head to head · open source
llama_index vs mcp-server-elasticsearch
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. mcp-server-elasticsearch has 728 stars, 160 forks, 23 open issues and last shipped 6 days ago. llama_index leads on adoption by 7,150% (52,779 vs 728 stars). llama_index is written in Python under MIT; mcp-server-elasticsearch is written in Rust under Apache-2.0. llama_index has attracted 16% as many forks as stars, mcp-server-elasticsearch 22%. mcp-server-elasticsearch was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (vector-database), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| llama_index | mcp-server-elasticsearch | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 728 |
| License | MIT | Apache-2.0 |
| Written in | Python | Rust |
| Last push | 2026-10-26 | 2026-10-28 |
| Forks | ⑂ 8.5K | ⑂ 160 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick mcp-server-elasticsearch if
- You want the mcp-server-elasticsearch feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Rust
- You evaluated both and mcp-server-elasticsearch fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About mcp-server-elasticsearch
Elasticsearch MCP Server [!CAUTION] This MCP server is deprecated and will only receive critical security updates going forward. It has been superseded by the Elastic Agent Builder MCP endpoint, which is available in Elastic 9.2.0+ and Elasticsearch Serverless projects. Use the Elasticsearch MCP Server for AI Agents The Elasticsearch MCP Server connects your AI agents to Elasticsearch data using the Model Context Protocol (MCP). It enables natural language interactions with your Elasticsearch indices, allowing agents to query, analyze, and retrieve data without custom APIs. Follow these steps to deploy and…
read the full mcp-server-elasticsearch overview →
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Frequently asked questions
Is llama_index or mcp-server-elasticsearch more popular?
llama_index has 52,779 GitHub stars and mcp-server-elasticsearch has 728. llama_index has the larger community by that measure.
Are llama_index and mcp-server-elasticsearch free?
Both are open source. llama_index is licensed under MIT and mcp-server-elasticsearch under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and mcp-server-elasticsearch?
llama_index is written in Python and mcp-server-elasticsearch in Rust. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, llama_index or mcp-server-elasticsearch?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose mcp-server-elasticsearch if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.