Compozy

What is Compozy?

Compozy is an AI multi-agent workflow and automation framework that helps development teams build, coordinate, and operate agent-based applications. It brings agents, tasks, tools, and signals together in declarative YAML workflows, giving developers a structured way to turn separate AI capabilities into connected processes. Instead of managing each agent interaction through custom orchestration code, teams can describe how work should move through the system and let Compozy coordinate execution.

Built with Go and Temporal, Compozy focuses on reliable execution for distributed AI workloads. Its architecture supports fault tolerance, workflow state management, and scalable processing, making it suitable for applications that extend beyond a single model request. With control over deployment infrastructure, organizations can align their AI automation environment with their operational requirements, data policies, and resource budgets.

How to Use Compozy

Start by defining a workflow in YAML and outlining the agents, tasks, and tools involved. Each workflow describes how inputs are processed, which operations should run, and how results move between steps. Dynamic variables and functions allow developers to adapt execution to incoming data rather than relying exclusively on fixed sequences. Agents can connect large language models (LLMs) with purpose-built tools, allowing them to perform actions as part of a larger automated process.

Next, choose task patterns that match the workload. Straightforward operations can run as individual steps, while independent activities can execute in parallel. Collection tasks support processing groups of items, and routing logic can direct work according to conditions. For more specialized integrations, developers can write JavaScript or TypeScript tools that run through Bun, or connect compatible services using the Model Context Protocol (MCP).

Once the workflow is configured, teams can use scheduling for recurring jobs and signals for processes that depend on external events. Memory capabilities help agents retain relevant context, while workflow debugging and monitoring support ongoing maintenance. This approach gives developers a path from an initial automation prototype to a more dependable multi-agent application.

Key Features of Compozy

• Declarative YAML Workflow Configuration: Describe agents, execution steps, and dependencies in readable configuration files. Keeping workflow definitions alongside application code makes changes easier to review, track, and share across a development team.
• Multi-Agent Orchestration: Organize multiple AI agents within a coordinated process. Developers can divide responsibilities among specialized agents and define how their outputs contribute to the overall workflow.
• Reliable Go and Temporal Foundation: Compozy combines Go with Temporal to support durable execution, retries, and workflow state management. These capabilities help long-running automation recover from interruptions and operational failures.
• Flexible Task Execution: Build workflows using basic tasks, parallel branches, collection processing, conditional routing, signals, and wait conditions. These patterns support both sequential automation and more involved distributed processes.
• JavaScript and TypeScript Tools: Extend agent functionality with custom tools executed through a Bun runtime. Teams can add API calls, application-specific logic, and data-processing operations to meet their particular requirements.
• Model Context Protocol Integration: Use MCP to connect agents with compatible external tools and services. This provides a standardized integration path when working with systems that expose MCP interfaces.
• Event-Driven Workflow Coordination: Use signals to communicate with running workflows and respond to external activity. Signal-based coordination is useful when a process must pause, receive additional information, or continue after another system completes its work.
• Built-In Workflow Scheduling: Run recurring automation without manually launching each execution. Scheduled workflows can support routine reporting, repository checks, and periodic data-processing tasks.
• Agent Memory and Context: Maintain conversation history and relevant state for applications that depend on previous interactions. Context-aware agents can use retained information to provide more consistent assistance across a continuing process.

Why Choose Compozy?

Compozy is designed for teams that need more than a collection of isolated AI prompts. Production automation often involves multiple services, changing inputs, asynchronous events, and tasks that may fail or take time to complete. By combining workflow definitions with a durable execution foundation, Compozy helps developers manage these concerns within a consistent orchestration model.

Infrastructure control is another reason to consider the framework. Self-hosting allows organizations to make deployment decisions around their own systems and operational policies. It also gives teams more direct control over resource allocation and associated infrastructure costs. These choices are especially relevant when AI workflows interact with internal repositories, business applications, or sensitive organizational data.

The YAML-based approach also supports collaborative development. Workflow changes can be versioned and reviewed, while custom tools provide flexibility where configuration alone is not enough. For teams evaluating AI automation frameworks, Compozy offers a combination of explicit workflow structure, extensible integrations, and reliability features suited to maintaining multi-agent applications over time.

Use Cases and Applications

Compozy can support AI-assisted code analysis by coordinating repository inspection, specialized review tasks, and report generation. A workflow might assign different agents to examine code quality, identify potential security concerns, or summarize performance-related findings. Scheduled execution makes this pattern useful for recurring repository checks, although the quality of the analysis depends on the models, tools, and review criteria configured by the team.

Document and data processing are another practical application. Collection tasks can apply a sequence of operations to multiple files or records, while parallel execution can handle independent items concurrently. Teams might use this approach to classify documents, extract information, produce summaries, or prepare structured outputs for downstream systems.

Event-driven integrations benefit from signals and wait conditions. For example, a workflow can begin processing a request, pause until an external service returns information, and then continue with the next stage. This supports coordination across applications without requiring every operation to finish within one synchronous request.

Compozy also supports context-aware assistants and customer support workflows. Memory can preserve relevant interaction history, while custom tools connect agents to approved business functions. Multiple agents can handle separate responsibilities, such as gathering information, evaluating a request, and preparing a response. Together, these capabilities make the framework useful for automation that combines reasoning, external actions, and ongoing context.