LangGraphGo

Abbreviated as lango, 中文: 懒狗

English | 简体中文

Website: http://lango.rpcx.io

🔀 Forked from paulnegz/langgraphgo - Enhanced with streaming, visualization, observability, and production-ready features.

This fork aims for feature parity with the Python LangGraph library, adding support for parallel execution, persistence, advanced state management, pre-built agents, and human-in-the-loop workflows.

📦 Installation

go get github.com/smallnest/langgraphgo

🚀 Features

• Core Runtime:

  • Parallel Execution: Concurrent node execution (fan-out) with thread-safe state merging.
  • Runtime Configuration: Propagate callbacks, tags, and metadata via RunnableConfig.
  • LangChain Compatible: Works seamlessly with langchaingo.

• Persistence & Reliability:

  • Checkpointers: Redis, Postgres, and SQLite implementations for durable state.
  • State Recovery: Pause and resume execution from checkpoints.

• Advanced Capabilities:

  • State Schema: Granular state updates with custom reducers (e.g., AppendReducer).
  • Smart Messages: Intelligent message merging with ID-based upserts (AddMessages).
  • Command API: Dynamic control flow and state updates directly from nodes.
  • Ephemeral Channels: Temporary state values that clear automatically after each step.
  • Subgraphs: Compose complex agents by nesting graphs within graphs.
  • Enhanced Streaming: Real-time event streaming with multiple modes (updates, values, messages).
  • Pre-built Agents: Ready-to-use ReAct, CreateAgent, and Supervisor agent factories.
  • Programmatic Tool Calling (PTC): LLM generates code that calls tools programmatically, reducing latency and token usage by 10x.

• Developer Experience:

  • Visualization: Export graphs to Mermaid, DOT, and ASCII with conditional edge support.
  • Human-in-the-loop (HITL): Interrupt execution, inspect state, edit history (UpdateState), and resume.
  • Observability: Built-in tracing and metrics support.
  • Tools: Integrated Tavily and Exa search tools.

🎯 Quick Start

package main

import (
	"context"
	"fmt"
	"log"

	"github.com/smallnest/langgraphgo/graph"
	"github.com/tmc/langchaingo/llms"
	"github.com/tmc/langchaingo/llms/openai"
)

func main() {
	ctx := context.Background()
	model, _ := openai.New()

	// 1. Create Graph
	g := graph.NewMessageGraph()

	// 2. Add Nodes
	g.AddNode("generate", func(ctx context.Context, state interface{}) (interface{}, error) {
		messages := state.([]llms.MessageContent)
		response, _ := model.GenerateContent(ctx, messages)
		return append(messages, llms.TextParts("ai", response.Choices[0].Content)), nil
	})

	// 3. Define Edges
	g.AddEdge("generate", graph.END)
	g.SetEntryPoint("generate")

	// 4. Compile
	runnable, _ := g.Compile()

	// 5. Invoke
	initialState := []llms.MessageContent{
		llms.TextParts("human", "Hello, LangGraphGo!"),
	}
	result, _ := runnable.Invoke(ctx, initialState)
	
	fmt.Println(result)
}

📚 Examples

• Basic LLM - Simple LangChain integration
• RAG Pipeline - Complete retrieval-augmented generation
• RAG with LangChain - LangChain components integration
• RAG with VectorStores - LangChain VectorStore integration (New!)
• RAG with Chroma - Chroma vector database integration (New!)
• Tavily Search - Tavily search tool integration (New!)
• Exa Search - Exa search tool integration (New!)
• Streaming - Real-time progress updates
• Conditional Routing - Dynamic path selection
• Checkpointing - Save and resume state
• Visualization - Export graph diagrams
• Listeners - Progress, metrics, and logging
• Subgraphs - Nested graph composition
• Swarm - Multi-agent collaboration
• Create Agent - Flexible agent creation with options (New!)
• Dynamic Skill Agent - Agent with dynamic skill discovery and selection (New!)
• State Schema - Complex state management with Reducers
• Smart Messages - Intelligent message merging (Upserts)
• Command API - Dynamic control flow
• Ephemeral Channels - Temporary state management
• Streaming Modes - Advanced streaming patterns
• Time Travel / HITL - Inspect, edit, and fork state history
• Dynamic Interrupt - Pause execution from within a node
• Durable Execution - Crash recovery and resuming execution
• GoSkills Integration - Integration with GoSkills (New!)
• PTC Basic - Programmatic Tool Calling for reduced latency (New!)
• PTC Simple - Simple PTC example with calculator tools (New!)
• PTC Expense Analysis - Complex PTC scenario with data processing (New!)

🔧 Key Concepts

Parallel Execution

LangGraphGo automatically executes nodes in parallel when they share the same starting node. Results are merged using the graph's state merger or schema.

g.AddEdge("start", "branch_a")
g.AddEdge("start", "branch_b")
// branch_a and branch_b run concurrently

Human-in-the-loop (HITL)

Pause execution to allow for human approval or input.

config := &graph.Config{
    InterruptBefore: []string{"human_review"},
}

// Execution stops before "human_review" node
state, err := runnable.InvokeWithConfig(ctx, input, config)

// Resume execution
resumeConfig := &graph.Config{
    ResumeFrom: []string{"human_review"},
}
runnable.InvokeWithConfig(ctx, state, resumeConfig)

Pre-built Agents

Quickly create complex agents using factory functions.

// Create a ReAct agent
agent, err := prebuilt.CreateReactAgent(model, tools)

// Create an agent with options
agent, err := prebuilt.CreateAgent(model, tools, prebuilt.WithSystemMessage("System prompt"))

// Create a Supervisor agent
supervisor, err := prebuilt.CreateSupervisor(model, agents)

Programmatic Tool Calling (PTC)

Generate code that calls tools directly, reducing API round-trips and token usage.

// Create a PTC agent
agent, err := ptc.CreatePTCAgent(ptc.PTCAgentConfig{
    Model:         model,
    Tools:         toolList,
    Language:      ptc.LanguagePython, // or ptc.LanguageGo
    ExecutionMode: ptc.ModeDirect,     // Subprocess (default) or ModeServer
    MaxIterations: 10,
})

// LLM generates code that calls tools programmatically
result, err := agent.Invoke(ctx, initialState)

See the PTC README for detailed documentation.

🎨 Graph Visualization

exporter := runnable.GetGraph()
fmt.Println(exporter.DrawMermaid()) // Generates Mermaid flowchart

📈 Performance

• Graph Operations: ~14-94μs depending on format
• Tracing Overhead: ~4μs per execution
• Event Processing: 1000+ events/second
• Streaming Latency: <100ms

🧪 Testing

go test ./... -v

Contributors

This project is open for contributions! if you are interested in being a contributor please create feature issues first, then submit PRs..

📄 License

MIT License - see original repository for details.