Inconvo
Open-source kit for building chat-with-data agents that query your production database in plain English.
Inconvo's story changed in July 2026: it's joining Attio, and the standalone product is no longer actively developed. The code is still on GitHub, so it remains a genuinely useful reference for anyone building secure multi-tenant chat-with-data — the semantic layer with computed columns and table prompts, pre-execution query validation, and row-level security are patterns worth reading. But treat it as a starting point you fork and own, not a dependency you can lean on for support or security patches. If you need a maintained path, compare against TextQL and Vanna before committing engineering time.
Verified 8d ago · liveness 69/100 · cite: rightaichoice.com/tools/inconvo
- Developers wanting a reference implementation of a safe, multi-tenant chat-with-data agent
- SaaS teams with a maintainer who can fork and self-host natural language querying
- Engineers evaluating semantic-layer and query-validation patterns before building their own
- Teams that need row-level security and tenant scoping in an open-source data agent
- Non-technical users who need a no-code setup or managed hosting
- Teams that require vendor support, security patches, or a maintained release cadence
- Production deployments without someone owning the forked codebase
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Skip Inconvo if you need a maintained, supported product with a vendor behind it — the standalone service stopped active development in July 2026 when the team joined Attio, leaving the open-source codebase as your only path.
Self-hosting means you pay for the infrastructure your data agent runs on and the engineer-hours to keep it patched after active development stopped.
Inconvo's codebase is open source, so the money question is engineering time rather than a subscription: you fund the infrastructure and the maintainer who owns the fork. That fits teams with spare backend capacity and a toleration for self-support. If you would rather buy a supported cadence, budget against managed natural-language analytics vendors such as TextQL or Vanna instead.
In short
Inconvo — Open-source kit for building chat-with-data agents that query your production database in plain English. Best for Developers wanting a reference implementation of a safe, multi-tenant chat-with-data agent, SaaS teams with a maintainer who can fork and self-host natural language querying, Engineers evaluating semantic-layer and query-validation patterns before building their own. Free to use.
What's new in Inconvo
Checked todayAcross the latest 1 update: 1 news mention.
What people actually say about Inconvo — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
11 mentions across 3 sources (Hacker News, GitHub, Lemmy) · researched Aug 16, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Semantic layer prevents SQL hallucination; builds safe, deterministic queries
- +Built-in multi-tenancy with tenant scoping for secure SaaS deployments
- +Observability dashboard with traces and usage analytics for debugging
- +MCP server support with OAuth enables easy integration into AI workflows
- +Supports a wide range of databases: PostgreSQL, MySQL, SQL Server, Redshift
- −Standalone service no longer actively maintained; self-support required
- −Missing popular databases like ClickHouse, Snowflake, and Oracle
- −Limited community documentation and examples beyond basic setup
- −Advanced configuration requires deep SQL and data modeling knowledge
- −No clear roadmap or release schedule post-Attio acquisition
- • No managed service available, so all infrastructure and maintenance costs are yours
- • Potential need to hire or spend time on security and updates
Viability Score
How well maintained and how widely used is Inconvo? 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: September 2026
How we score →Key Features
- Open-source codebase on GitHub for self-hosting and forking
- Semantic layer with computed columns, table prompts, and agent rules
- Query validation before execution to block unsafe operations
- Multi-tenancy with tenant scoping and row-level security
- User context configuration for per-user data scoping
- Dataset file upload, list, and delete endpoints
- Conversation state management for multi-turn interactions
- Response streaming (response.created, response.progress, response.completed)
- Agent traces and observability dashboard
- Usage analytics on messages and schema introspection
- Conversation feedback API (create and update feedback)
- MCP server deployment with OAuth support and tenant creation
- Integrations with Assistant-UI and the Vercel AI SDK
- Database connectivity: PostgreSQL, MySQL, Microsoft SQL Server, Amazon Redshift (beta), BigQuery, Supabase
- CLI for local development (npx inconvo@latest dev) and Node.js SDK
About Inconvo
Inconvo is an open-source toolkit for building chat-with-data agents that answer questions by querying your production database in plain language. Rather than letting an LLM write freehand SQL, Inconvo routes requests through a semantic layer and validates every query before it runs. On top of that sit multi-tenancy with tenant scoping and row-level security, conversation state for multi-turn follow-ups, response streaming, a CLI for local development, and a Node.js SDK for embedding agents in your own product. Database coverage includes PostgreSQL, MySQL, Microsoft SQL Server, Amazon Redshift (beta), BigQuery, and Supabase, and the docs describe getting started with production data in under five minutes via npx inconvo@latest dev. Observability is part of the package: agent traces, usage analytics on messages and schema introspection, and a conversation feedback API. Integration paths include Assistant-UI, the Vercel AI SDK, and MCP servers with OAuth support. The important context for anyone evaluating it now: Inconvo announced in July 2026 that it is joining Attio, the agentic CRM. The open-source codebase remains available, but the standalone service is no longer actively developed, so adopting it today means self-hosting and owning the code. It is a strong reference for how a safe multi-tenant data agent is assembled, not a dependency you can lean on for support or security patches.
