The operating layer for autonomous agents. Visual workflows, resumable runs, PII and sensitive data protection, and a complete audit trail. Built in from day one.
Most agent libraries help you build a demo. Brahmalabs is what takes agent code to production: visual workflows, durable execution, human-in-the-loop approvals, and live observability, composed into one coherent system.
Compose agents, tools, conditions, loops, code steps, HTTP calls, and human approval gates the way you would diagram them on a whiteboard. Agents are versioned, skills moderated through your private registry, and the platform validates the structure as you build. Broken connections are caught before deploy.
Runs survive crashes, restarts, and partial outages. Every step executes inside the sandbox image you chose for that workload; every output is captured as a versioned artifact. Pause, resume, replay, or cancel any in-flight workflow. A workflow that started yesterday can still finish today.
Single approvers, N-of-M multi-party sign-off, content guardrails, and configurable spend ceilings before agents take consequential action. Agents propose; humans decide; every decision is recorded against the run.
Watch every step in real time. Per-run token, latency, and cost accounting. Every artifact a run produced is tied back to the run that made it. Forward events to your existing on-call and analytics tooling; the audit log is queryable directly.
Identity, isolation, and least-privilege are not premium add-ons. They are the foundation. Brahmalabs is designed to pass review, not to negotiate around it.
Connect your existing identity provider. Users sign in with the credentials your security team already manages. Membership and group provisioning happen automatically.
Decide who can build, who can publish, and who can approve. Permissions are enforced server-side on every request. Never just hidden in the interface.
Every customer's workflows, runs, and credentials live in a bounded space scoped to their organisation. Cross-tenant access is structurally impossible, not just policy-prevented.
Each workflow runs inside the sandbox image that matches the work — a browser image for UI automation, a database-client image for migrations — with CPU, memory, network, and timeout ceilings you choose. One workflow's load can never starve another.
Agents see only the tools and MCP servers you explicitly grant them, at the workspace, workflow, and individual-agent level. Nothing outside the allow-list is reachable. No shadow capabilities, no surprise side-effects.
Agents never see raw credentials. Provider keys, API tokens, and OAuth secrets stay in the platform vault. The runtime injects scoped, short-lived references at call time and revokes them when the run ends. Even an exfiltrated prompt has nothing to leak.
Change control, human decisions, and every artefact a run produced are tracked continuously, so your compliance team doesn't have to assemble the evidence at audit time.
Agents are versioned and promote through draft, staged, and production, with rollback in one click. Skills are private to your organisation by default; moderation happens before anyone else can use them.
Single approver, N-of-M multi-party chains, or escalation when the first approver is unavailable. Configure per workflow and per action. Every decision and its reason attach to the run that requested it.
Every file a run produced — a drafted PR, a compliance report, a test fixture, a generated dataset — is a stable, versioned artifact tied to the run that made it. Auditors and downstream systems get a URL they can trust.
Every authentication, configuration change, workflow run, approval, key issuance, and integration call is recorded. Exportable, immutable, queryable directly by your compliance team.
Plus the Model Context Protocol. Bring any MCP-compatible server in, or call Brahmalabs out from your own MCP client. Credentials stay in the platform vault, scoped per workspace.
Credits are the unit of platform usage, metered against what costs us to run. Bring your own model keys to pay providers directly at provider pricing.
One unit. Predictable. Metered against actual platform cost.
Connect Anthropic, OpenAI, or any compatible provider key. You pay the model provider directly at their public rates; Brahmalabs charges 0.1 credits per 1k tokens for platform overhead. Typically a 90% reduction versus hosted inference.
Brahmalabs is the layer that makes agentic systems durable, governable, and inspectable. Start free, or talk to us about a sovereign deployment.
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