Context Runtime
Context Runtime is an open-source, self-hostable optimization layer that serves as a query planner for AI applications. It sits between application logic and AI models, dynamically determining what context the model sees to improve performance. The system plans, executes, and learns from each request, continuously adapting to provide better context selection over time. It is designed for production AI workloads with no vendor lock-in and can be deployed on your own infrastructure. Context Runtime includes a set of reference applications and modules that demonstrate different optimization strategies: retrieval, planning, verification, execution, and learning. For example, in an agentic billing module, it learns which billing signals to pull per account; in customer support, it learns which ticket context to retrieve. Each module shows a measured reward score compared to a learned reward score, illustrating the improvement gained through its adaptation. This learning mechanism allows the system to optimize context without manual tuning, reducing latency and increasing accuracy. Developers integrate Context Runtime via its API or by building agents that consume its optimization outputs. The reference implementations are available on GitHub, covering use cases like RAG, agent orchestration, security triage, compliance monitoring, business intelligence, and more. The runtime is developed by ReDevOps and positioned as the optimization layer that brings database-like query planning to AI.
- Category
- Analytics
- Pricing
- Paid
Problem solved
AI applications often struggle with selecting the right context to present to models, leading to suboptimal responses, higher costs, and manual engineering effort. Context Runtime automates context optimization by learning from each interaction, acting like a database query planner for AI to improve accuracy, reduce hallucinations, and increase efficiency without manual intervention.
Use cases
- Optimizing retrieval-augmented generation (RAG) pipelines
- Adaptive agent orchestration with dynamic skill and budget selection
- Automated billing and bookkeeping with learned context selection
- Customer support ticket triage with contextual retrieval
- Security incident triage with learned alert source prioritization
- Compliance monitoring with evidence gathering
- Business intelligence with learned query selection
- Competitive intelligence with automated competitor scanning
Audience
- Developers
- ML Engineers
- Data Scientists
- Enterprise
Pros
- Open-source and self-hostable, no vendor lock-in
- Learns from interactions to continuously improve context selection
- Demonstrated reward improvements across multiple domains
- Includes reference applications for quick start
- Unified optimization layer for various AI patterns (RAG, agents, planning)
Cons
- Requires integration effort for custom applications
- Learning mechanism depends on quality of reward signals
- Early-stage with potential breaking changes (active development)
- Limited documentation beyond reference apps and demo
- Steep learning curve to understand optimization strategies
Official website: https://redevops.io/
Canonical LambdaBase page: https://www.lambdabase.com/tools/context-runtime