Behind the Verdict
The strongest thing about Inconvo is what it refuses to do. It does not let the model write SQL and hope for the best — requests pass through a semantic layer of computed columns, table prompts, and agent rules, and every query is validated before execution, so unsafe or invalid queries get blocked rather than run against your production database. For a category where the failure mode is 'the agent dropped a table' or 'the agent leaked another tenant's rows,' that design choice is the product. The rest of the toolkit is aimed squarely at developers embedding this inside an existing product. Multi-tenancy with tenant scoping and row-level security means each customer only touches their own rows. Conversation state handles multi-turn follow-ups. Streaming keeps answers feeling live. There is a CLI (npx inconvo@latest dev) for local work and a Node.js SDK for shipping. Database coverage spans PostgreSQL, MySQL, Microsoft SQL Server, Amazon Redshift (beta), BigQuery, and Supabase, and the docs walk through connections, semantic model configuration, user context, and datasets. Observability is not an afterthought: agent traces, usage analytics on messages and schema introspection, and a feedback API round it out. Integration paths run through Assistant-UI, the Vercel AI SDK, and MCP servers with OAuth support. Where it falls down is continuity. The July 2026 announcement that Inconvo is joining Attio is on the homepage in plain language, and the standalone service is no longer actively developed. The open-source codebase stays up, but the docs themselves warn that data agents require far more engineering time, resources, and ongoing maintenance than you would first expect — which is exactly the burden that transfers to whoever forks it. Practically that means budget for a maintainer, not just an integration sprint. Where it fits: engineering teams who want a reference implementation of safe multi-tenant natural language querying, and who are comfortable reading the code, forking it, and owning the release cadence themselves. Where it doesn't: non-technical teams wanting no-code setup, anyone needing vendor support or guaranteed security patches, and production deployments without someone accountable for the forked codebase. If you are evaluating semantic-layer and query-validation patterns before building your own, it is worth the read. If you need a managed product with a support contract, TextQL and Vanna are further along.
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Real-world workflow fit
Concrete scenarios for the personas Inconvo actually fits — and what changes day-one when you adopt it.
Clones the Inconvo repo, connects a PostgreSQL instance via the CLI with npx inconvo@latest dev, defines a semantic layer with computed columns and table prompts, and enables tenant scoping plus row-level security so each customer only sees their own rows.
Outcome: An embedded natural language query endpoint that validates every query before execution and streams answers back through the Node.js SDK, with no SQL written by the end user.
Asks follow-up questions about a customer's account through the chat interface, relying on conversation state to keep context across turns and on the observability dashboard to check which tables the agent introspected.
Outcome: Answers to account questions without pulling in an analyst, plus a trace of exactly what the agent looked at and a feedback API to flag bad answers.
Reads the open-source codebase to study query validation, permission rules, and MCP server deployment with OAuth, then wires the agent into an existing Assistant-UI or Vercel AI SDK front end.
Outcome: A working prototype of a safe data agent, and a clear picture of the maintenance burden before deciding whether to fork it or build in-house.
Use Cases
- Embed a natural language query interface in your SaaS product for customer-facing analytics.
- Let support teams ask questions about customer data without writing SQL.
- Serve multi-tenant data access with row-level security so each organisation sees only its own rows.
- Scope answers per end user with user context rather than a single shared database view.
- Monitor query performance and agent decisions with built-in observability.
- Deploy a data agent via MCP so AI assistants can query your warehouse under OAuth.
- Use the codebase as a reference for semantic-layer and query-validation patterns before building your own.
Limitations
- Inconvo has joined Attio and the standalone platform is no longer actively developed, though the open-source codebase remains available for developers to fork and self-host.
- There is no ongoing vendor release cadence behind the standalone product, so security patches and upgrades become your responsibility.
