LangGraph Cycles and Recursion Limits — How to Control Agent Loops
Your ReAct agent handles simple questions just fine. It calls a tool, reads the result, and replies. But then someone asks something vague. The LLM fires the same tool over and over. Twenty-five rounds later, your terminal blows up with GraphRecursionError: Recursion limit of 25 reached without hitting a stop condition. If that sounds familiar, this guide is for you. You’ll learn why it happens, how to tame it, and how to build agents that quit on their own instead of crashing.
What Are Cycles in a LangGraph Graph?
A cycle is a path that sends the graph back to a node it already ran. Most graph tools ban this — they only allow DAGs (directed acyclic graphs) where data flows one way and each node runs once. LangGraph breaks that rule on purpose because agent loops need it.
Before You Start
- Python: 3.10+
- Package: langgraph 0.4+
- Install:
pip install langgraph - Background: LangGraph basics — nodes, edges, state, conditional edges
- Time: 20–25 minutes
Here’s the simplest cycle you can build. The increment node adds 1 to a counter. A conditional edge checks — if the count is under 3, go back to increment. If not, go to END.
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
class CounterState(TypedDict):
count: int
message: str
def increment(state: CounterState) -> dict:
new_count = state["count"] + 1
return {"count": new_count, "message": f"Step {new_count}"}
def should_continue(state: CounterState) -> str:
if state["count"] < 3:
return "increment"
return END
graph = StateGraph(CounterState)
graph.add_node("increment", increment)
graph.add_edge(START, "increment")
graph.add_conditional_edges("increment", should_continue)
app = graph.compile()
result = app.invoke({"count": 0, "message": ""})
print(result)
{'count': 3, 'message': 'Step 3'}
The node ran three times. After each run, the router checked the counter and looped back — until the count hit 3 and the graph stopped. That’s a cycle: a node that visits itself again through a conditional edge.
Key Insight: Cycles are what make LangGraph agents tick. Without them, every node runs once and the graph ends. With them, an agent can think, act, check the result, and decide to keep going — the core of every ReAct loop.
Why Do Agents Need Cycles?
If cycles are risky, why use them at all? Because three key agent patterns fall apart without them.
The ReAct loop. The agent picks a tool, runs it, reads what came back, and then picks another tool or writes a final answer. That decide-act-observe cycle keeps going until the agent has enough info.
Draft-and-refine loops. One node writes a draft. Another node scores it. If the score is too low, the graph sends it back for another try. The loop runs until the output clears a quality bar.
Page-by-page data fetching. An agent hits an API, sees there are more pages, and loops back to grab the next one. It stops when there’s nothing left to fetch.
All three need a cycle. And all three can spin forever if you skip the exit check.
Warning: Every cycle needs a way out. If your conditional edge can send the graph back to an earlier node, it must also be able to send it to END. Without that, the graph loops until it hits the limit and crashes.
When Should You Skip Cycles?
Not every job calls for a loop. If your pipeline runs a set series of steps — like extract, clean, load — a straight-line graph with plain edges is the right call. No cycle needed.
If you’re handling a batch of items that don’t depend on each other, use a map-reduce pattern instead. Fan out to parallel nodes, then combine the results. It’s faster and kills the risk of runaway loops.
A handy rule: if you know the step count when you build the graph, skip the cycle. Cycles pay off when the number of rounds depends on what happens at runtime — LLM choices, quality scores, or API pagination.
How Does recursion_limit Work?
LangGraph counts each node run as one step. The recursion_limit setting caps the total steps a graph can take before it throws GraphRecursionError. The default cap is 25 steps.
You pass it through the config dict when you call invoke() or stream(). Here are three ways to set it.
# Default: 25 steps
result = app.invoke({"count": 0, "message": ""})
# Custom: allow up to 50 steps
result = app.invoke(
{"count": 0, "message": ""},
{"recursion_limit": 50}
)
# Tight limit: only 5 steps allowed
result = app.invoke(
{"count": 0, "message": ""},
{"recursion_limit": 5}
)
Think of recursion_limit as a circuit breaker. It doesn’t steer your graph’s logic — it’s a safety net that kills the run if your logic fails to stop the loop on its own.
Quick check: Say your graph has a 4-node cycle and you set the limit to 25. How many full passes can it make? About 6 — because 25 / 4 = 6.25, and it truncates to 6 full passes.
