Show HN: LangAlpha – what if Claude Code was built for Wall Street?

A vibe investing agent harness

LangAlpha is built to help interpret financial markets and support investment decisions.

Every AI finance tool today treats investing as one-shot: ask a question, get an answer, move on. But real investing is Bayesian — you start with a thesis, new data arrives daily, and you update your conviction accordingly. It's an iterative process that unfolds over weeks and months: refining theses, revisiting positions, layering new analysis on top of old. No single prompt captures that.

Inspired by software engineering: a codebase persists, and every commit builds on what came before. Code agent harnesses like Claude Code and OpenCode succeeded by building agents that embrace this pattern, exploring existing context and building on prior work. LangAlpha brings that same insight: give the agent a persistent workspace, and research naturally compounds.

In practice, you create a workspace per research goal ("Q2 rebalance", "data center demand deep dive", "energy sector rotation"). The agent interviews you about your goals and style, produces its first deliverable, and saves everything to the workspace filesystem. Come back tomorrow and your files, threads, and accumulated research are still there.

Progressive Tool Discovery— Any MCP tools loaded as summary in context and full documentation dumped into the workspace, allowing the agent to discover and use tools truly on demand. Also supports binding json tools with skills and only expose to agent when skill is activated.

Programmatic Tool Calling (PTC)— The agent writes and executes Python to process financial data from mcp servers instead of pouring raw data into the LLM context window, enabling complex multi-step analysis while dramatically reducing token waste.

Financial data ecosystem— Multi-tier provider hierarchy with native tools for quick lookups and MCP servers for bulk data processing, charting, and multi-year analysis in sandboxes.

Persistent workspaces— Each workspace maps to a dedicated sandbox with structured directories and a persistent memory file (agent.md) that compounds research across sessions and threads.

Skills for Financial Research— Pre-built workflows for DCF models, initiating coverage reports, earnings analysis, morning notes, document generation, and more — activatable by slash command or auto-detection.

Finance Research Workbench— Web UI with inline financial charts, multi-format file viewer, TradingView charting, real-time WebSocket market data, shareable conversations, and subagent monitoring.

Multi-provider model layer— Provider-agnostic LLM abstraction and automatic failover on error.

Automations— Schedule recurring or one-shot tasks, or set price-triggered automations that fire when a stock or index hits a real-time price condition.

Secretary— Flash agent doubles as a secretary: create and manage workspaces, dispatch deep PTC analyses in the background, monitor running tasks, and retrieve results — all through conversational commands with human-in-the-loop approval.

Agent swarm— Parallel async subagents with isolated context windows, preloaded toolset/skills, mid-execution steering, checkpoint-based resume, and live progress monitoring in the UI.

Live steering— Send follow-up messages while the agent/subagent is working to course-correct, clarify, or redirect without waiting for it to finish.

Middleware stack— 24 composable layers handling skill loading, plan mode, multimodal input, auto-summarization, and context management support long-running agent sessions.

Security & workspace vault— Encryption at rest via pgcrypto, automatic credential leak detection and redaction, sandboxed execution, and per-workspace secret storage for safe agent access.

Channel integrations— Use LangAlpha from Slack, Discord with complete feature support.

Production-ready infrastructure— SSE-streamed agent activity with Redis-buffered reconnection replay, background execution decoupled from HTTP connections, and PostgreSQL-backed state persistence.