Tracecat is an innovative open-source automation platform designed for security teams, providing a robust alternative to existing solutions like Tines and Splunk SOAR. The platform leverages AI-assisted workflows, alert orchestration, and rapid case resolution capabilities. It integrates enterprise-grade tools with open-source AI infrastructure and GPT models, targeting accessibility particularly for small-to-mid-sized teams.
Critical Bug: CORS Issue on API Endpoint (#118): This issue, reported by Lockness (lockness-Ko), is critical as it hampers API functionality from browsers due to missing CORS headers. Immediate attention is required to resolve this issue permanently beyond the temporary nginx workaround.
Feature Implementation Partially Done: AWS GuardDuty Integration (#112): Initiated by Daryl Lim (daryllimyt), this feature lacks unit tests, which is crucial for ensuring reliability before it can be fully integrated and used.
The project shows a healthy pipeline of new features and integrations, alongside active efforts to enhance user experience and backend stability. However, areas such as telemetry and logging require further improvements as indicated by multiple open issues like #62.
tracecat/integrations/aws_cloudtrail.py
tracecat/runner/workflows.py
frontend/src/components/workspace/canvas/integration-node.tsx
docs/installation.mdx
tracecat/db.py
The Tracecat project is progressing well towards its goal of democratizing security automation with a strong emphasis on AI-driven workflows. The team's recent activities reflect a commitment to enhancing both the user experience and the technical robustness of the platform. However, attention is needed in areas like testing, error handling, and managing code complexity to ensure the platform's reliability as it scales.
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
Daryl Lim | 3 | 24/22/0 | 123 | 134 | 9175 | |
Chris Lo | 2 | 8/8/0 | 40 | 61 | 8052 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
TracecatHQ's project, Tracecat, is an innovative open-source automation platform tailored for security teams. It competes with established products like Tines and Splunk SOAR by offering AI-assisted workflows and integrations with enterprise-grade tools. The project is in its public alpha phase and demonstrates a robust development activity with a focus on expanding features, refining user experience, and enhancing backend stability.
The development team, led by Daryl Lim and Chris Lo, shows a high level of activity with significant recent contributions to both the project’s core functionalities and its integrations. The team's ability to collaborate effectively is evident from the frequent co-authored commits and shared responsibilities across different aspects of the project.
Tracecat positions itself as a tool that democratizes security automation by integrating open-source AI technologies. This strategic positioning could capture a significant market share among small-to-mid-sized teams who are currently underserved by more expensive solutions. The ongoing development of new integrations, such as those with AWS GuardDuty and Datadog, suggests a clear direction towards enhancing its appeal to enterprises looking for comprehensive security solutions.
The project's shift from using Supabase to direct Postgres access, as seen in the recent refactorings, indicates a strategic move to optimize operational costs and control data management more directly. Such decisions are crucial for maintaining scalability while managing financial overheads effectively.
Issues like the critical CORS bug (#118) highlight challenges in maintaining reliability during rapid development phases. However, the team’s responsiveness to such issues and their commitment to user experience enhancements suggest a proactive approach to product quality.
Prioritize Critical Bug Fixes: Immediate resolution of critical bugs such as #118 should be prioritized to prevent negative impacts on user trust and product usability.
Enhance Testing Practices: Integrating comprehensive unit tests for new features (e.g., AWS GuardDuty integration #112) will be crucial in minimizing future bugs and ensuring reliability as new updates are rolled out.
Expand Market Reach Through Integrations: Continued expansion of third-party integrations will not only enhance functionality but also improve market competitiveness by offering more comprehensive solutions to potential enterprise users.
Focus on Documentation and Developer Support: As the platform grows, maintaining detailed and up-to-date documentation will be essential for supporting new users and developers who adopt or contribute to Tracecat.
Monitor Refactoring Outcomes: Given the significant changes like removing Tantivy (#115) and migrating authentication systems (#106), it is vital to monitor these modifications closely to ensure they do not introduce new issues or degrade performance.
