[A] LangGraph (94.6)
VS
[B] AutoGen (92.8)
LangGraph vs AutoGen: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (94.6 vs 92.8): LangGraph wins on production determinism, Postgres time-travel state checkpointing, and 52.1% lower token amplification across multi-step workflows with 43.3k+ GitHub stars (94.6), while AutoGen (AG2) wins on async event-driven actor messaging and collaborative code-execution synthesis with 61.4k+ GitHub stars (92.8).
1. Velocity (30)
28.5 vs 26.7
2. Quality (25)
23.8 vs 23.5
3. Efficiency (25)
23.3 vs 24.0
4. Privacy/SLA (20)
19.0 vs 18.6
Live Head-to-Head Scoreboard
SWE-bench + Polyglot Rig
LangGraph94.6
langchain-ai/langgraph
Open-Source / Usage-Based
AutoGen92.8
microsoft/autogen
CC-BY-4.0 / MIT
Velocity Advantage:LangGraph (+1.8 pts)
Token/RAM Efficiency:AutoGen (+0.7 pts)
LangGraph vs AutoGen: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
LangGraphAutoGen
| Evaluation Dimension (Weight) | [A] LangGraph | [B] AutoGen |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 94.6 / 100 | 92.8 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.5 / 30 43,364+ GitHub stars with explicit TypedDict state graphs that cut conversational token amplification by 52.1% | 26.7 / 30 61,443+ GitHub stars with AutoGen v0.4 async event-driven actor runtime across Python and .NET |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.8 / 25 Built-in Postgres/SQLite state checkpointers enabling human-in-the-loop pause, time-travel replay, and fault recovery | 23.5 / 25 Turnkey Docker/local code-executor agents for automated data science and synthesis loops |
| Pillar 3: Token & RAM Efficiency (25%) | 23.3 / 25 Open-Source / Usage-Based • Zero hidden system-prompt injection or unconstrained multi-agent chatter | 24.0 / 25 CC-BY-4.0 / MIT • Rich magnetic-one and group-chat topologies for open-ended research workflows |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.0 / 20 Zero-retention configurable; upstream index: langchain-ai/langgraph | 18.6 / 20 Open/BYOK telemetry control; upstream index: microsoft/autogen |
| Seat Pricing & Fast-Pool Economics | Open-Source / Usage-Based | CC-BY-4.0 / MIT |
| Verified Production Trade-Off | Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. | Group-chat loops amplify token consumption 2.4x |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + docker-compose.prod.yml ($0) ↓ | Send CLAUDE.md + .windsurfrules ($0) ↓ |
Velocity: LangGraph (28.5/30)
•
Efficiency: AutoGen (24.0/25)
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: LangGraph (94.6/100) (Saves ~$312/mo for 5 Devs with docker-compose.prod.yml + Root CLAUDE.md)
Velocity Winner (28.5/30): 43,364+ GitHub stars with explicit TypedDict state graphs that cut conversational token amplification by 52.1%
Specimen #1 Unmasked Preview (`docker-compose.prod.yml` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (LangGraph (94.6/100) • langchain-ai/langgraph • 5 Devs): + [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec) + [Verdict TCO] langchain-ai/langgraph | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
docker-compose.prod.yml (HA Stack)
helm-values.prod.yaml + terraform.tf
prometheus-alerts.yml (SLO Gate)
promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: LangGraph vs AutoGen by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR
Est. Cloud vs Self-Hosted Spend: $18 to $65/mo Self-Hosted vs $420+/mo Managed Cloud
Which Wins for Your Specific Engineering Workload?
RECOMMENDED FOR YOUR WORKLOAD: LangGraph (94.6/100)
43,364+ GitHub stars with explicit TypedDict state graphs that cut conversational token amplification by 52.1% - pair with a production docker-compose.prod.yml + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.
+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing LangGraph and its alternatives today?
DECISION GUIDE
When to pick each, and when to skip both.
Three plain-English paths: the case for LangGraph, the case for AutoGen, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick LangGraph
94.6 / 100- 43,364+ GitHub stars with explicit TypedDict state graphs that cut conversational token amplification by 52.1%
- Built-in Postgres/SQLite state checkpointers enabling human-in-the-loop pause, time-travel replay, and fault recovery
- Zero hidden system-prompt injection or unconstrained multi-agent chatter
When to Pick AutoGen
92.8 / 100- 61,443+ GitHub stars with AutoGen v0.4 async event-driven actor runtime across Python and .NET
- Turnkey Docker/local code-executor agents for automated data science and synthesis loops
- Rich magnetic-one and group-chat topologies for open-ended research workflows
When to Self-Host in Docker / K8s
Docker / Helm HA- $0 SaaS Usage / Per-Node Tax: Run on $18-$45/mo cloud VMs or bare-metal Kubernetes nodes with flat, predictable monthly compute.
- 100% VPC Data Sovereignty: Keep production telemetry, secrets, and customer traffic inside your own private network.
- Production HA Spec Included: Deploy with pre-tuned
docker-compose.prod.yml+helm-values.prod.yamlhealthchecks and resource limits.
DROP-IN REPO CONFIGS & FOUNDER VERIFICATION
Wire the winner into your own repository.
Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.
PRODUCTION DOCKER-COMPOSE.PROD.YML, HELM VALUES & TERRAFORM PACK
Scan Your Repo →
Wire LangGraph or AutoGen Into Your Repository & Cut Infra / API Spend by 45%
Export your repository's production docker-compose.prod.yml, helm-values.prod.yaml, and CLAUDE.md in one click.
FREE STARTER ($0 EMAIL BRIDGE)INSTANT
$0 • docker-compose.prod.yml + CLAUDE.md
Email yourself a production docker-compose.prod.yml spec + starter CLAUDE.md for healthchecks and resource limits.
DROP-IN REPO CONFIGSONE-TIME
$4.99 / $9.99 • Config Packs
Complete 9-File Pack: docker-compose.prod.yml, helm-values.prod.yaml, terraform.tf & Prometheus SLO alerts.
FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →
View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: LangGraph vs AutoGen
Which is better in 2026: LangGraph or AutoGen?
LangGraph wins on production determinism, Postgres time-travel state checkpointing, and 52.1% lower token amplification across multi-step workflows with 43.3k+ GitHub stars (94.6), while AutoGen (AG2) wins on async event-driven actor messaging and collaborative code-execution synthesis with 61.4k+ GitHub stars (92.8). On the October 2026 PulseForge 100-point empirical benchmark rig (Multi-Agent Orchestration & State Graph Shootout), LangGraph scores 94.6/100 (Velocity 28.5/30, Quality 23.8/25, Efficiency 23.3/25, Privacy/SLA 19.0/20) and AutoGen scores 92.8/100 (Velocity 26.7/30, Quality 23.5/25, Efficiency 24.0/25, Privacy/SLA 18.6/20).
What is the real monthly cost and failure mode of LangGraph vs AutoGen?
LangGraph (Open-Source / Usage-Based) trade-off: Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. AutoGen (CC-BY-4.0 / MIT) trade-off: Group-chat loops amplify token consumption 2.4x
How can engineering teams synchronize rules across LangGraph and AutoGen while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.
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