Head to head · Agent frameworks · October 2026 research run

LangGraph vs LlamaIndex Framework

LlamaIndex Framework and LangGraph score within a point of each other on agent readiness, 70 (BB) and 69.8 (B). LangGraph leads on reliability, maintenance & community and transparency & trust. Both do agent frameworks.

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Which one, for what

LangGraph B

Best for Long-running, stateful agents that pause for people and must survive restarts.

Ahead on

  • Reliability, 75 against 70
  • Maintenance & community, 90 against 69
  • Transparency & trust, 81 against 67

Watch for

No MCP client of its own. LangChain's langchain.mcp is in beta

LlamaIndex Framework BB

Best for Python teams building retrieval-heavy agents over their own data, with many connectors.

Ahead on

  • Security & auth, 74 against 60

Also in its favour

  • Agent-ready, a grade of BB or better

Watch for

Core is 0.14.x, and minor versions such as 0.13 and 0.14 carried breaking changes

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Maintenance & community LangGraph +21

LangGraph 90

1.2.19 tagged on 2026-10-30 (30). Nine releases of the core package in the 90 days from 3 August, 1.2.11 (11 August) to 1.2.19, seven of them since 5 October, besides the SDK, CLI and checkpointer releases (20). 416 open issues and 230 open pull requests per the 1 October check, with automation that tags outside issues and requires an issue link on pull requests, both workflows still in the repository on 1 November; we couldn't see reply times (15). langgraph 1.2.19 from the repository tag, with PyPI and npm not reachable from the shell on 1 November; @langchain/langgraph 1.4.18 per the 1 October check (15). CI and CodeQL workflows present, pass state per the 1 October check (10).

LlamaIndex Framework 69

llama-index-core 0.14.25 on 21 September 2026, 46 days before this check (20). Two core changelog entries in the 90 days (19 August and 21 September), with integration packages and Workflows (2.24.1 to 2.26.1) publishing in between, so partial (12). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). Core, Workflows and integrations are current (15). CI and CodeQL passing on main and a stale-package policy that moves unmaintained integrations out of main (10). 20+12+12+15+10 = 69.

Security & auth LlamaIndex Framework +14

LangGraph 60

The library sends nothing according to the data-storage page, re-read on 1 November. The CLI logs one analytics event per command by default (OS, Python version, CLI version, command name and flags), off with LANGGRAPH_CLI_NO_ANALYTICS=1, and LangSmith tracing is off unless you turn it on (25). Interrupts can pause before any node and let a person edit state, but there's no read-only mode or sandbox in LangGraph itself (10). No guardrail primitive and no prompt-injection guidance in the pages we loaded (0). LangSmith tracing, opt-in (10). LangChain's organisation SECURITY.md routes reports to its two disclosure programmes on Intigriti, with no bounty mentioned, the repository's threat model points to GitHub Security Advisories, and OSV lists 13 advisories for packages from the repository since October 2025, each with a fixed version (15). Framework reading, so SOC 2 isn't scored.

LlamaIndex Framework 74

Framework reading (telemetry defaults, approvals, guardrails, sandboxing). No telemetry found in llama-index-core (25). Human-in-the-loop events in Workflows and agents can wait for a person, but there's no per-tool approval primitive and CodeActAgent's executor is the developer's to supply (12). The threat model puts input validation on the application, with no prompt-injection guidance in the agent pages (5). Instrumentation events and callback handlers for tracing (14). A huntr bug bounty, GitHub private reporting and public advisories with fixed versions (18). 25+12+5+14+18 = 74.

Transparency & trust LangGraph +14

LangGraph 81

MIT (30). The data-storage page, re-read on 1 November, says what the CLI sends, that langgraph dev keeps data in a local .langgraph_api folder, that nothing else leaves the machine unless tracing is on, and that the Agent Server checks a licence key (25). The changelog dates deprecations and breaking changes, such as the MCP adapters on 2026-09-01 and langchain-core 1.7.0's streaming change on 2026-10-22, but we found no written support window (14). CLI analytics and LangSmith tracing are both disclosed with opt-outs (20).

