wasmagent-js

WasmAgent adds a verifiable evidence layer to agent tool use: protect tool calls, record what happened, audit the result, and turn trusted traces into training data.

Protect → Record → Audit → Train

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Start in 30 seconds

Pick your entry point:

Goal	Install
Protect tools — runtime firewall, policy enforcement, taint tracking	npm add @wasmagent/mcp-firewall
Record evidence — signed AEP records after every agent run	npm add @wasmagent/aep
Train from traces — compliance scoring + DPO/PPO export	npm add @wasmagent/aep @wasmagent/compliance

Trust Pack — 30-minute end-to-end: docs/quickstarts/trust-pack-30min.md

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Quickstart

Three paths — pick the one that fits your use case:

Path 1 — Protect: MCP runtime firewall

Wrap any MCP server: vet tools before execution, enforce policy per call, track taint across results.

npm install @wasmagent/mcp-firewall

import { vetTool, evaluatePolicy, taintObservation, snapshotTool } from "@wasmagent/mcp-firewall";

// Before calling a tool
const snap     = snapshotTool(entry, "my-server");   // hash descriptor at registration
const vetting  = vetTool(entry);                     // static scan: injection / exfil / rug-pull
const decision = evaluatePolicy(entry.name, args, vetting, consentRecords);

if (decision.decision === "deny")   throw new Error(`Blocked: ${decision.reason}`);
if (decision.decision === "ask_user") {
  // surface consent UI, then call recordConsent(...)
}

// After receiving result
const obs = taintObservation(entry.name, rawResult);  // boundary-tagged, safe to assemble into prompt

→ Security pack · OWASP Agentic Top 10 · Attack demos

Path 2 — Record: AEP evidence export

Emit a signed evidence record after every agent run — consumable by trace-pipeline for audit and training.

npm install @wasmagent/aep

import { AEPEmitter } from "@wasmagent/aep";

const emitter = new AEPEmitter({ run_id: "run-001", model_id: "claude-sonnet-4-6" });

// During the run — add tool call evidence
emitter.addAction({ tool_name: "bash", outcome: "pass", exit_code: 0 });

// At the end — emit the record
const record = emitter.build();
// record satisfies aep/v0.1 JSON Schema — ready for evomerge validate-aep

→ AEP schema · trace-pipeline 10-min tutorial

Path 3 — Execute: Sandboxed code execution

Run agent-generated code in an isolated WASM kernel — no host-process access.

npm install @wasmagent/aisdk @wasmagent/kernel-quickjs

import { sandboxedJsTool } from "@wasmagent/aisdk";
import { QuickJSKernel } from "@wasmagent/kernel-quickjs";

// Drop into any AI SDK / LangChain / OpenAI Agents setup
const codeTool = sandboxedJsTool({ kernel: new QuickJSKernel() });

→ Kernel comparison · Getting started

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📚 Docs · Getting started · Kernels · OWASP governance · Security pack · Changelog

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What is shipped vs alpha

WasmAgent uses a five-tier maturity scale to prevent "shipped" from becoming a vague claim:

Tier	Meaning	Semver guarantee	Production use
stable	Public API locked; breaking changes require major-version bump	Yes	Yes
beta	Functional and used in production, but a specific limitation is documented (e.g. first-line filter only, contract still evolving)	Minor/patch only	Yes, with caveats documented
alpha	Schema versioned; fields may be added without a breaking-change bump	No	Informed use
demo	Demonstration or example code; not hardened for production	No	No
research	Research-grade prototype; interfaces may change without notice	No	No

Packages not listed here (model adapters, UI cards, etc.) follow the same scale — see each package's README or package.json wasmagent.stability field.

