Comparisons / LangGraph vs LlamaIndex
LangGraph vs LlamaIndex: Which Agent Framework to Use?
LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state. LlamaIndex started as a RAG framework — connect your data, query it with an LLM. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
LangGraph
18.9k
3.4k
Python
MIT
2024-01-17
LangChain Inc (Harrison Chase)
Sequoia Capital, Benchmark
Part of LangChain Inc — $50M raised across A and B
8.2M
LangGraph Platform (hosted), LangSmith (observability)
Yes
Used by: Replit, Klarna, Elastic
github.com/langchain-ai/langgraph→LlamaIndex
48.3k
7.2k
Python
MIT
2022-11-02
Jerry Liu
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LangGraph | LlamaIndex |
|---|---|---|
| Agent | A `StateGraph` with nodes, edges, and a typed `State` channel | `AgentRunner` with `AgentWorker`, or `ReActAgent` for tool-calling agents |
| Tools | `ToolNode(tools)` paired with a conditional edge for routing | `FunctionTool` for custom tools, `QueryEngineTool` to query an index as a tool |
| Loop | `add_conditional_edges` from a node back to itself until a `END` condition | — |
| State | Typed `State` channels with reducers (`Annotated[list, add_messages]`) | — |
| Checkpointing | `MemorySaver` / `PostgresSaver` persists state per `thread_id` | — |
| Human-in-loop | `interrupt_before` / `interrupt_after` pauses execution for review | — |
| Parallel fanout | Multiple edges from one node + reducers merge results | — |
| Agent Loop | — | `AgentRunner.chat()` manages step-by-step execution via `AgentWorker` tasks |
| RAG Integration | — | `VectorStoreIndex` + `QueryEngineTool` — the agent can query your data as a tool call |
| Memory | — | `ChatMemoryBuffer` with token limit, or custom memory modules |
| Orchestration | — | `AgentRunner` step API for custom control flow, or multi-agent pipelines |
LangGraph vs LlamaIndex, head to head
LangGraph LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state.
LlamaIndex LlamaIndex started as a RAG framework — connect your data, query it with an LLM.
Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.
Pick LangGraph if
Pick LangGraph if langGraph earns its weight when your agent is a workflow — explicit branches, checkpoints, parallel branches, or a human approval gate. For a single-agent loop, the graph machinery is overkill and a plain while loop is faster to write, debug, and ship. LangGraph is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LlamaIndex would force you to translate.
Pick LlamaIndex if
Pick LlamaIndex if llamaIndex adds genuine value when your agent needs to query structured or unstructured data as part of its reasoning — that's the index-as-tool pattern, and it's well-executed. But if you're building a general-purpose agent that doesn't need RAG, the agent framework is overhead. The plain Python version of the agent loop is the same 60 lines either way. LlamaIndex is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LangGraph would force you to translate.
What both add
Both LangGraph and LlamaIndex pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.
Or build your own in 60 lines
Both LangGraph and LlamaIndex implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.
No framework. No dependencies. No opinions. Just the code.
Build it from scratch →