
Agent Graphs
- 1.6k installs
- 23 repo stars
- Updated August 5, 2026
- launchdarkly/agent-skills
agent-graphs is a LaunchDarkly agent skill that designs and manages directed graphs of config nodes with handoff routing for developers who build multi-agent workflows requiring parallelizable subtask decomposition.
About
agent-graphs is an Apache-2.0 LaunchDarkly agent skill (version 0.1.0) that guides creation and management of agent graphs—directed graphs of configs connected by edges with handoff logic. The skill walks through graph topology design, node creation, edge wiring, and verification that routing between config nodes works correctly. It requires the remotely hosted LaunchDarkly MCP server as a prerequisite. Developers reach for agent-graphs when building multi-agent workflows where configs must route to each other based on handoff conditions. Claude or Cursor agents use it to break complex work into structured, parallelizable graphs of subtasks rather than monolithic single-agent prompts.
- Converts a single prompt into an executable graph of interdependent agent tasks
- Supports parallel execution of independent nodes for faster completion
- Maintains state and dependencies across multi-step agent workflows
- Works with Claude Code, Cursor, and compatible agent runtimes
- Reduces token waste and context drift on large projects
Agent Graphs by the numbers
- 1,595 all-time installs (skills.sh)
- +12 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #767 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 1.6k |
|---|---|
| repo stars | ★ 23 |
| Last updated | August 5, 2026 |
| Repository | launchdarkly/agent-skills ↗ |
How do you structure multi-agent handoff graphs in LaunchDarkly?
Let Claude or Cursor agents break complex work into structured, parallelizable graphs of subtasks.
Who is it for?
Developers orchestrating multiple LaunchDarkly agent configs who need explicit graph topology and handoff verification.
Skip if: Single-prompt agent setups without LaunchDarkly or teams not using the LaunchDarkly MCP server.
When should I use this skill?
The user wants to create agent graphs, wire config handoffs, or design parallelizable multi-agent workflows in LaunchDarkly.
What you get
A verified agent graph topology with config nodes, directed edges, and handoff routing rules deployed via LaunchDarkly MCP.
- agent graph topology
- config node edges
- handoff routing rules
By the numbers
- Apache-2.0 licensed skill version 0.1.0 in manifest metadata
Files
Config Agent Graphs
You're using a skill that will guide you through creating and managing agent graphs in LaunchDarkly. Your job is to design the graph topology, create it with the right edges and handoffs, and verify the routing between config nodes.
Prerequisites
This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.
Required MCP tools:
create-agent-graph-- create a new graph with nodes and edgesget-agent-graph-- inspect a graph's structure and edgeslist-agent-graphs-- browse existing graphs in the project
Optional MCP tools:
update-agent-graph-- modify edges, root config, or descriptiondelete-agent-graph-- permanently remove a graphget-ai-config-- inspect individual configs that serve as nodescreate-ai-config-- create new configs to use as graph nodes
Core Concepts
What Are Agent Graphs?
An agent graph is a directed graph where:
- Nodes are configs (each config is an agent with its own model, prompt, and tools)
- Edges define routing between configs (source -> target)
- Handoff data on edges controls how context is passed between agents
- Root config is the entry point — the first agent that receives user input
When to Use Agent Graphs
| Scenario | Example |
|---|---|
| Multi-step workflows | Triage agent -> Specialist agent -> Summary agent |
| Routing by intent | Router agent decides which specialist handles the request |
| Escalation chains | L1 support -> L2 support -> Human handoff |
| Pipeline processing | Extract -> Transform -> Validate -> Store |
Graph Structure
[Root Config] --edge--> [Config A] --edge--> [Config C]
\--edge--> [Config B]Each edge has:
key-- unique identifier for the edgesourceConfig-- the config key that routes FROMtargetConfig-- the config key that routes TOhandoff(optional) -- data/instructions passed during the transition
Core Principles
1. Design Before Building: Map out nodes and edges on paper/whiteboard first 2. One Agent, One Job: Each node should have a clear, focused responsibility 3. Root Config Is the Router: The entry point should understand how to dispatch 4. Handoff Data Matters: Define what context flows between agents 5. Verify the Full Path: Test that routing works end-to-end
Workflow
Step 1: Design the Graph
Before creating anything:
1. Identify the agents (configs) needed — each is a graph node 2. Map the routing: which agent hands off to which? 3. Define handoff data: what context does each edge carry? 4. Identify the root config: which agent receives initial input? 5. Check existing graphs with list-agent-graphs to avoid duplicates 6. Check existing configs with get-ai-config to see what nodes already exist
Step 2: Ensure Nodes Exist
Each node in the graph must be an existing config. If configs don't exist yet: 1. Use create-ai-config to create each agent config 2. Set up variations with appropriate models and prompts for each agent's role 3. Verify each config exists with get-ai-config
Step 3: Create the Graph
Use create-agent-graph with:
projectKey-- the project containing the configskey-- unique identifier for the graphname-- human-readable display namedescription(optional) -- explain the graph's purposerootConfigKey-- the entry-point config keyedges-- array of connections between configs
{
"projectKey": "my-project",
"key": "support-triage-graph",
"name": "Customer Support Triage",
"description": "Routes customer queries to the appropriate specialist agent",
"rootConfigKey": "triage-agent",
"edges": [
{
"key": "triage-to-billing",
"sourceConfig": "triage-agent",
"targetConfig": "billing-specialist",
"handoff": {"category": "billing", "priority": "normal"}
},
{
"key": "triage-to-technical",
"sourceConfig": "triage-agent",
"targetConfig": "technical-specialist",
"handoff": {"category": "technical", "priority": "normal"}
}
]
}Step 4: Verify
1. Use get-agent-graph to confirm the graph was created with the correct structure 2. Verify edges connect the right source and target configs 3. Check that the root config key matches the intended entry point 4. Confirm handoff data is present on edges that need it
Report results:
- Graph created with N nodes and M edges
- Root config set correctly
- All edges verified
Edge Cases
| Situation | Action |
|---|---|
| Config doesn't exist yet | Create it first with create-ai-config before referencing in a graph |
| Circular routing | Allowed but warn user — ensure there's a termination condition in the agent logic |
| Single-node graph | Valid but unusual — consider if a graph is actually needed |
| Updating edges | Use update-agent-graph — provide the complete new edge list |
What NOT to Do
- Don't create a graph before the config nodes exist
- Don't forget handoff data when agents need context from predecessors
- Don't create overly complex graphs — start simple and add nodes as needed
- Don't delete a graph without understanding if it's actively used in agent workflows
Related skills
How it compares
Use agent-graphs instead of single-config skills when multiple LaunchDarkly agent configs need explicit graph topology and handoff routing rather than one monolithic prompt.
FAQ
What does agent-graphs require before use?
agent-graphs requires the remotely hosted LaunchDarkly MCP server to create and manage agent graphs. The skill is version 0.1.0 under Apache-2.0 and guides topology design, edge creation, and handoff verification between config nodes.
What is an agent graph in LaunchDarkly?
An agent graph in LaunchDarkly is a directed graph of config nodes connected by edges with handoff logic. agent-graphs helps developers design that topology so multiple agent configs route work to each other in parallelizable multi-agent workflows.