Bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in
Weaviate is praised for its robust AI capabilities and ease of integration, often achieving high ratings ranging from 4 to 5 stars on platforms like G2. Users appreciate its open-source nature and ability to handle complex AI tasks efficiently, as noted in various social mentions on forums like Reddit and Hacker News. However, some users reference challenges with controlling AI functions, tracking costs, and debugging when running AI agents. The pricing sentiment is generally positive, with a focus on its value for open-source projects, contributing to an overall strong reputation in the AI tools market.
Mentions (30d)
1
Avg Rating
4.7
20 reviews
Platforms
3
GitHub Stars
15,926
1,241 forks
Weaviate is praised for its robust AI capabilities and ease of integration, often achieving high ratings ranging from 4 to 5 stars on platforms like G2. Users appreciate its open-source nature and ability to handle complex AI tasks efficiently, as noted in various social mentions on forums like Reddit and Hacker News. However, some users reference challenges with controlling AI functions, tracking costs, and debugging when running AI agents. The pricing sentiment is generally positive, with a focus on its value for open-source projects, contributing to an overall strong reputation in the AI tools market.
Features
Use Cases
Industry
information technology & services
Employees
71
Funding Stage
Series B
Total Funding
$67.7M
1,007
GitHub followers
138
GitHub repos
15,926
GitHub stars
20
npm packages
27
HuggingFace models
338,540
npm downloads/wk
100,424,094
PyPI downloads/mo
Show HN: Open-sourced AI Agent runtime (YAML-first)
Been running AI agents in production for a while and kept running into the same issues:<p>controlling what they can do tracking costs debugging failures making it safe for real workloads<p>So we built AgentRuntime, the infrastructure layer we wished we had. Not an agent framework, but the platform around agents:<p>policies memory workflows observability cost tracking RAG governance<p>Agents and policies are defined in YAML, so it's infrastructure-as-code rather than a chatbot builder. Example – agents and policies in YAML agent.yaml – declarative agent config name: support_agent<p>model: provider: anthropic name: claude-3-5-sonnet<p>context_assembly: enabled: true<p><pre><code> embeddings: provider: openai model: text-embedding-3-small providers: - type: knowledge config: sources: ["./docs"] top_k: 3 </code></pre> policies/safety.yaml – governance as code name: security-policy<p>rules: - id: block-file-deletion condition: tool.name == "file_delete" action: deny<p>CLI – run and inspect Create and run an agent agentctl agent create researcher --goal "Research AI safety" --llm gpt-4 agentctl agent run researcher agentctl runs watch <run-id><p>Manage policies agentctl policy list agentctl policy activate security-policy 1.0.0<p>RAG – ingest docs and ground responses in your knowledge base agentctl context ingest ./docs agentctl run --agent agent.yaml --goal "How do I deploy?"<p>Agent-level debugging agentctl debug -c agent.yaml -g "Analyze this dataset."<p>Cost tracking is exposed via the API (per agent/tenant), and the Web UI shows analytics. The workflow debugger (breakpoints, step-through) lives in the pkg layer; the CLI debug is for agent execution. What’s in there Governance<p>Policy engine (CEL) Risk scoring Encrypted audit logs RBAC Multi-tenancy Fully YAML-configurable<p>Orchestration<p>Visual workflow designer (React Flow) DAG workflows Multi-agent coordination Conditional logic Plugin hot-reload Workflow marketplace<p>Memory & Context<p>Working memory Persistent memory Semantic memory Event log<p>Context assembly combines:<p>policies workflow state memory tool outputs knowledge<p>RAG features:<p>embeddings (OpenAI or local) SQLite for development Postgres + vector stores in production<p>Observability<p>Cost attribution via API SLA monitoring Distributed tracing (OpenTelemetry) Prometheus metrics Deterministic replay (5 modes)<p>Production<p>Kubernetes operator (Agent, Workflow, Policy CRDs) Helm charts Istio config Auto-scaling Backup / restore GraphQL + REST API<p>Implementation<p>~50k LOC of Go Hundreds of tests Built for production (in mind)<p>Runs on: Local<p>SQLite In-memory runtime<p>Production<p>Postgres Redis Qdrant / Weaviate<p>Happy to answer questions or help people get started
View originalPricing found: $45 /mo, $400 /mo, $45 / month, $400 / month, $0.01668 / 1m
g2
What do you like best about Weaviate?i really like how quick it is to get going with weaviate. you don’t need to spend days messing around with configs or setups. just spin it up and start pushing data in, which makes it perfect when you’re prototyping or just testing ideas. i also like that it handles both vectors and metadata together, so you can try hybrid searches without building a whole extra system. overall, it feels beginner friendly but still powerful enough to run real demos fast Review collected by and hosted on G2.com.What do you dislike about Weaviate?the main issue is performance when you try to scale things up. it feels fine for small to medium datasets, but once the load grows the latency can get kinda unpredictable. sometimes queries just take longer than expected even with good hardware. for experiments it’s fine, but for production where speed really matters it can be frustrating. i’d say scaling is the weak point right now. Review collected by and hosted on G2.com.
