Ship agents
that actually do the work.
A managed runtime for autonomous AI agents — typed tools, vector memory, multi-model routing, policy guardrails, and a full audit trail. Define once in Python or YAML, run from CLI, REST, cron, or your app, with the same observability you expect from any production service.
Routes between the model providers and tool ecosystems you already use
One agent. Three surfaces.
Define in Python. Deploy via GitOps. Call from anywhere.
Author agents in idiomatic Python with typed tools and memory, or define them declaratively in YAML for GitOps. Either way, every workspace gets a signed REST endpoint, OpenTelemetry traces, and a one-click replay surface for every session.
- Typed tool schemas — no JSON-string juggling, no silent failures
- Per-tenant memory and Vault namespace — no cross-tenant leakage
- Budget + tool-call ceilings enforced server-side
- Sessions replayable in the dashboard or piped to OpenTelemetry
1# Pip install once: pip install vxcloud-agents2from vxcloud.agents import Agent, Tool, MemoryStore3 4stripe = Tool.openapi("https://api.stripe.com/openapi.json", auth="vault://stripe")5gmail = Tool.mcp("gmail", server="mcp://workspace/gmail")6policy = Tool.python(refund_policy) # local python function -> typed tool7 8memory = MemoryStore.pgvector("orders-refunds", scope="tenant")9 10agent = Agent(11 model="claude-opus-4-7",12 tools=[stripe, gmail, policy],13 memory=memory,14 guardrails={15 "max_amount_usd": 500, # above -> human approval16 "deny_paths": ["/users/delete"],17 "max_tool_calls": 12,18 "budget_usd": 0.05,19 },20)21 22result = agent.run(23 goal="Refund order #4821 if eligible and notify the customer.",24 context={"order_id": "4821", "actor": "[email protected]"},25)26 27print(result.summary) # plain-English summary28print(result.audit.url) # signed replay linkWant to draft an agent from a goal?
vxcli agent new "ship a refund bot for Stripe" — generates the Python and YAML, ready to commit.
The runtime, not just a wrapper
Everything an agent needs to ship to production
Memory, tools, planning, guardrails, audit, and routing — built in, not bolted on.
Plan, act, observe, repeat
Agents decompose a goal into steps, pick the right tool for each step, observe the result, and loop until success or a configured budget is hit.
Multi-agent orchestration
Compose specialist agents (planner, retriever, coder, reviewer) into supervised graphs. Hand off context cleanly; nothing runs unsupervised.
Long-term + episodic memory
Vector memory for semantic recall, episodic memory per task, and short-term scratchpads — all per-tenant, encrypted, and auditable.
Typed tool catalog
Wire HTTP, GraphQL, SQL, gRPC, and MCP servers as typed tools. Schema validation in, schema validation out, with retry and backoff baked in.
Human-in-the-loop guardrails
Policy engine gates risky actions: amount thresholds, PII writes, production resource changes. Approval via Slack, email, or dashboard.
Full audit trail
Every prompt, tool call, observation, and decision is recorded with cryptographic chain-of-custody. Replay any session in the dashboard.
Typed tool catalog
Every API your agent touches is a typed contract
Wire any HTTP, GraphQL, SQL, gRPC, or MCP endpoint as a typed tool. Schema validation runs on the way in and on the way out — no more silent JSON deserialization failures taking down your agent at 2am.
Multi-model routing
Pick the right brain for each step — automatically
Route reasoning to Opus, fast loops to Sonnet, classification to Haiku, and air-gapped tenants to your own self-hosted model. One config, one bill, one observability surface.
Claude Opus 4.7
reasoningClaude Sonnet 4.6
fastClaude Haiku 4.5
cheapGPT-class reasoning
reasoningSelf-hosted Llama
sovereignBYO endpoint
BYOMReal workloads, real numbers
Production-grade — measured on production traffic
Benchmarks aggregated from agent sessions running on the platform. Not synthetic; not hand-tuned demos.
Real-world agents teams have shipped
From customer ops to incident response
Refund and dispute bot
Reads order history, applies refund policy, calls Stripe, emails the customer, and writes the audit entry — under 2s, $0.005 per resolution.
Tier-1 support copilot
Answers product questions from your docs, files tickets when escalation is needed, and writes the postmortem when an incident closes.
PR triage agent
Watches GitHub PRs, runs targeted reviews, requests changes with line-level comments, and merges low-risk dependency bumps after CI is green.
Incident commander
Joins the on-call channel, pulls traces and logs, suggests a fix, and drafts the timeline as the incident unfolds.
Data-quality watcher
Profiles new ETL outputs, flags drift, opens Linear tickets with a reproducible query, and assigns the right data engineer.
Sales-ops research agent
Enriches inbound leads with firmographic data, scores them against your ICP, and drops them into the right Slack channel with context.
Ship your first agent before lunch.
Open a workspace, point it at a tool, and run a real goal. No credit card, no cluster to provision — and no synthetic demo data.