DEV Community: Tanmay Nilesh Shinde

Building Persistent AI Memory for Event-Driven Architectures Using Cognee and AsyncAPI

Tanmay Nilesh Shinde — Sat, 24 Oct 2026 19:47:48 +0000

As distributed systems expand, message brokers and event streams (such as Apache Kafka, RabbitMQ, WebSockets, and MQTT) form the central nervous system of modern enterprise architectures.

Teams standardize these event contracts using AsyncAPI (the Linux Foundation open standard). However, AI agents, architecture copilots, and debugging tools struggle to reason across event-driven systems:

They fail to connect producers, topics, and consumers into unified flows.
They cannot track how JSON schema payload structures evolve across microservices.
They lack persistent relational awareness of which services subscribe to which events.

To bridge this gap, we engineered the AsyncAPI Specification Data Connector for Cognee, transforming asynchronous event architectures into interconnected, queryable knowledge graphs.

Why Knowledge Graphs Are Crucial for Event-Driven Systems

Event-driven architectures are inherently graph networks:

Brokers/Servers host Channels/Topics.
Producers publish Messages with specific Payload Schemas.
Consumers subscribe to Topics to trigger downstream business logic.

Cognee ingests event contracts into persistent graph and vector stores, turning event topologies into queryable knowledge graphs for AI agents.

The Pipeline Architecture

AsyncAPI Spec (YAML/JSON)
  │
  ▼ (Server, Topic & Schema Discovery)
asyncapi_source() [dlt]
  │
  ▼ (Event Payload & Operation Normalization)
Semantic Markdown Documents (DOCUMENT_SOURCE_ATTR = 'asyncapi')
  │
  ▼ (cognify())
Persistent Knowledge Graph & Vectors (Cognee Engine)
  │
  ▼ (cognee.search(INSIGHTS))
Autonomous AI Architecture Agent

Technical Highlights of the AsyncAPI Connector

Full Protocol & Broker Support: Automatically discovers Kafka, WebSocket, RabbitMQ, and MQTT brokers defined in servers.
Channel & Operation Traversal: Parses channel paths, operations (publish/subscribe), message summaries, content types, and payload schema properties (field types, required flags, descriptions).
Full-Snapshot Sync + Forget-on-Delete: Built with write_disposition="replace". Deprecated topics or removed messages automatically drop out of Cognee's graph via orphan_cleanup.
Document-Mode Processing: Sets DOCUMENT_SOURCE_ATTR = "asyncapi", directing event schemas straight into Cognee's natural language cognify entity-extraction pipeline.

Quickstart Tutorial

1. Installation

pip install cognee-community-connector-asyncapi cognee

2. Ingestion & Graph Building

import asyncio
from pathlib import Path
import cognee
from cognee_community_connector_asyncapi import asyncapi_source

async def main():
    # 1. Point to your AsyncAPI file or directory
    source = asyncapi_source(spec_path="./path/to/asyncapi.yaml")

    # 2. Ingest AsyncAPI event schemas
    print("Ingesting AsyncAPI specification into Cognee...")
    await cognee.add(source)

    # 3. Cognify topology into knowledge graph
    print("Building event graph...")
    await cognee.cognify()
    print("Event knowledge graph persistent!")

if __name__ == "__main__":
    asyncio.run(main())

3. Querying the Graph

async def query_events():
    insights = await cognee.search(
        query_type="INSIGHTS",
        query_text="Which Kafka topics handle user registrations and what fields are required in the payload?",
    )
    print("Agent Insights:\n", insights)

asyncio.run(query_events())

Conclusion & Mergetober

The AsyncAPI connector equips autonomous AI agents with architectural memory across message streams and event topologies.

GitHub PR: topoteretes/cognee-community#477
Cognee Core: github.com/topoteretes/cognee
Sprint: Mergetober 2026 (WeMakeDevs x Hacker's Unity x Cognee)

Building Persistent AI Memory for VitePress Documentation Sites Using Cognee

Tanmay Nilesh Shinde — Sat, 24 Oct 2026 19:47:15 +0000

The modern frontend tooling ecosystem (including Vite, Vue.js, Rollup, Vitest, and Pinia) relies on VitePress to power its lightning-fast static documentation sites.

