AI Berkshire Framework for Multi-Agent Financial Research
AI Berkshire is an open-source research framework compatible with Claude Code and Codex that models value investing methodologies. It coordinates four independent parallel agents using Python's decimal module to avoid precision loss.
Impact: Medium
Why it matters
You can examine this code to learn advanced terminal-agent workflows, tool routing, and highly-structured multi-agent orchestration.
TL;DR
- 01AI Berkshire models complex financial decision-making using structured, multi-agent frameworks.
- 02The project demonstrates how to completely eliminate LLM math errors by offloading math to Python's decimal library.
- 03It provides concrete commands and skills for both Claude Code and Codex environments.
Key facts
- Supported Clients
- Claude Code, Codex
- Math Implementation
- Python decimal.Decimal
Orchestrating Multiple Experts
The primary command /investment-team executes parallel subagents mimicking expert personas. Rather than operating within a single context window, each analyst searches and gathers financial details independently:
git clone https://github.com/xbtlin/ai-berkshire.gitAll inputs are synthesized by a Team Lead agent, delivering concrete action items instead of conversational hedge-phrases.
Bypassing Tool Confirmation Prompts
Because this framework frequently triggers local commands and financial scraping, Claude Code normally asks for execution permissions at every turn. In trusted directories, you can bypass these hurdles via the danger flag:
claude --dangerously-skip-permissionsFinancial Validation Rigor
To ensure precision during calculations, the framework triggers an isolated checking script:
python3 tools/financial_rigor.py verify-market-cap \
--price 510 --shares 9.11e9 --reported 4.65e12 --currency HKDThis prevents the model from generating reports based on incorrect decimal alignments or mixed currencies.
Try it in 2 minutes
git clone https://github.com/xbtlin/ai-berkshire.gitbash
✓ When to use
- When studying complex agent orchestration design patterns, or building structured qualitative research pipelines.
✕ When NOT to use
- When seeking automated, high-frequency, or real-time trading logic.
What to do today
- Clone the AI Berkshire repository.
- Review the tools/financial_rigor.py logic to see how local Python scripts gatekeep LLM outputs.
- Test running Claude Code commands in permission-bypass mode to speed up heavy workflows.
Sources