LeanCTX vs Loops
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
LeanCTX compared with Loops
| Detail | 
LeanCTX | 
Loops |
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| What it does | The Context Gateway for AI systems
| Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
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| Stage | Not given
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| Founded | Not given
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| Based in | CH
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| Pricing model | Freemium
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| Pricing | Community: Free (open-source, local use). Pro: $9/month or $90/year (personal cloud sync, 1 GB hosted index). Team: $29/month per seat or $290/year per seat (team server, shared workspace, 20 GB index, 1-25 seats). Enterprise: Contact for pricing.
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| Key features | - Source selection and retrieval with task-relevant context at configurable depth
- Access controls via PathJail boundary enforcement and role-based permissions
- Read-time content filters for injection patterns and personal data detection
- Memory and continuity to retain findings and connections across sessions
- Evidence and outcomes recording with source references and usage audit trails
- Integration with AI tools via SDK, MCP, hooks and supported client adapters
- Local, VPC, on-premises and offline deployment options
- Optional production intelligence for planning, ranking and routing enhancements
- SDK with five stable primitives and six Agent Tools language contracts
- Context cache, reference routing and usage accounting
| - forward_backward() operation for computing loss and accumulating gradients
- optim_step() for updating the adapter
- sample() for generating from current or saved policy
- save_state() to save and resume training state
- Tinker API compatibility
- Separate trainer and sampler resources
- Synchronous and asynchronous RL patterns
- Frozen base model weights with LoRA adapter updates
- Support for open models from Qwen, Google, DeepSeek, Moonshot AI, NVIDIA, and Z.ai
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| What makes it different | - Deterministic, model-agnostic context processing independent of AI provider choice
- Governs context without controlling model calls, keeping AI decisions under user ownership
- Records measurable impact with 64.1% reported context reduction in field use
- Open-source core (Apache 2.0) with commercial tiers for teams and enterprises
- Transparent governance with separate evaluation of task outcome from context delivery
| - Managed training and sampling infrastructure, no setup required
- Tinker-compatible API for easy migration
- Ready-made SFT and RL recipes through Loops cookbook
- Support for vision-language models
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 42 ● no change since last check | 77 ● no change since last check |
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| Trust Flow (Majestic) | 3 | 13 |
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| Citation Flow (Majestic) | 35 | 43 |
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| Referring domains (Majestic) | 212 | 550 |
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3 details filled in for both companies.
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