Lyra-stellAI/BYO-LLM-WIKI

Description and use

**Brief Summary**
Lyra‑stellAI’s **BYO‑LLM‑WIKI** is an open‑source framework for turning a collection of documents into an LLM‑native knowledge base that supports search, extraction, summarisation, and contextual RAG‑driven Q&A. It automatically builds “skills” from selected context using Claude sub‑agents and a CodeAct pipeline, with a human‑in‑the‑loop review step to ensure quality. A live demo is available at https://byo‑wiki‑demo.onrender.com.

**Value**

• **From ad‑hoc prompts to repeatable workflows:** BYO‑LLM‑WIKI stitches together retrieval, reasoning, and tool‑use into deterministic agent pipelines, letting teams reuse knowledge across projects.
• **Layered knowledge graph + reinforced memory:** The system stores both raw documents and derived entities/relations, enabling richer, context‑aware answers and persistent agent memory.
• **Automated skill generation:** By feeding Claude sub‑agents with curated context, the platform creates reusable tool‑calling “skills” that can be invoked programmatically, accelerating the build‑out of multi‑agent applications.

**Practical Adoption Path**

1. **Prototype:** Clone the repo, run the Docker/CLI quick‑start, and point the ingestion pipeline at your existing docs (Markdown, PDFs, etc.).
2. **Validate:** Use the provided demo UI or API to run search, summarisation, and Q&A queries; iterate on chunking and metadata tagging to improve relevance.
3. **Skill creation:** Define the contexts you want to expose as skills, let the Claude sub‑agents generate the corresponding tool‑call specifications, and review the output via the built‑in human‑review UI.
4. **Integrate:** Wrap the generated skill APIs with your internal services (e.g., ticketing, CRM) using the exposed Python SDK or REST endpoints, and orchestrate multi‑agent flows via the provided orchestration layer.
5. **Scale & monitor:** Deploy the stack on Kubernetes or a managed cloud service, enable logging and RAG performance metrics, and progressively replace manual prompts with the automated pipelines.

**Production Readiness**

• **Activity & community:** 237 ★ on GitHub, recent commits (as of 2026‑06‑25), and clear Python SDK/CLI make the project easy to evaluate and extend.
• **Architecture:** Modular components (ingestion, graph store, RAG engine, skill generator) follow standard OSS patterns, facilitating containerisation and CI/CD integration.
• **Risk considerations:** The license, security audit, and long‑term maintainer commitment still need a final check, but there are no glaring metadata or dependency issues.
• **Overall:** With strong recent activity, clear integration points, and a well‑defined workflow from raw knowledge to reusable LLM‑driven agents, BYO‑LLM‑WIKI is ready for a serious pilot in production environments, provided the final security and licensing review is completed.

Use in a workflow

Lyra-stellAI/BYO-LLM-WIKI helps turn isolated prompts and tools into repeatable agent workflows.

• coordinate multi-agent workflows
• add tool-use pipelines
• standardize agent memory

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.