Open Source Search & Retrieval

Information retrieval, semantic search, text similarity and retrieval-augmented generation (RAG): everything for finding the right passage in a large pile of text.

1,485
projects
4.6M โ˜…
total stars
+7.5K
stars this week
Python
top language

ranked by GitHub stars ยท updated Nov 12, 2026

๐Ÿ”ฅ Trending this week in Search & Retrieval

Search & Retrieval

yjid/kensaku๐Ÿ”ฅ active

โ–ฒ +877 โ˜… this week

Search inside documents, code, and even screenshots - instantly and locally. A free, open-source file launcher with OCR, semantic search, and MCP agent access.

๐Ÿ“„ OCR & Documents
RustMITupdated Nov 10, 2026
GitHub โ†—โ˜… 1.7Kโ‘‚ 112
Search & Retrieval

Tencent/WeKnora๐Ÿ”ฅ active

โ–ฒ +725 โ˜… this week

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

โ“ Question Answering
GoOtherupdated Nov 9, 2026
GitHub โ†—โ˜… 37.3Kโ‘‚ 5.2K
Search & Retrieval

bojieli/ai-agent-book

โ–ฒ +610 โ˜… this week

ใ€Šๆทฑๅ…ฅ็†่งฃ AI Agent๏ผš่ฎพ่ฎกๅŽŸ็†ไธŽๅทฅ็จ‹ๅฎž่ทตใ€‹๏ผˆๆŽๅšๆฐ ่‘—๏ผ‰ๅผ€ๆบไธปไป“ๅบ“๏ผšๅ…จไนฆๆญฃๆ–‡ใ€็ผ–่ฏ‘็‰ˆ PDF ไธŽๆŒ‰็ซ ้…ๅฅ—ไปฃ็ 

๐Ÿค– Language Models
PythonApache-2.0updated Oct 8, 2026
GitHub โ†—โ˜… 57.9Kโ‘‚ 6.7K
Search & Retrieval

microsoft/generative-ai-for-beginners๐Ÿ”ฅ active

โ–ฒ +395 โ˜… this week

21 Lessons, Get Started Building with Generative AI

๐Ÿค– Language Models
Jupyter NotebookMITupdated Nov 12, 2026
GitHub โ†—โ˜… 123.4Kโ‘‚ 66.3K
Search & Retrieval

VectifyAI/PageIndex๐Ÿ”ฅ active

โ–ฒ +384 โ˜… this week

๐Ÿ“‘ PageIndex: Document Index for Vectorless, Reasoning-based RAG

PythonMITupdated Nov 12, 2026
GitHub โ†—โ˜… 40.4Kโ‘‚ 3.5K
Search & Retrieval

PaddlePaddle/PaddleOCR

โ–ฒ +327 โ˜… this week

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

๐Ÿ“„ OCR & Documents
PythonApache-2.0updated Sep 16, 2026
GitHub โ†—โ˜… 92.6Kโ‘‚ 11.7K

Top 30 semantic search and RAG tools by stars

Search & Retrieval๐Ÿฅ‡#1

open-webui/open-webui๐Ÿ”ฅ active

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

SvelteOtherupdated Nov 12, 2026
GitHub โ†—โ˜… 158.5Kโ‘‚ 23K
Search & Retrieval๐Ÿฅˆ#2

langchain-ai/langchain๐Ÿ”ฅ active

The agent engineering platform.

PythonMITupdated Nov 12, 2026
GitHub โ†—โ˜… 149.4Kโ‘‚ 25.1K
Search & Retrieval๐Ÿฅ‰#3

Graphify-Labs/graphify๐Ÿ”ฅ active

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

PythonApache-2.0updated Nov 11, 2026
GitHub โ†—โ˜… 146.6Kโ‘‚ 14K
Search & Retrieval#4

microsoft/generative-ai-for-beginners๐Ÿ”ฅ active

โ–ฒ +395 โ˜… this week

21 Lessons, Get Started Building with Generative AI

๐Ÿค– Language Models
Jupyter NotebookMITupdated Nov 12, 2026
GitHub โ†—โ˜… 123.4Kโ‘‚ 66.3K
Search & Retrieval#5

thedotmack/claude-mem๐Ÿ”ฅ active

Persistent Context Across Sessions for Every Agent โ€“ Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

TypeScriptApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 116.5Kโ‘‚ 10.1K
Search & Retrieval#6

infiniflow/ragflow๐Ÿ”ฅ active

RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.

GoApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 94.1Kโ‘‚ 11.3K
Search & Retrieval#7

PaddlePaddle/PaddleOCR

โ–ฒ +327 โ˜… this week

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

๐Ÿ“„ OCR & Documents
PythonApache-2.0updated Sep 16, 2026
GitHub โ†—โ˜… 92.6Kโ‘‚ 11.7K
Search & Retrieval#8

unclecode/crawl4ai๐Ÿ”ฅ active

Official Crawl4AI Cloud: a hosted web scraping, extraction, and search API for AI agents. Free API key, 1,000 credits a month, no card. Fully open source: this repo is the Apache-2.0 crawler that powers it, and you can self-host it.

PythonApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 87.3Kโ‘‚ 9.1K
Search & Retrieval#9

datawhalechina/hello-agents๐Ÿ”ฅ active

๐Ÿ“š ใ€ŠไปŽ้›ถๅผ€ๅง‹ๆž„ๅปบๆ™บ่ƒฝไฝ“ใ€‹โ€”โ€”ไปŽ้›ถๅผ€ๅง‹็š„ๆ™บ่ƒฝไฝ“ๅŽŸ็†ไธŽๅฎž่ทตๆ•™็จ‹

PythonOtherupdated Nov 11, 2026
GitHub โ†—โ˜… 86.8Kโ‘‚ 10.8K
Search & Retrieval#10

dair-ai/Prompt-Engineering-Guide

โ–ฒ +172 โ˜… this week

๐Ÿ™ Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

๐Ÿค– Language Models
MDXMITupdated Mar 11, 2026
GitHub โ†—โ˜… 80Kโ‘‚ 8.8K
Search & Retrieval#11

headroomlabs-ai/headroom๐Ÿ”ฅ active

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.

PythonApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 79.5Kโ‘‚ 6.2K
Search & Retrieval#12

mem0ai/mem0๐Ÿ”ฅ active

The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.

PythonApache-2.0updated Nov 6, 2026
GitHub โ†—โ˜… 68.9Kโ‘‚ 8.2K
Search & Retrieval#13

Mintplex-Labs/anything-llm๐Ÿ”ฅ active

Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience

JavaScriptMITupdated Nov 12, 2026
GitHub โ†—โ˜… 68.2Kโ‘‚ 7.7K
Search & Retrieval#14

meilisearch/meilisearch๐Ÿ”ฅ active

โ–ฒ +51 โ˜… this week

A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.

RustOtherupdated Nov 12, 2026
GitHub โ†—โ˜… 59.8Kโ‘‚ 2.8K
Search & Retrieval#15

pathwaycom/serviette๐Ÿ”ฅ active

A no-code RAG server that keeps answers in sync with your documents. Works with 8 vector databases. Built on Pathway.

PythonMITupdated Nov 10, 2026
GitHub โ†—โ˜… 58.7Kโ‘‚ 1.6K
Search & Retrieval#16

HKUDS/DeepTutor๐Ÿ”ฅ active

DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/.

PythonApache-2.0updated Nov 7, 2026
GitHub โ†—โ˜… 58.6Kโ‘‚ 7.1K
Search & Retrieval#17

bojieli/ai-agent-book

โ–ฒ +610 โ˜… this week

ใ€Šๆทฑๅ…ฅ็†่งฃ AI Agent๏ผš่ฎพ่ฎกๅŽŸ็†ไธŽๅทฅ็จ‹ๅฎž่ทตใ€‹๏ผˆๆŽๅšๆฐ ่‘—๏ผ‰ๅผ€ๆบไธปไป“ๅบ“๏ผšๅ…จไนฆๆญฃๆ–‡ใ€็ผ–่ฏ‘็‰ˆ PDF ไธŽๆŒ‰็ซ ้…ๅฅ—ไปฃ็ 

