SoloFlow

KO | EN

gitlite — search

#python #hacktoberfest #react #typescript #go #android #rust #macos #java #windows #cli #vue #security #openai

(★ 67)

Complete ETCLOVG framework for AI Agent workflows - DAG+FSM orchestration, Ebbinghaus memory, discipline routing, skill evolution, trace system, governance. 80+ tests, zero deps, 7/7 layers.

SoloFlow 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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.**

[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/SonicBotMan/SoloFlow/main/LICENSE)
[![Tests](https://img.shields.io/badge/tests-68%20passing-brightgreen.svg)](https://raw.githubusercontent.com/SonicBotMan/SoloFlow/main/tests)
[![Python](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/)
[![Dependencies](https://img.shields.io/badge/dependencies-0-brightgreen.svg)](https://raw.githubusercontent.com/SonicBotMan/SoloFlow/main/requirements.txt)

---

## Why SoloFlow?

AI Agents fail in predictable ways:

| Problem | SoloFlow Solution |
|---------|-------------------|
| **No Observability** — 8-step chain fails at step 5, no trace, no resume | **Trace System** — nested spans, token tracking, JSON export |
| **Amnesiac Agents** — every invocation starts from zero | **Ebbinghaus Memory** — three-tier memory with forgetting curve |
| **One-Size-Fits-All** — simple tasks waste deep reasoning | **Discipline Routing** — auto-classify to quick/deep/visual/ultrabrain |
| **No Learning** — repeated patterns stay manual | **Skill Evolution** — observe → detect → package → install |

---

## 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

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

### Create and Execute a Workflow

```python
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
bash install.sh
```

Or manually:

```bash
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

| Command | Description |
|---------|-------------|
| `/soloflow begin [name]` | Mark workflow start |
| `/soloflow end [name]` | Mark workflow end, record pattern |
| `/soloflow propose` | Analyze session, propose top skill |
| `/soloflow generate [name]` | Generate and install a skill |
| `/soloflow list` | List detected patterns |
| `/soloflow skills` | List generated skills |
| `/soloflow status` | Show tracking status |
| `/soloflow queue` | Show pending proposals |
| `/soloflow clear` | Clear session log |

### 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:

```python
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:

| Tool | Description |
|------|-------------|
| `soloflow_create` | Create a new workflow with steps and DAG edges |
| `soloflow_run` | Execute a workflow with DAG parallelism |
| `soloflow_status` | Get workflow status and progress |
| `soloflow_list` | List workflows with optional state filter |
| `soloflow_cancel` | Cancel a running workflow |

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

---

## Trace System

Track every workflow execution with nested spans:

```python
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:

```python
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:

```python
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:

```python
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

| Layer | Component | Status |
|-------|-----------|--------|
| **T** | MCP Tool Layer | ✅ 5 tools |
| **C** | Ebbinghaus Memory + Context Providers | ✅ Forgetting curve + pluggable context |
| **L** | DAG + FSM Engine + Pipeline | ✅ Core + Haystack-style components |
| **O** | Trace System + Hooks | ✅ Nested spans + lifecycle hooks |
| **V** | Quality Scorer + Output Validation | ✅ 4-dimension scoring + typed contracts |
| **E** | Execution + Dispatch + Handoff | ✅ Sub-agent dispatch + control transfer |
| **G** | Governance + Roles + Session + Boundary | ✅ Role permissions + session mgmt |

**Coverage: 7/7 layers (100%)**

---

## Testing

```bash
# 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](./CONTRIBUTING.md) for guidelines.

---

## License

MIT License - see [LICENSE](./LICENSE)

---

## Acknowledgments

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

SoloFlow

SoloFlow 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

SoloFlow

SoloFlow 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README