mango-ai 0.0.1

Generate NoSQL queries from natural language

🥭 Mango — MongoDB AI Agent

Natural language → MQL → Answers. The open-source AI agent for MongoDB.

Ask your MongoDB database anything in plain language. Mango translates your question into an MQL query, runs it, and gives you a clear answer — with memory that improves over time.

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What You Get

Ask a question in natural language and get back:

1. Real-time tool call visibility — see exactly which collections and queries Mango uses

2. A natural language answer — summarised from the actual query results

3. A system that learns — every successful query is stored in memory and reused for similar questions in the future

All streamed in real-time over SSE to your frontend.

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Why Mango?

Vanna.ai has solved text-to-SQL elegantly. MongoDB has no equivalent. The challenges are fundamentally different:

• No explicit schema — collections have no DDL. Mango infers schema by sampling documents.
• Nested documents — queries must navigate arrays, subdocuments, and dotted paths.
• Aggregation pipelines — complex analytics require multi-stage JSON pipelines, not flat SQL strings.
• No JOINs — relationships are handled via $lookup or application-level references.

Mango is the first production-grade framework for natural language interaction with MongoDB.

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Get Started

Install

pip install mango[anthropic]   # Claude
pip install mango[openai]      # GPT-5.4
pip install mango[gemini]      # Gemini 3.1 Pro Preview
pip install mango[all]         # all providers + ChromaDB memory

Minimal setup (3 minutes)

from mango import MangoAgent
from mango.tools import build_mongo_tools
from mango.tools.base import ToolRegistry
from mango.servers.fastapi import MangoFastAPIServer
from mango.integrations.anthropic import AnthropicLlmService
from mango.integrations.mongodb import MongoRunner
from mango.integrations.chromadb import ChromaAgentMemory

# 1. Connect your database
db = MongoRunner()
db.connect("mongodb://localhost:27017/mydb")

# 2. Choose your LLM
llm = AnthropicLlmService(model="claude-sonnet-4-6", api_key="...")

# 3. Add memory (optional but recommended)
memory = ChromaAgentMemory(persist_dir="./mango_memory")

# 4. Register tools and create agent
tools = ToolRegistry()
for tool in build_mongo_tools(db, memory):
    tools.register(tool)

agent = MangoAgent(llm_service=llm, tool_registry=tools, db=db, agent_memory=memory)

# 5. Run the server
MangoFastAPIServer(agent).run()  # http://localhost:8000

Your endpoint is live at POST /api/v1/ask/stream — ready to connect to any frontend.

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How It Works

User question
      │
      ▼
┌─────────────────────────────────────────────┐
│               MANGO AGENT                    │
│                                              │
│  1. Search memory for similar past queries   │
│  2. Build system prompt with schema context  │
│  3. LLM decides which tools to call          │
│  4. Execute tools against MongoDB            │
│  5. Feed results back to LLM                 │
│  6. Stream natural language answer           │
│  7. Auto-save successful queries to memory   │
└─────────────────────────────────────────────┘
      │
      ▼
SSE stream → your frontend

The learning loop: steps 1 and 7 make Mango smarter over time. A novel question triggers deep LLM reasoning. A similar question already in memory gets answered faster and more accurately.

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SSE Streaming API

Every question is streamed via Server-Sent Events. Each data: line is a JSON event:

POST /api/v1/ask/stream
{"question": "How many orders were placed last week?"}

data: {"type": "session",     "session_id": "abc123"}
data: {"type": "tool_call",   "tool_name": "list_collections", "tool_args": {}}
data: {"type": "tool_result", "tool_name": "list_collections", "success": true, "preview": "orders, customers, products..."}
data: {"type": "tool_call",   "tool_name": "run_mql", "tool_args": {"operation": "aggregate", "collection": "orders", ...}}
data: {"type": "tool_result", "tool_name": "run_mql", "success": true, "preview": "[{\"total\": 1247}]"}
data: {"type": "answer",      "text": "1,247 orders were placed in the last 7 days."}
data: {"type": "done",        "iterations": 2, "input_tokens": 1820, "output_tokens": 94}

Multi-turn conversations are supported — pass the same session_id to continue a thread.

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Agent Memory

Mango learns from every successful interaction using ChromaDB as a vector store.

