Julep

Julep — durable, composable AI agents. Flows that crash and resume, retry safely, and explain every step.

Julep builds agents as composable, durable dataflows instead of ad-hoc loops: flows can crash and resume, retry safely, explain every step through a derived projection, and deny any tool the model was not explicitly allowed to call. The primary authoring surface is define-by-construction @flow: ordinary Python names graph steps while registered tools, pures, reasoners, branches, fan-out, retries, and timeouts compile to the same frozen wire-format IR. The pure core stays dependency-free, while the Temporal layer is optional.

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Install

pip install --pre julep

Julep 3 currently ships as a release candidate, so the --pre flag is required; it drops once 3.0.0 is final.

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Quickstart

10 minutes, no API key. Install the base package and run this as a normal Python script:

from typing import TypedDict

from julep import Reasoner, deploy, flow, pure, think, tool


class SupportReply(TypedDict):
    reply: str


@tool(effect="read", idempotent=True)
def lookup_ticket(ticket: str) -> dict[str, str]:
    return {
        "ticket": ticket,
        "queue": "billing",
        "summary": "Use the duplicate-charge runbook.",
    }


@pure("ticket_prompt")
def ticket_prompt(hit: dict[str, str]) -> dict[str, str]:
    return {"queue": hit["queue"], "context": hit["summary"]}


support_reply = Reasoner(
    name="support_reply",
    model="anthropic:claude-haiku-4-5-20251001",
    system="Draft one concise support reply as JSON.",
    reply=SupportReply,
)


@flow
def triage(ticket: str) -> dict[str, str]:
    hit = lookup_ticket(ticket, retries=2, timeout_s=5)
    prompt = ticket_prompt(hit)
    answer = think(support_reply, prompt, timeout_s=10)
    return hit | answer


def fake_support_reply(value: dict[str, str]) -> SupportReply:
    return {"reply": f"{value['queue']}: {value['context']}"}


deployment = deploy(triage, tools=[lookup_ticket], reasoners=[support_reply])
result = deployment.dry_run(
    "Customer was charged twice.",
    reasoners={"support_reply": fake_support_reply},
)

print(result.value)

@flow runs once at definition time with data handles. Registered tools, registered pures, think(...), cond(...), switch(...), each(...), and reschedule(...) append graph steps instead of doing runtime work; | merges records and h["key"] plucks fields. deploy(..., tools=..., reasoners=...) freezes the tool and reasoner surface, and dry_run(...) executes locally with in-memory tools and deterministic fake reasoners. See the larger @flow examples in examples/episode_summary_flow.py and examples/cluster_labeling_flow.py.

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The CLI

julep is the developer CLI for a whole module of agents — "dbt for agents, terminal-native." Point it at a directory; it discovers every @flow/Agent(...), treats each as a node in a cross-agent graph, and gives you one selection grammar across every verb:

julep ls                                  # list agents (name · kind · tags)
julep show triage                         # one agent's kind, source location, tags, calls
julep graph                               # the cross-agent DAG as Graphviz DOT
julep run triage --input '"TICKET-42"'    # execute locally, stream the trace tree
julep lint +triage                        # validate an agent and everything it depends on
julep test triage                         # run pytest for the selected agents
julep trace <run-id>                      # render a cached run's trace tree + Langfuse link
julep doctor                              # preflight: discovery, git, Langfuse, Temporal
julep deploy triage --env staging         # freeze → publish → record in the deploy ledger

Selectors compose: tag:support, state:modified (Slim-CI), +agent/agent+/@agent graph traversal, a,b intersection, --exclude. Full reference: docs-site/content/docs/guides/using-the-cli.md.

For production applications, declare an explicit object instead of adding a second discovery convention:

from collections.abc import Mapping

from julep import Application, CapabilityManifest, McpSnapshot, PipelineSpec


def load_live_memory_tools_snapshot(
    environment: Mapping[str, str],
) -> McpSnapshot:
    # Use the selected deployment environment to call tools/list.
    ...

application = Application("memory", [
    PipelineSpec(
        name="episode_summary",
        flow=summary_flow,
        reasoners=(summary_reasoner,),
        capabilities=CapabilityManifest.from_file("summary-capabilities.yaml"),
        lane="summary",
        eval_packages=("prompts/episode_summary.ctx",),
        snapshot=memory_tools_snapshot,
        snapshot_source=load_live_memory_tools_snapshot,
    ),
])

For julep plan, apply, and application-level status, each snapshot_source receives a read-only mapping made from the selected environment's [vars] followed by [worker_environment] (worker values win on duplicate names). Secret-backed worker variables are intentionally absent: worker_secret_environment contains Kubernetes Secret references, not values, and those values exist only in the worker at runtime. The callback must return an McpSnapshot; pass any credential needed for schema discovery through a non-secret control-plane mechanism rather than expecting a Secret value here.

