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conversation-steganography
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FR-AI / ANALYSISWhy it is worth attention
It combines policy-driven cost control, cache-aware concurrency, and built-in spend analysis in a single Rust binary, making it easy to self-host and inspect routing decisions without a hosted control plane.
Who it is for
- Developers building AI applications that consume LLM APIs
- Engineering teams looking to manage and reduce LLM spend across multiple providers
- Infrastructure operators who want to keep provider credentials on their own infrastructure
- Organizations that need auditability and cost analysis for LLM usage
Use cases
- Route cheap tasks to low-cost models while reserving frontier models for complex or high-risk requests
- Maintain prompt-cache reuse by keeping the same session on the same provider and model
- Switch LLM providers (e.g., OpenAI to Anthropic) without changing application code
- Analyze historical spend and simulate cost savings with different routing policies
Strengths
- Single self-contained Rust binary with Docker support, no external dependencies beyond a store
- Policy engine classifies requests into cheap/mid/frontier roles and selects lowest-cost healthy route
- Cache-aware concurrency preserves independent affinity lanes per role within a session
- Built-in ledger and CLI commands (spend, models, trace, top, analyze) for cost visibility and planning
Considerations
- Early release (v0.1.0) with limited production track record; only Linux and macOS platforms currently supported
- Not an agent orchestrator—does not manage agent lifecycles or inspect prompts
- Requires Rust 1.97+ to build from source; cross-protocol routing translates only a subset of features
README quick start
Quick Start
Description
Self-hosted LLM gateway. Cost effective, deterministic, and fast. Secure and private by default.
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