| Name | flowllm JSON |
| Version |
0.1.11.6
JSON |
| download |
| home_page | None |
| Summary | A flexible framework for building LLM-powered flows and mcp services |
| upload_time | 2025-10-28 06:46:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | Apache License
Version 2.0, January 2004
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|
| keywords |
llm
ai
flow
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<p align="center">
<img src="docs/figure/logo.png" alt="FlowLLM Logo" width="50%">
</p>
<p align="center">
<strong>FlowLLM: A Flexible Framework for Building LLM-Powered Flows</strong><br>
<em>Flow with Intelligence, Build with Simplicity.</em>
</p>
<p align="center">
<a href="https://pypi.org/project/flowllm/"><img src="https://img.shields.io/badge/python-3.12+-blue" alt="Python Version"></a>
<a href="https://pypi.org/project/flowllm/"><img src="https://img.shields.io/badge/pypi-v0.1.11.3-blue?logo=pypi" alt="PyPI Version"></a>
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-black" alt="License"></a>
</p>
---
FlowLLM is a **configuration-driven** framework for building LLM-powered applications. Write operations once, compose them via YAML configuration, and automatically get HTTP APIs and MCP toolsβno boilerplate code needed.
## π Table of Contents
- [Why FlowLLM?](#-why-flowllm)
- [Getting Started](#-getting-started)
- [Core Workflow](#-core-workflow)
- [Architecture](#-architecture)
- [Features](#-features)
- [Resources](#-resources)
---
## π‘ Why FlowLLM?
**The Problem**: Building LLM services traditionally requires writing boilerplate routes, validation, documentation, and orchestration code for each endpoint.
**The Solution**: FlowLLM's configuration-driven approach lets you:
- β
**Write Operations Once** - Focus on business logic in reusable Python ops
- β
**Configure, Don't Code** - Compose workflows using YAML configuration
- β
**Auto-Generate Services** - HTTP and MCP endpoints created automatically
- β
**Built-in Orchestration** - Sequential (`>>`), parallel (`|`), and nested flows
- β
**Zero Boilerplate** - No routes, validators, or service code needed
| Feature | Traditional Approach | FlowLLM Approach |
|---------|---------------------|------------------|
| **Service Creation** | Write FastAPI/Flask routes, handlers, validation | Write YAML config - auto-registers HTTP + MCP |
| **API Documentation** | Manually write OpenAPI specs | Auto-generated from config |
| **Workflow Changes** | Modify Python code, test, redeploy | Update config, restart service |
| **Orchestration** | Write custom coordination code | Use expressions: `>>`, `\|`, `()` |
**Perfect For**: Rapid prototyping, microservices, AI agent tools, data pipelines, enterprise AI applications.
---
## π Getting Started
### Installation
```bash
pip install flowllm
```
For detailed setup instructions, see the [Installation Guide](INSTALLATION.md).
### Quick Start
See the [Quick Start Guide](QUICKSTART.md) to build your first LLM service in 30 seconds.
---
## π― Core Workflow
```
βββββββββββββββββββ ββββββββββββββββββββ ββββββββββββββββββββββββββββ
β Build Ops β β Configure YAML β β Auto-Register β
β (Python) β β β (Workflows) β β β Services β
β β β β β β
β β’ BaseOp β β flow: β β ββββββββββββββββββββββ β
β β’ BaseAsyncOp β β workflow: β β β HTTP Service β β
β β’ BaseMcpOp β β description β β β POST /workflow β β
β β’ BaseRayOp β β flow_contentβ β β OpenAPI docs β β
β β β tool: β β ββββββββββββββββββββββ β
β β β parametersβ β β
β β β β β ββββββββββββββββββββββ β
β β β backend: http β β β MCP Service β β
β β β or mcp β β β Tool: workflow β β
β β β β β β Auto-discovered β β
β β β β β ββββββββββββββββββββββ β
βββββββββββββββββββ ββββββββββββββββββββ ββββββββββββββββββββββββββββ
```
**Three Simple Steps:**
1. **Create an Op** - Write a Python class implementing your business logic
2. **Configure in YAML** - Define workflow and service endpoints
3. **Launch** - Run one command to start your HTTP or MCP service
**No manual routing, no endpoint definitions, no service code - just configuration!**
---