TracecatHQ's project is on a promising trajectory with active development, strategic feature enhancements, and a clear focus on creating a scalable, user-friendly platform for security automation. By addressing the current challenges related to testing and bug fixes, Tracecat can strengthen its market position as a viable alternative to more established competitors, particularly for budget-conscious teams seeking powerful automation tools.
Developer | Avatar | Branches | PRs | Commits | Files | Changes |
---|---|---|---|---|---|---|
Daryl Lim | 3 | 24/22/0 | 123 | 134 | 9175 | |
Chris Lo | 2 | 8/8/0 | 40 | 61 | 8052 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Resource.updated_at
field in the database model.There are several notable issues regarding pull requests being merged despite failing builds. This practice can introduce bugs or regressions into the main branch if not managed carefully. It is recommended that the team investigates these failures thoroughly before proceeding with merges in future cases.
The open pull requests seem to be well-managed overall, but attention is needed to ensure that unit tests are completed (PR #112) and that draft pull requests are finalized (PR #58).
The project appears active with recent merges addressing various aspects of functionality from documentation updates to major refactors like replacing Supabase with Postgres. It's important that these changes are well-tested given their potential impact on the application's stability.
Tracecat is an open-source automation platform for security teams, serving as an alternative to Tines and Splunk SOAR. It is designed to build AI-assisted workflows, orchestrate alerts, and close cases quickly. The project is managed by TracecatHQ and is currently in public alpha. The platform integrates enterprise-grade open-source tools with open-source AI infrastructure and GPT models, aiming to make security automation accessible to all, especially small-to-mid-sized teams.
Tracecat's features include drag-and-drop workflow builders, AI actions, secrets management, case management with AI-assisted labeling, unlimited logs storage, data validation using Pydantic V2 and Zod, and more. It supports Docker Compose deployment and is cloud-agnostic. The project's codebase is licensed under the Apache License 2.0.
The development team at TracecatHQ has been very active recently, with a strong emphasis on enhancing the project's stability, usability, and feature set. Daryl Lim has been instrumental in driving the project forward with numerous contributions across the stack. Chris Lo has been pivotal in ensuring that the project's infrastructure is robust and that new integrations are added to expand Tracecat's capabilities.
The team seems to be working well together, with frequent collaborations between members. The focus on both user-facing features and backend stability suggests a balanced approach to development. The detailed commit messages and thorough documentation updates indicate a commitment to quality and transparency.
Given the volume of recent activity and the trajectory of the work being done, Tracecat appears to be rapidly evolving towards its goal of providing an accessible security automation platform with a strong focus on AI-assisted workflows.
(Note: Specific commit details are omitted due to the extensive list provided earlier.)
tracecat/integrations/aws_cloudtrail.py
Structure and Quality:
typing
for type hints enhances code readability and maintainability.query_cloudtrail_logs
is well-documented with parameters and return types clearly specified.AWS_ACCOUNT_ID
, AWS_ORGANIZATION_ID
) directly in the function could be improved by encapsulating them within a configuration management system or class to avoid direct calls to os.environ
which can lead to issues if the environment is not properly configured.tracecat.etl.aws_cloudtrail
and tracecat.logger
, suggesting a good modular design.Potential Risks:
tracecat/runner/workflows.py
Structure and Quality:
Workflow
class that encapsulates workflow configurations, using Pydantic for data validation which is a good practice for ensuring data integrity.Potential Risks:
frontend/src/components/workspace/canvas/integration-node.tsx
Structure and Quality:
useCallback
) and context (useWorkflowBuilder
) to manage state and side effects efficiently.Potential Risks:
docs/installation.mdx
Structure and Quality:
<Note>
tags) helps highlight important information, improving readability.Potential Risks:
tracecat/db.py
Structure and Quality:
Potential Risks:
TRACECAT__DB_URI
) should be managed through a centralized configuration module to enhance security and maintainability.The Tracecat source code exhibits a sophisticated use of modern Python and TypeScript features with a focus on modularity, type safety, and clear documentation. However, there are areas where error handling, complexity management, and separation of concerns could be improved to enhance maintainability and robustness.