#55th of 5 in Search
MAESTRO
Multi-agent research platform that writes long reports from documents and web
- Stars
- 1.5k
- License
- AGPL-3.0
- Last commit
- Apr 2026
- Last release
- Oct 2025
Overview
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.
Who it is for: Researchers managing document-heavy research projects
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
What it needs
- 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
Also in Search
See all 5| Rank | Project | Score |
|---|---|---|
| 1 | MorphicSearch engine that answers with citations and renders rich inline components | 70 out of 100 |
| 2 | Local Deep ResearchAgentic research assistant with local LLMs, SearXNG and encrypted libraries | 65 out of 100 |
| 3 | GPT ResearcherResearch agent that writes cited reports from web and local documents | 64 out of 100 |
| #4 | VaneSelf-hosted answer engine with cited sources over SearXNG | 55 out of 100 |