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ECC Review

ECC is an open-source system designed to optimize the performance of AI coding agents and automate software development workflows.

shipped Jul 22, 2026freemium
Domain rating39Monthly visits45/mo
ECC — product screenshot

Why it matters

1Offers a freemium model with a free tier providing 10 analyses per month and up to 200 commits per run.
2The Pro tier is priced at $19/seat/month, allowing 50 analyses per active developer seat and 1,000 commits per run.
3ECC 2.0 introduces a local-first control plane for observability and orchestration.
4The open-source core includes over 260 skills, 60+ specialized agents, and 80+ commands.

About ECC

Business Model
Freemium SaaS
Funding
Bootstrapped
Platforms
Web, API
Target Audience
Developers and teams managing code repositories

Pricing Plans

Free
$0/month
  • • Install on public repositories
  • • Generate reviewable PRs from git history
  • • Keep the OSS install surface fully available
Pro
$19/seat
  • • Private repo GitHub App coverage
  • • Pooled usage with metered overage
  • • Deeper AgentShield-backed review checks
Enterprise
Contact
  • • SSO-ready governance and audit log surfaces
  • • Policy packs and custom rules
  • • Dedicated rollout support and procurement help

Leadership

Affaan MustafaFounder
GitHubOpen Source

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is ECC?

ECC is an AI agent harness system developed by Affaan Mustafa that enables developers, team leads, and platform teams to optimize the performance and security of AI coding agents. It provides a structured environment for AI assistants, enabling them to operate more effectively within a project's context by analyzing repository history and generating tailored skill files.

features

Key Features of ECC

ECC Tools provides a comprehensive suite of features designed to enhance AI coding agents and streamline development workflows. These capabilities range from deep code history analysis to robust security auditing, ensuring AI agents operate efficiently and securely within project parameters.

  • AI-powered code history analysis for generating context-aware skills.
  • Open-source compatibility (MIT license) fostering community contributions.
  • Custom skill generation from repository data, including rules, commands, and safety checks.
  • Security auditing with AgentShield, featuring over 102 rules for AI agent configurations.
  • Flexible pricing tiers, including a free tier and a Pro tier at $19/seat/month.
  • Automating GitHub workflows through agent-specific manifests and pull requests.
  • Enhancing security by integrating skills and agents to detect vulnerabilities.
  • Streamlining coding practices by enforcing consistent standards and TDD workflows.
  • Ensuring security compliance by auditing against rules like OWASP Top 10.
  • Analyzing repository history and agent-related configuration to provide reusable guidance.

use cases

Who Should Use ECC?

ECC Tools is designed for various roles within software development and engineering organizations seeking to leverage AI coding agents more effectively and securely. Its functionalities cater to teams focused on automation, standardization, and security compliance.

  • Developers: For automating code reviews, enforcing TDD, and generating code in frameworks like Django, Next.js, and React.
  • Team Leads: For managing team workflows, maintaining coding standards across projects, and ensuring consistent agent behavior.
  • Platform Teams: For standardizing agent configuration and workflows across multiple AI agent harnesses (e.g., Claude Code, Codex, Cursor).
  • Security Teams: For enhancing security through AgentShield, auditing AI agent configurations, and detecting prompt-injection attempts.
  • Engineering Leaders: For scaling AI assistance across numerous repositories, rapidly onboarding new developers, and ensuring continuous learning within development processes.

how to use

How to Use ECC

To begin using ECC, users typically integrate it with their GitHub repositories to leverage its AI agent harness capabilities. The system then analyzes repository history to generate project-specific guidance for AI coding agents.

  • 1Install the ECC GitHub App to enable automated workflows.
  • 2Configure ECC to analyze your repository's Git history and existing agent configurations.
  • 3Generate custom skill files that provide AI agents with specific rules, commands, and security checks.
  • 4Deploy AI agents (e.g., Claude Code, Cursor) within the ECC harness to perform tasks like code review or feature implementation.
  • 5Monitor agent performance and security compliance using ECC's observability features and AgentShield.
  • 6Utilize the continuous learning framework to refine agent behaviors and improve confidence scoring over time.

pricing

ECC Pricing & Plans

ECC Tools operates on a freemium model, offering a free tier for individual use and a Pro tier for teams requiring more extensive capabilities. An Enterprise plan is available for organizations with advanced needs.

  • Free: $0/month. Includes 10 analyses per month and supports up to 200 commits per run.
  • Pro: $19/seat/month. Offers 50 analyses per active developer seat per month (pooled) and supports up to 1,000 commits per run.
  • Enterprise: Contact for custom pricing. Designed for large organizations with specific requirements.

Pros

  • +Provides a structured environment for AI agents, enhancing consistency and context retention.
  • +Open-source core (MIT license) fosters community contributions and transparency.
  • +AgentShield offers robust security auditing with over 102 rules for AI agent configurations.
  • +Automates GitHub workflows, including pull request creation with agent-specific manifests.
  • +Supports multiple AI coding assistants (Claude Code, Codex, Cursor, Gemini, GitHub Copilot).
  • +Includes a continuous learning framework for improving agent behaviors over time.

Cons

  • −The free tier has limited analyses (10 per month) and commit processing (200 per run).
  • −Requires integration with GitHub, which might not suit all development environments.
  • −Specific details on supported AI models and multimodality are not explicitly stated.
  • −The effectiveness is dependent on the quality of repository history for skill generation.
  • −May require initial setup and configuration to tailor to specific project conventions.

Policies

Pricing Page

View Pricing→

Similar Tools

ECC vs Competitors

ECC Tools positions itself as an 'agent harness operating system' that enhances existing AI coding assistants by providing a persistent layer of context, memory, and security. This differentiates it from tools that primarily offer individual AI coding functionalities or general MLOps platforms.

1
AgentOpsOn Stork

AgentOps is a dedicated observability and debugging platform specifically designed for AI agents, offering session-level tracing, LLM call recording, cost tracking, replay debugging, and evaluation.

AgentOps directly competes with ECC's focus on agent performance optimization by providing detailed execution tracing and debugging for various AI agent frameworks and LLMs. Like ECC, it offers a free tier, focusing on developers and ML engineers for improving agent reliability and efficiency.

2
Galileo AIOn Stork

Galileo is a purpose-built AI evaluation and observability platform that offers real-time protection and automated failure detection specifically for autonomous systems and LLM applications.

Galileo directly competes with ECC by providing a comprehensive platform for ensuring the reliability and performance of AI agents through advanced evaluation, monitoring, and guardrails. It focuses on agent reliability challenges, similar to ECC's performance optimization and security aspects.

3
Weights & Biases (W&B)On Stork

W&B provides a comprehensive MLOps platform that extends to LLM applications through tools like Weave and Prompts, offering experiment tracking, model versioning, dataset management, and rigorous evaluation for LLMs and agents.

W&B offers a broader MLOps suite, with its LLM-specific tools (Weave, Prompts) competing with ECC by providing evaluation, monitoring, and optimization capabilities for LLM-powered agents. While ECC is more narrowly focused on agent harness optimization, W&B provides a more general platform that can be adapted for similar goals, though it's often seen as more for ML experiment tracking than direct agent operations.

4
LangSmithOn Stork

LangSmith is a managed observability, evaluation, and deployment platform specifically designed for building, debugging, evaluating, and shipping reliable AI agents and LLM applications, with native integration into the LangChain ecosystem.

LangSmith is a direct competitor to ECC, offering a comprehensive platform for agent engineering that includes observability, evaluation, and deployment features to improve agent performance, reliability, and security. It provides a similar "harness" for agents, particularly those built with LangChain, but is also framework-agnostic.

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