However, when engineering teams plug AI coding copilots or technical documentation bots into these repositories, traditional vector search (RAG) fails to capture the full picture:

It strips out navigation sidebars and group breadcrumbs.
It mangles custom container blocks (::: tip, ::: warning, ::: danger).
It cannot perform multi-hop semantic reasoning across interrelated configuration guides and API modules.

To solve this, we built the official VitePress Documentation Data Connector for Cognee, transforming VitePress hubs into persistent, interconnected knowledge graphs.

Why Knowledge Graphs Beat Vector Chunking for Docs

In developer documentation, hierarchy carries vital architectural context:

Guide > Getting Started dictates the baseline environment.
Configuration > Build Options defines parameter schemas.
Callout Containers flag critical performance tips and deprecations.

Cognee ingests documents into persistent graph and vector stores, extracting entities and relations into a queryable memory network.

The Ingestion Pipeline

VitePress Site (.vitepress/config + docs)
  │
  ▼ (Sidebar Traversal & Breadcrumbs)
vitepress_source() [dlt]
  │
  ▼ (Container Parsing & Frontmatter Normalization)
Semantic Markdown Documents (DOCUMENT_SOURCE_ATTR = 'vitepress')
  │
  ▼ (cognify())
Persistent Knowledge Graph & Vectors (Cognee Engine)
  │
  ▼ (cognee.search(INSIGHTS))
Autonomous AI Developer Copilot

Technical Highlights of the VitePress Connector

Sidebar Hierarchy Traversal: Automatically inspects .vitepress/config sidebar groups and multi-sidebar mappings, prefixing documents with navigation breadcrumbs (Section: Guide > Configuration).
Container Parser: Translates VitePress custom containers (::: tip, ::: warning, ::: info, ::: danger, ::: details) into semantic Markdown callout blocks.
Full-Snapshot Sync + Forget-on-Delete: Uses write_disposition="replace". Any deleted or moved documentation page automatically drops out of the knowledge graph via Cognee's orphan_cleanup.
Document-Mode Processing: Sets DOCUMENT_SOURCE_ATTR = "vitepress", routing pages directly into Cognee's natural language cognify entity-extraction engine.

Quickstart Tutorial

1. Installation

pip install cognee-community-connector-vitepress cognee

2. Ingestion & Graph Building

import asyncio
from pathlib import Path
import cognee
from cognee_community_connector_vitepress import vitepress_source

async def main():
    # 1. Point to your VitePress documentation directory
    source = vitepress_source(site_path="./path/to/vitepress-site")

    # 2. Ingest VitePress pages
    print("Ingesting VitePress documentation into Cognee...")
    await cognee.add(source)

    # 3. Build knowledge graph
    print("Cognifying graph memory...")
    await cognee.cognify()
    print("VitePress knowledge graph persistent!")

if __name__ == "__main__":
    asyncio.run(main())

3. Querying the Knowledge Graph

async def query_docs():
    insights = await cognee.search(
        query_type="INSIGHTS",
        query_text="What are the prerequisite setup steps and build configurations?",
    )
    print("AI Copilot Insights:\n", insights)

asyncio.run(query_docs())

Conclusion & Mergetober

The VitePress connector allows any team using VitePress to give their AI agents persistent, graph-structured technical memory in minutes.

GitHub PR: topoteretes/cognee-community#476
Cognee Core: github.com/topoteretes/cognee
Sprint: Mergetober 2026 (WeMakeDevs x Hacker's Unity x Cognee)

Building Persistent AI Memory for API Specifications Using Cognee and Fern

Tanmay Nilesh Shinde — Sat, 24 Oct 2026 19:40:58 +0000

API documentation is the bedrock of modern software engineering. High-growth developer platforms and AI companies (including Cohere, ElevenLabs, Scale AI, and AssemblyAI) standardize their API definitions and SDK generation using Fern.