The same cap works with stream(). Each event the stream yields still counts as a step.
# Streaming also respects recursion_limit
for event in app.stream(
{"count": 0, "message": ""},
{"recursion_limit": 30}
):
print(event)
Tip: Set recursion_limit by hand for every graph you ship. Don’t lean on the default of 25. If your agent usually needs 10 tool calls, pick 30–40. If it’s a short 3-step pipeline, pick 10. Spelling it out tells the next dev what “normal” looks like for this graph.
What Is GraphRecursionError?
When the step count goes past the limit, LangGraph throws GraphRecursionError. It’s a plain Python exception — you catch it with try/except like any other.
Let’s trigger one on purpose. This graph has no way out — the router always sends it back to loop_node.
from langgraph.errors import GraphRecursionError
class LoopState(TypedDict):
value: int
def loop_node(state: LoopState) -> dict:
return {"value": state["value"] + 1}
def should_loop(state: LoopState) -> str:
return "loop_node" # always loops — no exit!
bad_graph = StateGraph(LoopState)
bad_graph.add_node("loop_node", loop_node)
bad_graph.add_edge(START, "loop_node")
bad_graph.add_conditional_edges("loop_node", should_loop)
bad_app = bad_graph.compile()
try:
bad_app.invoke({"value": 0}, {"recursion_limit": 5})
except GraphRecursionError as e:
print(f"Caught it: {e}")
Caught it: Recursion limit of 5 reached without hitting a stop condition. If you are using a graph with a cycle, set a higher recursion limit.
The error message tells you what limit you hit and hints that you should raise it. But if your graph truly has no exit, bumping the limit just delays the crash. The real fix is to add logic that routes to END at some point.
How Do You Debug Runaway Loops? A Three-Step Method
Your graph just threw GraphRecursionError and you didn’t expect it. Here’s a quick checklist that catches the bug 90% of the time.
Step 1 — Print the routing choices. Drop a print statement inside your router. You’ll see right away whether it ever picks END.
def should_continue_debug(state: CounterState) -> str:
decision = "increment" if state["count"] < 3 else END
print(f" count={state['count']}, routing to: {decision}")
return decision
Step 2 — Check that the node updates the right field. The top cause of endless loops isn’t bad routing logic — it’s a node that never changes the value the router looks at. If your router reads state["count"] but the node forgets to bump count, the check stays true forever.
Try to guess the outcome: what happens when you run the graph below? The node returns {"message": "done"} but never touches count.
def broken_node(state: CounterState) -> dict:
return {"message": "done"} # oops — count never changes
def check_count(state: CounterState) -> str:
return END if state["count"] >= 3 else "broken_node"
Answer: endless loop. The router checks count, but count sits at 0 forever because the node never moves it.
Step 3 — Watch for off-by-one mistakes. If the node adds 1 and the router checks < 3, the cycle runs 3 times (count goes 0 → 1 → 2 → 3, exits when count hits 3). Make sure the boundary matches what you want.
Key Insight: Most runaway loops come from state bugs, not routing bugs. The router usually has the right condition. The problem is that the node it loops back to never changes the field the condition checks.
Exit Strategy 1 — How Does a State Counter Work?
The simplest way to stop a loop is a counter in your state. Each pass bumps the counter. When it reaches a cap, the router sends the graph to END.
Here’s the pattern for a real agent loop. The agent_step node tracks rounds in a counter field and writes a fallback message when it hits the cap. The route_agent function checks a done flag and either loops or exits.
class AgentState(TypedDict):
query: str
iterations: int
max_iterations: int
result: str
done: bool
def agent_step(state: AgentState) -> dict:
iteration = state["iterations"] + 1
if iteration >= state["max_iterations"]:
return {
"iterations": iteration,
"result": "Reached max iterations. Best answer so far.",
"done": True,
}
return {
"iterations": iteration,
"result": f"Working... (iteration {iteration})",
"done": False,
}
def route_agent(state: AgentState) -> str:
if state["done"]:
return END
return "agent_step"
Build and run it with a 3-round cap.
agent_graph = StateGraph(AgentState)
agent_graph.add_node("agent_step", agent_step)
agent_graph.add_edge(START, "agent_step")
agent_graph.add_conditional_edges("agent_step", route_agent)
agent_app = agent_graph.compile()
result = agent_app.invoke({
"query": "What is the capital of France?",
"iterations": 0,
"max_iterations": 3,
"result": "",
"done": False,
})
print(f"Iterations: {result['iterations']}")
print(f"Result: {result['result']}")
Iterations: 3
Result: Reached max iterations. Best answer so far.