What do you like best about Weaviate?Weaviate makes it incredibly easy to implement semantic search and generative AI applications. The integration with Python and REST APIs is smooth, and the support for hybrid search (vector + keyword) is powerful for real-world use cases. Its modular design and integrations with tools like OpenAI, Cohere, and Hugging Face let you plug in embeddings quickly. The documentation is clear, and the community is active and responsive, which shortens the learning curve. Review collected by and hosted on G2.com.What do you dislike about Weaviate?The cloud pricing can scale up quickly if you’re handling large datasets, and the learning curve for more advanced features (like sharding or schema design) can be a bit steep for beginners. Some SDKs lag slightly behind the core feature set, so you occasionally need to rely on REST calls. More built-in visualization or monitoring features would make it easier to track cluster performance without third-party tools. Review collected by and hosted on G2.com.
What do you like best about Weaviate?Weaviate stores the data objects as vectors in multidimensional space, so you can search and find relationships between the data based on semantic meaning, resulting in great and stable accuracy. Their customer support is impeccable, and there's a great community environment too in Slack. Review collected by and hosted on G2.com.What do you dislike about Weaviate?Could focus more on AI docs for direct API access. Review collected by and hosted on G2.com.
What do you like best about Weaviate?The interface is impressively clean and intuitive, making it easy to navigate even as a newcomer to vector databases. The setup and testing process is refreshingly straightforward - you can get up and running quickly without wrestling through complex configuration steps. What really stands out is their commitment to continuous improvement; they're consistently rolling out new products and features that genuinely make the developer experience easier. Their free office hours, workshops, and events are incredibly valuable for newcomers - having direct access to experts who can answer questions and provide guidance makes the learning curve much more manageable. The integration process feels well-thought-out, and the documentation guides you through implementation without unnecessary complexity. Review collected by and hosted on G2.com.What do you dislike about Weaviate?As someone just getting started, it's hard to identify major pain points yet. The learning curve for vector database concepts and who to use them themselves can be steep if you're new to the space, though that's more about the technology category than Weaviate specifically. Review collected by and hosted on G2.com.
What do you like best about Weaviate?The most helpful about Weaviate is that you can store, vectorize, and search data all within one system — no need to juggle multiple tools and no need to precompute embeddings it has built in vectorization. Also a good community as in it is actively maintained. Review collected by and hosted on G2.com.What do you dislike about Weaviate?If you're new, it can feel like you're piecing things together from scattered sources. Also, it is heavy to run locally. I used it in my windows laptop and my machine used to groan a bit. Review collected by and hosted on G2.com.
What do you like best about Weaviate?The tech support is fantastic: ticket ownership, fast turn-around times, professional, personable, and proactively willing share product knowledge with the end user to better help them understand the Weaviate product. Thank you. Review collected by and hosted on G2.com.What do you dislike about Weaviate?Nothing. We had one issue with our serverless cloud and Weaviate support assigned four engineers to quickly resolve the issue. Review collected by and hosted on G2.com.
What do you like best about Weaviate?Weaviate was so easy to integrate and use. The documentation is easy to follow, the Weaviate AI is super helpful for navigating common problems, and their customer support is next level! Facing a challenge is somehow a pleasant experience - you get a swift response and an expert perspective on your problem. Review collected by and hosted on G2.com.What do you dislike about Weaviate?It would've been great to have PHP instructions in the docs, or just simple HTTP requests. Review collected by and hosted on G2.com.
What do you like best about Weaviate?I really enjoyed learning about Query agents, transformation agent and personalized agent Review collected by and hosted on G2.com.What do you dislike about Weaviate?Nothing, everything was spectacular I enjoyed all the guest speakers Review collected by and hosted on G2.com.
