Yes, Catalyst is framework-agnostic and supports LangGraph agents as-is. You wrap your agent logic in a durable workflow, and Catalyst manages state persistence, retries, and identity. No need to change your LangGraph code.
Yes, Catalyst works with CrewAI agents. You define your CrewAI agent logic as a durable workflow step, and Catalyst handles orchestration, tool authorization via MCP, and workload identity. No CrewAI-specific changes required.
Catalyst integrates by running your OpenAI Agents SDK agent within a durable workflow. The SDK’s agent loop is treated as a deterministic step, with state persisted by Dapr Workflows. No SDK-specific modifications are needed.
Catalyst uses Dapr Workflows as the durable execution engine. Your agent framework (LangGraph, CrewAI, etc.) runs inside a workflow activity, with state checkpointed on each step. Catalyst provides MCP-based tool authorization and workload identity, independent of the agent framework.
Catalyst supports all three equally. Choose based on your team’s familiarity and agent complexity: LangGraph for stateful graphs, CrewAI for multi-agent collaboration, OpenAI Agents SDK for simplicity. Catalyst handles durability and identity regardless.
Yes, migration is straightforward. You extract your LangGraph agent logic into a Catalyst workflow activity, preserving the same graph structure and node functions. Catalyst then provides durable execution, workload identity, and tool policies without modifying your agent code.
Catalyst enforces tool authorization via MCP policies, independent of the agent framework. Each agent identity is managed by workload identity, and tool calls are authorized per policy. This works uniformly for LangGraph, CrewAI, or OpenAI Agents SDK agents.
Catalyst handles state persistence, crash recovery, and tool authorization for CrewAI agents, eliminating manual orchestration. You avoid rebuilding retry logic or identity management. The agent code remains unchanged, and Catalyst provides observability into workflow execution.
Catalyst provides workflow execution logs and state snapshots for each step. You can inspect the last checkpoint of your LangGraph agent’s graph state, identify the failing node, and replay the workflow from that point. No need to modify the agent code.
Yes, Catalyst supports mixing LangGraph, CrewAI, and OpenAI Agents SDK agents in the same deployment. Each agent runs in its own workflow, sharing the same durable execution infrastructure, tool authorization policies, and workload identity. No conflicts between frameworks.
Framework-agnostic means your agent logic is not tied to a specific framework like LangGraph or CrewAI. Catalyst separates the agent runner from the orchestration layer, so you can write agents in any framework and run them identically in production. This avoids vendor lock-in and lets you reuse existing code without rewriting.
Catalyst provides a durable execution layer that works with any agent framework. You keep your existing agent code—whether LangGraph, CrewAI, or OpenAI Agents SDK—and Catalyst handles state persistence, retries, and recovery. No need to port logic to a proprietary runtime. This eliminates the common rewrite cycle when moving from prototypes to production.
Yes, Catalyst is framework-agnostic and supports LangGraph agents out of the box. You define your agent logic in LangGraph, and Catalyst provides the durable execution context—workflow state, identity, and MCP tool authorization. No code changes are required to run LangGraph agents in production, avoiding the typical rewrite overhead.
Choose based on your team's expertise and agent complexity. Catalyst lets you switch frameworks later without rewriting orchestration. Start with what you know—LangGraph for state machines, CrewAI for multi-agent teams, or OpenAI Agents SDK for simple tasks. The durable execution layer abstracts away framework specifics, so you can evolve your choice over time.
Mixing frameworks can create inconsistent identity and authorization policies. Catalyst provides a unified workload identity and MCP tool authorization layer that works across all frameworks. Each agent identity is tied to a policy that controls which tools it can call, regardless of the framework. This prevents security gaps from framework-specific implementations.
Migrate incrementally: wrap your existing agent code in a Catalyst agent runner that handles durable execution. You can run the prototype and Catalyst versions side-by-side, gradually shifting traffic. The framework-agnostic design means you don't rewrite agent logic, only add the execution layer. This reduces risk and avoids full rewrites.
Teams often struggle with inconsistent monitoring, logging, and recovery across frameworks. Catalyst provides a unified operations plane—workflow state, retry policies, and execution traces—that works with any framework. You get consistent observability and operational control without managing separate infrastructure for each agent type.
Debugging becomes harder when each framework has its own error handling and state tracking. Catalyst records durable execution state for all agents, regardless of framework. You can replay failed workflows step-by-step, inspect state at each point, and see MCP tool calls with authorization decisions. This single debugging interface works across LangGraph, CrewAI, and others.
