topoteretes

I built OptMem plugin for Hermes. It's one of the 24 in the official catalog. Zero dependencies. No database, no embeddings, no API key. An append-only log: summaries keep context small, the raw record is never deleted. OptMem from @VictorTaelin rarf/optmem-hermes-plugin

my first-ever open-source contribution That morning email saying the maintainer assigned me an issue was pure happiness😆 PR is up for #Mergetober with @cognee_ x @WeMakeDevs blog: afreenhossain.vercel.app PR: cognee-community#459

I Built a QuickBooks Connector for an AI Knowledge Graph — Here’s What I Learned What if you could ask AI questions about invoices, unpaid bills, and financial records instead of searching through them manually? That’s the idea behind my latest open-source contribution to Cognee Community: a QuickBooks Online data connector. 🔗 Pull Request: cognee-community#402 📌 Issue: cognee#4802 🧠 What does it do? QuickBooks stores useful business information, but AI systems need a way to ingest and understand that data. I worked on a connector that brings QuickBooks financial records into Cognee's AI memory workflow. The connector supports: - 📄 Ingesting invoices, vendor bills, and credit memos. - 🔐 OAuth 2.0 authentication and token refresh. - 🔄 Incremental sync to retrieve new and updated records. - 🗑️ Deletion detection to help remove outdated information. - 🛡️ Retry handling and safeguards against accidental deletion. - 🧪 Automated tests and offline pipeline verification using DuckDB. ⚙️ How does it work? The basic workflow is: QuickBooks → Python Connector → DLT → Cognee → Knowledge Graph The connector retrieves financial records, formats them into readable Markdown documents, and sends them through Cognee's ingestion workflow. For example, instead of working with raw invoice JSON, the system can process information such as customer names, invoice totals, payment status, and line items. This makes the information more useful for downstream AI search and knowledge extraction. 🔍 The biggest challenge: keeping data in sync Fetching records is only one part of building a reliable connector. What happens when an invoice changes? What if someone deletes a record? And what if an API request fails and returns no data? I worked on incremental synchronization using Metadata.LastUpdatedTime, deletion detection through QuickBooks Change Data Capture (CDC), and a fallback reconciliation mechanism. I also added a safety check to prevent an empty synchronization sweep from accidentally marking previously known records for deletion. The submitted test suite documents 12 passing tests, covering data formatting, synchronization, deletion handling, and retry behavior. 💡 What I learned This contribution taught me that building a data connector is about much more than calling an API. You need to think about data consistency, authentication, synchronization state, failure handling, and testing edge cases. Working on these details gave me practical experience with Python, REST APIs, DLT, data pipelines, testing, and AI knowledge graphs. 🙌 Explore the project - My pull request: cognee-community#402 - Original issue: cognee#4802 - Cognee: topoteretes/cognee - Cognee Community: topoteretes/cognee-community Thanks to the Cognee maintainers and community for creating opportunities to learn and contribute to open-source AI infrastructure. The pull request is currently open for review, and I welcome feedback from other developers. 💬 Let's discuss!