๐Ÿค– Language Models
PythonApache-2.0updated Oct 8, 2026
GitHub โ†—โ˜… 57.9Kโ‘‚ 6.7K
Search & Retrieval#18

run-llama/llama_index๐Ÿ”ฅ active

LlamaIndex is the document processing platform for AI

PythonMITupdated Nov 11, 2026
GitHub โ†—โ˜… 52.9Kโ‘‚ 8.5K
Search & Retrieval#19

jeecgboot/JeecgBoot๐Ÿ”ฅ active

ไธ€ๆฌพๅ…่ดนๅผ€ๆบ็š„ AI ไฝŽไปฃ็ ๅนณๅฐ๏ผŒไนŸๆ˜ฏ AI ๆ—ถไปฃๆž„ๅปบๅคๆ‚ไธšๅŠก็ณป็ปŸ็š„ๅผ€ๆบๅŸบๅบง๏ผๆƒ้™ใ€ๅคš็งŸๆˆทใ€ๅทฅไฝœๆตใ€ๆŠฅ่กจใ€ๅคงๅฑใ€AI ็Ÿฅ่ฏ†ๅบ“็ญ‰้€š็”จ่ƒฝๅŠ›ๅผ€็ฎฑๅณ็”จ๏ผŒAI ๅช้œ€็”ŸๆˆไธšๅŠกๅทฎๅผ‚้ƒจๅˆ†ใ€‚AI Skills ไธ€ๅฅ่ฏ็”ŸๆˆๅฎŒๆ•ด็ณป็ปŸโ€”โ€”ๅปบ่กจใ€ๅ‰ๅŽ็ซฏไปฃ็ ใ€่œๅ•ๆƒ้™ใ€BPM ๆต็จ‹ใ€่กจๅ•ใ€ๆŠฅ่กจใ€ๅคงๅฑๅ…จ่‡ชๅŠจใ€‚ๅ†…็ฝฎ AI ๅบ”็”จๅนณๅฐ๏ผˆ่Šๅคฉใ€็Ÿฅ่ฏ†ๅบ“ใ€ๆต็จ‹็ผ–ๆŽ’ใ€MCP๏ผ‰๏ผŒๅ…ผๅฎนไธปๆตๅคงๆจกๅž‹

JavaApache-2.0updated Nov 11, 2026
GitHub โ†—โ˜… 48.4Kโ‘‚ 16.2K
Search & Retrieval#20

milvus-io/milvus๐Ÿ”ฅ active

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

GoApache-2.0updated Nov 12, 2026
GitHub โ†—โ˜… 46.6Kโ‘‚ 4.3K
Search & Retrieval#22

patchy631/ai-engineering-hub

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

Jupyter NotebookMITupdated Nov 1, 2026
GitHub โ†—โ˜… 41.8Kโ‘‚ 6.7K
Search & Retrieval#23

HKUDS/LightRAG๐Ÿ”ฅ active

โ–ฒ +125 โ˜… this week

[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

๐Ÿค– Language Models
PythonMITupdated Nov 12, 2026
GitHub โ†—โ˜… 40.7Kโ‘‚ 5.8K
Search & Retrieval#24

VectifyAI/PageIndex๐Ÿ”ฅ active

โ–ฒ +384 โ˜… this week

๐Ÿ“‘ PageIndex: Document Index for Vectorless, Reasoning-based RAG

PythonMITupdated Nov 12, 2026
GitHub โ†—โ˜… 40.4Kโ‘‚ 3.5K
Search & Retrieval#25

The-Vibe-Company/quivr๐Ÿ”ฅ active

An open-source engine that turns continuous content streams into search and monitoring. Durable ingestion, hybrid search, alerts, and plugins for formats, models and business rules.

