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.
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
- 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
- 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
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
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.
Self-hosting moves the cost off a subscription and onto you: you supply the compute and the underlying model access the agents call.
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
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
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
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.
Researching FinRobot? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas FinRobot actually fits — and what changes day-one when you adopt it.
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.
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.
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
- Generate a full equity research report for a public company from its ticker symbol.
- Run a DCF valuation where discount rate and cash flow assumptions are produced by deterministic operators rather than a language model.
- Compare valuation multiples across peer companies in the same sector.
- Assess key risk factors for a stock from recent financials and disclosures.
- Produce a 13-chapter structured research report on a fixed template for consistent output.
- Ask follow-up natural language questions about a company's financials through the conversational tools.
- Add a capability to the V2 harness by registering a new tool rather than editing a pipeline.
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.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — 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.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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.
- →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.
- ↗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
Tools that pair well with FinRobot
Common stack mates teams adopt alongside FinRobot, with the specific reason each pairing earns its keep.
Quant Python Ai
Open-source CLI AI agent that automates financial news research, sentiment analysis, and risk reports for quantitative researchers.
FinceptTerminal
FinceptTerminal is an open-source desktop financial terminal with AI research agents and 41 modules, free under AGPL-3.0 or from $15/user/mo for the private
finbar
AI-native equity research and financial modelling that runs in your browser and your Excel add-in.
Featured Head-to-Head Comparisons
Finrobot vs Bitsgap
If you're a crypto trader seeking automated bots across multiple exchanges, Bitsgap is the clear choice with its specialized bots, demo mode, and AI assistant. For equity research professionals needing automated financial statement analysis and valuation modeling, FinRobot's open-source platform offers customization and depth. Choose based on your asset class: crypto vs equities.
Finrobot vs Screenplayiq
ScreenplayIQ and FinRobot serve entirely different domains—screenwriting vs. equity research—so the choice depends on your profession. ScreenplayIQ is a niche tool for film professionals needing data-driven script feedback and marketability predictions, with a free tier for occasional use. FinRobot is an open-source platform for finance professionals automating research reports and valuations, ideal for those who want customizable, transparent analysis. Neither tool competes directly; pick based on your industry.
Finrobot vs Geologicai
Choose GeologicAI if you are in mining and need to accelerate core logging with an integrated sensor suite and AI modeling. Choose FinRobot if you are in finance and want an open-source tool to automate equity research reports. They serve entirely different domains with no overlap.
Alternatives to FinRobot
View allQuant Python Ai
Open-source CLI AI agent that automates financial news research, sentiment analysis, and risk reports for quantitative researchers.
FinceptTerminal
FinceptTerminal is an open-source desktop financial terminal with AI research agents and 41 modules, free under AGPL-3.0 or from $15/user/mo for the private
Frequently Asked Questions
Categories
Used FinRobot? Help shape our editorial sentiment research.