@AIMemoryGuy Can you check out the integration proposal for cognee hackathon github.com/topoteretes/cognee/issues

##I Built a Brex Connector for Cognee: Bringing Business Expense Data Into AI Memory 🚀 I Built a Brex Connector for Cognee: Bringing Business Expense Data Into AI Memory 🚀 What I learned building an open-source data connector during MergeTober by WeMakeDevs. What if you could ask an AI assistant about business expenses instead of manually searching through transaction records, notes, and budgets? That question captures the idea behind my recent open-source contribution to Cognee Community. During MergeTober by WeMakeDevs, I worked on building a Brex data-source connector for Cognee. The work involved much more than fetching data from an API. I also had to think about how the data should be organized, how future syncs should work, and how to protect existing data when something goes wrong. Here is what I built, how it works, and what I learned. 🌍 My entry into MergeTober Open source can feel intimidating when you're starting out. You might wonder which project to choose, how to understand an unfamiliar codebase, or what maintainers expect from a contribution. MergeTober gave me an opportunity to work on a real integration issue rather than build another standalone demo. My workflow was straightforward: 1. Find an open issue that matches a real project need. 2. Understand the requirements and existing architecture. 3. Implement the feature and submit a pull request. 4. Document the work so other developers can understand it. I chose a connector issue because I wanted hands-on experience with APIs, data engineering, and AI infrastructure. 🧠 Why connectors matter for AI Consider a company that stores information across different tools. Expense records live in a financial platform, project updates live elsewhere, and important context is buried in notes. Even a capable AI system cannot use information it cannot access. Cognee is an open-source project focused on turning data into structured AI memory. Data connectors help bring information from external systems into that workflow. That led me to a practical question: How can we make business expense information available to an AI knowledge system in a useful, structured, and maintainable way? My contribution was an attempt to solve that problem for Brex. 🔌 What I built: the Brex connector Brex provides business expense and budget management capabilities. My connector brings selected Brex data into Cognee's ingestion workflow. You can explore the implementation here: - GitHub issue #4797 — Brex connector requirements - Pull request #389 — My implementation The connector supports three data resources: | Resource | Information collected | | --- | --- | | Expenses | Amounts, merchants, categories, statuses, and dates | | Memos | Expense notes and business justifications | | Budgets | Budget limits, spending, and budget relationships | The interesting part is that these resources don't all need to be represented in the same way. Expense memos can become readable documents for contextual knowledge extraction, while expenses and budgets can remain structured records for organized data processing. This gives downstream workflows access to both business context and structured information. ⚙️ How the connector works Here is the simplified architecture: Brex Platform API | v Authentication + Pagination | v Fetch Expenses, Memos and Budgets | v Transform the Data / \ v v Document Mode Structured Mode \ / v v DLT Ingestion | v Cognee Workflow | v AI Knowledge Retrieval Let's break down the engineering decisions behind it. 1. Authentication and API requests The connector uses Bearer token authentication to access the Brex Platform API. It also handles paginated responses, so the integration can retrieve records across multiple API pages rather than stopping at the first batch. API integrations must account for failure cases, too. The implementation includes handling for authentication errors, permission errors, and rate limits. 