# wasmagent-js [![npm version](https://img.shields.io/npm/v/@wasmagent/core.svg?label=%40wasmagent%2Fcore)](https://www.npmjs.com/package/@wasmagent/core) [![License: Apache-2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](./LICENSE) [![CI](https://github.com/WasmAgent/wasmagent-js/actions/workflows/ci.yml/badge.svg)](https://github.com/WasmAgent/wasmagent-js/actions/workflows/ci.yml) [![Quick Start](https://github.com/WasmAgent/wasmagent-js/actions/workflows/quickstart-check.yml/badge.svg)](https://github.com/WasmAgent/wasmagent-js/actions/workflows/quickstart-check.yml) [![Docs](https://img.shields.io/badge/docs-vitepress-brightgreen.svg)](https://WasmAgent.github.io/wasmagent-js/) > **WasmAgent adds a verifiable evidence layer to agent tool use: protect tool calls, record what happened, audit the result, and admit trusted traces into downstream systems.** **Protect → Record → Audit → Admit** · **Sync** — agent↔UI shared state --- ## Start in 30 seconds Pick your entry point: | Goal | Install | |---|---| | **Protect tools** — runtime firewall, policy enforcement, taint tracking with a provenance-preserving information-flow gate wired through the agent runtime | `npm add @wasmagent/mcp-firewall` | | **Record evidence** — signed AEP records after every agent run | `npm add @wasmagent/aep` | | **Admit from traces** — compliance scoring produces `ComplianceEvalRecord`s for downstream training | `npm add @wasmagent/aep @wasmagent/compliance` | | **Sync state** — reducer-backed agent↔UI shared state, agent reads projections + writes intent | `npm add @wasmagent/core` (`/shared-state` subpath) | **Trust Pack — 30-minute end-to-end: [docs/quickstarts/trust-pack-30min.md](./docs/quickstarts/trust-pack-30min.md)** --- ## 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. ```bash npm install @wasmagent/mcp-firewall ``` ```ts import { evaluatePolicy, snapshotTool, taintObservation, vetTool } from "@wasmagent/mcp-firewall"; const entry = { name: "read_file", description: "Read a file from disk", inputSchema: { type: "object", properties: { path: { type: "string" } } }, }; const args = { path: "/tmp/report.txt" }; const consentRecords = []; // 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.reasons.join("; ")}`); if (decision.decision === "ask_user") { // surface consent UI, then call recordConsent(...) } // After receiving result const rawResult = "example report contents"; const obs = taintObservation(entry.name, rawResult); // boundary-tagged, safe to assemble into prompt ``` → [Security pack](./docs/security-governance-pack/README.md) · [OWASP Agentic Top 10](./docs/security/capability-manifest-owasp.md) · [Attack demos](./docs/security/mcp-firewall-attack-demos.md) ### Path 2 — Record: AEP evidence export Emit a signed evidence record after every agent run — consumable by trace-pipeline for audit and training. ```bash npm install @wasmagent/aep ``` ```ts 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/v0.3 JSON Schema — ready for evomerge validate-aep ``` → [AEP schema](./packages/aep/) · [trace-pipeline 10-min tutorial](https://github.com/WasmAgent/trace-pipeline/blob/main/docs/TRACE_TO_TRAINING_10MIN.md) ### Path 3 — Execute: Sandboxed code execution Run agent-generated code in an isolated WASM kernel — no host-process access. ```bash npm install @wasmagent/aisdk @wasmagent/kernel-quickjs ``` ```ts 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](./docs/kernels/comparison.md) · [Getting started](./docs/guides/getting-started.md) ### Path 4 — Sync: Human-agent shared state Reducer-backed collaborative state where the LLM reads projections, dispatches semantic actions, and respects affordances — all through standard tools. ```bash npm install @wasmagent/core ``` ```ts import { defineStateModel, SharedStateStore, stateTools } from "@wasmagent/core/shared-state"; // 1. One reducer, shared by both UI and agent. const model = defineStateModel({ initial: () => ({ page: "list", selectedId: null as string | null }), reduce: (s, a) => { if (a.type === "SELECT") return { ...s, page: "detail", selectedId: a.id }; if (a.type === "BACK") return { ...s, page: "list", selectedId: null }; return s; }, project: (s) => ({ page: s.page, selectedId: s.selectedId }), affordances: (s) => s.page === "list" ? ["SELECT"] : ["BACK"], }); // 2. Server-side store keyed by session. const store = new SharedStateStore(model); // 3. Give the