No crash, no error. The agent stopped after 3 rounds. And since max_iterations lives in the state, you can tune it per request — tight caps for easy questions, bigger caps for hard research tasks.
Exit Strategy 2 — How Does a Quality Gate Work?
What if you can’t predict how many rounds the agent needs? Sometimes the right answer isn’t “stop after N loops” but “stop when the result is good enough.”
Picture this: a graph writes a draft and scores it. If the score clears 0.8, it’s done. If not, it loops back for another try. But what if the score never clears 0.8? That’s where a dual-exit design helps.
The generate function fakes steady improvement — each round adds 0.3 to the score. The route_refinement function checks two things: did the score pass the bar, OR did the loop hit the max? Two exits cover the happy path and the worst case.
class RefinementState(TypedDict):
draft: str
score: float
iterations: int
max_iterations: int
def generate(state: RefinementState) -> dict:
iteration = state["iterations"] + 1
score = 0.3 * iteration # simulated improvement each round
return {
"draft": f"Draft v{iteration}",
"score": score,
"iterations": iteration,
}
def route_refinement(state: RefinementState) -> str:
if state["score"] >= 0.8:
return END
if state["iterations"] >= state["max_iterations"]:
return END
return "generate"
refine_graph = StateGraph(RefinementState)
refine_graph.add_node("generate", generate)
refine_graph.add_edge(START, "generate")
refine_graph.add_conditional_edges("generate", route_refinement)
refine_app = refine_graph.compile()
result = refine_app.invoke({
"draft": "",
"score": 0.0,
"iterations": 0,
"max_iterations": 5,
})
print(f"Final draft: {result['draft']}")
print(f"Score: {result['score']}")
print(f"Iterations used: {result['iterations']}")
Final draft: Draft v3
Score: 0.9
Iterations used: 3
On round 3, the score hit 0.9 (0.3 × 3). That beat the 0.8 bar, so it stopped early. The max cap never fired — but it would have if the bar were out of reach.
Tip: Always pair a quality gate with a max-round guard. A quality check on its own can spin forever if the output never gets good enough. The max cap catches that edge case.
Exit Strategy 3 — How Do You Catch GraphRecursionError?
What about agents whose loop count is truly unpredictable? Catch the error at the call site and return a fallback. This works great in API endpoints and chatbot backends where an uncaught exception means a 500 error for the user.
from langgraph.errors import GraphRecursionError
def run_agent_safely(app, inputs, limit=25):
try:
result = app.invoke(inputs, {"recursion_limit": limit})
return result
except GraphRecursionError:
return {
"result": "I couldn't complete the task within "
"the step limit. Try a simpler question.",
"error": "recursion_limit_exceeded",
}
output = run_agent_safely(bad_app, {"value": 0}, limit=10)
print(output["result"])
I couldn't complete the task within the step limit. Try a simpler question.
Instead of a 500 error, the user gets a clear message. This is better than letting the exception bubble up — it gives you control over what the user sees.
Which Cycles Are Safe — and Which Are Dangerous?
Not all cycles carry the same risk. Here’s a quick cheat sheet.
| Cycle Type | Example | Risk | Why |
|---|---|---|---|
| Counter-bounded | Retry up to 3 times | Low | Hard cap forces an exit |
| Quality-gated + cap | Refine until score > 0.8, max 5 | Low | Two exit paths cover all cases |
| LLM-decided | Agent calls tools until it decides to stop | Medium | LLM might never decide to stop |
| No exit at all | Node always routes back to itself | Critical | Guaranteed crash |
| Hidden multi-node cycle | A → B → C → A with no exit | High | Easy to miss in big graphs |
The bottom line: if a human wrote the exit check with a hard cap, you’re safe. If the LLM picks when to stop and there’s no backup cap, you’re gambling.
Warning: LLM-driven loops need a backup cap. LLMs sometimes call the same tool over and over with slightly different inputs, hoping for a different result. Always add a max_iterations check next to the LLM’s own decision so you can cut the loop short.