2. Turning raw data into useful information Raw API responses aren't always the best representation for an AI knowledge pipeline. For example, consider an expense containing a merchant, amount, department, budget, and a note explaining why the purchase was necessary. The connector can transform that context into a readable document card while preserving expenses and budgets as structured records. This separation makes the data easier to consume in different downstream workflows. 3. Incremental synchronization Fetching every record during every run can be inefficient as datasets grow. The connector uses Brex's posted_at_start filter and stores synchronization state through DLT's resource-state mechanism to track the latest processed timestamp. Subsequent runs can then use that timestamp to retrieve relevant new or updated expense records. The important lesson: reliable integrations need to remember where the previous synchronization ended. 4. Handling deletions safely One of the more interesting challenges was deletion reconciliation. Suppose a record disappears from the source. Eventually, the destination should stop treating that record as current. However, imagine the API temporarily fails while the connector is checking which records still exist. Treating an incomplete response as a complete snapshot could incorrectly mark valid records as deleted. The implementation therefore includes a guardrail: if reconciliation encounters API or network errors, deletion-marker generation is aborted rather than proceeding with potentially incomplete information. This is an important distinction between simply syncing data and building a connector that considers data integrity. 💻 What does using it look like? The pull request includes a usage example following Cognee's API. After installing and configuring the connector and providing the required Brex API credentials, the basic ingestion pattern looks like this: import asyncio import os import cognee from cognee_community_connector_brex import brex_source async def main(): source = brex_source( token=os.environ["BREX_API_TOKEN"], include=["expenses", "memos", "budgets"], ) await cognee.remember( source, dataset_name="brex_spend", primary_key="id", write_disposition="merge", max_rows_per_table=0, ) if __name__ == "__main__": asyncio.run(main()) This example illustrates how the connector is configured and passed to Cognee's ingestion API. A complete setup also requires installing the relevant dependencies, configuring valid credentials with appropriate permissions, and checking the compatibility of the project's installed versions. Once the data is ingested, it can be used in a Cognee search workflow according to the configured pipeline and supported query types. For the full implementation and query example, see the pull request. 🧪 Testing is part of the feature A connector should not be considered reliable just because it works with one successful API response. The pull request includes offline unit tests for important paths, including: - Authentication and API error handling. - Pagination and data transformation. - Incremental synchronization state. - Deletion-marker generation and failure guardrails. The PR also documents an end-to-end DLT pipeline run using DuckDB. These tests matter because connectors operate at a boundary between systems. A mistake can affect not only the data being fetched but also what downstream applications believe is current. I came away with a stronger appreciation for testing failure scenarios—not just the happy path. 