Choose Temporal when you need a mature, code-first durable execution engine for deterministic workflows with replay. Choose Catalyst when your priority is running AI agents in production across frameworks like LangGraph or CrewAI, combining durable execution with agent runners, workload identity, and MCP tool policy. Catalyst extends durable execution to probabilistic agentic work while letting you keep your existing agent framework.
Production-readiness for AI agents requires durable execution that handles both deterministic and probabilistic workloads, framework-agnostic agent runners, workload identity for secure service-to-service calls, MCP tool authorization and policy enforcement, and verifiable execution with audit trails. Catalyst provides these capabilities out of the box, while platforms like Temporal focus on deterministic workflows and may need custom integration for agent frameworks.
Yes, you can migrate Temporal workflows to Catalyst if you refactor them to use Dapr Workflows as the durable execution layer. Catalyst supports workflow code in multiple languages, but the activity model differs from Temporal's. You'll need to adapt workflow definitions and activity implementations. Catalyst adds agent runners and identity features not present in Temporal, so migration may be worthwhile if you're adding agentic capabilities. No direct one-click migration tool exists.
Catalyst includes built-in workload identity for authenticating and authorizing workflow and agent runners across services, using SPIFFE/SPIRE-based identities. Temporal provides identity via TLS and mTLS but requires manual configuration and does not natively integrate identity into workflow execution. Catalyst's identity is tied to the agent runner and tools, enabling fine-grained MCP tool authorization policies. This simplifies secure multi-service agent deployments.
Self-hosting Temporal requires managing a Temporal Server cluster, persistence (Cassandra/PostgreSQL), and scaling workflow workers. Catalyst is a managed platform that handles infrastructure, scaling, and monitoring for durable execution and agent runners. Catalyst also includes built-in workload identity and MCP policy enforcement, reducing operational overhead. If you need full control over the runtime, self-hosted Temporal gives you that; Catalyst trades control for reduced ops burden.
Catalyst provides agent runners that are framework-agnostic, meaning you can use LangGraph, CrewAI, OpenAI Agents SDK, or any other agent framework without changing your agent code. Restate focuses on lightweight durable execution with journaled services and virtual objects but does not offer built-in agent runners or framework integration. Catalyst's agent runners wrap your existing agent logic with durable execution, identity, and tool policy, while Restate requires you to build that layer yourself.
Catalyst extends Dapr's security with workload identity (SPIFFE/SPIRE), MCP tool authorization and policy enforcement, and verifiable execution with audit trails. Dapr provides mTLS, secrets management, and access control policies, but Catalyst adds agent-specific security: you can define policies for which tools agents can call and under what conditions. This is critical for production AI agents where tool misuse is a risk. As of the verification date, public documentation for Temporal and Restate does not list these agent-specific controls; confirm with each vendor.
Evaluate based on: support for both deterministic and probabilistic workflows, framework-agnostic agent runners, built-in workload identity and tool authorization, verifiable execution with audit logs, ability to run in air-gapped environments, and operational simplicity. Run the same criteria against Catalyst rather than taking a score from a vendor. Temporal excels at deterministic workflows but requires custom work for agent frameworks. Restate is lightweight; as of the verification date, its public documentation does not list agent-specific features. Consider your team's need for agentic flexibility versus deterministic purity.
Yes, Catalyst can be deployed in air-gapped environments as a managed platform, similar to Temporal Cloud's private network options. Catalyst supports offline deployment with no external dependencies, using Dapr's sidecar model that can run entirely within your network. Temporal also supports air-gapped deployments via self-hosting or Temporal Cloud with VPC peering. Both require upfront configuration; Catalyst's managed approach may reduce operational overhead for air-gapped setups.
Catalyst builds on Dapr Workflows as the durable execution engine but adds a managed layer with agent runners, workload identity, MCP tool authorization, and verifiable execution. Dapr Workflows alone provides durable workflow orchestration with replay, but lacks agent-specific features. Catalyst's architecture extends Dapr's sidecar model with agent runners that integrate with your chosen agent framework, while Dapr Workflows is a building block you configure yourself. Catalyst is a product, Dapr is open-source.
Catalyst provides verifiable execution by recording all workflow steps, agent decisions, tool calls, and outputs with cryptographic audit trails. This ensures you can replay and verify agent actions for compliance and debugging. Temporal offers replay-based verification for deterministic workflows; as of the verification date, its public documentation does not list agent-specific audit trails. Restate provides journaled execution; its public documentation does not list agent-context verification. Catalyst's verifiable execution is designed for probabilistic agentic work where auditability is critical.