GoMITupdated Nov 9, 2026
GitHub โ†—โ˜… 39.6Kโ‘‚ 3.7K
Search & Retrieval#26

chatchat-space/Langchain-Chatchat

Langchain-Chatchat๏ผˆๅŽŸLangchain-ChatGLM๏ผ‰ๅŸบไบŽ Langchain ไธŽ ChatGLM, Qwen ไธŽ Llama ็ญ‰่ฏญ่จ€ๆจกๅž‹็š„ RAG ไธŽ Agent ๅบ”็”จ | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain

PythonApache-2.0updated Nov 10, 2025
GitHub โ†—โ˜… 38.7Kโ‘‚ 6.3K
Search & Retrieval#27

ItzCrazyKns/Vane

Vane is an AI-powered answering engine.

TypeScriptMITupdated Sep 1, 2026
GitHub โ†—โ˜… 38.2Kโ‘‚ 4.3K
Search & Retrieval#28

khoj-ai/khoj

โ–ฒ +112 โ˜… this week

Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

PythonAGPL-3.0updated Aug 2, 2026
GitHub โ†—โ˜… 38.1Kโ‘‚ 2.5K
Search & Retrieval#29

virgiliojr94/book-to-skill๐Ÿ”ฅ active

Turn any technical book PDF into a Claude Code skill โ€” ready to study, reference, and use while you work.

PythonMITupdated Nov 8, 2026
GitHub โ†—โ˜… 37.7Kโ‘‚ 4K
Search & Retrieval#30

Tencent/WeKnora๐Ÿ”ฅ active

โ–ฒ +725 โ˜… this week

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

โ“ Question Answering
GoOtherupdated Nov 9, 2026
GitHub โ†—โ˜… 37.3Kโ‘‚ 5.2K

๐Ÿ“„ New research on Search & Retrieval

๐Ÿ“„ arXiv:2611.14493 ยท cs.AI45h ago

Comparative Analysis of Agent Evaluation Frameworks: Stability, Detection, and Discovery in Enterprise Analytics Agents

Howard Zhang, Chuck Arvin, Logan Brown +3

๐Ÿ’ฌ Accepted to KDD 2026, Jeju, Korea. 9 pages, 3 figures

Deploying LLM-based analytics agents in enterprise settings requires evaluation frameworks that can reliably detect failures across complex, multi-tool workflows. We present a three-phase comparative study of three evaluation frameworks, each representing a distinct evaluation paradigm (trace-based LLM judging, text-only LLM judging with red-teaming, and deterministic heuristics), applied to two analytics agents in a controlled research setting using frozen execution traces. Phase 1 quantifies evaluation harness stability through repeated measurements, finding that 97% of configurations meet the stability threshold and that stability patterns are agent-independent. Phase 2 measures known risk detection on 72 ground-truth test cases, where the trace-based framework achieves the best precision-recall balance (F1=0.90) while text-only evaluation matches on recall but generates 5x more false alarms. Phase 3 evaluates unknown risk discovery, uncovering 24 novel failures with zero overlap between functional testing and security red-teaming. Our results demonstrate that (1) the information available to the evaluation harness determines evaluation quality, (2) functional and security evaluation are complementary, and (3) LLM-based evaluation harnesses surface subtle errors that human reviewers miss. We recommend a two-framework strategy: trace-based evaluation for functional assessment and adversarial red-teaming for security validation.โ–ผ more
๐Ÿ” Search & Retrieval๐Ÿค– Language Models
๐Ÿ“„ arXiv:2611.14477 ยท cs.IR45h ago

FERN: Cross-Benchmark Table Retrieval for Text-to-SQL with Frozen Retrievers and Learned Ranking