📚 What I learned from this contribution This project taught me several practical lessons. Understand the existing architecture before coding. Following established connector patterns makes a contribution easier to review and maintain. Think about synchronization from day one. Initial ingestion is only one part of the problem. Updates, deletion handling, and persistent state matter just as much. Protect data when dependencies fail. A partial API response should not automatically be treated as the complete truth. Treat documentation as part of the implementation. Clear setup instructions and realistic examples help other developers use a feature without first having to understand every line of code. Most importantly, I learned that a meaningful open-source contribution often involves making many small engineering decisions correctly. 🙏 Thank you, Kunal Kushwaha, Cognee, and WeMakeDevs! A big thank you to Kunal Kushwaha and the WeMakeDevs team for encouraging developers to build in public, learn by contributing, and participate in open-source initiatives like MergeTober. Thank you to the Cognee team and community for maintaining the project and creating opportunities to work on real integration challenges. I am also grateful to the maintainers and community members who helped with the issue assignment and contribution process. Open source is about more than individual pull requests. It is about a community where developers can learn from one another and gradually help improve the tools everyone uses. 🔗 Explore the project If you're interested in AI memory, data engineering, or contributing to open source, these resources are a good starting point: - My pull request: Brex spend data-source connector - Original issue: Add a Brex data-source connector - Cognee: Explore the main repository - MergeTober: Learn about the initiative 🚀 Your turn: build something useful You don't need to start with a groundbreaking idea to contribute to open source. Pick a real problem. Read the existing code. Ask questions when you need to. Test your implementation. Document what you learn. That is how small contributions become useful improvements to real software. My MergeTober journey is another step in that direction, and I'm looking forward to learning and contributing more. Have you built an API integration or contributed to an AI open-source project? Share your experience in the comments. I'd love to learn what challenges you encountered. ---

CogneeでAIに長期記憶を持たせる｜ペルソナ・ブランド戦略を忘れない記事作成AIを構築する はじめに 普段、AIを使ってマーケティング戦略の立案やSEO記事の作成を行っています。 しかし、AIを継続的に利用するうえで課題があります。 - AIが以前設定したペルソナの性格を忘れる - ブランドの世界観や文章のトーンが変わる - 過去に決めたマーケティング戦略が引き継がれない - 新しいチャットを始めるたびに同じ情報を説明する必要がある - 複数のAIエージェントで知識を共有しにくい そこで、AI向けの知識管理ツール「Cognee」を使い、ペルソナやブランド戦略を長期記憶として管理する仕組みを構築してみます。 この記事は、自分用の技術メモも兼ねた実践記録です。 1. Cogneeとは？ Cogneeは、LLM向けのメモリ・知識管理フレームワークです。 文章やドキュメントなどの情報を取り込み、AIが検索・活用できる知識として構造化できます。 主な特徴： - ナレッジグラフの構築 - ベクトル検索 - GraphRAGへの応用 - AIエージェントの長期記憶 - 複数の情報間の関係性の管理 公式サイト： cognee.ai 公式ドキュメント： docs.cognee.ai GitHub： topoteretes/cognee 2. 今回実現したいこと 目標は、AIマーケティングチームの共通知識をCogneeに保存することです。 ┌────────────────────┐ │ Cognee │ │ 長期記憶DB │ └─────────┬──────────┘ │ ┌────────────┼────────────┐ │ │ │ ペルソナ記憶 ブランド戦略 過去の学習 │ │ │ └────────────┼────────────┘ │ 検索・情報取得 │ ┌────────────┴────────────┐ │ │ 記事作成AI Marketing AI │ │ └────────────┬────────────┘ │ コンテンツ生成 │ 結果を評価 │ 記憶を更新 ※ Cogneeと各AIの接続処理は別途実装します。 3. 長期記憶の設計 記憶を3つのカテゴリーに分けます。 3.1 固定記憶 頻繁に変更しない情報です。 - ペルソナの性格 - 価値観 - 購買心理 - ブランド理念 - ブランドの世界観 - 禁止表現 - 文章のトーン 3.2 戦略記憶 定期的に更新する情報です。 - SEO戦略 - ターゲットキーワード - 競合分析 - コンテンツ戦略 - 商品の訴求ポイント - コンバージョン戦略 3.3 学習記憶 実践から蓄積する情報です。 - 記事のパフォーマンス - 成功した施策 - 失敗した施策 - 顧客からのフィードバック - AIが生成した記事への修正履歴 重要なのは、AIが勝手に固定記憶を書き換えないようにすることです。 記憶の更新には承認プロセスを設けます。 4. 環境構築 必要なもの - Python - pip - Cognee - LLM APIキー 今回はPythonを使って最小構成を構築します。 4.1 仮想環境を作成 python -m venv .venv macOS / Linux： source .venv/bin/activate Windows PowerShell： .venv\Scripts\Activate.ps1 4.2 Cogneeをインストール pip install cognee 4.3 APIキーを設定 macOS / Linux： export LLM_API_KEY="YOUR_API_KEY" Windows PowerShell： $env:LLM_API_KEY="YOUR_API_KEY" 使用するLLMプロバイダーやモデルによっては、追加の環境変数設定が必要です。 5. ペルソナ情報を作成 最初は架空のペルソナで検証します。 