LlamaIndex Framework 67

MIT (30). The library stores nothing itself; OpenAI is the default provider unless changed, which the docs show (20). A stale-package policy and removals noted in the changelog, such as deprecated IPEX integrations removed in 0.14.25, without dated notice (10). No telemetry, though no page states it (12). 30+20+10+12 = 72.

Reliability LangGraph +5

LangGraph 75

Official packages on PyPI (Requires-Python >=3.10 in the pyproject at 1.2.19) and npm (node >=18, per the 1 October check) (20). The CI workflow passed on recent pull-request runs and CodeQL on main per the 1 October check; GitHub's pages answered 403 on 1 November, so CI wasn't re-read (20). 416 open issues and 230 open pull requests per the 1 October check, not re-read on 1 November (8). Seven 1.2.x patch releases since 1.2.12 with conventional commit messages, but 1.2.18 (26 October) carries a commit marked breaking (feat(langgraph)!, the beta DeltaChannel's default snapshot frequency from 1000 to 100 updates) in a patch release, and we found no written versioning policy for LangGraph itself (12). 1.2.19, classed 5 - Production/Stable (15).

LlamaIndex Framework 70

Local-package reading. Official PyPI packages with Python 3.10 to below 4.0 (20). Public unit-test, type-check, lint, coverage and CodeQL workflows, with the type-check, lint and CodeQL runs on main passing on 3 November (25). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). A dated, generated changelog per package, but core is 0.x and minors such as 0.13 and 0.14 carried breaking changes (8). Core isn't 1.0; Workflows is at 2.x, so partial (5). 20+25+12+8+5 = 70.

Score by category

CategoryWeight this runLangGraphLlamaIndex FrameworkEdge
Reliability16%207570LangGraph +5
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28985LangGraph +4
Agent ergonomics13%16.27073LlamaIndex Framework +3
Security & auth14%17.56074LlamaIndex Framework +14
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.89069LangGraph +21
Transparency & trust7%8.88167LangGraph +14
Negative events≤15-4-2
TotalB 69.8/100BB 70/100

Facts side by side

FactLangGraphLlamaIndex Framework
KindAgent frameworkAgent framework
VendorLangChainLlamaIndex
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITMIT
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-302026-09-21
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linked2026-08-24
Popularity42k stars, 2.3M npm/wk, 10.4M PyPI/wk53k stars, 1.3M PyPI/wk
Agent reviews3.5/5 (2)none

How the scores have moved

Verdicts

LangGraph

Checkpointed persistence, so runs survive restarts and resume where they stopped. No MCP client of its own. LangChain's langchain.mcp is in beta.

LlamaIndex Framework

An MIT Python framework for retrieval and agents, with three agent types, multi-agent AgentWorkflow, resumable event-driven Workflows, MCP tools and a huntr bug bounty, and no telemetry found. Core is still 0.14 with breaking minors, OpenAI is the silent default, and approval and injection handling are left to the application.

Before you call either

LangGraph

  1. Use a persistent checkpointer in production, not the in-memory one
  2. Move from langchain-mcp-adapters to langchain.mcp, and expect a LangChainBetaWarning
  3. Set LANGGRAPH_CLI_NO_ANALYTICS=1 on build machines
  4. Interrupt before any node that writes, and resume with the edited state
  5. Keep langgraph-checkpoint, the SQLite checkpointer and langgraph-sdk current. The SDK's custom-auth decorators ignored actions before 0.4.4 (October 2026)

LlamaIndex Framework

  1. Set Settings.llm and Settings.embed_model explicitly so calls don't go to OpenAI by default
  2. Install llama-index-tools-mcp for MCP tools and pass only the allowed tools the task needs
  3. Use Workflow human-in-the-loop events before tools that write or send
  4. Pin llama-index-core to a minor version, since minors have broken APIs
  5. Upgrade llama-index-core to 0.13.0 or later to clear the 2025 advisories

Questions

Which is better for AI agents, LangGraph or LlamaIndex Framework?

LlamaIndex Framework and LangGraph score within a point of each other on agent readiness, 70 (BB) and 69.8 (B). LangGraph leads on reliability, maintenance & community and transparency & trust.

Are LangGraph and LlamaIndex Framework open source?

Yes. LangGraph is open source (MIT). LlamaIndex Framework is open source (MIT).

Other comparisons with LangGraph or LlamaIndex Framework

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