However, when engineering teams plug AI agents into their API documentation, standard vector retrieval (RAG) fails. It treats endpoint schemas as plain text, fragmenting path parameters from query parameters, request bodies from response types, and error definitions from endpoint paths.

To give AI agents structured, relational memory of API architectures, we built the official Fern API Documentation Connector for Cognee.

Why Knowledge Graphs Are Essential for APIs

APIs are graphs by definition:

Services contain Endpoints.
Endpoints require Authentication and accept Parameters.
Parameters adhere to Type Schemas.
Endpoints throw specific Error Models.

Cognee is an open-source framework that maps documentation and schemas into persistent knowledge graphs and vector embeddings for autonomous agents.

The Pipeline Architecture

Fern Workspace (fern.config.json + definition)
  │
  ▼ (YAML Schema & Endpoint Discovery)
fern_source() [dlt]
  │
  ▼ (Endpoint Schema & Markdown Normalization)
Semantic Markdown Documents (DOCUMENT_SOURCE_ATTR = 'fern')
  │
  ▼ (cognify())
Persistent Knowledge Graph & Vectors (Cognee Engine)
  │
  ▼ (cognee.search(INSIGHTS))
Autonomous AI API Copilot

Technical Features of the Fern Connector

Structured Endpoint Extraction: Walks definition/**/*.yml files, parsing HTTP methods, paths, path parameters, query parameters, request body types, response schemas, and errors.
Guide Integration: Ingests markdown documentation and conceptual guides in pages/ alongside endpoint definitions.
Full Snapshot + Forget-on-Delete: Uses write_disposition="replace". Deprecated or removed endpoints automatically drop out of Cognee's graph via orphan_cleanup.
Document-Mode Processing: Sets DOCUMENT_SOURCE_ATTR = "fern", ensuring schemas flow through Cognee's natural language cognify entity-extraction engine.

Quickstart Tutorial

1. Installation

pip install cognee-community-connector-fern cognee

2. Ingestion & Graph Cognification

import asyncio
from pathlib import Path
import cognee
from cognee_community_connector_fern import fern_source

async def main():
    # 1. Point to your Fern directory
    source = fern_source(fern_path="./path/to/fern")

    # 2. Ingest API definitions and guides
    print("Ingesting Fern workspace into Cognee...")
    await cognee.add(source)

    # 3. Cognify into knowledge graph
    print("Cognifying API graph...")
    await cognee.cognify()
    print("API knowledge graph persistent!")

if __name__ == "__main__":
    asyncio.run(main())

3. Querying the Knowledge Graph

async def query_api():
    insights = await cognee.search(
        query_type="INSIGHTS",
        query_text="What endpoints handle user authentication and what error types can they throw?",
    )
    print("AI Copilot Insights:\n", insights)

asyncio.run(query_api())

Conclusion & Mergetober

The Fern connector provides AI agents with persistent, multi-hop architectural memory across entire API ecosystems.

GitHub PR: topoteretes/cognee-community#475
Cognee Core: github.com/topoteretes/cognee
Sprint: Mergetober 2026 (WeMakeDevs x Hacker's Unity x Cognee)

Building Persistent AI Memory for Docusaurus Documentation Sites Using Cognee

Tanmay Nilesh Shinde — Sat, 24 Oct 2026 19:40:22 +0000

Documentation is the single most critical asset for open-source libraries and developer-focused platforms. Flagship technologies like React, Supabase, Redis, Algolia, and Babel rely on Docusaurus to organize their guides, API documentation, and architecture manuals.

However, standard LLM applications struggle with technical docs. When using basic chunked vector retrieval (RAG), the AI loses structural context:

It fails to understand sidebar categories and prerequisite hierarchies.
It mangles custom documentation admonitions (:::note, :::tip, :::danger).
It cannot answer multi-hop architectural questions connecting setup steps to API schemas.

To eliminate this bottleneck, we engineered the Docusaurus Data Connector for Cognee, transforming Docusaurus sites into persistent, interconnected knowledge graphs.

