler(Scheduler(store, ws))

    # Create a DAG workflow with parallel branches
    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"),     # parallel branches
            ("search_a", "outline"), ("search_b", "outline"), # merge
            ("outline", "write"), ("write", "review"),
        ],
    )

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

asyncio.run(main())

SoloFlow Plugin — Automatic Skill Detection

SoloFlow includes a Hermes plugin that watches your workflows and automatically generates reusable skills.

Install

bash install.sh

Or manually:

cp plugins/soloflow.py ~/.hermes/plugins/
cp -r skills/meta/soloflow ~/.hermes/skills/meta/
cp -r evolution ~/.hermes/plugins/
hermes skills reload

How It Works

tool_call events → WorkflowBuilder (aggregate) → PatternDetector (fingerprint)
                                                       ↓
                                              Pattern (2+ occurrences)
                                                       ↓
                                              SkillPackager → SKILL.md + plugin.py
                                                       ↓
                                              QualityScorer → grade (A-F)

• WorkflowBuilder accumulates consecutive tool_call events into multi-step workflows (auto-flushes after 60s idle)
• PatternDetector fingerprints workflow structure (step names + edges + tools) and groups identical executions
• SkillPackager generates Hermes-native SKILL.md and plugin.py with rich step descriptions
• QualityScorer rates skills on 4 dimensions: reliability, efficiency, maturity, reusability

Commands

Natural Language Triggers

Tell Hermes naturally — no commands needed:

• "Save this as a skill"
• "Remember how to do this"
• "Turn this workflow into a reusable skill"
• "I always do this manually..."
• "Let's automate this"

DAG Engine Integration

When a workflow completes through the DAG engine, SoloFlow automatically feeds the execution data to PatternDetector:

from hermes_plugin.services.workflow_service import WorkflowService

ws = WorkflowService(store)
ws.set_on_complete(lambda wf_id, success, duration, wf_def: ...)
# Completed workflows are automatically recorded for pattern detection

MCP Tools

5 MCP tools for integration with AI agents:

# 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)

span = collector.start_span(operation="workflow", node_name="research")
step = collector.start_span(
    operation="step", node_name="search",
    parent_id=span.span_id, trace_id=span.trace_id,
)
collector.finish_span(
    step.span_id,
    status=SpanStatus.SUCCESS,
    token_usage=TokenUsage(prompt_tokens=100, completion_tokens=200),
)
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"))

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

entry = await consolidator.get_memory("user_preference")
stats = await consolidator.consolidate_all()

Human-in-the-Loop

Approval system for sensitive workflow steps:

from hermes_plugin.human import HumanApprovalManager

manager = HumanApprovalManager()
request = manager.create_request(
    workflow_id="wf_123",
    step_id="review",
    prompt="Please review and approve",
)
result = await manager.wait_for_approval(request.request_id)

Governance

Role-based permissions, audit logging, and policy enforcement:

from hermes_plugin.governance import GovernanceManager, Permission

governance = GovernanceManager()
governance.grant_permission("user_1", Permission.EXECUTE)
has_perm = governance.check_permission("user_1", Permission.EXECUTE)
governance.log_audit(
    action=AuditAction.WORKFLOW_STARTED,
    workflow_id="wf_123",
    user_id="user_1",
)

Architecture

SoloFlow/
├── hermes-plugin/          # Core engine
│   ├── core/               # DAG + FSM
│   ├── services/           # WorkflowService + Scheduler
│   ├── memory/             # Three-tier memory
│   ├── store/              # SQLite persistence
│   ├── checkpoint/         # LangGraph: resumable execution
│   ├── dispatch/           # DeerFlow: sub-agent dispatch
│   ├── roles/              # CrewAI: permission boundaries
│   ├── output/             # PydanticAI: typed contracts
│   ├── boundary/           # Mastra: workflow vs agent control
│   ├── handoff/            # OpenAI Agents SDK: control transfer
│   ├── session/            # Google ADK: session + context budget
│   ├── hooks/              # Claude Agent SDK: lifecycle hooks
│   ├── pipeline/           # Haystack: component orchestration
│   ├── context/            # Microsoft: pluggable context providers
│   ├── governance/         # Permissions + audit
│   ├── human/              # Human approval
│   └── visualization/      # Mermaid diagrams
├── plugins/                # Hermes plugins
│   └── soloflow.py         # Skill detection plugin
├── skills/                 # Hermes skills
│   └── meta/soloflow/      # AI behavior guidance
├── evolution/              # Skill auto-evolution
│   ├── pattern_detector.py # Fingerprint + detect
│   ├── skill_packager.py   # Generate SKILL.md + plugin.py
│   └── quality_scorer.py   # 4-dimension scoring
├── mcp/                    # MCP Tool Layer
├── trace/                  # Observability
├── memory/forgetting/      # Ebbinghaus forgetting curve
├── routing/                # Discipline-aware routing
├── install.sh              # One-command installer
└── tests/                  # Test suite (68 tests)

ETCLOVG Coverage

Coverage: 7/7 layers (100%)

Testing

# Run all tests
python3.11 -m pytest tests/ -v

# Run specific module
python3.11 -m pytest tests/evolution/ -v
python3.11 -m pytest tests/hermes-plugin/ -v
python3.11 -m pytest tests/mcp/ -v

68 tests, all passing.

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