finrobot
FinRobot is an open-source AI agent framework tailored for financial analysis, built using a modular four-layer architecture that supports multi-agent orchestration with state‑of‑the‑art LLMs.
The full architecture, tutorials, and code are on FinRobot’s GitHub repository at AI4Finance‑Foundation/FinRobot.
Meanwhile, below is a quick deep dive in the FinRobot Tech Stack.
1. Multi-Source LLM Foundation Models Layer
Under the hood, FinRobot supports plug-and-play with a variety of LLMs—including FinGPT, LLaMA series, ChatGLM, Falcon, multimodal LLMs, and FinRL for quantitative tasks—enabling flexible model selection based on task requirements
2. LLMOps & DataOps Layer
This layer implements:
- Smart Scheduler – a dynamic orchestrator that auto-selects and switches between LLMs based on task and performance metrics
- DataOps pipelines – real-time feeds from financial APIs (e.g., Finnhub, YFinance, FMP, SEC filings) that continuously update model inputs
3. Financial LLM Algorithms Layer
This layer applies:
- Chain-of-Thought prompting – decomposing complex financial problems into smaller reasoning steps for clarity and accuracy
- Task-specific fine-tuned LLMs – leveraging models like FinGPT or region-specific LLaMA/ChatGLM variants for improved domain performance
4. Financial AI Agents Layer
FinRobot defines multiple agent types:
- Perception – ingests market, news, filings using utilities (e.g., FinnhubUtils, SECUtils)
- Brain – uses LLMs to interpret data via CoT prompting and reasoning chains
- Action – executes outcomes like equity research, portfolio signals, trades, visual reports
5. Agent Workflow & Orchestration
FinRobot supports both single-agent and multi-agent execution:
- SingleAssistantRAG – RAG-enabled agents combining retrieval and generation
- MultiAssistantWithLeader – a hub-and-spoke model where a Director agent delegates work to specialized agents and aggregates results via the Smart Scheduler
Deployment & Implementation
FinRobot is implemented in Python, structured into modules like agent_library.py, workflow.py, and utilities under data_source/ and functional/, all orchestrated via the Smart Scheduler framework
Advantages of This Stack
- Flexible multi‑LLM support for best-fit models
- Explainable reasoning via chain-of-thought
- Robust, real‑time data integration
- Scalable agent orchestration for complex workflows