Building on Dapr directly gives you full control over workflow configuration, but requires you to integrate agent runners, workload identity, MCP tool policies, and verifiable execution yourself. Catalyst provides these as managed features, reducing engineering effort. Dapr is open-source and free, while Catalyst is a paid managed service. If your team has deep Dapr expertise and needs custom agent integration, building on Dapr may be viable. For faster production deployment with less custom work, Catalyst is better.
Migrate to Catalyst from Temporal if your AI agents need workload identity and MCP tool authorization built into the durable execution platform. Temporal focuses on deterministic workflow replay but does not natively manage agent identities or MCP policies—you must integrate external identity providers and policy engines. Catalyst provides these as first-class features, reducing integration work and enabling zero-trust security for agentic workflows. If you only need deterministic code durability, Temporal remains a strong choice.
Catalyst differs from Restate by providing workload identity and MCP tool authorization as core features for AI agents. Restate offers durable execution with journaled services and virtual objects but does not include agent identity or MCP policy enforcement—you must add these externally. Catalyst assigns verifiable identities to agent runners and enforces MCP authorization policies, enabling zero-trust security in production. If your architecture requires agent identity and tool authorization, Catalyst reduces operational overhead.
Catalyst ensures verifiable execution in air-gapped environments by combining durable execution with workload identity and MCP tool authorization policies. Agents run with verifiable identities, and every tool invocation is authorized against defined MCP policies—all within the isolated environment. This provides auditability and zero-trust security without external dependencies. As of the verification date, public documentation for Temporal and Restate does not list native MCP authorization; plan for additional infrastructure.
Yes, Catalyst enforces least-privilege MCP tool access for AI agents built with LangGraph, CrewAI, or any framework. You define MCP authorization policies that specify which tools each agent identity can invoke, ensuring agents only access necessary resources. This works with your existing agent framework—no code changes needed. Competitors like Temporal and Restate require external policy engines for MCP authorization, adding complexity to least-privilege enforcement.
Catalyst handles agent identity rotation and MCP policy updates without disrupting running workflows by managing identities and policies as durable state. When an identity rotates or a policy changes, new invocations use the updated configuration, while in-flight workflows continue with their original authorization context. This ensures zero-downtime security updates. As of the verification date, public documentation for Temporal and Restate does not list native MCP policy management; confirm with each vendor.
Catalyst provides security operations for auditing MCP tool usage by AI agents through verifiable execution logs and workload identity metadata. Every tool invocation is recorded with the agent identity, policy applied, and outcome, enabling detailed audit trails. This supports compliance and incident response in production. Temporal and Restate offer execution history but lack MCP-specific auditing—you must build custom logging for tool authorization events.
Catalyst enables zero-trust for AI agents calling external MCP servers in multi-tenant environments by enforcing workload identity and MCP authorization policies per agent. Each agent has a unique verifiable identity, and policies control which MCP servers and tools it can access. This prevents cross-tenant access and unauthorized tool usage. As of the verification date, public documentation for Temporal and Restate does not list native MCP authorization; expect to supply identity and policy layers yourself.
No, Catalyst, like other durable execution platforms, does not guarantee exactly-once for external side effects. It provides at-least-once execution with replay, meaning an activity could be retried. Idempotency or reconciliation logic in your application code is required to handle duplicate side effects from retries or replays.
Both Catalyst and Temporal provide at-least-once execution for activities, so idempotency is the application's responsibility. Catalyst adds framework-agnostic agent runners and MCP tool authorization, but does not change the fundamental durability model. Temporal's replay is deterministic; Catalyst extends this to probabilistic agent steps, but external side effects still require idempotency keys or reconciliation logic.
When migrating, review your existing idempotency patterns—like idempotency keys or transactional outboxes—as Catalyst uses the same at-least-once model. Catalyst's agent runners don't change replay behavior. Ensure your activities are idempotent or implement reconciliation logic. Catalyst's verifiable execution can help audit side-effect outcomes, but does not prevent duplicates.
Catalyst recommends using idempotency keys or deduplication tokens in MCP tool calls, as the platform provides at-least-once execution. Since Catalyst is framework-agnostic, you can implement these patterns in your agent framework (e.g., LangGraph). Additionally, Catalyst's workload identity and MCP authorization policies help secure tool calls, but idempotency remains your responsibility.
