Building Multi-Step Research Agents with Oxlo.ai and Llama 3.3

Short summary

Build an autonomous research agent that decomposes questions into sub-questions, gathers evidence across multiple model calls, and synthesizes findings into structured reports. Uses Oxlo.ai's flat-rate pricing and Llama 3.3 70B with the OpenAI SDK. Demonstrates how orchestrated reasoning loops replace monolithic chat for complex knowledge work.

• •Step-by-step Python tutorial for multi-step research agent pipelines
• •Leverages Oxlo.ai's flat-rate pricing to keep costs predictable with long contexts
• •Shows planning, evidence gathering, and synthesis—practical agentic workflow pattern

Generated with AI, which can make mistakes.