What do you like best about Weaviate?Participated in workshop by Weaviate in Dallas and these guys know what they are doing and built an amazing product. The hand holding we had during the session is amazing. Review collected by and hosted on G2.com.What do you dislike about Weaviate?Cant think of any!, It was both great education and we will explore of feasibility. Review collected by and hosted on G2.com.
What do you like best about Weaviate?The responsiveness of the support team and the ability to speak to real people about issues you may be having. The product has great functionality and enables quick wins in terms of integrating to our systems Review collected by and hosted on G2.com.What do you dislike about Weaviate?Release process is usually smooth but there have been some "undocumented" gotchas. But team helped to resolve. Review collected by and hosted on G2.com.
Skill Seekers v3.5: 10 new source types, 12 LLM platforms, marketplace pipeline, agent-agnostic AI, and prompt injection scanner
Hey r/ClaudeAI — sharing the latest update on Skill Seekers, the open-source tool that converts documentation into Claude Code skills. A lot has changed since the v3.2 post, so here's what's new across 3 releases (v3.3 → v3.5.1). What's new 10 new source types (17 total) You can now generate skills from Notion, Confluence, HTML files, OpenAPI specs, AsciiDoc, PowerPoint, RSS feeds, man pages, chat exports (Slack/Discord), and unified multi-source configs — on top of the original web, GitHub, PDF, Word, EPUB, video, and local codebase sources. 12 LLM platforms Skills now package for Claude, OpenAI, Gemini, Kimi, DeepSeek, Qwen, OpenRouter, Together AI, Fireworks AI, OpenCode, Markdown, and MiniMax. Plus RAG framework exports for LangChain, LlamaIndex, Haystack, ChromaDB, FAISS, Weaviate, Qdrant, and Pinecone. Agent-agnostic AI enhancement Enhancement is no longer locked to Claude. The new AgentClient abstraction supports Claude, Kimi, Codex, Copilot, OpenCode, and custom agents. It auto-detects which agent to use from your API keys, or you can specify with --agent. Marketplace pipeline You can now publish skills directly to Claude Code plugin marketplace repositories and manage multiple marketplace registries. Config sources can be pushed and synced across repos. Prompt injection scanner A built-in workflow scans scraped content for injection patterns — role assumption, instruction overrides, delimiter injection, hidden instructions. Runs automatically as the first stage in default and security-focused workflows. Flags suspicious content without removing it so you can review. One-command auto-detection skill-seekers create https://docs.example.com/ skill-seekers create owner/repo skill-seekers create ./my-project skill-seekers create document.pdf One command figures out the source type and routes to the right scraper. No more separate subcommands. Headless browser rendering JavaScript SPA sites (React, Vue, etc.) that return empty HTML shells now work with --browser. Uses Playwright under the hood. Other highlights skill-seekers doctor health check command Kotlin language support in the C3.x codebase analysis pipeline Smart SPA discovery (sitemap.xml + llms.txt + browser nav) Unlimited pages by default (was capped at 500) 3100+ tests passing Full MCP server with 40 tools (works in Claude Code and Cursor/Windsurf) Links GitHub: github.com/yusufkaraaslan/Skill_Seekers PyPI: pip install skill-seekers Free and open source Built with Claude Code. Happy to answer questions or take feedback. submitted by /u/Critical-Pea-8782 [link] [comments]
View originalI built an open source AI Memory Storage that scales, easily integrates, and is smart
I built a super easy to integrate memory storage and retrieval system for NodeJS projects because I saw a need for information to be shared and persisted across LLM chat sessions (and many other LLM feature interactions). It started as a fun side project but it worked really well and I thought others might find it useful as well. I used Claude Opus to code the unit tests and a developer UI sandbox but coded the rest myself. I tried to keep the barrier to use as low as possible so I included built-in support for major LLMs (GPT, Gemini, and Claude) as well as major vector store providers (Weaviate and Pinecone). The memory store works by ingesting and automatically extracting “memories” (summarized single bits of information) from LLM interactions and vectorizing those. When you want to provide relevant context back to the LLM (before a new chat session starts or even after every user request) you just pass the conversation context to the recall method and an LLM quickly searches the vector store and returns only the most relevant memories. This way, we don’t run context size issues as the history and number of memories grows but we ensure that the LLM always has access to the most important context. There’s a lot more I could talk about (like the deduping system or the extremely configurable pieces of the system), but I’ll leave it at that and point you to the README if you’d like to learn more! Also check out the dev client if you’d like to test out the memory palace yourself! https://github.com/colinulin/mind-palace submitted by /u/colin3440 [link] [comments]
