The execution layer for reliable AI agents. Trace every run, enforce runtime policies, and replay safely — so you can ship agents with operational confidence.
Without execution control
- Looped 14 times before timeout
- 38k tokens consumed
- Duplicate tool calls undetected
With Paprika
- Halted at step 6 by policy
- Replayed safely without side effects
- Root cause isolated in trace
The problem
AI agents break production trust.
Probabilistic systems need deterministic control. Without execution governance, failures are invisible, costly, and unreproducible.
Infinite loops
Agents repeat without termination, burning compute and burning budgets.
Runaway costs
Unchecked LLM calls accumulate with no budget enforcement.
Duplicate side effects
Same tool called twice with identical inputs — redundant and risky.
Unreproducible failures
No structured trace to replay, audit, or diagnose when things break.
How it works
Three layers of execution control.
Capture execution traces
Every LLM call and tool invocation is recorded as a structured, audit-ready event with timestamps, token usage, and input hashes.
Enforce runtime policies
Set hard limits on steps, tokens, and repeated inputs. Paprika halts execution before damage is done.
Replay runs safely
Re-execute any prior run using recorded outputs. No live APIs, no side effects. Mismatches surface immediately.
Why Paprika
Built differently.
Enforcement at runtime
Limits are enforced in the execution path, not after the fact. Incidents are prevented, not detected.
Safe replay
Re-run any execution with recorded outputs. Debug, audit, and validate without hitting live APIs.
Platform-agnostic
Wraps execution, not frameworks. Works with LangGraph, CrewAI, AutoGen, or custom agent stacks.
Platform architecture
An execution layer, not a framework.
Paprika wraps your agent runtime to record traces, enforce policies, and enable replay. Minimal surface, maximum control.
- Decorator-based agent registration
- Context-injected LLM and tool adapters
- Structured traces for audit and debugging
- CLI and API for inspection and diffing
#a3d95f]">"text-[#9ecbff]">from paprika "text-[#9ecbff]">import PaprikaRuntime, PolicyConfig
runtime = PaprikaRuntime(
policy=PolicyConfig(max_steps=20)
)
@runtime.agent()
#a3d95f]">"text-[#9ecbff]">def agent(ctx, prompt):
result = ctx.llm.call(
provider=#a3d95f]">"openai",
model=#a3d95f]">"gpt-4.1-mini",
input={#a3d95f]">"messages": [
{#a3d95f]">"role": "user", "content": prompt}
]},
)
#a3d95f]">"text-[#9ecbff]">return resultIntegrations
Works with your stack.
Platform-agnostic. Wraps execution at the runtime level — works with any agent framework or custom stack.
LangGraph
CrewAI
AutoGen
Vanilla Python
Security
Built for operational trust.
Every runtime decision is traceable. Every replay is side-effect free. Audit-ready by design.
FAQ
Frequently asked questions.
Operational confidence for AI agents.
Add execution control to your agent stack. Trace, enforce, replay — and ship with confidence.