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Package maturity

Package	Maturity	Notes
@wasmagent/core	stable	Public API; semver guaranteed
@wasmagent/kernel-quickjs	stable
@wasmagent/kernel-remote	stable
@wasmagent/mcp-gateway	stable	Published 0.1.0; gateway composes all firewall layers
@wasmagent/mcp-firewall	beta	First-line filter, not adversarial-grade — keyword bag + lightweight n-gram classifier; use defence-in-depth
@wasmagent/aep	beta	v0.2 signature contract (Ed25519) shipped; schema versioned
@wasmagent/otel-exporter	alpha	GENAI_SEMCONV, AEP↔OTel bridge
@wasmagent/aisdk / @wasmagent/mastra-sandbox	alpha	API stable, may add fields
@wasmagent/compliance	alpha	Schema versioned; may add fields without breaking
@wasmagent/mcp-policy	alpha — private	Not yet published to npm
@wasmagent/mcp-attestation	alpha — private	Not yet published to npm
@wasmagent/evals-runner	alpha
@wasmagent/devtools	alpha

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WasmAgent Ecosystem

WasmAgent is a portable, governable agent runtime for safe code execution, verifiable rollouts, and post-training data loops.

Repo	Role
wasmagent-js (this repo)	Embedded Agent Runtime / WASM Kernel / policy / verifier / adapters
bscode	Cloudflare flagship demo and deploy template for safe coding agents
trace-pipeline	Public datafactory and eval-trust backend for rollout data

Task → Safe Runtime → Verifiable Rollout → Trajectory Export → DPO/PPO Data → Better Models

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What makes wasmagent different

Three wedges where wasmagent stands apart from generic agent frameworks:

Wedge	What it means
Sandboxed execution	Three isolation tiers — VmKernel / WASM (QuickJS·Pyodide·Wasmtime) / microVM — with a single CapabilityManifest and MCP runtime firewall across all
Runtime compliance	TaskSpec → ConstraintIR → ComplianceEvalRecord — every run produces an auditable, cross-repo training contract, not just a log
Trace-to-training contract	Verifiable rollout branching, objective scoring, DPO/PPO export — the loop from runtime evidence to training data is first-class, not an afterthought

Full feature axis table (10 axes vs. other JS agent frameworks)

#	Axis	Status
1	Multi-provider adapters — one Model interface across Anthropic, OpenAI, Doubao, DeepSeek, Kimi, Qwen, GLM, MiniMax, local llama.cpp	shipped
2	Three isolation tiers — VmKernel (in-process) / QuickJS·Pyodide·Wasmtime (WASM) / RemoteSandboxKernel (microVM) — same CapabilityManifest across all	shipped
3	Cross-runtime + offline — Node / edge / browser / air-gapped laptop; @wasmagent/model-local + WASM kernel = zero outbound traffic	shipped
4	Memory layers — MemoryBlockSet (prompt-cache stable) + observational memory + Checkpointer + 4 KV backends	shipped
5	Durable workflows — LocalWorkflowEngine + CloudflareWorkflowEngine — observable, terminable, resumable	shipped
6	Code-mode MCP — N tools → 2 tools (docs_search + execute_code); 13.6% token cost at N=30	shipped
7	Devtools + OTel — local Studio, gen_ai.* semantic conventions (Datadog / Honeycomb / Grafana)	shipped
8	Goal-directed loop — agent synthesises success criteria, verifies, retries with hints	shipped 2026-06-18
9	Adaptive execution — registered fallbacks (L1) → synthesised tool (L2) → relaxed goal (L3)	shipped 2026-06-18
10	MCP runtime firewall — @wasmagent/mcp-firewall: descriptor snapshot, static vetting (injection / exfiltration / rug-pull / taint), per-call policy, consent ledger	shipped 2026-06-25

Full comparison with Vercel AI SDK, LangGraph.js, OpenAI Agents JS, Mastra, CF Agents SDK: docs/compare.md

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Quick Start

Tool-Calling Agent

import { ToolCallingAgent, AnthropicModel } from "@wasmagent/core";
import { z } from "zod";

const agent = new ToolCallingAgent({
  model: new AnthropicModel("claude-haiku-4-5-20251001"),
  tools: [{
    name: "search", description: "Search the web",
    inputSchema: z.object({ query: z.string() }),
    readOnly: true, idempotent: true,
    forward: async ({ query }) => `Results for: ${query}`,
  }],
  stopPolicies: ["steps:10", "cost:0.5"],
});

for await (const ev of agent.run("Search for recent AI news")) {
  if (ev.event === "final_answer") console.log(ev.data.answer);
}

