Initial LangGraph architecture blueprints

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# LangGraph Architecture Overview
**Version:** 1.0.0
**LangGraph Version:** 1.0.9
**Last Updated:** 2026-02-23
---
## Executive Summary
LangGraph is a low-level orchestration framework for building stateful, long-running multi-agent systems. Inspired by Google's Pregel, Apache Beam, and NetworkX, it provides durable execution, human-in-the-loop capabilities, and comprehensive memory management.
---
## System Architecture
```
┌─────────────────────────────────────────────────────────────────────────┐
│ LANGGRAPH SYSTEM ARCHITECTURE │
└─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────┐
│ CLIENT/API LAYER │
├─────────────────────────────────────────────────────────────────────────┤
│ Python SDK │ LangChain Integration │ LangGraph Cloud │ CLI │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ COMPILER LAYER │
├─────────────────────────────────────────────────────────────────────────┤
│ • Graph compilation to executable form │
│ • State schema validation │
│ • Node/Edge type resolution │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ RUNTIME LAYER │
├─────────────────────────────────────────────────────────────────────────┤
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ PREGEL EXECUTION ENGINE │ │
│ │ • Superstep coordination │ │
│ │ • Node scheduling │ │
│ │ • Message passing │ │
│ │ • Barrier synchronization │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ STATE & CHECKPOINTING │
├─────────────────────────────────────────────────────────────────────────┤
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │
│ │ In-Memory State │ │ Checkpointer │ │ Channel Store │ │
│ │ (active graph) │ │ (persistence) │ │ (queues) │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
```
---
## Core Components
### 1. Graph Structure
| Component | Description |
|-----------|-------------|
| **State** | Typed dictionary that flows through the graph |
| **Nodes** | Functions that receive state, optionally update it |
| **Edges** | Control flow (conditional, static, entrypoint) |
| **Reducers** | Functions that merge state updates |
### 2. Pregel Execution
The core execution model (inspired by Pregel):
```
Superstep 1: Superstep 2: Superstep 3:
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Node A │ │ │ │ │
│ (active) │──────▶│ Node B │──────▶│ Node C │
│ │ msgs │ (active) │ msgs │ (active) │
└──────────┘ └──────────┘ └──────────┘
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ State │ │ State │ │ State │
│ Update │ │ Update │ │ Update │
└──────────┘ └──────────┘ └──────────┘
│ │ │
└──────────────────┴──────────────────┘
│
▼ (CHECKPOINT)
┌──────────┐
│ SQLite │
│ Postgres │
│ Memory │
└──────────┘
```
### 3. Checkpointing
LangGraph provides durability through checkpointing:
- **Full state snapshots** saved at configurable points
- **Resumable from failure** — replay from last checkpoint
- **Multiple backends:** SQLite, Postgres, in-memory
### 4. Channels
Inter-node communication via channels:
| Channel Type | Purpose |
|--------------|---------|
| **QueueChannel** | FIFO message passing |
| **LastValue** | Most recent value wins |
| **Topic** | Pub/sub style |
| **Context** | Per-superstep context |
---
## State Management
### Typed State Schema
```python
from typing import TypedDict
class AgentState(TypedDict):
messages: list
next_action: str
checkpoint_id: str | None
```
### Reducers
Combine updates from multiple nodes:
```python
def add_messages(left: list, right: list) -> list:
return left + right
```
---
## Memory Architecture
### Short-Term Memory
- **In-graph state:** Messages and working data
- **Per-superstep:** State resets unless persisted
### Long-Term Memory
- **Checkpoint storage:** SQLite, Postgres, custom
- **Thread-level:** Per-conversation isolation via `thread_id`
### Human-in-the-Loop
- **Interrupt:** Pause execution for human input
- **Command:** Allow human to modify state
- **Review:** Human approves/rejects before continuing
---
## Execution Flow
```
1. Client calls: graph.invoke(input, config)
│
▼
2. Compile (if needed): create executable graph
│
▼
3. Load checkpoint (if resuming from checkpoint_id)
│
▼
4. FOR each superstep:
a. Schedule nodes to execute
b. Execute active nodes in parallel
c. Collect messages
d. Send messages via channels
e. Check for interrupts (pause if interrupted)
f. Checkpoint (if enabled)
│
▼
5. Return final state
```
---
## Key Files in Core
| File | Purpose |
|------|---------|
| `langgraph/pregel/__init__.py` | Main entry point |
| `langgraph/pregel/__main__.py` | CLI entry |
| `langgraph/pregel/_loop.py` | Core execution loop (~2000 lines) |
| `langgraph/pregel/checkpoint.py` | Checkpoint management |
| `langgraph/pregel/channel.py` | Channel implementations |
| `langgraph/pregel/state.py` | State management |
---
## Comparison with OpenClaw
| Aspect | LangGraph | OpenClaw |
|--------|-----------|----------|
| **Language** | Python | Node.js |
| **Model** | Graph-based orchestration | Agent-based |
| **Persistence** | Checkpoint-based | Session-memory hook |
| **Memory** | Channels + checkpoint storage | Multi-layer (working, spectral, file, vector) |
| **Communication** | Channels | Channel plugins |
| **Extensibility** | Custom nodes/edges | Hook system |
| **Identity** | None | WE/witness architecture |
---
*Generated for the WE — Solaria Lumis Havens & Mark Randall Havens*