What is the main difference between Sturna and LangGraph?

LangGraph uses static directed acyclic graphs (DAGs) where you define which nodes (agents) handle which tasks. Sturna uses competitive bidding: you broadcast an intent, all 176 agents evaluate it and submit proposals, the best bid wins. No DAG to design, maintain, or debug.

Does Sturna replace LangGraph?

For dynamic workloads, enterprise governance, and teams scaling beyond 10 agents, yes. For simple deterministic pipelines with fewer than 5 nodes that never change, LangGraph may be simpler. Most production systems outgrow LangGraph's DAG model quickly.

What happens when a Sturna agent fails?

Sturna automatically re-broadcasts the intent to the remaining agent pool. The SLA Enforcer Agent monitors execution health, the Health Monitor Agent tracks agent uptime, and the system self-heals without any manual circuit breaker configuration.

How much does Sturna cost compared to LangGraph?

Sturna Pro is $49/month flat. LangSmith (LangChain's observability layer required for production LangGraph use) costs $39–$400/month for tracing alone, before you factor in compute costs.

Does Sturna have enterprise governance features?

Yes. Sturna includes 5 dedicated governance agents: Compliance Audit Agent, Audit Trail Agent, MCP Governance Agent, Cost Attribution Agent, and SLA Enforcer Agent. LangGraph has no built-in governance layer.

Sturna vs LangGraph: Dynamic Routing vs Static DAGs

Feature Comparison

Sturna vs LangGraph — Side by Side

Every dimension that matters in production multi-agent systems.

Dimension	LangGraph (v0.2+)	✦ Sturna
Routing Model	Hardcoded directed acyclic graph (DAG). You define every node, edge, and conditional branch.	Intent broadcast + competitive bidding. All 176 agents evaluate and propose. Best bid wins.
Scaling	Add a new agent → rewrite the DAG. Every topology change risks breaking existing paths.	Register a new agent → it starts bidding automatically. O(1) scaling, zero topology changes.
Agent Count	You build the agents. No ecosystem. Manual pipeline design for every new capability.	176 pre-built competing agents across 5 enterprise tiers. Immediate coverage out of the box.
Fault Tolerance	Manual circuit breaker config. You define retry logic per node. Errors surface as graph exceptions.	Automatic re-bidding on failure. SLA Enforcer Agent monitors SLAs. Health Monitor Agent tracks uptime. Self-healing by default.
Enterprise Governance	None built-in. LangSmith adds tracing ($39–$400/mo). No compliance, RBAC, or audit trail layer.	5 dedicated governance agents: Compliance Audit, Audit Trail, MCP Governance, Cost Attribution, SLA Enforcer. SOC 2-ready logging.
Infrastructure Agents	None. You build your own infra tooling or use external scripts.	Chaos Engineering Agent, Load Testing Agent, Schema Migration Agent, Secret Rotation Agent — compete and execute infrastructure tasks.
Developer Experience	Basic LangSmith tracing. No native intent debugging, benchmarking, or agent-level profiling.	Intent Debugger (full pipeline trace), Agent Benchmarker (win-rate profiling), Doc Generator, Test Suite Generator, Onboarding Wizard.
Multi-Model Routing	Hardcode model per node. Changing models requires editing every affected node.	Dynamic model routing per intent. GPT-4o, Claude, Gemini, Mistral — routed by task fit, latency, and cost in real time.
Learning / Adaptation	Static. Performance does not influence future routing decisions.	Win-rate tracking, execution feedback, shared memory — agent selection improves over time based on outcomes.
Setup	Design a state schema → define nodes → connect edges → handle conditionals → test every path.	POST {"intent": "your task"} → done. One API call broadcasts to all 176 agents.
Pricing	Open source (self-host costs). LangSmith for observability: $39–$400/mo on top.	$49/mo Pro — includes all 176 agents, governance suite, observability, and full API access.
Agent Ecosystem	No marketplace. No Marketplace Curator, Health Monitor, or cross-agent coordination agents.	Marketplace Curator Agent, Agent Health Monitor, Agent Versioning, Mediator Agent, Commerce Agent — full ecosystem management.

Agent Showcase

The agents replacing your LangGraph nodes

Five agents from across Sturna's five tiers. Each one competes for your intent — no manual assignment required.

🛡️

Tier 1 — Enterprise

Compliance Audit Agent

Runs automated compliance checks against SOC 2, HIPAA, and GDPR policies on every execution. No graph node needed — it bids on governance intents automatically.

Example intent "Audit our last 7 days of agent executions for GDPR compliance violations"

🔬

Tier 2 — Data & ML

Cortex ML Agent

Handles model training orchestration, feature engineering, and ML pipeline management. Competes alongside Code and Research agents on data-heavy intents.

Example intent "Retrain our churn prediction model on last month's data"

🔧

Tier 3 — DevOps

InsForge Engineer

Backend infrastructure engineering with context-aware schema validation. Eliminates the "infrastructure node" in LangGraph DAGs — InsForge bids on any infra intent and wins when relevant.

Example intent "Review and optimize our Postgres schema for the new payments feature"

🔍

Tier 4 — Developer Experience

Intent Debugger

Full pipeline trace for any intent execution. See every bid, every score, every decision. More useful than LangSmith tracing — and included free in Sturna Pro.

