FinRobot

FinRobot

Open-source equity research agent that turns a ticker into a structured report — with every number produced by deterministic operators, not by a language model.

53/100MonitorFree planFreemium

FinRobot is worth a look if you want an open-source equity research workflow you can read and modify. The V2 architecture's compute boundary — 26 deterministic operators as the sole route to a number, with the eight-step pipeline demoted to one of seven callable tools — is a genuine answer to the arithmetic problem that quietly breaks naive finance wrappers, and the docs are candid that V1 still hands that job to the model. If you want general financial Q&A, a general model is cheaper and simpler. If you need a vendor-hosted product with a published service commitment, be aware the scraped pages don't document one.

Verified 57m ago · liveness 53/100 · cite: rightaichoice.com/tools/finrobot

Best for
  • Equity research analysts wanting an inspectable first draft
  • Portfolio managers and investment professionals needing quick structured analysis
  • Financial advisors producing client-ready report templates
  • Finance students learning DCF and comparable valuation
Not ideal for
  • Casual investors wanting stock tips or trading signals
  • Users needing real-time market data feeds
  • Teams that need the vendor to operate the service under a published service commitment
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AdvancedDevelopers comfortable self-hosting should expect the long pole to be standing up the stack and wiring the model access the agents call, not the report itself — the eight-step pipeline is fixed and template-driven, so once it runs, the first report comes out in a single pass. Analysts using someone else's deployment get to first value on the first ticker they enter.WebNo public APIVerified 57m ago
Pricing
Free plan
FreemiumFree tier2 plans2 hidden costs
Learning curve
Advanced
Developers comfortable self-hosting should expect the long pole to be standing up the stack and wiring the model access the agents call, not the report itself — the eight-step pipeline is fixed and template-driven, so once it runs, the first report comes out in a single pass. Analysts using someone else's deployment get to first value on the first ticker they enter.
Runs on
Web
No public API
Who it's for
Equity research analystPortfolio managerDeveloper self-hosting FinRobot
Live sentiment
Is FinRobot actually worth it?

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Skip it if

Skip FinRobot if you want a hosted product where someone else runs and supports the pipeline, or if you need real-time market data and trading signals rather than a structured research draft.

The 30-second take
Biggest gripe

Self-hosting moves the cost off a subscription and onto you: you supply the compute and the underlying model access the agents call.

Price reality

The scraped pages this run did not include a pricing page, so we can't compare FinRobot's cost against named peers — and the earlier seed profile noted only that a Pro tier existed behind a contact step. What we can say from the architecture: the open-source codebase means the recurring cost of V2 is your own compute plus model access, which scales differently from a per-seat research terminal.

In short

FinRobot — Open-source equity research agent that turns a ticker into a structured report — with every number produced by deterministic operators, not by a language model. Best for Equity research analysts wanting an inspectable first draft, Portfolio managers and investment professionals needing quick structured analysis, Financial advisors producing client-ready report templates. Free to use.

Viability Score

53/100
Monitor

How well maintained and how widely used is FinRobot? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this

Recent activity
not measured
Traction
not measured
Site health
95
User sentiment
47
What the vendor publishes
20

Last calculated: October 2026

How we score →

Key Features

  • Automated equity research report generation from a ticker
  • Financial statement analysis (income, balance sheet, cash flow)
  • DCF valuation modeling
  • Comparable company analysis
  • Risk assessment and factor identification
  • Natural language query on financial data
  • 13-chapter structured report on a fixed template
  • V2 lead agent plus 5 role sub-agents (data, analysis, modeling, synthesis, report)
  • 26 deterministic operators as the only path to a number
  • 15 lead-agent tools: 8 conversational, 7 pipeline dispatchers
  • Seven-pipeline registry generated as callable tools
  • Pydantic output_type validated returns on V1's 8 agents
  • ModelRetry recovery when a tool step fails
  • Tauri desktop shell via React + Vite front end
  • Jinja2 + Vue 3 front end for V1

About FinRobot

FreemiumAdvancedNo APIWeb

FinRobot is an open-source AI agent platform for automated equity research. You give it a company ticker or financial data and it returns a structured report covering financial statement analysis, valuation, risk assessment and forward-looking commentary. The docs describe two agent stacks deliberately kept side by side. V1 Report Studio is built on the OpenAI Agents SDK with eight peer agents that each declare a Pydantic output_type — but no tool calls, no handoffs, and no shared state: Python flattens the financials into one text prompt first, so the report is fixed at 13 chapters and every valuation number is produced by a model doing arithmetic in tokens. V2 Research Desk is built on pydantic-ai with one lead agent plus five role sub-agents (data, analysis, modeling, synthesis, report). It carries 15 tools on the lead agent — 8 conversational plus 7 pipeline dispatchers generated from a pipeline registry — and 26 deterministic operators are the only path for a model to obtain a number. That compute boundary is the point: because the harness lets the model choose its next step, the facts had to be put somewhere the model cannot reach. Front ends are Jinja2/Vue 3 for V1 and React/Vite plus a Tauri desktop shell for V2. It suits equity research analysts, portfolio managers, financial advisors and finance students who want a customizable, inspectable first draft, and developers willing to self-host it.