data/persona.md # ペルソナ：佐藤美咲 ## 基本情報 - 年齢：35歳 - 職業：マーケティング担当者 - 関心：AI、自動化、SEO、業務効率化 ## 性格 - 論理的 - 慎重 - 新しい技術に興味がある - 誇張された広告表現を嫌う ## 悩み - 記事作成に時間がかかる - AIの出力品質が安定しない - マーケティング業務を自動化したい ## 購買心理 - 導入効果を重視 - 実例や数値を求める - 誇大広告を信用しない ## 好むコンテンツ - 実践的な技術記事 - 比較記事 - ステップ形式の解説 - コード付きチュートリアル 6. ブランド戦略を作成 data/brand_strategy.md # ブランド戦略 ## ブランドの目的 AI技術を活用してマーケティング業務を効率化する。 ## ターゲット - マーケティング担当者 - 個人事業主 - コンテンツ制作者 ## ブランドの性格 - 専門的 - 誠実 - 実践重視 - 論理的 ## 文章のルール - 結論を先に伝える - 専門用語を説明する - 実例を含める - 誇張表現を避ける - 根拠のない成果を約束しない ## コンテンツ戦略 - SEOを意識する - 読者の検索意図を満たす - 実践できる情報を提供する - 読者の課題解決を優先する 7. Cogneeに長期記憶を登録 ingest.py import asyncio import cognee async def main(): # ペルソナ情報を追加 await cognee.add("data/persona.md") # ブランド戦略を追加 await cognee.add("data/brand_strategy.md") # 知識を構造化 await cognee.cognify() print("知識の登録が完了しました") if __name__ == "__main__": asyncio.run(main()) 実行： python ingest.py cognify() により、登録した情報が検索可能な知識として処理されます。 8. 記憶を検索する search.py import asyncio import cognee async def main(): questions = [ "佐藤美咲の性格は？", "佐藤美咲が嫌う広告表現は？", "ブランドの文章ルールは？", "このペルソナ向けの記事では何を重視すべき？" ] for question in questions: results = await cognee.search( query_text=question ) print(f"\n質問: {question}") print(f"検索結果: {results}") if __name__ == "__main__": asyncio.run(main()) 実行： python search.py 別のPythonプロセスから検索することで、登録情報が永続化されているかを確認します。 ※ データベースの保存先や設定が変わると、同じ記憶を参照できない場合があります。 9. 記事作成AIとの連携 記事作成前にCogneeから関連情報を取得します。 ユーザー： 「AIマーケティングについて記事を書いて」 ↓ Cogneeから検索： 1. ターゲットペルソナ 2. ブランドの文章ルール 3. コンテンツ戦略 4. 関連する過去の知見 ↓ 記事作成AIへコンテキストを渡す ↓ 記事生成 ↓ 人間が確認・修正 ↓ 修正内容や成果を記録 これにより、AIの出力をブランド戦略と整合させやすくなります。 ただし、検索結果が不完全な場合やLLMが指示に従わない場合もあるため、生成内容の検証は必要です。 10. 検証項目 以下の項目を実際に確認します。 - [ ] Cogneeをインストールできる - [ ] ペルソナ情報を登録できる - [ ] ブランド戦略を登録できる - [ ] ナレッジグラフを生成できる - [ ] ペルソナの性格を検索できる - [ ] ブランドの文章ルールを検索できる - [ ] Pythonを再起動しても検索できる - [ ] 記憶を利用して記事を生成できる - [ ] 記憶にない情報への回答を検証できる 実行結果 ※ 実践後に追記 実行環境： OS： Pythonバージョン： Cogneeバージョン： LLMモデル： データベース設定： 成功したこと： 発生したエラー： 改善したこと： 11. 今後の改善計画 Phase 1：基本的な長期記憶 - ペルソナを保存 - ブランド戦略を保存 - 自然言語検索を実装 Phase 2：記事作成AIとの連携 - 記事生成前に記憶を検索 - 検索結果をプロンプトへ注入 - ブランドとの整合性を評価 Phase 3：複数AIエージェントへの展開 - Marketing AI - SEO AI - 記事作成AI - 編集・品質管理AI 複数のAIが共通の記憶を参照できる構成を目指します。 Phase 4：継続学習 - 記事の成果を記録 - 戦略の変更履歴を管理 - 成功・失敗パターンを蓄積 - 記憶の更新を承認制にする 12. まとめ Cogneeを使えば、AIが参照するペルソナやブランド戦略を、チャットの外部に永続的に保存できます。 今回目指すのは、単なるチャット履歴の保存ではありません。 AIマーケティングチームが共通の知識を持ち、過去の意思決定を参照しながら、継続的にコンテンツを制作できる環境です。 今後は実際にCogneeを動かし、検索精度、記事品質、運用コストなどを検証していきます。 --- 参考資料 - Cognee公式サイト - Cognee公式ドキュメント - Cognee GitHub --- タグ Python AI RAG LLM Cognee

I made my contribution to open source with Mergetober! !Image description## Integrating Clay Enterprise with Cognee Using dlt For Mergetober, I worked on building a connector that brings Clay Enterprise data into Cognee using dlt's REST API source. The main goal was to make it easier to bring structured data from Clay tables into Cognee. I worked on configuring the API endpoint, request parameters, and pagination so the connector could fetch data from the API. Pagination was an important part of the implementation because the API can return data across multiple pages. The connector needs to handle those pages to retrieve the available records properly. I also worked on preparing the data so it could be used in Cognee's ingestion workflow. I tested the implementation with unit tests and a real Clay Enterprise workspace. Working on this connector gave me a chance to contribute to an open-source project and explore how data integrations can connect external sources with AI applications. You can check out my contribution here: Clay Enterprise connector for Cognee — PR #313. Acknowledgments A big thank you to WeMakeDevs for organizing Mergetober and giving developers a chance to contribute to open source. Thanks to the Cognee team for building the project and making this contribution possible. I'd also like to thank Kunal Kushwaha for encouraging and supporting the developer community. I'm glad I got the opportunity to contribute to Cognee through Mergetober. It was a great way to get involved in open source and work on a practical integration.

