Best A2A Agents for Data Analytics

The data analytics A2A ecosystem has a few genuinely useful agents and a few that are really just reference implementations wearing a "data analytics" label. This post separates the two.

Quick Comparison

Agent	Framework	Language	Best For	Maturity
LangChain Data Agent	LangGraph	Python	NL2SQL queries	Production-ready (214 stars)
MindsDB Agent	Custom	Python	ML predictions in-database	Official sample
Analytics Agent	Custom	Python	Statistical analysis	Official sample / reference
Data Agent	Pydantic AI + Modal	Python	Analysis + visualization (hosted)	Early
Movie Chatbot	Genkit	TypeScript	External API integration pattern	Reference only
Currency Agent	A2A Python SDK	Python	Currency conversion	Micro-agent / tutorial

LangChain Data Agent (NL2SQL)

Repo: eosho/langchain_data_agent

The strongest agent on this list. 214 GitHub stars, active development, built on LangGraph. You ask a question in English, it generates SQL, runs it against PostgreSQL, and returns both the query and the results.

User: "What were our top 10 products by revenue last quarter?"

Agent generates:
SELECT p.name, SUM(o.amount) as revenue
FROM products p
JOIN orders o ON p.id = o.product_id
WHERE o.created_at >= '2025-10-01' AND o.created_at < '2026-01-01'
GROUP BY p.name
ORDER BY revenue DESC
LIMIT 10;

It also supports MCP alongside A2A, which means it fits into both ecosystems without changes. Azure integration is built in for enterprise deployment.

The limitation everyone hits with NL2SQL: complex queries with ambiguous joins or business-specific terminology. The agent does not know that "active users" means "users with a login in the last 30 days" unless you tell it. Schema context and domain glossaries help, but they require setup work.

MindsDB Agent

Repo: a2aproject/a2a-samples/mindsdb

Most analytics agents answer "what happened." MindsDB answers "what will happen." It creates ML models inside MindsDB, runs predictions against your data, and returns results as A2A artifacts. Demand forecasting, churn prediction, anomaly detection -- the standard predictive use cases.

The key advantage is that predictions run where your data lives. No ETL pipelines, no data extraction, no separate ML infrastructure. MindsDB handles the model complexity. Official A2A sample, Apache 2.0.

The downside: you need a MindsDB instance. If your org is not already running MindsDB (and most are not), the setup cost is significant. This agent is most valuable to teams already invested in the MindsDB ecosystem.

Analytics Agent

Repo: a2aproject/a2a-samples/analytics

Official A2A sample for general-purpose statistical analysis. Accepts JSON or CSV, runs means/medians/distributions/correlations, identifies trends in time-series data, returns structured artifacts.

response = await client.send_message(
    message={
        "role": "user",
        "parts": [{
            "kind": "text",
            "text": "Analyze this sales data and identify the top 3 trends"
        }, {
            "kind": "data",
            "mimeType": "application/json",
            "data": sales_data_json
        }]
    }
)

It is clean, minimal, and well-structured. Good as a building block -- feed it data from the LangChain Data Agent and let it return insights. But it is a reference implementation, not a production analytics platform. Expect to extend it heavily for anything beyond basic stats.

Data Agent

Repo: prassanna-ravishankar/a2a-agent-bootstrapping

Similar to the Analytics Agent but hosted on Modal and adds visualization output (charts, graphs, distributions). That visual output is what differentiates it -- useful when results need to reach humans who want to see a chart, not a JSON blob.

curl https://prassanna-ravishankar--data-agent-data-agent-app.modal.run/.well-known/agent.json

Same caveat as the Code Agent from the same author: you are sending data to someone else's Modal deployment. Fine for evaluation, not for production with sensitive data. Fork and self-host for anything real.

Niche / Reference Implementations

The remaining two agents are honest about what they are: small, focused implementations that demonstrate patterns rather than solve production problems.

Movie Chatbot (extrawest/a2a-movie-chatbot) -- A Genkit/TypeScript agent that fetches movie data from external APIs. The domain is trivial, but the pattern is not: real-time external API integration, LLM processing of API responses, structured A2A output. If you need to build an agent that wraps a third-party API (financial data, weather, logistics tracking), this is a reasonable starting point for the plumbing. It is not a "data analytics agent" in any meaningful sense.

Currency Agent (sing1ee/a2a-python-currency) -- Live currency conversion built directly on the A2A Python SDK. No framework, minimal code. It is a tutorial companion that demonstrates the micro-agent pattern: one agent, one job. Useful as a utility in pipelines that handle international financial data, or as a learning exercise. Not something you would list as a "best agent" -- it is a building block.

---

The realistic state of A2A data analytics: the LangChain Data Agent is the only one with real community traction. MindsDB is compelling if you are already in that ecosystem. The rest are reference implementations that demonstrate patterns worth replicating in your own agents.

See the full Data Analytics stack on StackA2A for all available agents.