Why Knowledge Graphs Beat Vector Chunking for Docs

In technical documentation, structure carries meaning:

Prerequisites depend on Environment Setup.
Configuration Parameters relate to specific SDK Version Features.
Admonitions alert developers to breaking changes and security considerations.

Cognee ingests documents into a persistent graph and vector store, mapping entities and semantic relationships directly into nodes and edges.

The Pipeline Architecture

Docusaurus Site (sidebars.json + docs/)
  │
  ▼ (Sidebar Categories & Breadcrumbs)
docusaurus_source() [dlt]
  │
  ▼ (Admonition & Frontmatter Sanitization)
Semantic Markdown Documents (DOCUMENT_SOURCE_ATTR = 'docusaurus')
  │
  ▼ (cognify())
Persistent Knowledge Graph & Vectors (Cognee Engine)
  │
  ▼ (cognee.search(INSIGHTS))
Autonomous AI Developer Assistant

Technical Highlights of the Docusaurus Connector

Sidebar Hierarchy Traversal: Automatically inspects sidebars.json to recursively extract documentation categories and navigation paths, prefixing every document with contextual breadcrumbs (Section: Sidebar > Category > Subcategory).
Admonition Parser: Translates Docusaurus-specific admonition syntax (:::note, :::tip, :::danger) into semantic Markdown callout blocks, preserving developer warnings without cluttering LLM prompts with custom JSX.
Full-Snapshot Sync + Forget-on-Delete: Built with write_disposition="replace" under dlt. Any document deleted or moved in docs/ is automatically forgotten from the graph via Cognee's orphan_cleanup.
Document-Mode Processing: Sets DOCUMENT_SOURCE_ATTR = "docusaurus", directing documents straight into Cognee's natural language cognify entity-extraction pipeline.

Quickstart Tutorial

1. Installation

pip install cognee-community-connector-docusaurus cognee

2. Ingestion & Graph Building

import asyncio
from pathlib import Path
import cognee
from cognee_community_connector_docusaurus import docusaurus_source

async def main():
    # 1. Point to your Docusaurus directory
    source = docusaurus_source(site_path="./path/to/docusaurus-site")

    # 2. Ingest documentation pages
    print("Ingesting Docusaurus pages into Cognee...")
    await cognee.add(source)

    # 3. Extract knowledge graph entities and relations
    print("Building knowledge graph...")
    await cognee.cognify()
    print("Graph memory successfully built!")

if __name__ == "__main__":
    asyncio.run(main())

3. Querying for Semantic Insights

async def query_docs():
    insights = await cognee.search(
        query_type="INSIGHTS",
        query_text="What are the breaking changes and migration steps described in the documentation?",
    )
    print("Agent Insights:\n", insights)

asyncio.run(query_docs())

Cognee navigates the knowledge graph edges directly connecting breaking change nodes to their relevant migration sections.

Conclusion & Mergetober

The Docusaurus connector allows any team using Docusaurus to give their AI agents persistent, graph-structured technical memory in minutes.

Pull Request: topoteretes/cognee-community#474
Cognee Core: github.com/topoteretes/cognee
Sprint: Mergetober 2026 (WeMakeDevs x Hacker's Unity x Cognee)

Building Persistent AI Memory for Developer Documentation Using Cognee and Mintlify

Tanmay Nilesh Shinde — Sat, 24 Oct 2026 18:47:13 +0000

Modern developer platforms, AI tools, and API-first companies (such as Anthropic, Cursor, Pinecone, Resend, and ElevenLabs) rely on Mintlify as their primary documentation engine. Mintlify offers MDX-based documentation, interactive API playgrounds, and structured navigation hierarchies.

However, when engineering teams deploy AI coding assistants, IDE plugins, or developer support agents, they encounter a persistent bottleneck: context fragmentation.

Standard vector retrieval (RAG) breaks documentation into arbitrary 500-token chunks. It completely misses the hierarchical relationship between navigation groups, SDK reference guides, API parameter schemas, and code examples.

