Comparison
Cordum vs LangChain Callbacks
Observation hooks vs governance enforcement: understanding where each tool sits in the agent safety stack.
LangChain Callbacks are post-execution observer hooks that fire after LLM calls, tool runs, and chain steps. Cordum enforces policy before agent actions are dispatched. One observes; the other governs.
This page helps teams searching for cordum vs langchain callbacks understand the difference between observation and enforcement for agent safety.
| Evaluation Area | Cordum | LangChain Callbacks |
|---|---|---|
| Enforcement Model | Pre-dispatch enforcement: the Safety Kernel blocks or constrains actions before they execute. Decisions are ALLOW, DENY, REQUIRE_APPROVAL, or ALLOW_WITH_CONSTRAINTS. | Post-execution observation: callbacks fire after LLM calls, tool invocations, and chain steps. They observe and log but do not block execution by default. |
| Policy Architecture | Centralized, version-controlled policy bundles with hot-reload. Policies are declarative and apply across all agents, services, and runtimes. | Callback handlers are Python classes attached per chain or agent. No centralized policy registry or versioning system. |
| Approval Workflows | Built-in REQUIRE_APPROVAL decision path with approval records tied to policy version and request context. | No native approval workflow. Would require custom callback logic to pause execution and collect approvals. |
| Output Safety | Dedicated output safety layer: ALLOW, REDACT, or QUARANTINE results after execution before downstream delivery. | Callbacks can inspect outputs but lack built-in redaction or quarantine semantics. Custom code required for filtering. |
| Audit and Traceability | Structured run timeline with policy decisions, approval chains, state transitions, and evidence pointers per job. | Callback logs depend on handler implementation. Tracing integrations (LangSmith) available but separate from governance. |
| Runtime Scope | Runtime-agnostic via CAP v2 protocol. SDKs in Go, Python, Node.js, and C++. Works with any agent framework or custom runtime. | LangChain ecosystem only. Callbacks are tightly coupled to LangChain chain and agent abstractions. |
Decision checklist
- Do you need to prevent harmful actions before they happen, or observe and log what already happened?
- Are your agents confined to the LangChain ecosystem, or do they span multiple runtimes and languages?
- Do you need centralized policies that apply across all agents without per-chain configuration?
- Are human-in-the-loop approval workflows a requirement for high-risk actions?
- Would callbacks and governance complement each other: callbacks for tracing and Cordum for enforcement?
Related comparisons
Move from observation to enforcement
See how Cordum's Safety Kernel enforces policy before dispatch, with structured audit trails and approval workflows.