JamJet vs Helicone
two different layers
Helicone is an open-source LLM proxy. You point your OpenAI / Anthropic / Bedrock client at
helicone.ai, and you get observability, cost tracking, caching, rate limiting,
and request logging — for any provider you proxy. One-line setup, generous free tier, MIT
license. It is the simplest path to LLM observability that exists.
JamJet operates one layer up. It is not a proxy for LLM calls; it wraps the agent's whole decision loop — tool calls, memory operations, MCP server invocations, human approvals — and enforces policy on each of them. The two products solve different problems and compose naturally: Helicone watches the LLM API; JamJet shapes the agent that calls it.
Last updated 2026-05-15
The quick version
Choose Helicone when
- You want LLM observability with one-line setup — change the base URL and you're done
- Cost tracking, request logging, and caching across multiple LLM providers is the goal
- You care about LLM-level rate limiting and prompt caching, not agent-level policy
- You're not running agentic workflows yet — just direct LLM API calls
- You want a generous free tier (100K requests/mo) and MIT-licensed self-host
Choose JamJet when
- Your agent makes tool calls, MCP invocations, or memory writes — not just LLM calls
- You need to block unsafe tool calls before they execute, not just log them
- You need durable execution that survives crashes and replays the event log
- You need human-in-the-loop approval nodes with audit-grade evidence
- You need signed audit exports for EU AI Act / financial / healthcare compliance
The core difference: API layer vs agent layer
Both tools intercept calls. The question is which calls. Helicone sits on the wire between your code and the LLM provider. JamJet sits inside your agent loop, before any tool call (LLM or otherwise) executes.
| Layer | What it intercepts | What it does |
|---|---|---|
| Helicone | HTTPS calls to LLM providers (OpenAI, Anthropic, Bedrock, Together, Groq, …). | Logs the request and response. Tracks cost and latency. Caches identical prompts. Rate-limits per user or key. Acts as a transparent proxy — the LLM call still goes through. |
| JamJet | Every agent action: tool call (filesystem, HTTP, database), MCP server invocation, memory write, sub-agent delegation, model selection. | Evaluates a policy hierarchy. Blocks unsafe calls before they execute. Routes high-risk actions to approval. Persists every decision as a durable event. Enforces cost caps that halt the run. |
Helicone is fantastic for LLM-call observability. JamJet is for the rest of the agent's surface — the part where an LLM tells your code to delete files, hit a payment API, or write to a database.
Feature comparison
| Capability | Helicone | JamJet |
|---|---|---|
| Setup effort | One line — change the base URL. | One install + wrap call (Python decorator, Spring Boot starter, OpenAI patcher, etc.). |
| LLM request logging | Every request and response, full payloads, multiple providers. | Through OTel spans; configurable redaction. Less LLM-payload-focused than Helicone. |
| Cost tracking | Per request, per user, per session, per custom property. Multi-provider. | Per workflow, per agent, per run. Enforces caps at runtime. |
| Prompt caching | Built-in semantic + exact-match caching at the proxy. | Not in scope — caching is a proxy concern. |
| Rate limiting | Per user / key / custom property at the proxy. | Per workspace and per agent at the runtime; tower-governor backed. |
| Tool-call policy enforcement | Not in scope (proxy only sees LLM calls). | 4-level hierarchy enforced at every tool invocation. Glob blocking, model allowlists, delegation scoping. |
| MCP support | Not in scope. | Client + server, full spec, 11 Engram tools, MCP Registry listed. |
| Durable execution | Not in scope (proxy is stateless). | Event-sourced + checkpoint snapshots. Rust core + Java native runtime. |
| Human-in-the-loop | Not in scope. | First-class pause/resume/approval nodes, durable across restarts. Slack + email routing. |
| Audit exports for compliance | Request logs available as export. | Ed25519-signed evidence packages. PDF / OTLP / Splunk / Datadog renderers. Per-entry retention. |
| Agent memory | Not in scope. | Engram — temporal knowledge graph, fact extraction, conflict detection, 11 MCP tools. |
| Provider coverage | 10+ providers via the proxy (OpenAI, Anthropic, Bedrock, Together, Groq, Anyscale, Mistral, Cohere, …). | Provider-agnostic — JamJet wraps the agent, not the API; works with any provider your code already calls. |
| Framework integrations | SDK wrappers + base-URL config for major LLM SDKs. | LangChain4j, Spring AI, OpenAI Agents SDK (via patcher), CrewAI (wrap), LangGraph (wrap), MCP. |
| Deployment | SaaS (helicone.ai). Open-source self-host (Docker, Kubernetes). | OSS self-host today (Docker, JVM, Kubernetes roadmap). JamJet Cloud beta available. |
| License | MIT. | Apache 2.0. |
| Pricing | Free up to 100K requests/mo. Pro $20/mo (1M requests), Team $200/mo, Enterprise custom. | Free up to 5K traces/mo. Starter $29, Team $99, Business $499, Enterprise custom. |
Where Helicone goes deep
Helicone is the most ergonomic LLM observability tool in the market. If your problem is "I have LLM API calls and I want to see them," it is the right tool.