How it works:

• After each successful tool call, Mango automatically saves the (question, tool, args, result) tuple
• On the next similar question, the saved example is injected as a few-shot prompt
• The more you use it, the faster and more accurate it gets

Pre-load your domain knowledge:

# Teach Mango about your business terminology
memory.save_text("'active customer' means a customer who placed an order in the last 90 days")
memory.save_text("'revenue' always refers to the total_amount field in the orders collection")
memory.save_text("the 'status' field uses: 1=pending, 2=shipped, 3=delivered, 4=cancelled")

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Available Tools

Tool	Description
list_collections	List all collections. Grouped view for large databases (100+ collections).
search_collections	Search collections by name pattern (supports glob: order*, *_log).
describe_collection	Full schema for a collection: field types, frequencies, indexes, references.
collection_stats	Document count and storage size for a collection.
run_mql	Execute a read-only MongoDB query: find, aggregate, count, distinct.
search_saved_correct_tool_uses	Search memory for similar past interactions.
save_text_memory	Save free-form knowledge about the database for future queries.

Read-only by design. run_mql only accepts find, aggregate, count, distinct. Write operations are rejected at the tool level.

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Pluggable Architecture

Mango is built on abstract interfaces — swap any component without touching your agent code.

LLM Providers

from mango.integrations.anthropic import AnthropicLlmService
from mango.integrations.openai import OpenAILlmService
from mango.integrations.google import GeminiLlmService

llm = AnthropicLlmService(model="claude-sonnet-4-6")
llm = OpenAILlmService(model="gpt-5.4")
llm = GeminiLlmService(model="gemini-3.1-pro-preview")

Custom Tools

Extend Mango with your own tools:

from mango.tools.base import Tool, ToolResult
from mango.llm import ToolDef, ToolParam

class MyCustomTool(Tool):
    @property
    def definition(self) -> ToolDef:
        return ToolDef(
            name="my_tool",
            description="Does something useful",
            params=[ToolParam(name="input", type="string", description="...")]
        )

    async def execute(self, **kwargs) -> ToolResult:
        result = do_something(kwargs["input"])
        return ToolResult(success=True, data=result)

tools.register(MyCustomTool())

Memory Backends

The MemoryService ABC makes it easy to plug in any vector store:

# Default: ChromaDB (local, no infrastructure needed)
from mango.integrations.chromadb import ChromaAgentMemory
memory = ChromaAgentMemory(persist_dir="./mango_memory")

# Implement your own: just inherit MemoryService
class MyPineconeMemory(MemoryService):
    async def store(self, entry: MemoryEntry) -> None: ...
    async def retrieve(self, question: str, top_k: int) -> list[MemoryEntry]: ...
    async def save_text(self, content: str) -> str: ...

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Large Databases

Mango handles databases with hundreds or thousands of collections without token explosion.

• Adaptive collection listing — databases with 100+ collections are automatically grouped by name pattern (contest_*, user_*) instead of listing every single one
• On-demand schema — schema details are only fetched when the LLM needs them, not injected upfront
• Turn-based conversation pruning — conversation history is automatically trimmed to keep token usage stable across long sessions
• Auto-save to memory — schema discoveries are persisted so the LLM doesn't re-introspect the same collections repeatedly

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Multi-turn Conversations

Mango maintains conversation history across questions in the same session:

User: How many orders were placed last week?
Mango: 1,247 orders were placed in the last 7 days.

User: And how many of those were delivered?
Mango: Of last week's 1,247 orders, 891 (71%) have been delivered.

User: Which customer placed the most?
Mango: Alice Johnson (customer_id: 64a3f...) placed 8 orders last week.

Follow-up questions work naturally — no need to repeat context.

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Technology Stack

Component	Library
MongoDB driver	pymongo 4.x
LLM (primary)	anthropic (Claude)
LLM (secondary)	openai (GPT-4), google-generativeai (Gemini)
Vector store	chromadb 1.x
Server	FastAPI + uvicorn
CLI	rich + prompt_toolkit
Data	pandas
Testing	pytest + mongomock

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Roadmap

• MongoDB backend with schema introspection
• Pluggable LLM providers (Anthropic, OpenAI, Gemini)
• ChromaDB agent memory with auto-save
• Streaming FastAPI server (SSE)
• Adaptive collection grouping for large databases
• Multi-turn conversation with automatic pruning
• ValidatorTool — pre-execution MQL validation
• ExplainQueryTool — pipeline explanation in plain language
• VisualizeDataTool — charts and tables in CLI
• Memory export/import (JSON)
• Atlas Vector Search backend
• Redis backend (experimental)

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Contributing

Contributions are welcome. Please open an issue before submitting a large PR so we can discuss the approach.

git clone https://github.com/francesco-bellingeri/mango
cd mango
pip install -e ".[dev]"
pytest

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License

MIT License — see LICENSE for details.

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Built with ❤️ by Francesco Bellingeri | Inspired by Vanna.ai