Arjun Rao, Nishant Gopinath

๐Ÿ’ฌ 19 pages

Text-to-SQL systems over large pooled catalogs must first retrieve the tables a question needs. We compare against the published table recall of RASL, which uses Claude 3.5 models to extract keywords and select tables, on pooled BIRD, Spider, and Fiben catalogs. FERN (Feature-Engineered Ranking with No generative LLM) instead fuses three frozen retrievers into 100 candidates, describes each with up to 120 features, and reorders them with LightGBM rankers fitted once on BIRD's training questions. On Spider, where it was not fitted, its candidate pool alone reaches RASL's published recall at fifteen tables (98.0), and the ranker raises the share of questions with every gold table in the top five (complete@5) from 83.9% to 95.6%. At five tables, FERN's recall is within one point of RASL's on Spider (96.7 vs 97.0) and on BIRD with its evidence hints (96.6 vs 97.5), and 8.2 points below on BIRD without them (89.3). On 121 real BEAVER questions, without BEAVER fitting, its recall at fifteen (82.1) falls between two BEAVER-trained retrievers (80.8, 84.5), but its complete@15 is below both (49.6 vs 54.5 and 62.0). On Fiben, it falls far short at fifteen tables (54.2 vs 69.2, different question sets), although its recall at fifty is numerically close to RASL's question-only retrieval result (80.7 vs 79.4). On BIRD dev with evidence, non-retrieval features raise complete@5 by 7.7 points over retrieval signals alone. On BIRD dev, median latency is 208 ms per question on one A100, without hosted model calls.โ–ผ more
๐Ÿ” Search & Retrieval๐Ÿค– Language Models
๐Ÿ“„ arXiv:2611.14325 ยท cs.CL47h ago

Point-in-Time Encoders for Earnings Calls

Alessio Brini

๐Ÿ’ฌ 10 pages, 3 figures, 7 tables. Accepted at the 3rd IEEE International Workshop on Large Language Models for Finance, IEEE BigData 2026. Code: https://github.com/Alessiobrini/point-in-time-encoders-for-earnings-calls

Text encoders turn earnings calls into vectors for search, for language models that answer from retrieved passages, and for forecasting models. A live retriever can use an encoder trained once on all available text, as finance retrievers are, but a backtest needs an encoder that read no text written after the date of each vector. To address this look-ahead bias, from 412,248 calls we train vintages under four pretraining recipes, with yearly cutoffs from 2010 to 2024, and each vintage reads only text dated on or before its cutoff. We then fine-tune each vintage on the question-answer pairs of the calls themselves. The vintages we train on calls alone, with 33.8M parameters, retrieve answers on held-out calls better than the general retrievers we tested in every test quarter. Their vectors also raise by about half the rank correlation between next-day returns and forecasts built from past returns and firm size. When each vintage continues the previous one, a later vintage retrieves better on earlier quarters than the vintage available at the time, and a point-in-time language model that answers from the passages the later vintage retrieves inherits the gain. The 2024 vintage of our families also forecasts earnings surprises better, although the interval of this gain includes zero on calls that no vintage read. It forecasts returns no better, so point-in-time vintages matter most for retrieval and little for forecasts of returns.โ–ผ more
๐Ÿ” Search & Retrieval๐Ÿค– Language Models
๐Ÿ“„ arXiv:2611.14236 ยท cs.CL2d ago

StaRSE: Compact Russian Sentence Embeddings with a Sign-Coded Static Encoder

Boris Malashenko, Ivan Jarsky, Egor Soldatov +6

๐Ÿ’ฌ Accepted to ReNeuIR @ SIGIR 2026. 16 pages

Transformer sentence encoders provide strong retrieval quality, but their inference cost becomes a bottleneck when embeddings must be produced on CPUs or refreshed frequently. We introduce StaRSE, a compact Russian static sentence encoder that removes Transformer inference at deployment time: token directions are stored as sign bits, token norms are stored separately, and sentences are encoded by mean pooling over norm-scaled sign directions. StaRSE is initialized from ruBERT-base, fine-tuned on Russian adjacent-sentence and task-shaped pairs, and post-trained with a contrastive stage that directly optimizes the sign-projected token table. On RuMTEB, StaRSE reaches a task mean of $51.16$, $7.98$ points above the strongest included static baseline. The packaged model occupies $11.3$ MiB and has a mean CPU latency of $0.08$ ms per sentence, compared with an estimated $55.7$ MiB 16-bit parameter footprint and $1.43$ ms mean CPU latency for the closest-quality contextual encoder. Ablations examine adjacent-sentence supervision, token norms, and direct sign-projected post-training.โ–ผ more
๐Ÿงฌ Embeddings๐Ÿ” Search & Retrieval๐Ÿค– Language Models