Behind the Verdict

What separates FinRobot from the pile of LLM finance demos is that its documentation is willing to argue with itself. The /docs page states the distinction plainly: in V1 the code decides what happens next and the model fills slots; in V2 the model decides and the code supplies tools, guardrails and a loop. Once the model picks its own path, you can no longer enumerate what it will do — which is exactly why V2 had to take the model's ability to produce a number away. That causal chain, rather than a feature list, is the strongest signal here that someone thought about failure modes. The practical consequences are concrete. V1's eight agents each run once through Runner.run(agent, prompt) with no shared state; nothing learns what anything else produced; wanting a specific sentence out of a 10-K or a different discount-rate assumption leads nowhere. V2's lead agent sees tool results as observations and retries via ModelRetry when a step fails. The pricing consequence is that the report stops being fixed at 13 chapters. Data still arrives through the same funnel — FMP financials, with 26 deterministic operators the only route to a figure. The weaknesses are equally concrete, and they come from the evidence rather than from the architecture. The scrape shows no named underlying models, no context limits and no model-level rate limits. No third-party integrations and no public API documentation appear in the pages reached. V1 is still shipped, and V1 is the version that lets a model do DCF arithmetic in tokens — so the honest advice is to confirm you are on V2 before trusting a valuation section, whatever the interface implies. Where it fits: analysts who want a reproducible first draft on a fixed template, quants and developers comfortable self-hosting something they can read end to end, and students who want to see how a DCF is actually assembled rather than generated. Where it doesn't: anyone wanting stock tips, anyone who needs the vendor to be the one holding the pager, and anyone without the domain knowledge to notice a wrong discount rate.

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Real-world workflow fit

Concrete scenarios for the personas FinRobot actually fits — and what changes day-one when you adopt it.

Equity research analyst

You pull up a ticker in the V2 Research Desk, let the lead agent dispatch the eight-step pipeline through the seven pipeline dispatchers, and get back a 13-chapter draft with the valuation section produced by the 26 deterministic operators.

Outcome: An inspectable first draft you can defend line by line, because the numbers came from functions and not from tokens — and you can ask follow-up questions through the eight conversational tools without regenerating the whole report.

Portfolio manager

You want a quick comparable-multiples read across sector peers before a morning meeting, so you run the comparable-analysis path and then query the financials conversationally.

Outcome: A structured sector view in the time it takes to read it, with the option to trace any figure back to the operator that computed it.

Developer self-hosting FinRobot

You need a capability the harness doesn't have, so you add a tool to the lead agent rather than editing a fixed pipeline — the pattern the docs describe as the difference between V1 and V2.

Outcome: A new capability in the agent's repertoire without forking the report sequence, since the eight-step pipeline is just one of seven tools the agent can choose to call.

Use Cases

Limitations

  • The scraped pages disclose no underlying model names, context limits or model-level rate limits.
  • No third-party integrations and no public API documentation appear in the sources reached this run, and the changelog/release-notes page was not reached, so release cadence is unverified.
  • Both stacks ship at once: V1 lets a model produce valuation numbers in tokens, so the compute boundary only holds on V2.
  • No dated changelog entries are visible in the evidence.

as of 2026-10-09

Verification history

We have re-verified FinRobot 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.

  1. — re-checked, vendor evidence unchanged
  2. — re-checked, vendor evidence unchanged
  3. — re-checked, vendor evidence unchanged
  4. — re-checked, vendor evidence unchanged
  5. — re-checked, vendor evidence unchanged
  6. — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it

Showing the 6 most recent of 7 verification passes.

Free to cite with attribution — this page re-verifies continuously.

12-month cost

Project the real annual outlay, including the implied monthly cost when only an annual tier is published.

Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly

Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.

Hidden costs & gotchas

What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.

  • Self-hosting moves the cost off a subscription and onto you: you supply the compute and the underlying model access the agents call.
  • V1 and V2 are both shipped, so the deterministic-coverage you assumed from the docs only holds if the report you run actually goes through V2's operator path.

Where the pricing makes sense

The company stage and team size where FinRobot's pricing actually pencils out — and where peers do it cheaper.

The scraped pages this run did not include a pricing page, so we can't compare FinRobot's cost against named peers — and the earlier seed profile noted only that a Pro tier existed behind a contact step. What we can say from the architecture: the open-source codebase means the recurring cost of V2 is your own compute plus model access, which scales differently from a per-seat research terminal.

Setup time & first value

How long it actually takes to get something useful out of FinRobot — broken out by persona, not the marketing-page minute.

Developers comfortable self-hosting should expect the long pole to be standing up the stack and wiring the model access the agents call, not the report itself — the eight-step pipeline is fixed and template-driven, so once it runs, the first report comes out in a single pass. Analysts using someone else's deployment get to first value on the first ticker they enter.

Switching to or from FinRobot

How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.

Migrating in
  • →From a manual spreadsheet DCF: move the discount-rate and cash-flow assumptions into the editor's inputs and let the deterministic operators compute the outputs.
  • →From a general-purpose chat assistant: switch to the 13-chapter template when you need consistent, comparable output across companies rather than an ad-hoc answer.
  • →From a closed research terminal you can't inspect: run FinRobot alongside it and diff the valuation section against what you already trust.
Migrating out
  • ↗To a general-purpose model: if you only need occasional financial Q&A, the template and pipeline are overhead you don't need.
  • ↗To a commercial research terminal: when the blocking requirement becomes the vendor operating the service and holding the support obligation, not the analysis itself.

Resources & Guides

Tutorials & Learning

YouTube returned 6 videos for “FinRobot”, and we withheld 6: 6 could not be judged, because “FinRobot” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about FinRobot.

Official links

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