Run AI agents securely.
AI agent orchestration on WebAssembly.
AI agents are powerful.
We make them safe.
credential leaks
Agents with tool access need API keys. A prompt injection or misbehaving agent can exfiltrate secrets and make unauthorized calls.
lost context on crash
Multi-turn agentic loops are long-running and fragile. A crash mid-loop loses all context, wastes tokens, and leaves inconsistent state.
idle resource waste
Agents sleeping between steps — waiting for approvals or scheduling future actions — hold processes and resources open for the entire duration.
tenant cross-contamination
Running agents for different customers on shared runtimes creates data leaks most workflow engines don't address.
How Runtara solves it
WASM-sandboxed agents
Workflows compile to WebAssembly and run in wasmtime. No filesystem, no raw network, no credential access. The sandbox enforces what agents can and cannot do.
credential isolation
API keys live in the connection service. A host proxy injects them at request time. Even a compromised agent cannot exfiltrate secrets.
crash-proof agentic loops
Every tool call is checkpointed. Crashes resume from the last step with full conversation history intact. No repeated API calls, no wasted tokens.
compiled, not interpreted
JSON workflows compile to WASM modules — immutable, versioned artifacts. Zero interpreter overhead, smaller attack surface than scripted engines.
tenant isolation
Each customer gets a fully isolated environment — own database, credentials, and runtime. No shared state, no cross-tenant risks.
durable sleep
Agents waiting for approvals or external events release all resources. The engine wakes them automatically when it's time — hours or days later.
Define agent workflows as data
JSON DSL compiles to sandboxed WASM. Tools are graph edges. Credentials stay server-side.
{
"name": "support-agent",
"steps": [
{
"id": "triage",
"type": "AiAgent",
"connection": "conn_openai_gpt4o",
"systemPrompt": "Triage tickets. Look up customers and issue refunds.",
"maxIterations": 10
},
{
"id": "lookup_customer",
"type": "Http",
"connection": "conn_crm",
"url": "/customers/:id"
},
{
"id": "issue_refund",
"type": "Http",
"connection": "conn_stripe",
"method": "POST",
"url": "/v1/refunds"
}
],
"edges": [
{ "from": "triage", "to": "lookup_customer", "label": "lookup_customer" },
{ "from": "triage", "to": "issue_refund", "label": "issue_refund" }
]
} How it works
define
Write agent workflows as JSON DSL with steps, tools, and connections.
compile
Build to a standalone WASM module — immutable, versioned, reproducible.
sandbox
Run in wasmtime. No filesystem, no raw network. All I/O mediated by the host.
execute
AI agent calls tools via host proxy. Credentials injected at request time, invisible to agent code.
checkpoint
After each tool call: conversation history, tool results, and iteration count saved to database.
recover
Crash at any point? Relaunch from last checkpoint. No lost context, no repeated calls.
How Runtara compares
| Runtara | LangGraph | CrewAI | Temporal + LLM | |
|---|---|---|---|---|
| Agent isolation | WASM sandbox (enforced) | Optional (Pyodide) | Docker (optional) | Process-level |
| Credential security | Proxy injection | Manual | Manual | Manual |
| Crash recovery | Per-tool checkpoint | Optional checkpoint | Task replay | Event sourcing |
| Tenant isolation | Dedicated env | Application-level | Enterprise only | Namespaces |
| Runtime | Compiled WASM | Python / JS/TS | Python | Multi-language |
Built for real workloads
autonomous support agents
LLM triages tickets, looks up customer data, takes actions. Checkpoints after each step. Survives deploys without losing conversation state.
approval workflows with AI
AI analyzes a request, produces a recommendation, sleeps until human approval — days later, resumes from checkpoint and executes the decision.
secure third-party AI
Run customer-provided AI logic in WASM sandboxes within dedicated per-customer environments. No access to other tenants' data or credentials.
multi-agent collaboration
Chain AI agent steps — one analyzes, one plans, one executes — each with different LLM providers and tool sets, all checkpointed independently.
Ready to run AI agents securely?
Open-source. Self-hosted or cloud. Written in Rust.