To solve this, we built the official Mintlify Data Connector for Cognee, transforming documentation repositories into living, interconnected knowledge graphs.

Why Knowledge Graphs Are Essential for Documentation

API documentation is inherently relational:

Endpoints link to Authentication Schemes.
Request Parameters have parent-child schemas.
Guides reference prerequisite Quickstarts.

Cognee is an open-source framework that turns text, chats, and structured data into persistent knowledge graphs and vector embeddings for AI agents.

The Ingestion Pipeline

Mintlify Repo (mint.json + MDX)
  │
  ▼ (Navigation & Breadcrumbs)
mintlify_source() [dlt]
  │
  ▼ (Frontmatter & MDX Sanitization)
Semantic Markdown Docs (DOCUMENT_SOURCE_ATTR = 'mintlify')
  │
  ▼ (cognify())
Persistent Knowledge Graph & Vectors (Cognee Engine)
  │
  ▼ (cognee.search(INSIGHTS))
AI Support & Coding Agent

Architecture: How the Mintlify Connector Works

Following the architecture of Cognee’s reference Notion connector, the Mintlify connector is built as a dlt (data load tool) source that implements:

Navigation Traversal: Reads mint.json to recursively extract page hierarchies (tabs, groups, and sub-groups), injecting breadcrumbs like API Reference > Authentication > POST /v1/auth/token directly into document headers.
MDX Tag Sanitization: Mintlify uses custom React/JSX components such as <ParamField>, <ResponseField>, <Card>, and <Accordion>. Our regex parser transforms these components into semantic Markdown bullet points and code blocks, stripping clutter while preserving parameter types and descriptions.
Full Snapshot + Forget-on-Delete: Using write_disposition="replace", each ingestion run represents the complete state of the documentation. If a page is removed or renamed upstream, Cognee's built-in orphan_cleanup automatically prunes the obsolete nodes from the knowledge graph.
Document-Mode Routing: Sets DOCUMENT_SOURCE_ATTR = "mintlify", ensuring pages are routed through the LLM-powered entity and relation extraction pipeline (cognify()).

Step-by-Step Code Tutorial

1. Installation

pip install cognee-community-connector-mintlify cognee

2. Ingesting Documentation into Cognee

import asyncio
from pathlib import Path
import cognee
from cognee_community_connector_mintlify import mintlify_source

async def main():
    # Point to your Mintlify documentation directory
    source = mintlify_source(docs_path="./path/to/docs")

    # Ingest pages into Cognee memory
    print("Ingesting documentation pages...")
    await cognee.add(source)

    # Cognify documents into a knowledge graph
    print("Extracting entities and graph relationships...")
    await cognee.cognify()
    print("Documentation memory is now persistent!")

if __name__ == "__main__":
    asyncio.run(main())

3. Querying the Knowledge Graph

async def query_docs():
    insights = await cognee.search(
        query_type="INSIGHTS",
        query_text="What are the required headers and error responses for the authentication endpoint?",
    )
    print("AI Agent Insights:\n", insights)

asyncio.run(query_docs())

Instead of retrieving disconnected text snippets, Cognee traverses graph edges linking the endpoint, parameter constraints, and troubleshooting tips.

Real-World Use Case: Automated Developer Support Agent

Imagine an open-source library where users constantly ask:

"Does the SDK support streaming responses with custom timeouts?"

With the Mintlify connector:

The CI/CD pipeline triggers mintlify_source on every documentation commit.
Cognee updates the knowledge graph with zero manual re-indexing.
A support bot in Slack or Discord queries Cognee's graph to provide instant, hallucination-free answers with exact parameter types and code samples.

Conclusion & Mergetober

The Mintlify connector bridges modern developer documentation directly into Cognee's persistent memory layer.

Pull Request: topoteretes/cognee-community#473
Cognee Core: github.com/topoteretes/cognee
Sprint: Mergetober 2026 (WeMakeDevs x Hacker's Unity x Cognee)