LangChain (framework) vs LlamaIndex (framework)
LangChain (framework) vs LlamaIndex (framework): side-by-side comparison of two agent frameworks (no first-party memory layer) systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
How they relate
- LlamaIndex (framework) integrates with LangChain (framework) — LlamaIndex has an official LLM integration package llama-index-llms-langchain in its repo.
Cost & capability
| LangChain (framework) | LlamaIndex (framework) | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 72 | 70 |
| Cost tier | searched not found | searched not found |
| $/Mtok input | searched not found | searched not found |
| $/Mtok output | searched not found | searched not found |
| Use cases | Scoped Agentic, Multi Agent Coordination, Memory Augmented Chat, Analytical Summarization | Analytical Summarization, Memory Augmented Chat, Scoped Agentic, Multi Agent Coordination |
Where they differ (9)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| LangChain (framework) | LlamaIndex (framework) | |
|---|---|---|
| Capability composite | 72 | 70 |
| Use cases | Scoped Agentic, Multi Agent Coordination, Memory Augmented Chat, Analytical Summarization | Analytical Summarization, Memory Augmented Chat, Scoped Agentic, Multi Agent Coordination |
| Type | LLM orchestration framework | Data framework for LLM apps |
| Latest release | v1.4.7 (2026-10-21) | v0.14.25 (2026-09-21) |
| License | MIT (OSS framework) | MIT |
| GitHub | langchain-ai/langchain — ~149k stars, last commit 2026-10 | run-llama/llama_index — ~53k stars, last commit 2026-10 |
| Funding | LangChain Inc raised $25M Series A 2024-02 (Sequoia led; $200M valuation rumored); ~$110M total per later rounds 2024-2025. | $27M total raised through Series A 2024-06 ($19M, Norwest led; $200M valuation rumored). |
| Deployment | Both (self-hosted + managed cloud) — OSS library; agents deploy to LangSmith Deployment (cloud, hybrid or self-hosted) | Both (self-hosted + managed cloud) — OSS library; LlamaAgents workflows deploy to LlamaCloud or self-host |
| MCP | yes — MCP client tools via MCPAdapter in langchain.mcp (langchain[mcp] 1.4+, beta; successor to langchain-mcp-adapters) | yes — use MCP server tools in workflows and serve LlamaIndex workflows as MCP servers |
At a glance
| LangChain (framework) | LlamaIndex (framework) | |
|---|---|---|
| Section | Agent frameworks (no first-party memory layer) | Agent frameworks (no first-party memory layer) |
| Tier | T1 | T1 |
| Type | LLM orchestration framework | Data framework for LLM apps |
| Created | 2022 | 2022 |
| Latest release | v1.4.7 (2026-10-21) | v0.14.25 (2026-09-21) |
| License | MIT (OSS framework) | MIT |
| GitHub | langchain-ai/langchain — ~149k stars, last commit 2026-10 | run-llama/llama_index — ~53k stars, last commit 2026-10 |
| Pricing | searched not found | searched not found |
| Funding | LangChain Inc raised $25M Series A 2024-02 (Sequoia led; $200M valuation rumored); ~$110M total per later rounds 2024-2025. | $27M total raised through Series A 2024-06 ($19M, Norwest led; $200M valuation rumored). |
| Deployment | Both (self-hosted + managed cloud) — OSS library; agents deploy to LangSmith Deployment (cloud, hybrid or self-hosted) | Both (self-hosted + managed cloud) — OSS library; LlamaAgents workflows deploy to LlamaCloud or self-host |
| API surface | searched not found | searched not found |
| Multi-tenancy | searched not found | searched not found |
| MCP | yes — MCP client tools via MCPAdapter in langchain.mcp (langchain[mcp] 1.4+, beta; successor to langchain-mcp-adapters) | yes — use MCP server tools in workflows and serve LlamaIndex workflows as MCP servers |
| A2A | searched not found | searched not found |
| OpenTelemetry | searched not found | searched not found |
| Optimised for | searched not found | searched not found |
| Anti-fit | searched not found | searched not found |
Pros & cons
LangChain (framework)
Pros: searched not found
Cons: searched not found
LlamaIndex (framework)
Pros: searched not found
Cons: searched not found