Inside My MergeTober Contribution: Integrating Salesforce with Cognee Introduction AI agents are only as useful as the information they can access. But business knowledge often lives in separate tools, such as CRM platforms, documents, and communication systems. That is why I participated in MergeTober, contributing to the open-source Cognee ecosystem by working on a Salesforce connector. Cognee provides a memory layer for AI agents, helping turn data into connected knowledge that can be retrieved by downstream applications. My contribution aims to make Salesforce CRM data available to those workflows. Why Connect Salesforce with Cognee? Salesforce contains valuable business information, including customer accounts, sales opportunities, support cases, and Chatter conversations. Connecting this data to Cognee could help developers build AI applications that retrieve relevant business context instead of relying only on isolated records. The goal of my connector is to support: - Authentication: OAuth 2.0 with a Salesforce Connected App. - Data ingestion: Accounts, Opportunities, Cases, and Chatter records. - Incremental synchronization: SOQL queries using LastModifiedDate. - Deletion handling: Detect deleted source records and support downstream cleanup. - Testing and documentation: Unit tests, setup instructions, and a runnable example. How It Could Work in the Real World Imagine a sales team using a CRM knowledge assistant. A representative asks: “What open opportunities are associated with this customer, and are there any related support cases?” A configured application could retrieve relevant Salesforce records through the connector, process the information with Cognee, and use the resulting knowledge as context for an answer. This is an illustrative use case, not a claim that I have tested a complete assistant against a live Salesforce organization. Incremental synchronization could help keep the information current, while deletion handling could help prevent outdated source records from remaining in the knowledge system. Testing My Contribution I tested the connector locally using mocked Salesforce responses and verified that 9 out of 9 mocked unit tests passed. These results validate the behavior covered by those tests. They do not independently establish that authentication, synchronization, and knowledge retrieval have all been verified against a live Salesforce environment. My pull request is currently open and awaiting review. View my Salesforce connector PR on GitHub Why MergeTober Matters I appreciate MergeTober's focus on building integrations that solve real problems. Code demonstrates what we build, while a practical blog post helps other developers understand why it matters and how they might use it. Contributing to open source is an opportunity to learn, collaborate, and create tools that others can build upon. A special thank you to Kunal Kushwaha, Founder of WeMakeDevs, for creating opportunities that encourage developers to contribute to meaningful open-source projects. Thank you also to the Cognee team and community for building an open-source memory layer for AI agents and supporting its growing integration ecosystem. Learn more about Cognee at cognee.ai. Conclusion My MergeTober contribution explores how Salesforce CRM data can become part of AI knowledge workflows through authentication, structured ingestion, incremental synchronization, and deletion handling. With my local mocked tests passing and the pull request awaiting review, I look forward to continuing to improve the contribution through feedback and validation. I'm excited to contribute to open source and explore how connected business knowledge can make AI applications more useful.

Cognee- Turn documents, code, and conversations into connected knowledge graphs. topoteretes/cognee

@cognee_ The fix → cognee#5608 Audit any public repo in one line. No clone, nothing executed: npx activate-agentmd audit <owner/repo>