Different category. Different architecture.
Cordum is not a replacement for agent frameworks or workflow engines. It is the governance layer that sits above them — a control plane that enforces policy before every action reaches production.
How Cordum compares
Cordum vs LangChain / LangGraph
Agent Framework- LLM reasoning chains and prompt templates
- Tool calling and retrieval pipelines
- Graph-based agent state machines
- Rapid prototyping of AI applications
- Pre-dispatch policy enforcement via Safety Kernel
- Human-in-the-loop approval gates
- Append-only audit trail for every action
- Deterministic execution with constraint enforcement
LangChain agents submit jobs via CAP protocol. The Safety Kernel evaluates every action before dispatch. Cordum provides the governance layer; LangChain provides the reasoning layer.
Cordum vs Temporal
Workflow Engine- Durable execution with automatic retries
- Workflow state persistence and replay
- Activity-based task execution model
- Multi-language SDK support
- Safety Kernel policy evaluation before every step
- Pack-based extensibility without core code changes
- Agent-native governance with risk tags and capabilities
- Policy-as-code with simulation and explain APIs
Temporal handles durable execution and state management. Cordum sits above as the governance layer, enforcing policy on what workflows can do.
Cordum vs CrewAI
Agent Framework- Multi-agent role-play and delegation
- Task decomposition across agent roles
- Collaborative agent conversations
- Python-first developer experience
- Deterministic control over which actions agents can take
- Policy-as-code with YAML configuration
- Constraint enforcement (budgets, deny-paths, limits)
- Immutable policy snapshots with hash versioning
CrewAI agents operate within Cordum-enforced boundaries. Every action is evaluated by the Safety Kernel before execution.
Cordum vs MCP (Model Context Protocol)
Tool Protocol- Single-model tool calling standard
- JSON-RPC transport for tool invocation
- Standardized tool discovery and schemas
- IDE and editor integration
- Distributed multi-agent control plane
- Pre-dispatch governance over MCP tool calls
- MCP allow/deny rules in policy configuration
- CAP protocol for agent-to-platform communication
MCP tools can be called from within CAP workers. Cordum enforces policy on which MCP tools are allowed via allow/deny rules in the Safety Kernel.
Cordum vs Prefect / Airflow
Data Pipeline- DAG-based data pipeline orchestration
- Scheduled batch job execution
- Data transformation and ETL workflows
- Monitoring dashboards for data pipelines
- AI-agent-native governance and policy enforcement
- Real-time Safety Kernel evaluation (not batch)
- Human approval gates bound to policy snapshots
- Pack system for domain-specific extensions
Use Airflow/Prefect for data pipelines. Use Cordum when your workflows involve autonomous AI agents that need governance and policy enforcement.
Feature comparison
| Feature | Cordum | LangChain | Temporal | CrewAI | MCP |
|---|---|---|---|---|---|
| Pre-dispatch policy enforcement | |||||
| Human-in-the-loop approvals | |||||
| Policy-as-code (YAML) | |||||
| Append-only audit trail | |||||
| Wire protocol (protobuf) | |||||
| Durable messaging | |||||
| DAG workflows | |||||
| Multi-language SDKs | |||||
| Pack / plugin system | |||||
| Source-available core |
When to use Cordum
Use Cordum when
- AI agents take actions in production
- You need audit trails for compliance
- Actions require human approval gates
- Policy must be enforced before execution
Use Cordum with
- LangChain/LangGraph for reasoning chains
- CrewAI for multi-agent collaboration
- Temporal for durable execution
- MCP for tool calling within workers
Skip Cordum for
- Simple single-model tool calling (use MCP)
- Data pipelines without AI agents (use Airflow)
- Prompt engineering prototypes
- Static batch processing jobs
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