--- title: "WeKnora vs deep-searcher" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tencent-weknora-vs-zilliztech-deep-searcher" tools: ["tencent-weknora", "zilliztech-deep-searcher"] --- # WeKnora vs deep-searcher *GraphCanon updated Aug 18, 2026* ## Verdict Pick WeKnora if weKnora is an open-source LLM knowledge platform that transforms raw documents into a queryable RAG, autonomous reasoning agent, and self-maintaining Wiki. It is built in Go and offers flexibility through its Docker Com포; pick deep-searcher if deepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under. [WeKnora](https://weknora.weixin.qq.com) reports 20k GitHub stars, 2.9k forks, and 556 open issues, last pushed Aug 16, 2026. [deep-searcher](https://zilliztech.github.io/deep-searcher/) has 8.1k stars, 775 forks, and 53 open issues, last pushed Nov 19, 2025. Figures are from public GitHub metadata via [WeKnora's repository](https://github.com/Tencent/WeKnora) and [deep-searcher's repository](https://github.com/zilliztech/deep-searcher). | | [WeKnora](/tools/tencent-weknora.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Tagline | Open-source LLM knowledge platform for creating a queryable RAG, autonomous reasoning agent, and self-maintaining Wiki. | Open Source Deep Research Alternative to Reason and Search on Private Data. | | Stars | 19,992 | 8,060 | | Forks | 2,877 | 775 | | Open issues | 556 | 53 | | Language | Go | Python | | Adopt for | WeKnora is an open-source LLM knowledge platform that transforms raw documents into a queryable RAG, autonomous reasoning agent, and self-maintaining Wiki. It is built in Go and offers flexibility through its Docker Com포 | DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [WeKnora](/tools/tencent-weknora.md) | [deep-searcher](/tools/zilliztech-deep-searcher.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 272d | | Open issues (now) | 556 | 53 | | Stars delta | +1.5k (30d) | +59 (30d) | | Open issues delta | +145 (30d) | 0 (30d) | | Full report | [trust report](/tools/tencent-weknora/trust.md) | [trust report](/tools/zilliztech-deep-searcher/trust.md) | ## Decision facts: WeKnora - **Pricing:** freemium - Free and open-source under the MIT license. - **Adopt for:** WeKnora is an open-source LLM knowledge platform that transforms raw documents into a queryable RAG, autonomous reasoning agent, and self-maintaining Wiki. It is built in Go and offers flexibility through its Docker Com포 ## Decision facts: deep-searcher - **Adopt for:** DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license. ## Choose when ### Choose WeKnora if… - WeKnora is primarily Go; deep-searcher is Python. - License: WeKnora is Other, deep-searcher is Apache-2.0. - Pricing: Free and open-source under the MIT license.. - Tags unique to WeKnora: agentic, ai, chatbot, embeddings. - Also covers Evaluation & Observability. - Use WeKnora if you prefer the Go (Golang) language ecosystem. ### Choose deep-searcher if… - deep-searcher is primarily Python; WeKnora is Go. - License: deep-searcher is Apache-2.0, WeKnora is Other. - Tags unique to deep-searcher: agentic-rag, deep-research, llm, vector-database. - When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ## When NOT to use WeKnora - Avoid WeKnora if your team's primary expertise is not in Go (Golang). - If you require real-time updates that are more seamlessly integrated with external systems, as WeKnora focuses on internal maintenance processes. - WeKnora might not be the best fit if your specific needs require proprietary licensing or access to features beyond its MIT License. ## When NOT to use deep-searcher - Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. - Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ## Common questions ### What is the difference between WeKnora and deep-searcher? WeKnora: Open-source LLM knowledge platform for creating a queryable RAG, autonomous reasoning agent, and self-maintaining Wiki.. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose WeKnora over deep-searcher? Choose WeKnora over deep-searcher when WeKnora is primarily Go; deep-searcher is Python; License: WeKnora is Other, deep-searcher is Apache-2.0; Pricing: Free and open-source under the MIT license.; Tags unique to WeKnora: agentic, ai, chatbot, embeddings; Also covers Evaluation & Observability; Use WeKnora if you prefer the Go (Golang) language ecosystem. ### When should I choose deep-searcher over WeKnora? Choose deep-searcher over WeKnora when deep-searcher is primarily Python; WeKnora is Go; License: deep-searcher is Apache-2.0, WeKnora is Other; Tags unique to deep-searcher: agentic-rag, deep-research, llm, vector-database; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3. ### When should I avoid WeKnora? Avoid WeKnora if your team's primary expertise is not in Go (Golang). If you require real-time updates that are more seamlessly integrated with external systems, as WeKnora focuses on internal maintenance processes. WeKnora might not be the best fit if your specific needs require proprietary licensing or access to features beyond its MIT License. ### When should I avoid deep-searcher? Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily. ### Is WeKnora or deep-searcher more popular on GitHub? WeKnora has more GitHub stars (19,992 vs 8,060). Stars measure visibility, not whether either tool fits your constraints. ### Are WeKnora and deep-searcher open source? Yes - both are open-source projects on GitHub (WeKnora: Other, deep-searcher: Apache-2.0). ### Where can I find alternatives to WeKnora or deep-searcher? GraphCanon lists graph-backed alternatives at [WeKnora alternatives](/tools/tencent-weknora/alternatives) and [deep-searcher alternatives](/tools/zilliztech-deep-searcher/alternatives) ([WeKnora markdown twin](/tools/tencent-weknora/alternatives.md), [deep-searcher markdown twin](/tools/zilliztech-deep-searcher/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/tencent-weknora-vs-zilliztech-deep-searcher.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, WeKnora or deep-searcher? WeKnora: Very active. deep-searcher: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for WeKnora and deep-searcher? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [WeKnora trust report](/tools/tencent-weknora/trust); [deep-searcher trust report](/tools/zilliztech-deep-searcher/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tencent-weknora`](/api/graphcanon/graph?tool=tencent-weknora) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_