- One-line integration — change the base URL and you're done
- 10+ LLM provider coverage out of the box (OpenAI, Anthropic, Bedrock, Together, Groq, …)
- Semantic + exact-match prompt caching at the proxy layer
- Cost attribution by user, session, and custom property
- Open-source self-host with MIT license
- Generous free tier (100K requests/month) — enough for most early-stage projects
- Webhooks, alerts, and a clean trace UI
Where JamJet goes further
Helicone is excellent at the LLM-call layer. But agentic workflows do more than call LLMs — they call tools, write to memory, invoke MCP servers, and trigger downstream effects. JamJet addresses the part of the stack a proxy cannot see.
Tool-call policy enforcement
Most agent risk is in tool calls, not LLM calls. The LLM call is a token-prediction request; the tool call is what actually deletes files, hits payment APIs, or writes to databases. Helicone proxies the LLM call. JamJet evaluates a policy hierarchy at every tool invocation and blocks unsafe ones before they execute.
Durable execution that survives crashes
Helicone is a stateless proxy — when your worker dies, the agent run dies with it. JamJet's event-sourced runtime replays the event log and resumes from the failed step. Rust core or embedded Java runtime; crash recovery is the execution model, not an add-on.
Human-in-the-loop as a workflow primitive
JamJet has approval nodes — the workflow pauses, routes the decision to Slack or email with SLA tracking, survives restarts, and resumes when the human decides. Helicone doesn't address this layer.
Signed audit evidence for compliance
Helicone has request logs. EU AI Act, financial, and healthcare auditors want signed evidence packages. JamJet ships Ed25519-signed exports in PDF, OTLP, Splunk, and Datadog formats with per-entry retention windows.
Agent memory governed by the same runtime
A proxy has no opinion about what the agent remembers between sessions. Engram is JamJet's memory primitive — temporal knowledge graph, fact extraction, conflict detection — governed by the same policy engine as everything else.
Can you use both?
Yes, and it's a sensible combination. Keep Helicone proxying your LLM API calls for the caching, multi-provider observability, and cost reporting it does well. Add JamJet around the agent loop for tool-call policy, durable execution, HITL, signed audit, and memory.
They sit at different layers — Helicone is below your agent code (between code and provider); JamJet is above it (between code and the world). Neither blocks the other.
The capability tradeoff
Helicone gives you
Frictionless LLM observability across 10+ providers, prompt caching, cost attribution, rate limiting, and request logging — with a one-line setup and a generous free tier. The proxy layer is its scope; agent-level policy, durable execution, HITL, and audit signing are not in scope.
JamJet gives you
Policy enforcement at every tool call, durable execution, memory (Engram), replay, signed audit evidence, HITL nodes, and cost caps in one runtime — across LangChain4j, Spring AI, OpenAI Agents SDK, MCP, and your custom agent loop. LLM-call observability today is OTel-emitted but the proxy-style UX and prompt caching are not what JamJet is for.
Ready to try JamJet?
Keep Helicone for the LLM proxy layer. Add JamJet for the agent layer above it.