## β¨ Architecture
FlowLLM adopts a **three-layer configuration-driven architecture**:
```
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Service Layer (ε€ε±) β
β ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ β
β β HTTP Service β β MCP Service β β CMD Service β β
β β FastAPI β β FastMCP β β Command Lineβ β
β ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ β
β Auto-Register from Configuration β
βββββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββββ΄ββββββββββββββββββββββββββββββββββββββββ
β Flow Layer (δΈε±) β
β β’ Sequential: op1 >> op2 >> op3 β
β β’ Parallel: (op1 | op2 | op3) β
β β’ Nested: op1 >> (op2 | op3) >> op4 β
βββββββββββββββββββββββββββββββ¬ββββββββββββββββββββββββββββββββββββββββ
β
βββββββββββββββββββββββββββββββ΄ββββββββββββββββββββββββββββββββββββββββ
β Foundation Layer (εΊε±) β
β ββββββββββββ ββββββββββββ ββββββββββββ ββββββββββββ β
β β Op Lib β β LLM Lib β βEmbedding β β Storage β β
β ββββββββββββ€ ββββββββββββ€ ββββββββββββ€ ββββββββββββ€ β
β β BaseOp β β OpenAI β β OpenAI β βElasticS. β β
β βBaseAsync β β LiteLLM β βCompatibleβ βChromaDB β β
β βBaseTool β βDashScope β β β β Local β β
β βBaseMcpOp β β Custom β β Custom β β Cache β β
β βBaseRayOp β β β β β β β β
β ββββββββββββ ββββββββββββ ββββββββββββ ββββββββββββ β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
```
### Key Design Principles
1. **Separation of Concerns** - Ops (business logic), Flows (orchestration), Services (protocol handling)
2. **Configuration over Code** - Ops in Python, Flows in YAML, Services auto-generated
3. **Dependency Injection** - ServiceContext manages shared resources (LLM, VectorStore, etc.)
4. **Registry Pattern** - Dynamic loading and discovery based on configuration
### Complete Data Flow
```
Request β Service Layer (HTTP/MCP)
β
Flow Layer (Parse expression β Build DAG)
β
Foundation Layer (Execute ops with context)
β
Response (JSON/MCP result)
```
---
## π― Features
### π¦ Pre-built Operations
**Gallery Ops**: `SimpleLLMOp`, `ReactLLMOp`, `ExecuteCodeOp`, `TranslateCodeOp`
**Search Ops**: `TavilySearchOp`, `DashScopeSearchOp`, `McpSearchOp`
**Research Ops**: `DashScopeDeepResearchOp`, `LangChainDeepResearchOp`
**Data Ops**: Various extraction and processing operations
### π§ Advanced Capabilities
- **Multi-LLM Support** - OpenAI, LiteLLM (100+ providers), DashScope, custom providers
- **Vector Storage** - Elasticsearch, ChromaDB, local file-based, in-memory
- **Async/Streaming** - Full async support with SSE streaming responses
- **Distributed Computing** - Ray integration for scaling operations
- **Caching** - Intelligent caching with TTL and automatic serialization
- **Web Crawling** - Integrated `crawl4ai` for content extraction
### π§ͺ Workflow Patterns
- **Simple LLM Chat** - Direct model interaction
- **Multi-Step Research** - Sequential search, summarization, validation
- **Parallel Processing** - Concurrent sentiment analysis, keyword extraction
- **Complex Pipelines** - Nested sequential and parallel operations
---
## π Resources
### Documentation
- **[Installation Guide](INSTALLATION.md)** - Setup and environment configuration
- **[Quick Start Guide](QUICKSTART.md)** - Build your first service
- **Specialized Guides** in `doc/`:
- [Deep Research Guide](docs/deep_research.md)
- [Financial Supply Guide](docs/fin_supply_readme.md)
- [Vector Store Guide](docs/vector_store.md)
### Examples & Configuration
- **Examples**: `test/` directory for practical examples
- **Configuration**: `flowllm/config/` for sample configs
### Latest Updates
- **[2025-10]** FlowLLM v0.1.10 - Enhanced async support and stability
- **[2025-09]** Financial data modules with 26+ pre-built flows
- **[2025-09]** Deep research with multiple search backends
- **[2025-08]** MCP (Model Context Protocol) support
- **[2025-06]** Multi-backend vector storage
---
## βοΈ License
Apache License 2.0 - see [LICENSE](./LICENSE) file for details.
---
## π Star History
If you find FlowLLM useful, please consider giving it a star!