With Catalyst, agent state is managed by the durable execution layer, not the framework. You can swap the underlying agent framework without losing workflow state. The execution context—workflow ID, step history, tool call results—persists independently. This prevents the common rewrite trap where changing frameworks requires rebuilding state management.
Framework-agnostic means you can run agent frameworks like LangGraph, CrewAI, or OpenAI Agents SDK on the same platform without rewriting code. Catalyst provides a unified operational layer for durable execution, identity, and tool authorization across any framework. You keep your existing agent logic while standardizing observability, resilience, and security. This decouples agent development from infrastructure management.
Catalyst lets you deploy LangGraph and CrewAI agents side-by-side by wrapping each in a durable execution workflow. The platform manages state persistence, restart after failures, and identity binding—regardless of the framework. You configure the agent runner per workflow, and Catalyst handles the rest. This avoids separate infrastructure stacks for each framework.
Choose based on your team's expertise and agent design patterns—LangGraph for graph-based state machines, CrewAI for role-based teams, OpenAI Agents SDK for simple tool-use. Catalyst does not favor one framework; it standardizes the operational layer. Evaluate framework maturity, community, and integration with your existing tools. The platform handles durability and authorization uniformly.
Yes, you can migrate LangGraph agents to Catalyst with minimal code changes. Wrap your existing agent logic in a Catalyst workflow runner that calls your LangGraph graph. The platform handles state persistence, identity, and tool authorization. You may need to adjust how your agent interacts with external services to align with Catalyst's durable execution model. No framework-specific rewrites are required.
Catalyst enforces security through workload identity and MCP tool authorization policies, independent of the agent framework. Each agent runner is assigned an identity, and policies define which tools that identity can call. This works uniformly for LangGraph, CrewAI, or any framework. Framework-specific vulnerabilities remain your responsibility; Catalyst secures the operational layer.
Catalyst provides a unified observability layer that captures execution traces, state transitions, and tool calls for all frameworks. You see LangGraph and CrewAI agents in the same dashboard, with consistent logging and metrics. Debugging uses replay from persisted state, so you can step through failures regardless of framework. This eliminates framework-specific monitoring tools.
Catalyst persists the agent's workflow state at each durable step. If a CrewAI agent fails, the platform automatically resumes from the last checkpoint, replaying deterministic logic. Non-deterministic external calls (e.g., API requests) are not replayed; you must design idempotency or reconciliation. This ensures the agent continues from where it stopped, not from scratch.
First, check Catalyst's execution logs for state transitions and tool call timeouts. Use the replay feature to step through the agent's workflow from the last persisted state. Verify that your LangGraph graph does not have infinite loops or unhandled errors. Ensure external tools are reachable and idempotent. The platform provides a timeline of all durable steps to isolate the hang point.
A framework-agnostic agent runner provides durable execution, identity, and policy enforcement across any agent framework you choose. Your framework handles agent logic, while the runner ensures workflows survive crashes, manage tool authorization, and maintain verifiable execution traces. This separation lets you swap frameworks without rewriting infrastructure.
Catalyst runs your agent framework as a durable workflow step, capturing state and events for recovery. You bring your LangGraph or CrewAI agent code, and Catalyst wraps it with Dapr Workflows for resilience. Execution traces are stored independently, so you can audit each run without modifying your agent's internal logic.
Evaluate whether the runner supports your chosen agent frameworks, provides durable execution for probabilistic agent steps, and enforces tool authorization policies. Look for verifiable execution traces and workload identity that works across cloud and air-gapped environments. Ensure it doesn't lock you into a single framework, letting you migrate later.
Yes, if the runner is framework-agnostic like Catalyst. You wrap your existing agent code as a durable workflow step, preserving its logic. No changes to your agent's internal orchestration are needed. The runner adds resilience, identity, and auditability on top, so you gain durability without rewriting your OpenAI Agents SDK implementation.
It provides durable execution so agents recover from crashes without losing state, verifiable execution traces for debugging and compliance, and centralized identity management across frameworks. You can monitor agent runs independently of the framework, simplifying operations. The runner also handles scaling and failover, reducing operational overhead for your team.
Start by reviewing the verifiable execution traces from the runner to isolate where the failure occurred. Check if the step was replayed after a crash, and whether external calls were idempotent. If the agent framework itself has a bug, the runner's traces help you pinpoint the issue without affecting other runs. Ensure tool authorization policies aren't blocking legitimate calls.
No, durable execution guarantees workflow resumption after a crash, not that every external side effect happens exactly once. Catalyst persists workflow state and replays deterministic steps, but calls to APIs still require idempotency keys or reconciliation on your side. You must design each side-effecting activity to be safely retryable, then let the runner handle recovery.