- Building and maintaining data agents is genuinely hard engineering — the docs are explicit that agents generating unsafe or invalid queries, managing permissions across evolving systems, fragile integrations as schemas change, and limited production visibility are the recurring problems Inconvo was built to handle, and those problems do not disappear just because you own the code.
- Amazon Redshift support is marked beta.
- Getting answers you can trust depends on how well you configure the semantic layer, computed columns, and rules for your own schema.
- There is no documented visual builder for non-developers; setup runs through the CLI and SDK.
as of 2026-09-22
Verification history
We have re-verified Inconvo 8 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-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — 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 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Inconvo's pricing actually pencils out — and where peers do it cheaper.
Inconvo's codebase is open source, so the money question is engineering time rather than a subscription: you fund the infrastructure and the maintainer who owns the fork. That fits teams with spare backend capacity and a toleration for self-support. If you would rather buy a supported cadence, budget against managed natural-language analytics vendors such as TextQL or Vanna instead.
Setup time & first value
How long it actually takes to get something useful out of Inconvo — broken out by persona, not the marketing-page minute.
Developers can reasonably reach a first query quickly — the docs promise querying production data in under five minutes using npx inconvo@latest dev. Realistically, the fast win is connecting a database; the longer work is defining the semantic layer, computed columns, table prompts, and row-level rules so answers are accurate for your schema. Teams planning multi-tenant row-level security and
Switching to or from Inconvo
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From hand-rolled LLM-to-SQL scripts: replace raw SQL generation with Inconvo's semantic layer and pre-execution query validation.
- →From a BI tool with saved dashboards: keep the warehouse, expose the same tables through Inconvo's semantic model so users can ask ad-hoc questions instead.
- →From a hosted chat-with-data vendor: fork the Inconvo codebase, port your table definitions into computed columns and table prompts, then self-host the agent.
- ↗To TextQL: move your semantic definitions across and adopt a managed natural-language analytics product with vendor support.
- ↗To Vanna: reimplement retrieval and SQL generation against your own schema if you want a maintained open-source alternative.
- ↗To an in-house build: keep Inconvo's query-validation and tenant-scoping patterns as a design reference and own the implementation end to end.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Inconvo”, and we withheld 6: 6 could not be judged, because “Inconvo” 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 Inconvo.
Official links
Tools that pair well with Inconvo
Common stack mates teams adopt alongside Inconvo, with the specific reason each pairing earns its keep.
Chat2DB
Chat2DB turns plain English into optimized SQL across 40+ databases, with queries processed locally.
Formula Bot
Better Analyst (formerly Formula Bot) turns plain-English questions into AI data analysis, charts, and dashboards.
Quadratic
Quadratic is an AI spreadsheet where the grid runs Python, SQL, JavaScript, and formulas against live data sources.
Featured Head-to-Head Comparisons
Inconvo vs Geologicai
For mining companies needing rapid, AI-accelerated core analysis with cutting-edge sensor integration, GeologicAI is a clear leader backed by recent $44M funding and LIBS acquisition. Inconvo offers a free, open-source chat-with-data platform for SaaS developers, but its recent sunset means no future support or development—choose only if you can maintain it yourself. These tools serve entirely different domains; pick based on your industry and need for managed vs self-hosted solutions.
Inconvo vs Versatile
These are not competitors and you should never be choosing between them. Versatile is a physical crane-monitoring system sold to steel erectors — hardware gets installed on your tower or crawler crane, and the software passively reports picks, sequences and delays. Inconvo is a free, self-hosted open-source codebase for developers building natural-language database agents, and as of July 2026 it's joining Attio, so the repo stays up but the company's focus is moving. Buy Versatile if you run crane-heavy steel erection; fork Inconvo only if you have an engineer and want a reference implementation of query validation and multi-tenancy.
Inconvo vs Screenplayiq
ScreenplayIQ and Inconvo serve completely different markets: ScreenplayIQ is a specialized AI tool for screenwriters and film professionals seeking data-driven script analysis and box office predictions, while Inconvo is a sunset open-source platform for developers building chat-with-data agents. Unless you need both a screenplay analyzer and a data-querying agent (unlikely), choose based on your domain: pick ScreenplayIQ for film industry insights, or explore alternatives to Inconvo for SQL chatbots.
Alternatives to Inconvo
View allChat2DB
Chat2DB turns plain English into optimized SQL across 40+ databases, with queries processed locally.
Formula Bot
Better Analyst (formerly Formula Bot) turns plain-English questions into AI data analysis, charts, and dashboards.
Frequently Asked Questions
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