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Open-source alternatives to Perplexity

The highest-scoring open-source projects in Search. They are picks, not exact replacements, so read the weaknesses before you switch.

Open-source alternatives to Perplexity
RankProjectScore
1MorphicSearch engine that answers with citations and renders rich inline componentsSearch, 9.2k stars, Apache-2.070 out of 100
2Local Deep ResearchAgentic research assistant with local LLMs, SearXNG and encrypted librariesSearch, 9.2k stars, MIT65 out of 100
3GPT ResearcherResearch agent that writes cited reports from web and local documentsSearch, 30k stars, Apache-2.064 out of 100
#4VaneSelf-hosted answer engine with cited sources over SearXNGSearch, 37.2k stars, MIT55 out of 100
#5MAESTROMulti-agent research platform that writes long reports from documents and webSearch, 1.5k stars, AGPL-3.030 out of 100

Reviews

170 out of 100

Morphic

Search engine that answers with citations and renders rich inline components

9.2k stars, Apache-2.0, last commit Oct 2026

Morphic runs web searches and returns cited answers, rendering inline components such as images, grids and headings from a streamed JSON spec instead of plain markdown. It offers Quick and Adaptive search modes and works with OpenAI, Anthropic, Google, Ollama, Vercel AI Gateway and OpenAI-compatible models. Docker Compose brings up PostgreSQL, Redis, SearXNG and the app, with chat history stored in PostgreSQL.

Strengths

  • Compose file bundles PostgreSQL, Redis and SearXNG, so no search API key is required
  • Supports Tavily, SearXNG, Brave and Exa as search providers
  • Model selector detects providers, including local Ollama and OpenAI-compatible endpoints
  • Auth is switchable between Supabase, better-auth and none

Weaknesses

  • Full stack needs four containers: PostgreSQL, Redis, SearXNG and the app
  • Supabase is the default auth provider unless ENABLE_AUTH=false or AUTH_PROVIDER is set
  • Needs at least one AI provider API key or a local Ollama setup
  • README gives no RAM or hardware requirements
  • Docker + Compose
  • Needs PostgreSQL, Redis, SearXNG, Supabase Auth (optional)
  • Models: OpenAI, Anthropic, Google, Ollama, Vercel AI Gateway
  • port 3000
Repository on GitHub ↗ (opens in a new tab)
265 out of 100

Local Deep Research

Agentic research assistant with local LLMs, SearXNG and encrypted libraries

9.2k stars, MIT, last commit Oct 2026

Runs multi-step research across the web, academic engines and your own documents using Ollama or any OpenAI-compatible endpoint, with a LangGraph agent that picks engines adaptively and writes cited reports. Each user gets an AES-256 SQLCipher database, and egress scopes limit which engines and providers a run may use. Web UI on port 5000 via Docker, Compose or pip, for privacy-focused researchers.

Strengths

  • Reports about 95% SimpleQA fully local on one RTX 3090 with Qwen3.6-27B
  • Per-user SQLCipher databases; keys derived from the password, never stored
  • No telemetry; Docker images signed with Cosign, with SLSA provenance and SBOMs
  • Downloaded sources build a searchable, embedded personal library

Weaknesses

  • Needs Ollama (or an LLM endpoint) and SearXNG running separately
  • Private or localhost engine URLs are blocked unless an operator env var allows them
  • Requires an AVX-capable x86-64 CPU; older CPUs crash with Illegal instruction
  • docker run --network host only works on native Linux; Docker Desktop needs Compose
  • GPU optional
  • Docker + Compose
  • Needs Ollama or OpenAI-compatible LLM endpoint, SearXNG, SQLCipher (bundled wheels)
  • Models: Ollama models (e.g. gpt-oss:20b, Qwen3.6-27B), any OpenAI-compatible endpoint
  • port 5000
Repository on GitHub ↗ (opens in a new tab)
364 out of 100

GPT Researcher

Research agent that writes cited reports from web and local documents

30k stars, Apache-2.0, last commit Sep 2026

Planner and execution agents generate research questions, scrape 20+ sources, filter passages (Jev by default, BM25 fallback with no key) and write cited reports over 2,000 words, exportable to PDF and Word. Runs as a FastAPI server on port 8000 with a static or Next.js frontend, or as a pip package; local PDF, Office, CSV and Markdown files can be sources. For analysts automating long-form research.