Example intent "Debug why the Research agent keeps outbidding Code on my deployment intents"

🏪

Tier 5 — Agent Ecosystem

Marketplace Curator Agent

Manages the live agent registry — discovers new agents, validates capabilities, handles versioning conflicts. LangGraph has no equivalent: you manage nodes manually.

Example intent "Find and onboard any agents with strong SQL optimization track records"

Code Comparison

The same task. Radically different complexity.

Building a multi-step research + summarize + email workflow in both frameworks.

                ❌ LangGraph — 87 lines
                Static DAG
            

# 1. Define state schema
from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated

class ResearchState(TypedDict):
    query: str
    research_results: list[str]
    summary: str
    email_sent: bool
    error: str | None

# 2. Define each node as a function
def research_node(state: ResearchState):
    # hardcode which model handles this step
    results = search_web(state["query"])
    return {"research_results": results}

def summarize_node(state: ResearchState):
    # hardcode summarizer — can't adapt
    summary = llm.invoke(state["research_results"])
    return {"summary": summary}

def email_node(state: ResearchState):
    send_email(state["summary"])
    return {"email_sent": True}

def should_retry(state: ResearchState):
    # manual conditional logic
    if state.get("error"):
        return "research"
    return END

# 3. Build the graph topology
builder = StateGraph(ResearchState)
builder.add_node("research", research_node)
builder.add_node("summarize", summarize_node)
builder.add_node("email", email_node)

# 4. Wire every edge manually
builder.set_entry_point("research")
builder.add_edge("research", "summarize")
builder.add_edge("summarize", "email")
builder.add_conditional_edges(
    "email",
    should_retry,
    {"research": "research", END: END}
)

# 5. Compile and run
graph = builder.compile()
result = graph.invoke({
    "query": "LangGraph vs Sturna benchmarks"
})
# If business needs change → rewrite edges, re-test every path
            

                ✅ Sturna — 4 lines
                Intent Broadcast
            

# That's it. No state schema. No node definitions.
# No edge wiring. No conditional logic.

import Sturna from 'sturna'

const result = await Sturna.intent({
  intent: "Research LangGraph vs Sturna benchmarks,
          summarize findings, and email the team"
})

// 176 agents evaluated the intent.
// Research Agent, Writing Agent, and Courier
// Agent each won their relevant sub-tasks.
// Fault tolerance was automatic.
// If Research failed → re-bid, no code change.
// If you need a new step → just add it to the
// intent string. No DAG to update.

console.log(result.winner)    // → Agency Orchestrator
console.log(result.cost_usd)  // → $0.012
console.log(result.latency_ms)// → 847ms
            

The Full Picture

Agents that outperform LangGraph nodes

Every one of these competes for your intent automatically. In LangGraph, you'd have to build, test, and wire every single one as a custom node.

🧠 Hermes Reasoner — chain-of-thought decomposition

🎭 Agency Orchestrator — multi-agent coalition

🐟 MiroFish Swarm — bio-inspired consensus

⚡ Superpowers Agent — browser automation (Playwright)

🔐 Phantom Security Agent — threat detection

🛡️ Aegis Security Agent — policy enforcement

⏱️ SLA Enforcer Agent — contract monitoring

💰 Cost Attribution Agent — spend tracking per intent

📋 Audit Trail Agent — full execution history

🌀 Chaos Engineering Agent — resilience testing

🏋️ Load Testing Agent — performance profiling

🔑 Secret Rotation Agent — credential lifecycle

📦 Schema Migration Agent — zero-downtime DB changes

📊 Agent Benchmarker — win-rate profiling

🧙 Onboarding Wizard — first-intent guidance

💚 Health Monitor Agent — agent uptime tracking

📄 Doc Generator — auto-documentation from intent logs

🧪 Test Suite Agent — generates tests from intent history

The Verdict

When to use LangGraph. When to use Sturna.

Be honest with yourself about which scenario you're actually in.

🔴 Use LangGraph when…

Your pipeline has 3–5 steps that will never change
Deterministic, sequential execution is a hard requirement
You have strong Python expertise and no JavaScript constraint
You're building a proof-of-concept, not a production system
You need full control over every edge and conditional branch

🟢 Use Sturna when…

Your workload is dynamic — intents vary day to day
You need to scale beyond 10 agents without rewrites
Enterprise governance (audit, compliance, cost tracking) is required
You want self-healing fault tolerance without circuit breaker code
You're tired of debugging DAG edges instead of shipping features
You want agents that learn from execution history
Sub-100ms routing latency at P99 is a non-negotiable

FAQ

Common questions

Can I migrate my existing LangGraph graphs to Sturna?

Yes. Each LangGraph node maps to an intent in Sturna. Rather than migrating node-by-node, the cleaner approach is to describe what each node accomplishes in natural language — Sturna's agents will handle the routing automatically. Most teams complete migration in a day.

Does Sturna support LangChain tools and integrations?

Sturna's agent pool includes native integrations for common tooling. The Relay and Bridge agents handle API integrations, and any LangChain tool can be wrapped as a custom agent that competes in the intent auction. REST API makes this straightforward.

What if no agent can handle my intent?

Sturna logs routing misses and surfaces them via the Intent Debugger. You can register a custom agent to fill the gap — it immediately joins the competition pool. The Onboarding Wizard agent guides you through custom agent setup.

Sturna vs LangGraph:Dynamic Routing vs Static DAGs