Sandboxed Code Agent

import { CodeAgent, AnthropicModel } from "@wasmagent/core";

const agent = new CodeAgent({
  model: new AnthropicModel("claude-sonnet-4-6"),
  tools: [],  // kernel executes code; no extra tools needed
  maxSteps: 10,
});

for await (const ev of agent.run("What is 42 * 1337?")) {
  if (ev.event === "final_answer") console.log(ev.data.answer);
}

CLI

npm install -g @wasmagent/cli

# Agent runs
wasmagent run "What is the square root of 144?"
wasmagent run "Summarise AI news" --stream | jq .

# Rollout / training data
wasmagent rank-rollout rollouts.jsonl --out ranked.jsonl
wasmagent validate-rollouts ranked.jsonl
wasmagent export-rollouts --in ranked.jsonl --format dpo --out dpo.jsonl

# MCP security (scan → guard → evidence)
wasmagent init --guard               # generate wasmagent.policy.yaml
wasmagent scan-mcp tools.json        # static risk scan, exits 1 on critical findings
wasmagent guard --config wasmagent.policy.yaml --upstream tools.json
wasmagent evidence export --input aep-records.jsonl --format json

GitHub Action — enforce policy in CI:

- uses: WasmAgent/wasmagent-js/.github/actions/agent-evidence-gate@main
  with:
    policy: wasmagent.policy.yaml
    tools-file: mcp-tools.json
    fail-on-policy-violation: "true"

→ MCP Guard guide · Attack demos

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Key Capabilities

Capability	Guide
MCP firewall — vetTool, ScopeLease, ApprovalReceipt	docs/guides/mcp-guard.md
AEP v0.2 evidence — causal chain, scope lease, taint, memory refs	packages/aep/src/types.ts
OWASP MCP Top 10 crosswalk	docs/security/standards-crosswalk.yaml
OWASP security demo (10 scenarios)	examples/owasp-demo/
Security benchmark runner	examples/security-benchmark/
AEP ↔ OTel bidirectional mapping	packages/otel-exporter/src/aep-otel-bridge.ts
AgentTeam delegation chain	packages/core/src/agents/AgentTeam.ts
Claim dashboard	node scripts/verify-claims.mjs --html → docs/claims/claims.html
Quality runners (self-consistency, reflect-refine, parallel fork-join)	docs/guides/quality-runners.md
Durable runtime (checkpoints, SSE resume, HITL)	docs/guides/durable-runtime.md
Observational memory — ~22% tokens on 50-turn traces	docs/guides/observational-memory.md
Goal-directed agent with verifiers	docs/guides/goal-directed.md
Production APIs (retry, evals, OTel, React hook)	docs/api/production-apis.md
API stability policy	docs/api/stability-policy.md

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Model Providers

First-class adapters: Anthropic · OpenAI · Doubao · DeepSeek · Kimi · Qwen · GLM · MiniMax · local llama.cpp

// Chinese providers with thinking support
import { DoubaoModel, DoubaoModels } from "@wasmagent/model-doubao";
import { DeepSeekModel, DeepSeekModels } from "@wasmagent/model-deepseek";

// Local / offline
import { LocalModel } from "@wasmagent/model-local";  // node-llama-cpp, multi-mirror download

Full provider reference and proxy/custom endpoint setup: docs/guides/openai-compat-recipes.md

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Ecosystem

Project	Role
bscode	Flagship Cloudflare deploy template — wires every wasmagent-js capability into a real edge product
trace-pipeline	Training data factory — converts ranked rollouts into DPO/PPO datasets

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Development

bun install && bun run build
bun test packages/
bun run typecheck
bun run bench          # reproduce all README benchmarks
bun run check:branding # CI guard: no old brand references
bun run verify:claims  # CI guard: all benchmark claims have evidence scripts

See CONTRIBUTING.md · Changelog · License: Apache-2.0