📈 This is a great opensource platform showing a solid usecase of how LLMs, RAG+memory can be leveraged for live financial trading.
5.8K Github stars ⭐️
ValueCell is an open source multi agent platform for financial applications where a group of specialized AI agents can research markets, monitor portfolios, and execute trades inside 1 unified system that users run on their own machines.
⚙️ The Core Concepts are that ValueCell separates a user facing orchestrator from the individual agents, so planning, memory, storage, and routing live in a central controller while each agent process focuses only on its own financial job.
The orchestrator receives the query from the user, streams partial responses back to the browser, supports human in the loop corrections, and can push notifications later when an agent finishes a long running task.
Under that controller, an Agent Clients layer speaks a common A2A protocol to external agent frameworks such as LangChain and Agno.
Means the same ValueCell core can host agents written with different toolkits without adding new plumbing every time.
Out of the box the platform ships with 3 main agents, a DeepResearch Agent for fundamental and document analysis, a Strategy Agent for multi asset trading strategies, and a News Retrieval Agent that can track topics and send scheduled news updates.
The DeepResearch Agent automatically retrieves filings and other fundamental documents, analyzes them into structured insights, and then uses large language models to produce interpretable summaries instead of raw dumps of text, which is where the project leans on retrieval augmented generation ideas backed by an embedding powered memory store.
The Strategy Agent is wired for multiple crypto assets and multiple strategies at once, so it can translate natural language trading logic into executable orders and run them continuously while logging every decision and trade.
The News Retrieval Agent behaves like a lightweight scheduler, for example a user can say they want Tesla news every 5 minutes, confirm the schedule, and then see a stream of timestamped headlines and summaries arrive in the chat panel with the option to cancel the job later.
Underneath these agents, ValueCell normalizes access to several LLM providers, right now OpenRouter, SiliconFlow, Google, and OpenAI, so the same agent code can run against different model backends simply by changing environment variables in the `.env` configuration.
You can configuring OpenRouter together with any provider that exposes embedding models, because that combination gives fast model switching plus RAG+Memory features for storing vector representations of documents and conversations that agents can recall later.