Llama Agents + Workflows (429 Stars) 2026: Event-Driven, Async-First Multi-Agent Orchestration

Llama Agents + Workflows (429 stars) is LlamaIndex's event-driven, async-first framework for building step-based multi-agent systems. Here is a working example.

💡 What You Will Learn

Llama Agents + Workflows (429 stars) is LlamaIndex's event-driven, async-first framework for building step-based multi-agent systems. Here is a working example.

📜 Table of Contents

• The short answer
• Why event-driven agents
• Minimal workflow example
• When to use it
• FAQ

The short answer

run-llama/llama-agents (429 stars, Python) provides Llama Agents + Workflows - an event-driven, async-first, step-based way to build multi-agent systems. Instead of hard-coding agent call chains, you define steps that react to events, which makes complex orchestrations easier to build and debug.

Why event-driven agents

• Decoupled: agents communicate via events, not direct calls
• Async-first: steps run concurrently where possible, cutting total latency
• Step-based: each step is a discrete unit you can test and reuse
• Observable: event streams make the system flow visible

Minimal workflow example

from llama_agents import AgentWorkflow, HumanAgent, ToolAgent

# Define two agents
researcher = ToolAgent(name="researcher", tools=[search_tool])
writer = AgentWorkflow(name="writer")

# Orchestrate with a workflow
workflow = AgentWorkflow(
    agents=[researcher, writer],
    root_agent="writer",
)

result = await workflow.run(user_msg="Research topic X and write a summary")
print(result)

Install: pip install llama-agents llama-index

When to use it

• You already use LlamaIndex and want agents with minimal new concepts.
• Your orchestration has parallelizable branches (research, summarize, review).
• You want explicit control over each step instead of magic chains.

FAQ

Is it stable? It is early-stage (429 stars) and evolving quickly; APIs may change between releases.

Does it need a server? No - workflows run in-process; there are optional server components for distributed agents.

How does it compare to LangGraph? Both are graph-style orchestrators; Llama Agents stays closer to the LlamaIndex ecosystem, while LangGraph (38,984 stars) is framework-agnostic.

❓ FAQ

Is it stable?

It is early-stage (429 stars) and evolving quickly; APIs may change between releases.

Does it need a server?

No - workflows run in-process; there are optional server components for distributed agents.

How does it compare to LangGraph?

Both are graph-style orchestrators; Llama Agents stays closer to the LlamaIndex ecosystem, while LangGraph (38,984 stars) is framework-agnostic.