Raw data
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"description": "<p align=\"center\">\n <img src=\"docs/figure/logo.png\" alt=\"FlowLLM Logo\" width=\"50%\">\n</p>\n\n<p align=\"center\">\n <strong>FlowLLM: A Flexible Framework for Building LLM-Powered Flows</strong><br>\n <em>Flow with Intelligence, Build with Simplicity.</em>\n</p>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/flowllm/\"><img src=\"https://img.shields.io/badge/python-3.12+-blue\" alt=\"Python Version\"></a>\n <a href=\"https://pypi.org/project/flowllm/\"><img src=\"https://img.shields.io/badge/pypi-v0.1.11.3-blue?logo=pypi\" alt=\"PyPI Version\"></a>\n <a href=\"./LICENSE\"><img src=\"https://img.shields.io/badge/license-Apache--2.0-black\" alt=\"License\"></a>\n</p>\n\n---\n\nFlowLLM is a **configuration-driven** framework for building LLM-powered applications. Write operations once, compose them via YAML configuration, and automatically get HTTP APIs and MCP tools\u2014no boilerplate code needed.\n\n## \ud83d\udcd6 Table of Contents\n\n- [Why FlowLLM?](#-why-flowllm)\n- [Getting Started](#-getting-started)\n- [Core Workflow](#-core-workflow)\n- [Architecture](#-architecture)\n- [Features](#-features)\n- [Resources](#-resources)\n\n---\n\n## \ud83d\udca1 Why FlowLLM?\n\n**The Problem**: Building LLM services traditionally requires writing boilerplate routes, validation, documentation, and orchestration code for each endpoint.\n\n**The Solution**: FlowLLM's configuration-driven approach lets you:\n\n- \u2705 **Write Operations Once** - Focus on business logic in reusable Python ops\n- \u2705 **Configure, Don't Code** - Compose workflows using YAML configuration\n- \u2705 **Auto-Generate Services** - HTTP and MCP endpoints created automatically\n- \u2705 **Built-in Orchestration** - Sequential (`>>`), parallel (`|`), and nested flows\n- \u2705 **Zero Boilerplate** - No routes, validators, or service code needed\n\n| Feature | Traditional Approach | FlowLLM Approach |\n|---------|---------------------|------------------|\n| **Service Creation** | Write FastAPI/Flask routes, handlers, validation | Write YAML config - auto-registers HTTP + MCP |\n| **API Documentation** | Manually write OpenAPI specs | Auto-generated from config |\n| **Workflow Changes** | Modify Python code, test, redeploy | Update config, restart service |\n| **Orchestration** | Write custom coordination code | Use expressions: `>>`, `\\|`, `()` |\n\n**Perfect For**: Rapid prototyping, microservices, AI agent tools, data pipelines, enterprise AI applications.\n\n---\n\n## \ud83d\ude80 Getting Started\n\n### Installation\n\n```bash\npip install flowllm\n```\n\nFor detailed setup instructions, see the [Installation Guide](INSTALLATION.md).\n\n### Quick Start\n\nSee the [Quick Start Guide](QUICKSTART.md) to build your first LLM service in 30 seconds.\n\n---\n\n## \ud83c\udfaf Core Workflow\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Build Ops \u2502 \u2502 Configure YAML \u2502 \u2502 Auto-Register \u2502\n\u2502 (Python) \u2502 \u2192 \u2502 (Workflows) \u2502 \u2192 \u2502 Services \u2502\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\n\u2502 \u2022 BaseOp \u2502 \u2502 flow: \u2502 \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n\u2502 \u2022 BaseAsyncOp \u2502 \u2502 workflow: \u2502 \u2502 \u2502 HTTP Service \u2502 \u2502\n\u2502 \u2022 BaseMcpOp \u2502 \u2502 description \u2502 \u2502 \u2502 POST /workflow \u2502 \u2502\n\u2502 \u2022 BaseRayOp \u2502 \u2502 flow_content\u2502 \u2502 \u2502 OpenAPI docs \u2502 \u2502\n\u2502 \u2502 \u2502 tool: \u2502 \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n\u2502 \u2502 \u2502 parameters\u2502 \u2502 \u2502\n\u2502 \u2502 \u2502 \u2502 \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n\u2502 \u2502 \u2502 backend: http \u2502 \u2502 \u2502 MCP Service \u2502 \u2502\n\u2502 \u2502 \u2502 or mcp \u2502 \u2502 \u2502 Tool: workflow \u2502 \u2502\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502 Auto-discovered \u2502 \u2502\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n**Three Simple Steps:**\n\n1. **Create an Op** - Write a Python class implementing your business logic\n2. **Configure in YAML** - Define workflow and service endpoints\n3. **Launch** - Run one command to start your HTTP or MCP service\n\n**No manual routing, no endpoint definitions, no service code - just configuration!