JamJet vs Temporal
for AI Agents

The quick version

Feature comparison

What the same agent step looks like

Same task: an LLM call that needs durability, a policy check, and human approval if the model wants to do something high-risk.

@activity.defn
async def call_llm(prompt: str) -> str:
    # durability via @activity is here, but...
    return await openai.chat(prompt)

@workflow.defn
class AgentWorkflow:
    @workflow.run
    async def run(self, prompt: str) -> str:
        result = await workflow.execute_activity(
            call_llm, prompt,
            start_to_close_timeout=timedelta(minutes=5),
        )
        # policy check? you write it
        # approval? signal handler + custom UI
        # audit? workflow history (not designed for compliance)
        # cost cap? you track tokens yourself
        return result

import jamjet
import jamjet.cloud as cloud

# One-time setup: connect to JamJet Cloud, push a policy, set a cost cap
jamjet.configure(api_key="jj_...", project="research-agent")
cloud.policy("block", "payments.*")
cloud.budget(max_cost_usd=50.0)

# Define an LLM-backed task: one decorator, docstring = instructions
from jamjet import task, tool

@tool
async def web_search(query: str) -> str:
    ...  # call your search API

@task(model="gpt-4o", tools=[web_search], max_cost_usd=5.0)
async def call_llm(prompt: str) -> str:
    """Research assistant. Search first, then summarize findings."""

# Call it like any async function — durability and policy apply automatically
result = await call_llm(prompt)

Same durability. Policy and budget configured at SDK setup; cost cap on the decorator; approval via cloud.require_approval() when you need it. Wrap mode (drop-in for an existing LangGraph or Spring AI agent) gets the same primitives without rewriting the function — see the docs.

Where Temporal excels

Temporal's durability model is the best in the industry. If your primary concern is "my distributed workflows must never lose progress," Temporal is the safe, proven choice. It handles:

• Massive-scale durable execution (trillions of actions)
• Complex workflow patterns (signals, queries, child workflows, continue-as-new)
• Multi-language support (5 SDKs, all production-grade)
• Enterprise support with SLAs and managed cloud
• Active integrations with OpenAI Agents SDK, Vercel AI SDK, Pydantic AI

At Replay 2026, Temporal launched serverless workers, durable execution for the OpenAI Agents SDK, and durable execution for the Vercel AI SDK. The agent-integration surface is closing fast.

Where JamJet goes further

JamJet's thesis is that durable execution alone isn't enough for AI agents in production. Agents create unique challenges:

• Agents call tools autonomously — you need policy enforcement to prevent unauthorized actions, not just retry logic
• Agents make decisions with real consequences — you need audit trails that are separate from execution history and designed for compliance
• Agents need human oversight — you need first-class pause/resume/approval, not manual signal handling
• Agents accumulate context — you need governed memory with conflict detection, not just a key-value store
• Agents spend money (LLM calls) — you need runtime-enforced cost budgets, not just monitoring
• Agents need to interoperate — you need MCP/A2A protocol support, not custom integration for each tool

These are not features you can easily add to Temporal. They require agent-native abstractions built into the runtime from the ground up.

Can you use both?

Technically, yes — but it would be unusual. Both are execution runtimes. Using both would mean running Temporal for durability and JamJet for governance, which adds operational complexity.

More practically: if you already use Temporal for non-AI workflows and want to add AI agent governance, JamJet's MCP Gateway (on the roadmap) can sit between your agent and its tools to add policy enforcement and audit trails without replacing Temporal.

If you're starting fresh with AI agents, JamJet gives you durability and governance in one runtime.

The honest tradeoff

Ready to try JamJet?

Start with a 60-second quickstart. Add governance controls as you need them.

Also comparing JamJet with Microsoft Agent Governance Toolkit? See the AGT comparison.