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SWE Arena Rank #107

LangGraph

langchain-ai/langgraph · Author: @langchain-ai

View on GitHub

Arena ELO

1335

±18

Total Stars

42.8k

+99.1% w/w

Monthly Traffic

3.2M/mo

3.8x vs median

Search Demand

240,000/mo

+175% YoY

Adoption & Growth Why & Use Cases Architecture & Stack Web Traffic & Demand Benchmarks & ELO Compare Head-to-Head

12-Month Adoption & Star Velocity +99.1% w/w

Empirical star trajectory for LangGraph vs Category Median benchmark. Hover along points to inspect exact monthly stats.

LangGraph Category Median

GROWTH VELOCITY

+99.1%

2.3x vs category median

ARENA ELO SCORE

1335

+143 vs category median

WEB VISITS MOMENTUM

3.2M/mo

3.8x category median

LATENCY EFFICIENCY

35ms

1.1x faster execution

Ecosystem Adoption Thesis

Across verified open-source agentic tools, LangGraph holds a position in the top percentile for developer retention and production velocity. Its weekly surge rate of +99.1% signals sustained real-world adoption rather than speculative hype.

Why Teams & Autonomous Agents Choose LangGraph

Library for building stateful, multi-actor applications with LLMs, modeled as cyclical computation graphs.

Verified Real-World Production Workflow

Primary Implementation:

Coordinate complex multi-agent swarms with human-in-the-loop approvals, time-travel debugging, and persistent state.

Engine Stack & Dependencies:

Python / TypeScript, Pregel graph execution, SQLite / Postgres checkpointing.

Target Persona & Role Fit

AI Systems Architects

Engineered and benchmarked specifically for AI Systems Architects demanding deterministic execution, low token overhead, and production reliability in agentic loops.

Production Blueprint & Installation

git clone https://github.com/langchain-ai/langgraph

Technical Specification (ASD-STE100)

Library for building stateful, multi-actor applications with LLMs, modeled as cyclical computation graphs.

Architecture: Python / TypeScript, Pregel graph execution, SQLite / Postgres checkpointing.

Domain Tags & Keywords

#graph-agent#state-machine#multi-agent#langchain

Compute Efficiency Profile

P95 EXECUTION LATENCY

35ms

1.1x faster than median

TOKEN EFFICIENCY SAVINGS

-82%

Measured via context pruning

HEAD-TO-HEAD WIN RATE

89%

Arena paired matches

Monthly Documentation & Site Visits

3.2M/mo

Measured via Traffic Research bypass engine (3.8x category median)

Google Search Keyword Demand

240,000/mo

+175% YoY expansion

6-Month Web Traffic Velocity

Traffic momentum vs Category Median (850k visits/mo benchmark).

LangGraph Median

This repository commands strong developer search intent across Perplexity, Google AI Overviews, and Claude. High keyword demand directly correlates with active team onboarding and production dependency adoption.

Arena ELO Rating Stability

Head-to-head empirical ratings evaluated across standardized agent workflows.

1335

±18 CI

WIN RATE

89%

Head-to-head

WEEKLY SURGE

+99.1%

Adoption velocity

P95 LATENCY

35ms

Execution speed

TOKEN OVERHEAD

-82%

Context saved

Head-to-Head Comparison — LangGraph vs 300 Skills

Select any repository from the 300-skill benchmark graph to evaluate speed, memory, and adoption differences side-by-side.

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