Observed Signal · Apr 27, 2026 · Product Launch · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Asqav Ships TypeScript SDK on npm
Asqav published a TypeScript SDK (@asqav/sdk) on npm that mirrors its existing Python SDK and brings agent governance and tamper-evident signing to TypeScript-based AI agents. The npm package is a thin client calling api.asqav.com, exposes the same REST API and behaviour as the Python SDK, and produces ML-DSA-65 (FIPS 204) signatures anchored to Bitcoin via OpenTimestamps for auditability. The SDK is intended to integrate with TypeScript agent frameworks such as Vercel AI SDK, LangChain.js and Mastra to sign tool calls, enabling an audit trail and policy enforcement. The SDK source is available in a GitHub monorepo (MIT license) and the TypeScript and Python SDKs are version-locked at the API level while releases remain independent.
A developer-facing technical release that expands AI agent governance to TypeScript ecosystems and integrates with popular JS agent frameworks; useful for compliance and observability but not an industry-shifting platform change.
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Key Takeaways & Evidence Grounding
- Asqav published a TypeScript SDK package @asqav/sdk on npm.
- The TypeScript SDK mirrors the Python SDK API and is version-locked at the API level (releases independent).
- SDK generates ML-DSA-65 signatures (FIPS 204) anchored to Bitcoin via OpenTimestamps for tamper-evident audit trails.
- The npm package is a thin client that calls api.asqav.com and keeps cryptographic operations on Asqav infrastructure.
- Source code is in a GitHub monorepo (python/ and typescript/), MIT licensed; docs at asqav.com/docs.
Connected Companies & Entities
4 Entities mappedOntology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
asqav SDK v0.2.9 Adds Verification, Attestations, Preflight, Budgets
asqav v0.2.9 was released on PyPI, introducing four developer-facing features for LLM agent workflows: output verification (binding and later verifying hashes of inputs and outputs), portable attestations (self-contained signed documents for external auditors), a consolidated preflight check (single call for status, policy and certificate checks), and client-side budget tracking (BudgetTracker enforces spend limits and signs spend records). The release focuses on tamper-evidence, external verifiability, preventing wasted LLM calls by checking agent readiness, and enforcing API spend ceilings. Documentation and source code are referenced on GitHub and at asqav.com/docs.
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Desktop AI Agent Writes Its Own Tools Behind Verification Gates
The author announces AMA-teras, an open-source desktop AI agent (AGPL, Electron + TypeScript) that can generate and install new tool plugins when it lacks capabilities. The system is designed with safety gates: generation happens in an isolated git worktree, generated code must pass typechecking, unit tests and a smoke run, and a human must approve diffs before promotion (git tag). Shared community plugins include a verification-evidence record and failed health checks auto-rollback. The agent supports swappable models (examples cited: Anthropic, OpenAI, Moonshot) and offers features like mobile approval and nightly autonomous mode that stacks changes for morning review. Limitations include an unsigned Windows installer and scoped self-evolution restricted to plugins rather than the core. The project repository is published on GitHub.
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