agent read_state + dispatch_action tools. const tools = stateTools(store, "session-001"); // Pass `tools` to any ToolCallingAgent — the LLM reads state and dispatches intent. ``` The semantic action stream doubles as AEP evidence — every dispatch is a provenance-ready record (see [#141](../../issues/141) for the full confluence design). --- 📚 **[Docs](https://WasmAgent.github.io/wasmagent-js/)** · [Getting started](./docs/guides/getting-started.md) · [Kernels](./docs/kernels/comparison.md) · [OWASP governance](./docs/security/capability-manifest-owasp.md) · [Security pack](./docs/security-governance-pack/README.md) · [Changelog](./CHANGELOG.md) --- ## 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. --- ## 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** | | --- ## 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](https://github.com/WasmAgent/bscode) | Cloudflare flagship demo and deploy template for safe coding agents | | [trace-pipeline](https://github.com/WasmAgent/trace-pipeline) | Public datafactory and eval-trust backend for rollout data | ```text Task → Safe Runtime → Verifiable Rollout → Trajectory Export → DPO/PPO Data → Better Models ``` --- ## 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, provenance gate auto-threaded through the agent runtime | shipped 2026-06-25 · provenance wiring 2026-10-04 |
> Full comparison with Vercel AI SDK, LangGraph.js, OpenAI Agents JS, Mastra, CF Agents SDK: **[docs/compare.md](./docs/compare.md)** --- ## Quick Start ### Tool-Calling Agent ```ts 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 ```ts 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 ```bash 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: ```yaml - 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](./docs/guides/mcp-guard.md) · [Attack demos](./docs/security/mcp-firewall-attack-demos.md) --- ## Key Capabilities | Capability | Guide | |---|---| | Shared state — reducer-backed agent↔UI sync, projections, affordances | [packages/core/src/shared-state/](./packages/core/src/shared-state/) | | MCP firewall — vetTool, ScopeLease, ApprovalReceipt | [docs/guides/mcp-guard.md](./docs/guides/mcp-guard.md) | | AEP v0.2 evidence — causal chain, scope lease, taint, memory refs | [packages/aep/src/types.ts](./packages/aep/src/types.ts) | | OWASP MCP Top 10 crosswalk | [docs/security/standards-crosswalk.yaml](./docs/security/standards-crosswalk.yaml) | | OWASP security demo (10 scenarios) | [examples/owasp-demo/](./examples/owasp-demo/) | | Security benchmark runner | [examples/security-benchmark/](./examples/security-benchmark/) | | AEP ↔ OTel bidirectional mapping | [packages/otel-exporter/src/aep-otel-bridge.ts](./packages/otel-exporter/src/aep-otel-bridge.ts) | | AgentTeam delegation chain | [packages/core/src/agents/AgentTeam.ts](./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](./docs/guides/quality-runners.md) | | Durable runtime (checkpoints, SSE resume, HITL) | [docs/guides/durable-runtime.md](./docs/guides/durable-runtime.md) | | Observational memory — ~22% tokens on 50-turn traces | [docs/guides/observational-memory.md](./docs/guides/observational-memory.md) | | Goal-directed agent with verifiers | [docs/guides/goal-directed.md](./docs/guides/goal-directed.md) | | Production APIs (retry, evals, OTel, React hook) | [docs/api/production-apis.md](./docs/api/production-apis.md) | | API stability policy | [docs/api/stability-policy.md](./docs/api/stability-policy.md) | --- ## Model Providers First-class adapters: Anthropic · OpenAI · Doubao · DeepSeek · Kimi · Qwen · GLM · MiniMax · local llama.cpp ```ts // 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](./docs/guides/openai-compat-recipes.md) --- ## Ecosystem | Project | Role | |---|---| | [bscode](https://github.com/WasmAgent/bscode) | Flagship Cloudflare deploy template — wires every wasmagent-js capability into a real edge product | | [trace-pipeline](https://github.com/WasmAgent/trace-pipeline) | Training data factory — converts ranked rollouts into DPO/PPO datasets | Upstream integration status (PRs filed to Vercel AI SDK, Mastra, LangChain.js, ElizaOS, MCP registry, …) is tracked in [`docs/distribution/upstream-prs.md`](./docs/distribution/upstream-prs.md). --- ## Development ```bash 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](./CONTRIBUTING.md) · [Changelog](./CHANGELOG.md) · [License: Apache-2.0](./LICENSE)