ClawHub vs Experiential Labs: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ClawHub and Experiential Labs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ClawHub
OpenClaw (openclaw)
A fast skill registry for agents that provides semantic (vector) search and a CLI + web interface for publishing and discovering skills.
Key features
- Vector Search: Uses OpenAI embeddings (text-embedding-3-small) combined with Convex vector search to enable semantic discovery of skills by meaning rather than exact keywords.
- Convex-backed Storage and API: Backend built on Convex for database, file storage, and HTTP actions, with a predefined API schema (clawhub-schema) exposing routes for programmatic access and automation.
- CLI Publishing & Sync: A command-line tool to publish, sync, and manage skills from local projects or GitHub imports, enabling CI-friendly skill deployment and updates.
- GitHub OAuth & Auth: Authentication via Convex Auth with GitHub OAuth and support for API tokens for CLI workflows (with telemetry opt-out available).
- Web UI Skill Registry: A hosted web application (default site at clawhub.ai) showing skill listings, details, and search capabilities with performance optimizations for browsing large registries.
- Local Dev & Self-Hosting Support: Environment variables and local development instructions for running the app locally, including Nix plugin support and guidance for deploying with Convex and OpenAI keys.
- Skill registry and directory for agent skills with support for multiple versions and metadata
- Semantic vector search using OpenAI embeddings (text-embedding-3-small) and Convex vector search
- clawhub CLI for publishing, syncing, login, and managing skills from local/workflow environments
- Backend powered by Convex (DB + file storage + HTTP actions) with Convex Auth (GitHub OAuth)
- Shared API schema and routes in packages/schema (clawhub-schema) for CLI and web app integration
- Telemetry emitted on certain operations (e.g., clawhub sync) with opt-out via CLAWHUB_DISABLE_TELEMETRY
- Local development configuration via environment variables (VITE_SITE_MODE, CONVEX_SITE_URL, SITE_URL, AUTH_GITHUB_ID/SECRET, JWT_PRIVATE_KEY/JWKS, OPENAI_API_KEY)
- Deployment-friendly config (example: vercel.json proxying /api to Convex)
- Support for Nix-mode skill packaging/plugins and skill metadata (metadata.clawdbot preferred)
- MIT-licensed, self-hostable, and open-source with public GitHub repositories
Best for
- Skill Catalog for Agents: Maintain a centralized registry of agent skills (actions, plugins) so agents can discover and call capabilities at runtime using semantic search.
- Semantic Discovery in Agent Orchestration: Use embedding-based search to match user intents to the best skill implementations, improving agent routing and orchestration decisions.
- CI/CD for Skills: Publish and update skills from CI pipelines or developer machines via the clawhub CLI, enabling automated versioning and deployment of skill artifacts.
- Integrating with OpenClaw/Clawdbot: Serve as the authoritative skill directory for the OpenClaw ecosystem, allowing the assistant to query available skills and metadata.
- Self-Hosted Registries: Organizations can self-host ClawHub to keep skill metadata and artifacts on-premises while leveraging the same API and CLI integration.
- Developer Discovery & Moderation: Curate, search, and moderate community-contributed skills with title/summary management, sorting, and metadata controls exposed in the web UI.
- Host and browse a central registry of agent skills for reuse across agents and projects
- Perform semantic search over skill descriptions and metadata to discover relevant skills
- Publish, update, and remove skills programmatically or from CI using the clawhub CLI
- Integrate skill discovery into agent runtimes (e.g., Clawdbot / OpenClaw) via the shared API schema
- Self-host a private skill registry for organizations with GitHub OAuth and Convex backend
- Index skill repositories using OpenAI embeddings to improve discovery and recommendation of capabilities
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.