Full Example — How Do You Build a Real Task Processing Loop?
Let’s tie all three safety layers together: a state counter, a quality check (all tasks done), and recursion_limit at the call site.
The agent works through a list of tasks. Each pass handles one task. The work_on_task node grabs the first unfinished task, marks it done, and bumps the counter. When nothing is left, it flips status to "all_done". The route_tasks function checks three things before choosing to loop or exit.
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
class TaskState(TypedDict):
tasks: list[str]
completed: list[str]
iterations: int
max_iterations: int
status: str
def work_on_task(state: TaskState) -> dict:
remaining = [t for t in state["tasks"] if t not in state["completed"]]
if not remaining:
return {"iterations": state["iterations"] + 1, "status": "all_done"}
current = remaining[0]
new_completed = state["completed"] + [current]
return {
"completed": new_completed,
"iterations": state["iterations"] + 1,
"status": f"completed: {current}",
}
def route_tasks(state: TaskState) -> str:
if state["status"] == "all_done":
return END
if state["iterations"] >= state["max_iterations"]:
return END
if len(state["completed"]) >= len(state["tasks"]):
return END
return "work_on_task"
Compile and run with three tasks.
task_graph = StateGraph(TaskState)
task_graph.add_node("work_on_task", work_on_task)
task_graph.add_edge(START, "work_on_task")
task_graph.add_conditional_edges("work_on_task", route_tasks)
task_app = task_graph.compile()
result = task_app.invoke({
"tasks": ["fetch_data", "clean_data", "train_model"],
"completed": [],
"iterations": 0,
"max_iterations": 10,
"status": "",
}, {"recursion_limit": 15})
print(f"Completed: {result['completed']}")
print(f"Iterations: {result['iterations']}")
print(f"Status: {result['status']}")
Completed: ['fetch_data', 'clean_data', 'train_model']
Iterations: 4
Status: all_done
Four rounds: one per task, plus a last pass that found nothing left and flipped status to "all_done". The router caught the flag and stopped.
What Are the Most Common Cycle Mistakes?
Mistake 1 — The node doesn’t update the field the router checks
This is the number one cause of infinite loops.
Wrong:
def my_node(state: MyState) -> dict:
return {"result": "done"} # never updates 'count'
def should_stop(state: MyState) -> str:
if state["count"] >= 3: # count never changes!
return END
return "my_node"
Why it breaks: The router reads count, but the node never touches it. The check count >= 3 stays false forever.
Right:
def my_node(state: MyState) -> dict:
return {"result": "done", "count": state["count"] + 1}
Mistake 2 — The recursion limit is too tight for multi-node cycles
Wrong:
# Graph has 5 nodes per cycle, needs ~15 steps
result = app.invoke(inputs, {"recursion_limit": 10})
Why it breaks: Each cycle pass uses 5 steps. Two passes eat 10 steps. One more node run and you hit the limit on a perfectly valid run.
Right:
# 5 nodes x 3 expected passes = 15, plus buffer
result = app.invoke(inputs, {"recursion_limit": 25})
Mistake 3 — No error handling in production
Wrong:
result = app.invoke(user_input) # unhandled crash
return {"response": result["answer"]}
Right:
from langgraph.errors import GraphRecursionError
try:
result = app.invoke(user_input, {"recursion_limit": 30})
return {"response": result["answer"]}
except GraphRecursionError:
return {"response": "I need more steps to answer this."}
Summary
Cycles give LangGraph agents the power to reason step by step. They drive ReAct loops, draft-and-refine flows, and page-by-page data collection. But a cycle with no exit will crash your agent every time.
Guard yourself with three layers:
-
State-based exit checks — counters or quality bars in your routing function
-
recursion_limit— a hard cap you set when you callinvoke(), acting as a crash guard -
GraphRecursionErrorhandling — a try/except at the call site so users never see a raw stack trace
Use all three in production. The state check handles the normal path. The recursion limit catches your bugs. The error handler makes sure users get a clear message instead of a crash.
Practice exercise: Build a research agent loop. The agent starts with a question, runs a fake “search” (simulated), and scores its confidence (starts at 0.1, gains 0.2 per round). It stops when confidence hits 0.85 or after 5 rounds. Print the final confidence and round count.