View originalShow HN: Open-sourced AI Agent runtime (YAML-first)
Been running AI agents in production for a while and kept running into the same issues:<p>controlling what they can do tracking costs debugging failures making it safe for real workloads<p>So we built AgentRuntime, the infrastructure layer we wished we had. Not an agent framework, but the platform around agents:<p>policies memory workflows observability cost tracking RAG governance<p>Agents and policies are defined in YAML, so it's infrastructure-as-code rather than a chatbot builder. Example – agents and policies in YAML agent.yaml – declarative agent config name: support_agent<p>model: provider: anthropic name: claude-3-5-sonnet<p>context_assembly: enabled: true<p><pre><code> embeddings: provider: openai model: text-embedding-3-small providers: - type: knowledge config: sources: ["./docs"] top_k: 3 </code></pre> policies/safety.yaml – governance as code name: security-policy<p>rules: - id: block-file-deletion condition: tool.name == "file_delete" action: deny<p>CLI – run and inspect Create and run an agent agentctl agent create researcher --goal "Research AI safety" --llm gpt-4 agentctl agent run researcher agentctl runs watch <run-id><p>Manage policies agentctl policy list agentctl policy activate security-policy 1.0.0<p>RAG – ingest docs and ground responses in your knowledge base agentctl context ingest ./docs agentctl run --agent agent.yaml --goal "How do I deploy?"<p>Agent-level debugging agentctl debug -c agent.yaml -g "Analyze this dataset."<p>Cost tracking is exposed via the API (per agent/tenant), and the Web UI shows analytics. The workflow debugger (breakpoints, step-through) lives in the pkg layer; the CLI debug is for agent execution. What’s in there Governance<p>Policy engine (CEL) Risk scoring Encrypted audit logs RBAC Multi-tenancy Fully YAML-configurable<p>Orchestration<p>Visual workflow designer (React Flow) DAG workflows Multi-agent coordination Conditional logic Plugin hot-reload Workflow marketplace<p>Memory & Context<p>Working memory Persistent memory Semantic memory Event log<p>Context assembly combines:<p>policies workflow state memory tool outputs knowledge<p>RAG features:<p>embeddings (OpenAI or local) SQLite for development Postgres + vector stores in production<p>Observability<p>Cost attribution via API SLA monitoring Distributed tracing (OpenTelemetry) Prometheus metrics Deterministic replay (5 modes)<p>Production<p>Kubernetes operator (Agent, Workflow, Policy CRDs) Helm charts Istio config Auto-scaling Backup / restore GraphQL + REST API<p>Implementation<p>~50k LOC of Go Hundreds of tests Built for production (in mind)<p>Runs on: Local<p>SQLite In-memory runtime<p>Production<p>Postgres Redis Qdrant / Weaviate<p>Happy to answer questions or help people get started
View originalRepository Audit Available
Deep analysis of weaviate/weaviate — architecture, costs, security, dependencies & more
Yes, Weaviate offers a free tier. Pricing found: $45 /mo, $400 /mo, $45 / month, $400 / month, $0.01668 / 1m
Weaviate has an average rating of 4.7 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
Key features include: Weaviate Agents, Deployment, Introducing Weaviate Agents, Weaviate Shared Cloud, Weaviate Dedicated Cloud, Query Agent, Transformation Agent, Personalization Agent.
Weaviate is commonly used for: Smart contextual search across unstructured data, Personalization of user experiences, Measuring advertising effectiveness, Building knowledgeable AI agents, Creating agentic workflows, Embedding services for machine learning models.
Weaviate integrates with: OpenAI, AWS Lambda, Google Cloud, Microsoft Azure, TypeScript, Python, Go, JavaScript, GraphQL, REST APIs.
Andrew Ng
Founder at DeepLearning.AI / Coursera
1 mention
Late Interaction combines the best of Keyword and Semantic Search
Mar 24, 2026
Weaviate has a public GitHub repository with 15,926 stars.
Based on user reviews and social mentions, the most common pain points are: cost tracking.
