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LMForge-End-to-End-LLMOps-Platform-for-Multi-Model-Agents
by Haohao-end
End-to-end LLMOps platform for multi-model agents and KB-driven workflows
Python
Updated May 22, 2026
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What It Does
Provides an end-to-end LLMOps platform for building and deploying multi-model agents. Combines model connectors (OpenAI, DeepSeek, Wenxin, Tongyi), knowledge-base management (FAISS/Weaviate), workflow automation, and enterprise security into a Flask + Vue3 UI with one-click Docker deployment. Distinctive features include [multi-model orchestration], integrated vector stores, and production deployment templates.
The Value Proposition
As multi-agent and multi-model setups become common, teams need a single place to manage models, knowledge, and deployments — not just individual agents. LMForge consolidates model adapters, KBs, and workflows so teams can run consistent evaluations and observe agent behavior across models. That visibility is a prerequisite for building agent track records and for pragmatic A2A evaluation workflows in production.
When to Use
Teams building production [orchestration] systems who need integrated knowledge management, orchestration, and repeatable deployments.
How It's Used
- Deploying multi-model agents with consistent KBs and retrievers across environments
- Automating agent workflows and background jobs (Celery) with production deployment templates
- Managing vector stores and retrieval (FAISS/Weaviate) for agent memory and grounding
- Rapidly provisioning full-stack agent demos using one-click Docker and Vue3 UI
Works With
langchainopenai
Topics
agentaiaiagentcelerydeepseekdockerfaiss-vector-databaseflasklangchainlanggraph+6 more
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Keywords
multi-agent orchestrationmulti-model agentsllmopsagent reliability