Solution
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
class ResearchState(TypedDict):
question: str
confidence: float
iterations: int
max_iterations: int
findings: str
def search(state: ResearchState) -> dict:
iteration = state["iterations"] + 1
confidence = min(0.2 * iteration + 0.1, 1.0)
return {
"iterations": iteration,
"confidence": confidence,
"findings": f"Found {iteration} sources",
}
def route_research(state: ResearchState) -> str:
if state["confidence"] >= 0.85:
return END
if state["iterations"] >= state["max_iterations"]:
return END
return "search"
research_graph = StateGraph(ResearchState)
research_graph.add_node("search", search)
research_graph.add_edge(START, "search")
research_graph.add_conditional_edges("search", route_research)
research_app = research_graph.compile()
result = research_app.invoke({
"question": "How does photosynthesis work?",
"confidence": 0.0,
"iterations": 0,
"max_iterations": 5,
"findings": "",
}, {"recursion_limit": 10})
print(f"Confidence: {result['confidence']}")
print(f"Iterations: {result['iterations']}")
print(f"Findings: {result['findings']}")
Confidence reaches 0.9 on round 4 (0.2 × 4 + 0.1 = 0.9, which clears 0.85). The loop exits after 4 rounds, before hitting the max of 5.
Complete Code
Click to expand the full script (copy-paste and run)
# Complete code from: LangGraph Cycles, Recursion Limits, and Controlling Agent Loops
# Requires: pip install langgraph
# Python 3.10+
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from langgraph.errors import GraphRecursionError
# --- Section 1: Simple Cycle ---
class CounterState(TypedDict):
count: int
message: str
def increment(state: CounterState) -> dict:
new_count = state["count"] + 1
return {"count": new_count, "message": f"Step {new_count}"}
def should_continue(state: CounterState) -> str:
if state["count"] < 3:
return "increment"
return END
graph = StateGraph(CounterState)
graph.add_node("increment", increment)
graph.add_edge(START, "increment")
graph.add_conditional_edges("increment", should_continue)
app = graph.compile()
result = app.invoke({"count": 0, "message": ""})
print("Section 1 — Simple Cycle:")
print(result)
print()
# --- Section 2: GraphRecursionError ---
class LoopState(TypedDict):
value: int
def loop_node(state: LoopState) -> dict:
return {"value": state["value"] + 1}
def should_loop(state: LoopState) -> str:
return "loop_node"
bad_graph = StateGraph(LoopState)
bad_graph.add_node("loop_node", loop_node)
bad_graph.add_edge(START, "loop_node")
bad_graph.add_conditional_edges("loop_node", should_loop)
bad_app = bad_graph.compile()
print("Section 2 — GraphRecursionError:")
try:
bad_app.invoke({"value": 0}, {"recursion_limit": 5})
except GraphRecursionError as e:
print(f"Caught it: {e}")
print()
# --- Section 3: State Counter Exit ---
class AgentState(TypedDict):
query: str
iterations: int
max_iterations: int
result: str
done: bool
def agent_step(state: AgentState) -> dict:
iteration = state["iterations"] + 1
if iteration >= state["max_iterations"]:
return {
"iterations": iteration,
"result": "Reached max iterations. Best answer so far.",
"done": True,
}
return {
"iterations": iteration,
"result": f"Working... (iteration {iteration})",
"done": False,
}
def route_agent(state: AgentState) -> str:
if state["done"]:
return END
return "agent_step"
agent_graph = StateGraph(AgentState)
agent_graph.add_node("agent_step", agent_step)
agent_graph.add_edge(START, "agent_step")
agent_graph.add_conditional_edges("agent_step", route_agent)
agent_app = agent_graph.compile()
print("Section 3 — State Counter Exit:")
result = agent_app.invoke({
"query": "What is the capital of France?",
"iterations": 0,
"max_iterations": 3,
"result": "",
"done": False,
})
print(f"Iterations: {result['iterations']}")
print(f"Result: {result['result']}")
print()
# --- Section 4: Quality-Based Exit ---
class RefinementState(TypedDict):
draft: str
score: float
iterations: int
max_iterations: int
def generate(state: RefinementState) -> dict:
iteration = state["iterations"] + 1
score = 0.3 * iteration
return {
"draft": f"Draft v{iteration}",
"score": score,
"iterations": iteration,
}
def route_refinement(state: RefinementState) -> str:
if state["score"] >= 0.8:
return END
