AI Agent Frameworks Compared
A practical guide to choosing between LangChain, AutoGen, CrewAI, Temporal, and Cordum — and when to combine them.
Quick overview
These tools solve different layers of the stack: agent logic, workflow orchestration, and governance. Use this overview to map the right tool to the right job.
Tooling + RAG + chains for fast LLM app development.
Multi-agent conversation orchestration with role-based chat flows.
Role-based agent teams with explicit tasks and handoffs.
Durable workflow engine with strong execution guarantees.
Governance control plane for agent actions in production.
Decision guide
Choose by your workflow stage: build agent logic first, then add governance before production.
Start with LangChain. It has the largest ecosystem of integrations and fast iteration loops.
Start with AutoGen. Conversation-first coordination shines for critique and exploration.
Start with CrewAI. Clear mental model for research + writing + review pipelines.
Start with Temporal. Strong workflow guarantees for mission-critical systems.
Add Cordum. Policy checks, approvals, and audit trails for AI actions.
Feature comparison matrix
This matrix is directional rather than exhaustive. Use it to compare categories at a glance.
Full supportPartialNot available
| Feature | LangChain | AutoGen | CrewAI | Temporal | Cordum |
|---|---|---|---|---|---|
| Multi-agent orchestration | |||||
| Policy enforcement | |||||
| Human approval gates | |||||
| Audit trail | |||||
| MCP support | |||||
| DAG workflows | |||||
| Deterministic execution | |||||
| Production-ready | |||||
| Self-hosted | |||||
| Enterprise support |
LangChain
A general-purpose framework for building LLM apps with tools, retrieval, and agent-style chains.
A toolkit for composing prompts, tools, retrievers, and output parsers into reusable flows.
- Largest integration ecosystem.
- Excellent for RAG and tool use.
- Fast prototyping cycles.
- Debugging complex chains can be hard.
- No built-in governance or audit trail.
- Agent loops can be nondeterministic.
RAG apps, internal tools, and rapid experimentation.
AutoGen
A conversation-first framework for orchestrating multiple agents that collaborate through dialogue.
Role-based agents and message flows optimized for critique, refinement, and multi-step reasoning.
- Strong multi-agent dialogue patterns.
- Human-in-the-loop fits naturally into chat flow.
- Great for exploratory tasks.
- Conversation-centric, not workflow-centric.
- Limited native governance and audit controls.
- Cost can grow with long conversations.
Research assistants, collaborative reasoning, code review loops.
CrewAI
A role-based framework that assigns agents explicit responsibilities and handoffs.
Agent teams organized around tasks with a clear mental model for delegation and review.
- Easy to understand and explain.
- Good fit for content and research workflows.
- Growing community examples.
- Limited governance and audit trail.
- Workflow control is simpler than full DAGs.
- Not designed for high-stakes production ops.
Content generation, research automation, structured review flows.
Temporal
A durable workflow engine that many teams adapt for AI systems that need reliability.
A workflow runtime with strong guarantees for long-running, stateful processes.
- Battle-tested durability and reliability.
- Excellent observability and retries.
- Enterprise-ready at scale.
- Not AI-native; you wire agent logic yourself.
- No policy enforcement for agent actions.
- Steeper learning curve.
Mission-critical workflows and long-running processes.
Cordum
A governance control plane that sits between agents and production systems to enforce policy and approvals.
A Safety Kernel and workflow engine that evaluates every job before execution and records a full audit trail.
- Policy-as-code enforcement before dispatch.
- Human approvals where risk demands it.
- Immutable audit trail for every run.
- Works with any agent framework.
- Not an agent framework; it governs them.
- Requires infrastructure (NATS + Redis).
- Best value appears at production scale.
Production deployments, regulated industries, and audit-ready AI systems.
FAQ
Common questions we hear from teams evaluating agent frameworks for production use.
Yes. Use LangChain, AutoGen, or CrewAI for agent logic, then route actions through Cordum for policy checks, approvals, and audit trails.
Temporal is a workflow engine. It provides reliable execution, but you still supply the agent logic and governance.
When agents can modify production systems, write data, or open pull requests. Approvals keep risky actions safe without blocking low-risk automation.
Not always. But once a workflow touches real systems or sensitive data, add policy enforcement and audit trails before scaling.
Recommendations
Most teams start with a framework for agent logic, then layer governance as soon as workflows touch production.
- You are prototyping fast.
- RAG or tool chains are core.
- You want the largest ecosystem.
- Dialogue and critique drive quality.
- Multi-agent collaboration is key.
- Exploration beats determinism.
- Role-based teams fit your workflow.
- Content or research automation is primary.
- You want a simple mental model.
- You need durable, long-lived workflows.
- AI is one part of a larger system.
- Reliability beats speed of iteration.
- Agents can touch production or sensitive data.
- You need approvals and policy enforcement.
- Audit trails are required for compliance.
- You want deterministic control over agent actions.
These tools are not mutually exclusive. Many production teams use LangChain, AutoGen, or CrewAI for agent logic, then route actions through Cordum for policy checks, approvals, and audit trails.
A technical comparison focused on retry behavior, rollback semantics, and control-plane governance.
A deep dive into CAP's core primitives - BusPacket, JobRequest, safety hooks, compensation, and checkpoint heartbeats.
A practical governance playbook for MCP: policy checks, approvals, constraints, and audit trails.