Caddi vs TradingAgents: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Caddi and TradingAgents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Caddi

Caddi, Inc.

Freemium

Agent that discovers repetitive back-office work, learns it from a screenshare, and automates it across the tools a firm already uses.

Automatic Process Discovery: Reads the systems a firm already uses and surfaces the most repetitive jobs with volume and confidence scores, so nobody has to guess what to automate first.
Record-to-Code Training: An employee screenshares and narrates a workflow once, and Caddi converts that recording into an API-driven hybrid agent with the rules it captured.
Loop Studio and Loopy: A chat and studio interface where staff start sessions, ask what to automate, inspect the loop diagram, and question any past run in plain language.
150+ Native Integrations: Runs inside Clio, Salesforce, Microsoft 365, NetDocuments, DocuSign, iManage, Wealthbox, Tamarac and dozens more, including cross-app pairs.
Exception Flagging: Executes the job as demonstrated and raises anything unusual, such as a half-signed envelope or an existing document version, instead of overwriting silently.
Governed Runs and Audit Trail: Every run is logged with run history from 30 days up to 2 years, MFA, security logs, SAML SSO and action-level audit logging on higher tiers.
Scout and Advanced Integrations: Business and Enterprise tiers add Scout plus advanced and centrally governed integrations, multi-instance support and priority loop execution.
Credit-Based Metering: Work is metered in credits (standard AI data extraction costs one credit per page) with active loop limits that scale by plan.

Client Intake and Conflict Checks: Automate new-business intake and run conflict checks across matter and CRM systems without manual re-keying.
Email Triage at Volume: Classify a full inbox, route remittances to payables, file executed contracts, and leave personal mail untouched.
Document Filing and Version Control: Pull the executed copy from DocuSign, verify both signatures, and file it into NetDocuments as a new version with the firm's naming convention.
Pre-Billing and Time Entry: Draft time narratives from calendar blocks and push prebill batches through review in Clio or Aderant.
Cross-System Data Sync: Keep matters, contacts and amounts consistent between Salesforce, document management and billing systems as records change.
Report Generation and Follow-Ups: Produce recurring reports and schedule client follow-ups on a fixed cadence with every step logged.

Tauric Research

Free

An open-source multi-agent LLM framework that mirrors a trading firm, with analyst, researcher, trader and risk agents debating each decision.

Analyst Team: Four specialized agents — fundamentals, sentiment, news and technical — each producing an independent report on a ticker before any decision is made.
Bull vs Bear Debate: Opposing researcher agents critically assess the analyst reports through structured debate, balancing potential gains against inherent risks.
Risk Management Chain: A risk team evaluates volatility and liquidity and reports to a portfolio manager agent who approves or rejects each proposed transaction.
Look-Ahead Protection: A verified data-access contract with point-in-time filtering across FRED macro data, Alpha Vantage and social sentiment so backtests do not leak future information.
Multi-Provider LLM Registry: Configurable backbones across OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, GLM, MiniMax, Mistral, Groq, NVIDIA, Kimi, Bedrock, Azure and local Ollama endpoints.
Checkpoint Resume: LangGraph graph-shape-aware checkpointing with a persistent decision log, so long runs can resume from where they stopped.
CLI and Package Interfaces: A command-line runner for interactive use plus an importable Python package for embedding the agent graph in other research code.
Docker and Local Deployment: Prebuilt Docker usage and Ollama support for running the whole agent stack against local models.

Agent Architecture Research: Studying how debate and role separation between LLM agents changes the quality of a complex decision.
Strategy Backtesting: Replaying historical periods with point-in-time data to evaluate how an agent-driven approach would have behaved.
Model Comparison: Swapping backbone LLMs across providers to measure how model choice affects reasoning quality on the same task.
Financial NLP Pipelines: Reusing the fundamentals, news and sentiment analyst components as building blocks in other market-research tooling.
Multi-Agent Teaching Material: Demonstrating analyst, debate, execution and risk-review roles as a worked example of an agentic workflow.
Local and Private Experimentation: Running the full framework against self-hosted Ollama models when market data or prompts cannot leave an environment.