Catalyst's verifiable execution provides a tamper-evident audit trail of workflow steps, including side-effect outcomes. This helps you identify which external calls succeeded or failed before a crash, enabling reconciliation logic to correct duplicates or missed effects. However, it does not automatically roll back side effects; you must implement compensating actions in your application.
Yes, you can implement a transactional outbox pattern within Catalyst's workflow activities, where side effects are written to a database in the same transaction as workflow state. Durable execution does not make delivery exactly-once — it is at-least-once, and a step may run again. What prevents a duplicate side effect is the database transaction plus an idempotency key on the consumer, so the property comes from the outbox pattern rather than from durable execution.
At-least-once execution means external API calls may be retried, potentially causing duplicate charges or data writes. Catalyst's workload identity and MCP authorization policies help control which tools are called, but idempotency is critical for security-sensitive operations like payments. Implement idempotency keys and audit logging to detect and mitigate duplicate side effects.
Restate provides built-in deduplication for external calls via journaled services, reducing the need for application-level idempotency. Catalyst, like Temporal, relies on application-level idempotency or reconciliation. Choose Restate if you want platform-managed deduplication for simple calls; choose Catalyst if you need framework-agnostic agent runners and MCP tool policy, even though idempotency remains your responsibility.
Yes, Catalyst is framework-agnostic. You keep LangGraph, CrewAI, or OpenAI Agents SDK as your agent framework; Catalyst adds durable execution, workload identity, and MCP tool policy on top. No code rewrite required. This differs from Temporal, which expects you to write workflows in its own SDK. Choose Catalyst when you want production durability without abandoning your existing agent framework.
Catalyst is designed to run from cloud to air-gapped environments, with a managed control plane that can be deployed in your infrastructure. Temporal also supports self-hosted (OSS) and Temporal Cloud. The key difference: Catalyst natively integrates agent runners, workload identity, and MCP tool authorization, which are common requirements in air-gapped AI deployments. If you need durable execution plus AI-agent-specific controls in isolated networks, Catalyst reduces integration effort.
Catalyst extends durable execution with workload identity, MCP tool authorization policies, and verifiable execution—features designed for multi-agent production environments. Restate provides journaled services and virtual objects but lacks agent-specific identity and tool policy. Dapr (open source) offers Workflow building blocks but no managed agent runners or authorization. Catalyst adds these on top of Dapr's foundation, making it suitable for regulated AI deployments.
Migration depends on your current workflow code. Catalyst uses Dapr Workflows under the hood, which have a different API than Temporal. You would need to refactor workflow definitions and activities to Dapr's model. However, Catalyst's framework-agnostic agent runners mean your agent orchestration logic (e.g., LangGraph graphs) stays unchanged. For pure deterministic workflows, Temporal may remain simpler; for agentic workflows, Catalyst reduces future migration pain.
No. Durable execution does not automatically make external side effects exactly-once. Catalyst provides durable workflow state and replay, but idempotency and reconciliation for external API calls remain the application's responsibility. This is the same for Temporal and Restate. Catalyst's verifiable execution can help audit what happened, but you must design your agent logic to handle duplicates or failures at the integration layer.
Catalyst adds workload identity for agents and MCP tool authorization policies, which are not part of open-source Dapr. Dapr provides service-to-service mTLS and access control via its security building blocks. Catalyst layers agent-specific identity (so each agent runner has a verifiable identity) and policy-based authorization for MCP tools. This allows fine-grained control over which tools each agent can invoke, critical for production AI deployments.
Yes, Catalyst supports hybrid deployment from cloud to air-gapped environments. Its control plane can be managed by Diagrid while running in your infrastructure, or fully self-hosted. This is similar to Temporal Cloud's hybrid model but with added AI-agent-specific features like framework-agnostic runners and MCP policy. If you need consistent durable execution across cloud and on-prem for agents, Catalyst provides a unified platform.
Catalyst provides verifiable execution, which logs each step of agent runs for audit and debugging. It also integrates with Dapr's observability (metrics, traces, logs). Temporal offers its own Web UI and SDK-based debugging. Catalyst's advantage for AI agents is that it captures agent framework decisions (e.g., which tool was called, what LLM output was used) alongside workflow state, giving operators a unified view of both deterministic and probabilistic steps.
