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Context Retrieval for AI Agents across Apps & Databases

·385 words·2 mins
GitHub Natural Language Processing AI Python Open Source AI Agent
Articoli Interessanti - This article is part of a series.
Part : Everything as Code: How We Manage Our Company In One Monorepo At Kasava, we've embraced the concept of "everything as code" to streamline our operations and ensure consistency across our projects. This approach allows us to manage our entire company within a single monorepo, providing a unified source of truth for all our configurations, infrastructure, and applications. **Why a Monorepo?** A monorepo offers several advantages: 1. **Unified Configuration**: All our settings, from development environments to production, are stored in one place. This makes it easier to maintain consistency and reduces the risk of configuration drift. 2. **Simplified Dependency Management**: With all our code in one repository, managing dependencies becomes more straightforward. We can easily track which versions of libraries and tools are being used across different projects. 3. **Enhanced Collaboration**: A single repository fosters better collaboration among team members. Everyone has access to the same codebase, making it easier to share knowledge and work together on projects. 4. **Consistent Build and Deployment Processes**: By standardizing our build and deployment processes, we ensure that all our applications follow the same best practices. This leads to more reliable and predictable deployments. **Our Monorepo Structure** Our monorepo is organized into several key directories: - **/config**: Contains all configuration files for various environments, including development, staging, and production. - **/infrastructure**: Houses the infrastructure as code (IaC) scripts for provisioning and managing our cloud resources. - **/apps**: Includes all our applications, both internal tools and customer-facing products. - **/lib**: Stores reusable libraries and modules that can be shared across different projects. - **/scripts**: Contains utility scripts for automating various tasks, such as data migrations and backups. **Tools and Technologies** To manage our monorepo effectively, we use a combination of tools and technologies: - **Version Control**: Git is our primary version control system, and we use GitHub for hosting our repositories. - **Continuous Integration/Continuous Deployment (CI/CD)**: We employ Jenkins for automating our build, test, and deployment processes. - **Infrastructure as Code (IaC)**: Terraform is our tool of choice for managing cloud infrastructure. - **Configuration Management**: Ansible is used for configuring and managing our servers and applications. - **Monitoring and Logging**: We use Prometheus and Grafana for monitoring,
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#### Source

Type: GitHub Repository Original link: https://github.com/airweave-ai/airweave Publication date: 2025-11-12


Summary
#

WHAT - Airweave is an open-source context retrieval layer for AI agents that operates on apps and databases. It provides a semantic search interface accessible via REST API or MCP, integrating with various productivity tools and databases.

WHY - It is relevant for AI business because it allows improving the ability of AI agents to retrieve contextual information from different sources, thus increasing the effectiveness of the agents’ responses and actions.

WHO - The main actors are the Airweave company and the community of developers contributing to the open-source project. Competitors include other context retrieval platforms and knowledge graph management solutions.

WHERE - It positions itself in the market of context retrieval solutions for AI agents, integrating with various productivity tools and databases.

WHEN - The project is active and growing, with a community of developers actively contributing. The project’s maturity is in the consolidation phase, with an expanding user base.

BUSINESS IMPACT:

  • Opportunities: Integration with our existing stack to improve the context retrieval capabilities of AI agents. Possibility of partnerships with Airweave to develop joint solutions.
  • Risks: Competition with other context retrieval solutions. Dependence on an open-source project for critical functionalities.
  • Integration: Possible integration with our existing stack via REST API or MCP, allowing the extension of AI agents’ capabilities.

TECHNICAL SUMMARY:

  • Core technology stack: Python, Docker, Docker Compose, Node.js, REST API, MCP. Supports integrations with various productivity tools and databases.
  • Scalability: Container-based architecture that facilitates horizontal scalability. Limitations depend on the configuration of the underlying infrastructure.
  • Technical differentiators: Support for semantic search, integration with various productivity tools, flexible API interface.

Use Cases
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  • Private AI Stack: Integration in proprietary pipelines
  • Client Solutions: Implementation for client projects
  • Development Acceleration: Reduction of project time-to-market
  • Strategic Intelligence: Input for technological roadmap
  • Competitive Analysis: Monitoring AI ecosystem

Resources
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Original Links #


Article recommended and selected by the Human Technology eXcellence team, elaborated through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-11-12 17:59 Original source: https://github.com/airweave-ai/airweave

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Articoli Interessanti - This article is part of a series.
Part : Everything as Code: How We Manage Our Company In One Monorepo At Kasava, we've embraced the concept of "everything as code" to streamline our operations and ensure consistency across our projects. This approach allows us to manage our entire company within a single monorepo, providing a unified source of truth for all our configurations, infrastructure, and applications. **Why a Monorepo?** A monorepo offers several advantages: 1. **Unified Configuration**: All our settings, from development environments to production, are stored in one place. This makes it easier to maintain consistency and reduces the risk of configuration drift. 2. **Simplified Dependency Management**: With all our code in one repository, managing dependencies becomes more straightforward. We can easily track which versions of libraries and tools are being used across different projects. 3. **Enhanced Collaboration**: A single repository fosters better collaboration among team members. Everyone has access to the same codebase, making it easier to share knowledge and work together on projects. 4. **Consistent Build and Deployment Processes**: By standardizing our build and deployment processes, we ensure that all our applications follow the same best practices. This leads to more reliable and predictable deployments. **Our Monorepo Structure** Our monorepo is organized into several key directories: - **/config**: Contains all configuration files for various environments, including development, staging, and production. - **/infrastructure**: Houses the infrastructure as code (IaC) scripts for provisioning and managing our cloud resources. - **/apps**: Includes all our applications, both internal tools and customer-facing products. - **/lib**: Stores reusable libraries and modules that can be shared across different projects. - **/scripts**: Contains utility scripts for automating various tasks, such as data migrations and backups. **Tools and Technologies** To manage our monorepo effectively, we use a combination of tools and technologies: - **Version Control**: Git is our primary version control system, and we use GitHub for hosting our repositories. - **Continuous Integration/Continuous Deployment (CI/CD)**: We employ Jenkins for automating our build, test, and deployment processes. - **Infrastructure as Code (IaC)**: Terraform is our tool of choice for managing cloud infrastructure. - **Configuration Management**: Ansible is used for configuring and managing our servers and applications. - **Monitoring and Logging**: We use Prometheus and Grafana for monitoring,
Part : This Article