**\n\n---\n\n## \u2728 Architecture\n\nFlowLLM adopts a **three-layer configuration-driven architecture**:\n\n```\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Service Layer (\u5916\u5c42) \u2502\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n\u2502 \u2502 HTTP Service \u2502 \u2502 MCP Service \u2502 \u2502 CMD Service \u2502 \u2502\n\u2502 \u2502 FastAPI \u2502 \u2502 FastMCP \u2502 \u2502 Command Line\u2502 \u2502\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n\u2502 Auto-Register from Configuration \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Flow Layer (\u4e2d\u5c42) \u2502\n\u2502 \u2022 Sequential: op1 >> op2 >> op3 \u2502\n\u2502 \u2022 Parallel: (op1 | op2 | op3) \u2502\n\u2502 \u2022 Nested: op1 >> (op2 | op3) >> op4 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2502\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Foundation Layer (\u5e95\u5c42) \u2502\n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n\u2502 \u2502 Op Lib \u2502 \u2502 LLM Lib \u2502 \u2502Embedding \u2502 \u2502 Storage \u2502 \u2502\n\u2502 \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524 \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524 \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524 \u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524 \u2502\n\u2502 \u2502 BaseOp \u2502 \u2502 OpenAI \u2502 \u2502 OpenAI \u2502 \u2502ElasticS. \u2502 \u2502\n\u2502 \u2502BaseAsync \u2502 \u2502 LiteLLM \u2502 \u2502Compatible\u2502 \u2502ChromaDB \u2502 \u2502\n\u2502 \u2502BaseTool \u2502 \u2502DashScope \u2502 \u2502 \u2502 \u2502 Local \u2502 \u2502\n\u2502 \u2502BaseMcpOp \u2502 \u2502 Custom \u2502 \u2502 Custom \u2502 \u2502 Cache \u2502 \u2502\n\u2502 \u2502BaseRayOp \u2502 \u2502 \u2502 \u2502 \u2502 \u2502 \u2502 \u2502\n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n```\n\n### Key Design Principles\n\n1. **Separation of Concerns** - Ops (business logic), Flows (orchestration), Services (protocol handling)\n2. **Configuration over Code** - Ops in Python, Flows in YAML, Services auto-generated\n3. **Dependency Injection** - ServiceContext manages shared resources (LLM, VectorStore, etc.)\n4. **Registry Pattern** - Dynamic loading and discovery based on configuration\n\n### Complete Data Flow\n\n```\nRequest \u2192 Service Layer (HTTP/MCP)\n \u2193\nFlow Layer (Parse expression \u2192 Build DAG)\n \u2193\nFoundation Layer (Execute ops with context)\n \u2193\nResponse (JSON/MCP result)\n```\n\n---\n\n## \ud83c\udfaf Features\n\n### \ud83d\udce6 Pre-built Operations\n\n**Gallery Ops**: `SimpleLLMOp`, `ReactLLMOp`, `ExecuteCodeOp`, `TranslateCodeOp` \n**Search Ops**: `TavilySearchOp`, `DashScopeSearchOp`, `McpSearchOp` \n**Research Ops**: `DashScopeDeepResearchOp`, `LangChainDeepResearchOp` \n**Data Ops**: Various extraction and processing operations\n\n### \ud83d\udd27 Advanced Capabilities\n\n- **Multi-LLM Support** - OpenAI, LiteLLM (100+ providers), DashScope, custom providers\n- **Vector Storage** - Elasticsearch, ChromaDB, local file-based, in-memory\n- **Async/Streaming** - Full async support with SSE streaming responses\n- **Distributed Computing** - Ray integration for scaling operations\n- **Caching** - Intelligent caching with TTL and automatic serialization\n- **Web Crawling** - Integrated `crawl4ai` for content extraction\n\n### \ud83e\uddea Workflow Patterns\n\n- **Simple LLM Chat** - Direct model interaction\n- **Multi-Step Research** - Sequential search, summarization, validation\n- **Parallel Processing** - Concurrent sentiment analysis, keyword extraction\n- **Complex Pipelines** - Nested sequential and parallel operations\n\n---\n\n## \ud83d\udcda Resources\n\n### Documentation\n\n- **[Installation Guide](INSTALLATION.md)** - Setup and environment configuration\n- **[Quick Start Guide](QUICKSTART.md)** - Build your first service\n- **Specialized Guides** in `doc/`:\n - [Deep Research Guide](docs/deep_research.md)\n - [Financial Supply Guide](docs/fin_supply_readme.md)\n - [Vector Store Guide](docs/vector_store.md)\n\n### Examples & Configuration\n\n- **Examples**: `test/` directory for practical examples\n- **Configuration**: `flowllm/config/` for sample configs\n\n### Latest Updates\n\n- **[2025-10]** FlowLLM v0.1.10 - Enhanced async support and stability\n- **[2025-09]** Financial data modules with 26+ pre-built flows\n- **[2025-09]** Deep research with multiple search backends\n- **[2025-08]** MCP (Model Context Protocol) support\n- **[2025-06]** Multi-backend vector storage\n\n---\n\n\n## \u2696\ufe0f License\n\nApache License 2.0 - see [LICENSE](./LICENSE) file for details.\n\n---\n\n## \ud83c\udf1f Star History\n\nIf you find FlowLLM useful, please consider giving it a star!\n",
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