ByteDance-Seed/m3-agent

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1445 stars · 117 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

M3-Agent is a multimodal agent framework from ByteDance Seed that processes real-time visual and auditory inputs to build entity-centric long-term memory (episodic and semantic) and performs iterative memory-based reasoning. It is released alongside M3-Bench, a long-video question-answering benchmark, and RL-trained agent models on Hugging Face.

Use cases

• build a multimodal agent with long-term memory
• question answering over long videos
• research on memory-based reasoning in agents
• evaluate multimodal agents on long-video benchmarks
• train an agent with reinforcement learning for video understanding
• process real-time camera and audio streams into semantic memory

When to choose

• you need a research-grade multimodal agent with persistent, entity-centric memory
• you want to benchmark long-video QA and memory-based reasoning
• you want to build on or fine-tune the released M3-Agent models

When to avoid

• you need a production-ready personal assistant out of the box
• you only need simple video transcription or captioning without memory
• you cannot run GPU-heavy multimodal inference

Facets

framework · maturity active

agent-framework machine-learning llm-training rag video-processing speech-recognition computer-vision artificial-intelligence large-language-models machine-learning python multimodal-agent long-term-memory reinforcement-learning video-qa benchmark iclr-2026 semantic-memory episodic-memory ai-agents video research linux gpu

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markdown · JSON · MCP: product_card(name="ByteDance-Seed/m3-agent")