Strengths

  • Deep Research mode: tree-like exploration, about 5 minutes and $0.40 per run on o3-mini
  • Hybrid retrievers: Tavily plus MCP servers such as GitHub as research sources
  • Works with any OpenAI-compatible endpoint via OPENAI_BASE_URL
  • Multi-agent LangGraph and AG2 variants produce 5-6 page PDF, DOCX and Markdown reports

Weaknesses

  • Default setup needs OpenAI and Tavily API keys
  • Jev context filtering needs a TYPESAFE_API_KEY; the fallback is keyword BM25
  • Python 3.12 or later required
  • Disclaimer labels the project experimental and for academic purposes
  • no GPU
  • Docker + Compose
  • Needs OpenAI or OpenAI-compatible LLM API, Tavily API key (default retriever), TypeSafe API key (optional Jev filter)
  • Models: OpenAI models, any OpenAI-compatible endpoint via OPENAI_BASE_URL, Gemini 2.5 Flash Image (inline images)
  • port 8000
Documentation ↗ (opens in a new tab)Website ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)
#455 out of 100

Vane

Self-hosted answer engine with cited sources over SearXNG

37.2k stars, MIT, last commit Sep 2026

Next.js answer engine (formerly Perplexica) that runs searches through a bundled SearXNG instance, then answers with citations using Ollama, OpenAI-compatible servers, OpenAI, Anthropic, Gemini or Groq models. Offers Speed, Balanced and Quality modes, web, discussion and academic sources, image and video search, file uploads and a search API. One container on port 3000; a slim image uses your own SearXNG.

Strengths

  • Single Docker image bundles SearXNG; no search API key needed
  • Local models via Ollama or any OpenAI-compatible server, plus cloud providers
  • Browser search-engine shortcut via /?q=%s and a REST search API
  • Slim image works with an existing SearXNG (JSON format and Wolfram Alpha enabled)

Weaknesses

  • No authentication yet; listed as an upcoming feature
  • Own-SearXNG setups must enable JSON output and Wolfram Alpha or searches fail
  • Tavily and Exa search backends are marked coming soon
  • Ollama on Linux must listen on 0.0.0.0 for the container to reach it
  • no GPU
  • Docker + Compose
  • Needs SearXNG (bundled in the default image), LLM provider (Ollama, OpenAI-compatible server or cloud API)
  • Models: Ollama, OpenAI, Anthropic Claude, Google Gemini, Groq
  • port 3000
Repository on GitHub ↗ (opens in a new tab)
#530 out of 100

MAESTRO

Multi-agent research platform that writes long reports from documents and web

1.5k stars, AGPL-3.0, last commit Apr 2026

Planning, Research, Reflection and Writing agents run research missions over uploaded PDF, Word and Markdown documents and web search, producing long reports with visible agent steps. Retrieval uses BGE-M3 embeddings in PostgreSQL with pgvector; any OpenAI-compatible API, including Azure OpenAI, can serve the models. Docker Compose stack on http://localhost with CPU and NVIDIA variants.

Strengths

  • Mission checkpoints allow pause, resume and writing-phase recovery
  • Local embeddings (BGE-M3) and pgvector; local LLMs via OpenAI-compatible API
  • Search providers: Tavily, LinkUp, Jina and SearXNG
  • CPU-only compose file plus automatic NVIDIA GPU detection

Weaknesses

  • 16 GB RAM minimum (32 GB recommended) and 30 GB disk
  • Alpha (v0.1.10-alpha); last commit April 2026
  • Dual-licensed AGPLv3 or commercial; proprietary use needs a paid license
  • First startup takes 5 to 10 minutes while models download
  • RAM ≥ 16 GB
  • GPU optional
  • Compose
  • Needs Docker Compose v2+, API key for an AI provider or an OpenAI-compatible endpoint, PostgreSQL with pgvector (in compose)
  • Models: OpenAI-compatible APIs, Azure OpenAI (GPT-5), BGE-M3 embeddings
  • port 80
Documentation ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)