Asimov vs Weave: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Asimov and Weave — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Asimov

ASIMOV Platform

Freemium

Foundational search and module platform enabling AI agents to discover, compose, and manage neurosymbolic capabilities.

Foundational Search: A dedicated search layer that indexes modules, capabilities, and artifacts so AI agents can discover relevant components at runtime or during planning.
Polyglot SDKs: Language SDK support (notably Rust SDKs) to build, register, and integrate neurosymbolic modules into agent pipelines and services.
Module CLI Management: Command-line tools for module lifecycle tasks—publishing, versioning, dependency resolution, and module snapshots for reproducible deployments.
Snapshot Tooling: Snapshot CLI for capturing module states and dependencies to enable reproducible rollbacks and audited deployments of agent stacks.
Installer Integration: Packaging formulas and installers (Homebrew, Scoop) to simplify local developer setup and runtime installation on developer machines and CI.
Module Ecosystem & Specs: A module specification and repository model that standardizes how cognitive, symbolic, and neural components are described and consumed.
Trust & Verification Primitives: Built-in emphasis on versioning, provenance, and auditable snapshots to improve reliability and governance of neurosymbolic agents.
Foundational search for agent knowledge
Indexing and retrieval for agent workflows
API access for integrations
Documentation and terms of service referencing subscriptions/billing
Foundational search functionality targeted at AI agents

Agent Capability Discovery: Allow autonomous agents to query a searchable registry to locate vetted neurosymbolic modules (e.g., planners, perception connectors) during task planning.
Building Neurosymbolic Pipelines: Developers assemble pipelines combining neural components and symbolic logic using SDKs and module specs to create explainable agent behaviors.
Module Lifecycle Management: Teams publish, version, and snapshot modules via the CLI to ensure reproducible experiment runs and safe rollouts to production agents.
Edge and Developer Deployment: Use Homebrew/Scoop installers or packaged snapshots to rapidly provision developer machines or edge nodes with specific module sets.
Auditability & Governance: Capture module snapshots and provenance for compliance, postmortem analysis, and to enable trusted rollbacks after model or module updates.
Integration with Rust Workflows: Rust developers build high-performance modules using the ASIMOV Rust SDK and manage them via the platform CLIs.
Provide retrieval/knowledge access to autonomous agents
Index and surface documents for agent decision-making
Integrate search into multi-agent systems and pipelines
Provide retrieval/search primitives for autonomous agents to obtain context and knowledge during decision-making

WorkWeave

Freemium

Engineering intelligence platform that measures the ROI of AI coding spend and routes every prompt to the most cost-efficient model.

Prompt-to-Production Analysis: LLM and ML models analyse commits, tokens, pull requests, reviews, deploys, and AI telemetry as a single pipeline rather than isolated metrics.
AI ROI Scoring: Token consumption is scored for cost, efficiency, and quality, benchmarked against thousands of engineering organisations, so spend is measured by value rather than volume.
Per-Engineer AI Impact: A breakdown of AI usage rate, AI score, code quality, and output change versus baseline for each engineer over a rolling window.
Weave Prompt Router: Classifies every prompt and routes it to the most cost-efficient model without compromising speed or quality, learning from individual and organisation-level feedback.
One-Command Router Install: Running npx @workweave/router detects your existing clients and writes one env var per provider for Anthropic, OpenAI, and Google, with the bearer token staying on your device unless you export it.
Wooly Engineering Agent: An AI agent that reviews all your engineering data to suggest where and how to improve, answering questions grounded in your own records with citations, available in-app or over MCP.
Standard Framework Reporting: DORA and SPACE metrics plus survey data combined with AI-specific measures in one pane of glass for executive reporting.
Enterprise Compliance Controls: SOC 2 Type II certification with regular third-party audits, GDPR and HIPAA compliance, SSO via SAML and OIDC, SCIM provisioning, and role-based access.

Justifying AI Tooling Spend: Producing an executive report on what a Claude Code or Cursor rollout actually returned, benchmarked against peer organisations.
Cutting Inference Costs: Routing routine edits to cheaper models and reserving frontier models for work that needs them, without changing how developers work.
Finding SDLC Bottlenecks: Identifying where pull requests, reviews, or deploys stall using DORA and SPACE metrics alongside AI telemetry.
Coaching Engineers on AI Use: Seeing which engineers get real quality and output gains from AI assistance and which are consuming tokens without effect.
Agent Observability: Tracking what autonomous coding agents contribute to the codebase separately from human-authored work.
Ad-Hoc Engineering Questions: Asking Wooly where deployment cycles are getting stuck and receiving an answer cited back to the organisation's own records.