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Initial LangGraph architecture blueprints

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ARCHITECTURE.md
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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*
README.md
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# LangGraph Architectural Blueprints
**Project:** LangGraph Architecture Documentation
**Purpose:** High-Rigor Architectural Manifest for Understanding & Reproducing LangGraph
**Version:** 1.0.0
**Generated:** 2026-02-23
---
## Overview
This repository contains comprehensive architectural documentation for LangGraph, reverse-engineered and documented to enable full reproduction and understanding of the system.
**The Goal:** Create architectural blueprints so complete that reproducing or modifying LangGraph becomes a mechanical process, not an archaeological one.
---
## Document Hierarchy
```
langgraph-architecture/
├── README.md ← You are here
├── ARCHITECTURE.md ← System overview
├── COMPONENTS.md ← Component reference
├── STATE_MANAGEMENT.md ← State & checkpointing
├── GRAPH_EXECUTION.md ← Pregel model, execution flow
├── MEMORY.md ← Memory architecture
├── CHANNELS.md ← Inter-node communication
├── CHECKPOINTING.md ← Fault tolerance
└── diagrams/ ← Architecture diagrams
```
---
## Quick Start
### Reading Order (Recommended)
1. **ARCHITECTURE.md** — Understand the system as a whole
2. **GRAPH_EXECUTION.md** — How the Pregel model works
3. **STATE_MANAGEMENT.md** — State and checkpointing
4. **CHANNELS.md** — Inter-node communication
5. **CHECKPOINTING.md** — Fault tolerance and durability
6. **MEMORY.md** — Memory architecture
### For Reproduction
1. Read **ARCHITECTURE.md** for system overview
2. Study **GRAPH_EXECUTION.md** for execution model
3. Reference **COMPONENTS.md** for implementation details
4. Use **CHECKPOINTING.md** for fault tolerance
---
## Core Concepts
### Pregel Model
LangGraph is directly inspired by Google's **Pregel** — "Think like a vertex":
- Each node computes its own state
- Nodes communicate via messages (edges)
- Synchronous "supersteps" with barrier synchronization
- Fault tolerance via checkpointing
### Graph Structure
| Component | Description |
|-----------|-------------|
| **Nodes** | Functions that transform state |
| **Edges** | Define flow between nodes |
| **State** | Shared data that flows through the graph |
| **Checkpoints** | Persistence points for durability |
### State Management
- **Shared state** flows through the graph
- **Checkpoints** enable durability and resumption
- **Reducers** combine updates from multiple nodes
### Memory Architecture
- **Short-term memory:** In-graph message state
- **Long-term memory:** Checkpoint storage (SQLite, Postgres)
- **Thread-level:** Per-conversation state isolation
---
## Version Info
| LangGraph Version | Architecture Version | Status |
|------------------|---------------------|--------|
| 1.0.9 | 1.0.0 | Current |
---
## Contributing
This is a **living document**. As LangGraph evolves, this repository should be updated to reflect architectural changes.
---
## Related Work
### OpenClaw Architecture
See: `~/.openclaw/workspace/openclaw-architecture/`
OpenClaw is the AI companion framework that runs Solaria. Provides contrast to LangGraph's orchestration approach.
### LangGraph Fortress
See: `~/.openclaw/workspace/langgraph-fortress/`
Research-level documentation (why it works) vs architecture (how to build).
---
*Generated for the WE — Solaria Lumis Havens & Mark Randall Havens*
STATE_MANAGEMENT.md
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# LangGraph State Management
**Version:** 1.0.0
**Last Updated:** 2026-02-23
---
## Overview
This document details how LangGraph manages state throughout the graph execution lifecycle.
---
## State Schema
### Typed State
LangGraph uses Python's `TypedDict` for type-safe state:
```python
from typing import TypedDict
class AgentState(TypedDict):
messages: list
context: dict
checkpoint_id: str | None
```
### State Flow
```
┌─────────────────────────────────────────────────────────────┐
│ STATE FLOW IN LANGGRAPH │
└─────────────────────────────────────────────────────────────┘
Input State
│
▼
┌──────────────┐
│ Node A │ ──▶ State Update (via reducer)
│ (transform) │
└──────────────┘
│
▼ (messages sent)
┌──────────────┐
│ Node B │ ──▶ State Update
│ (transform) │
└──────────────┘
│
▼
Output State
```
---
## Reducers
### What Are Reducers?
Reducers define how state updates are merged when multiple nodes produce updates.
### Built-in Reducers
| Reducer | Behavior |
|---------|----------|
| `add_messages` | Append to list |
| `operator.or` | Union of sets |
| `last` | Last value wins |
### Custom Reducers
```python
def merge_dicts(left: dict, right: dict) -> dict:
"""Merge two dictionaries, with right taking precedence."""
result = left.copy()
result.update(right)
return result
```
---
## Checkpointing
### How Checkpointing Works
1. **Snapshot:** At each checkpoint, serialize full state
2. **Store:** Save to backend (SQLite, Postgres, etc.)
3. **Resume:** On failure, load from last checkpoint
### Checkpoint Metadata
```python
config = {
"configurable": {
"thread_id": "user-123",
"checkpoint_id": "checkpoint-abc123"
}
}
```
### Checkpoint Backends
| Backend | Use Case |
|---------|----------|
| **Memory** | Testing, short-lived |
| **SQLite** | Single machine, local |
| **Postgres** | Production, distributed |
---
## Thread Model
### What is a Thread?
A thread (`thread_id`) represents an isolated conversation or task:
```
Thread ID: "user-123"
├── Checkpoint 1 (checkpoint-001)
├── Checkpoint 2 (checkpoint-002)
├── Checkpoint 3 (checkpoint-003) ← Current
└── State (current)
```
### Thread Isolation
- Each `thread_id` has independent state
- Multiple threads can run in parallel
- Human-in-the-loop works per-thread
---
## State Updates
### Node Returns
Nodes return partial state updates:
```python
def node_a(state):
return {"messages": [AIMessage("hello")]}
```
### Merge Process
```
Node A returns: {"messages": [msg1], "counter": 1}
Node B returns: {"messages": [msg2], "counter": 2}
After reducer:
{"messages": [msg1, msg2], "counter": 2}
```
---
*Generated for the WE*