Point [tool.ca].application at that object with a module:attribute value. Each deployable environment also names the worker's explicit context factory; ordinary and Secret-backed worker environment can be reconciled with the lane:

[tool.ca.env.staging]
temporal_address = "temporal-frontend.temporal.svc.cluster.local:7233"
release_store = "s3://julep-releases/julep"
worker_image = "registry.example/memory@sha256:<digest>"
worker_context_factory = "memory.worker:build_context"
worker_service_account = "julep-worker"
worker_priority_class = "julep-model-worker"
payload_encryption_secret = "temporal-payload-codec"

[tool.ca.env.staging.worker_environment]
MEMORY_TOOLS_MCP_URL = "http://memory-tools/mcp-internal"
CA_BUNDLE_ALLOWED_SIGNERS = "<64-hex-ed25519-public-key>"

[tool.ca.env.staging.worker_secret_environment.MEMORY_TOOLS_JWT_PRIVATE_KEY]
secret_name = "memory-tools-jwt"
key = "private-key"

payload_encryption_secret is required for application releases and names an existing Kubernetes Secret in kubernetes_namespace with keyring and active-key-id entries. The PriorityClass is optional: set worker_priority_class only when shared cluster infrastructure provisions it (the EKS demo does); omit it on ordinary clusters.

julep plan --env staging reports artifact, MCP-schema, Helm/KEDA, and runtime drift; julep apply --env staging publishes an immutable S3-CAS release and reconciles one digest-pinned Helm release per lane and immutable release on a release-specific task queue, without changing traffic; julep status --env staging aggregates the release and live lane state. That application-level status path is selected only when [tool.ca].application is configured and no selector or --exclude is supplied; selected status queries continue to inspect the legacy per-agent deploy ledger.

Application publishing requires a 64-hex Ed25519 seed (or a file containing one) in CA_BUNDLE_SIGNING_KEY. In production, set the corresponding 64-hex public key in the non-secret CA_BUNDLE_ALLOWED_SIGNERS worker environment; apply rejects a configured allow-list that does not contain the publishing key. Read-only plan and application-level status need that public allow-list (or the private key as a local fallback). Install julep[store,temporal] for S3 publication and Temporal workers, plus any pipeline-specific extras. The control-plane host also needs authenticated helm, kubectl, and temporal CLIs; apply --publish-only skips Helm reconciliation.

julep worker runs continuously from its environment contract. A positive --smoke-test-seconds N verifies Temporal connectivity, polls the configured queue for N seconds, drains, and exits; the default 0 keeps serving.

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Extras

The base install is authoring + compile only (PyYAML). Optional extras add runtime surfaces:

Extra	pip install --pre 'julep[...]'	Adds
temporal	julep[temporal]	durable execution on Temporal (workflows, activities, worker, client helpers)
dbos	julep[dbos]	durable execution on DBOS / Postgres (steps, flow workflow, chaining runner)
http	julep[http]	native HTTP tool calls from the callTool activity
dotctx	julep[dotctx]	rich .ctx layout (Jinja2 templates compiled into registered renderers)
yglu	julep[yglu]	Yglu-evaluated settings.yaml (mem-mcp .ctx compatibility)
providers	julep[providers]	multi-provider LlmCaller via any-llm (pair with provider extras)
otel	julep[otel]	OpenTelemetry span export of the projection
langfuse	julep[langfuse]	Langfuse OTLP/HTTP export of the projection
store	julep[store]	artifact distribution stores + bundle signing primitives
wasm	julep[wasm]	sandboxed wasm execution of bundle-sourced pures (wasmtime host)

julep.HAVE_TEMPORAL reports whether the runtime is available; the package imports and compiles flows either way.

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Looking for Julep v1?

Julep v1 (the agents API platform) is preserved on the v1 branch and its docs at v1.docs.julep.ai. Julep 3 is a ground-up rewrite; there is no migration path — v1 and v3 are different products.

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License

This project is licensed under Apache-2.0. See LICENSE.