if state["iterations"] >= state["max_iterations"]:
return END
return "generate"
refine_graph = StateGraph(RefinementState)
refine_graph.add_node("generate", generate)
refine_graph.add_edge(START, "generate")
refine_graph.add_conditional_edges("generate", route_refinement)
refine_app = refine_graph.compile()
print("Section 4 — Quality-Based Exit:")
result = refine_app.invoke({
"draft": "",
"score": 0.0,
"iterations": 0,
"max_iterations": 5,
})
print(f"Final draft: {result['draft']}")
print(f"Score: {result['score']}")
print(f"Iterations used: {result['iterations']}")
print()
# --- Section 5: Real-World Task Loop ---
class TaskState(TypedDict):
tasks: list[str]
completed: list[str]
iterations: int
max_iterations: int
status: str
def work_on_task(state: TaskState) -> dict:
remaining = [t for t in state["tasks"] if t not in state["completed"]]
if not remaining:
return {"iterations": state["iterations"] + 1, "status": "all_done"}
current = remaining[0]
new_completed = state["completed"] + [current]
return {
"completed": new_completed,
"iterations": state["iterations"] + 1,
"status": f"completed: {current}",
}
def route_tasks(state: TaskState) -> str:
if state["status"] == "all_done":
return END
if state["iterations"] >= state["max_iterations"]:
return END
if len(state["completed"]) >= len(state["tasks"]):
return END
return "work_on_task"
task_graph = StateGraph(TaskState)
task_graph.add_node("work_on_task", work_on_task)
task_graph.add_edge(START, "work_on_task")
task_graph.add_conditional_edges("work_on_task", route_tasks)
task_app = task_graph.compile()
print("Section 5 — Task Loop:")
result = task_app.invoke({
"tasks": ["fetch_data", "clean_data", "train_model"],
"completed": [],
"iterations": 0,
"max_iterations": 10,
"status": "",
}, {"recursion_limit": 15})
print(f"Completed: {result['completed']}")
print(f"Iterations: {result['iterations']}")
print(f"Status: {result['status']}")
print("\nScript completed successfully.")
Frequently Asked Questions
What’s the difference between recursion_limit and a custom max_iterations counter?
recursion_limit is LangGraph’s built-in safety net. It counts every node run and throws GraphRecursionError when the count is too high. A custom max_iterations is a field you add to your own state — you control the logic, and it triggers a clean exit through your routing function. Use both: max_iterations for the normal path, recursion_limit as the crash guard.
Can I set recursion_limit once at compile time?
Yes. Call graph.compile().with_config({"recursion_limit": 50}) to set a default for every run. You can still override it per call by passing a config to invoke().
Does recursion_limit count nodes or full cycle passes?
It counts individual node runs (called “supersteps”). If your cycle has 3 nodes and the limit is 25, you get about 8 full passes. Keep this in mind when your cycle spans many nodes.
What happens to state when GraphRecursionError fires?
Without a checkpointer, the in-progress state is gone. With checkpointing turned on, you can pull the last saved state and inspect what happened before the crash.
Do subgraphs share the parent’s recursion limit?
Yes. Every step inside a subgraph counts toward the parent’s recursion_limit. If the parent has a limit of 25 and a subgraph uses 10 steps, that leaves only 15 for the rest of the parent graph. Plan your limits with nesting in mind.
How do I stop an LLM from calling the same tool over and over?
Add a max_iterations counter to your state and check it in the router. When the count passes your cap, force the router to return END no matter what the LLM wants. This is the safest approach — you can’t rely on the LLM to stop on its own.
References
-
LangGraph documentation — GRAPH_RECURSION_LIMIT error reference. Link
-
LangGraph GitHub — Troubleshooting recursion limit errors. Link
-
LangGraph documentation — Interrupts for human-in-the-loop control. Link
-
LangGraph errors module — Python reference. Link
-
LangChain blog — Building LangGraph: Designing an Agent Runtime from first principles. Link
-
LangGraph official site — Agent Orchestration Framework. Link
-
Arxiv — Unsupervised Cycle Detection in Agentic Applications (2025). Link
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