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* feat(routing): cache-aware routing and zero-config prompt caching
Reconcile prompt caching with intelligent routing: zero-config implicit
session affinity, cache_control preservation/injection across transforms,
cache-adjusted routing economics, and a response-driven cache-hit feedback loop.
* refactor(caching): model-aware cache markers, drop cache-aware routing
* feat(routing): session stickiness cache-regret cost gate
* refactor(routing): expose per-model input and cached rates
* fix(docker): patch libssh2 CVEs by upgrading after package install
* feat: record counterfactual route on vetoed session switches
Add opt-in session_stickiness.record_counterfactual that emits the
plano.switch.counterfactual_route OTEL attribute when the cache-regret
gate retains the previous model, capturing the route it would have taken
had the switch been allowed. Telemetry only; the candidate is never
dispatched. Includes schema, demo config, and a getting-started guide.
* refactor: group prompt-caching and session-stickiness logic into cohesive modules
Extract the two concerns interleaved in the LLM handler into dedicated
sibling modules for readability: prompt_caching.rs (cache-marker injection)
and session_stickiness.rs (session key/prefix-hash resolution, pin lookup,
cache-regret gate, and pin planning). handlers/llm/mod.rs is now a thin
orchestrator that calls them at clear phase boundaries. Behavior-preserving.
* refactor(routing): unify session routing into a cumulative switch budget
Replace observed_cache_hit + per-request threshold with time/provider-TTL
warmth and a per-session switch budget in a single session_router::route()
used by both the full-proxy and /routing decision paths.
* refactor(routing): move switch budget to routing.routing_budget and rename telemetry
* refactor(routing): drop negative-cost budget refund from routing_budget
* refactor(routing): express routing_budget as a percentage overhead cap
Replace the absolute seed_usd pool with max_overhead_pct (whole-number
percent) measured against the session's running never-switch baseline:
allow a paid switch only while cumulative switch spend stays within
max_overhead_pct% of what staying on the anchor would have cost. Track
baseline_usd and switch_spend_usd on the binding (monotonic, no refunds).
* refactor(observability): align routing-budget telemetry with the overhead cap
Rename the switch-decision reasons (within_budget/over_budget -> within_cap/
over_cap) and the session span attributes to match the percentage overhead-cap
model: budget_remaining_in_usd -> overhead_pct + switch_spend_in_usd +
baseline_in_usd, and switch threshold_in_usd -> overhead_ceiling_in_usd.
* feat(observability): emit per-request and cumulative session cost
Price each turn from the catalog rates and surface it as llm.usage.{input,
output,total}_cost_usd on the (llm) span, then accumulate into a conversation-
level session_cost_usd on the binding and emit plano.session.total_cost_in_usd
on the routing span. Cache-creation tokens are priced at the plain input rate,
and the OpenAI vs Anthropic prompt-token convention (cached folded in vs
reported separately) is captured at parse time so the cost math is correct for
both. Rates are resolved request-side since the response path is synchronous.
* refactor(routing): price the never-switch baseline against the session default model
Distinguish the session's default_model (the model it started on, i.e. what it
would have cost by never switching) from anchor_model (the model that handled
the latest request, which the session is warm on). The never-switch baseline now
grows at the default model's cached rate rather than the drifting anchor's, so
the overhead-cap denominator stays true to "% above never-switching"; switch cost
is still measured against the current anchor. Also rename estimate_context_tokens
-> actual_context_tokens (and est_context_tokens -> context_tokens) since the
router prefers the provider's real prompt-token count when the session is warm.
* feat(routing): track bounded route history and price returns to still-warm models
Record a bounded (LRU, capped) per-model visit history on the session binding
and use it to sharpen the switch-cost estimate: a return to a model still within
its cache window re-reads only the tokens appended since its last visit (at its
cached rate) instead of the whole context at the uncached rate, so an A->B->A
switch is no longer over-charged as a full re-ingest. History is carried through
the response-side refresh (refining the anchor's entry from real usage) and
cleared on prefix drift. Adds the plano.switch.candidate_warm_tokens span
attribute.
* refactor(routing): tidy test names and rename pin events to binding events
Shorten run-on test names and replace stale "pin" vocabulary in the session
binding metric (brightstaff_session_pin_events_total -> _binding_events_total),
dropping lifecycle event labels that are no longer emitted.
* feat(routing): price switch cost against uncached rates when caching is off
The overhead-cap gate previously always priced the anchor at its cached rate,
assuming a warm provider cache. When prompt caching is disabled there is no
warm cache to lose, so both the switch cost and the never-switch baseline are
now priced at the plain uncached input rate:
switch_cost = context_tokens x (candidate_uncached - anchor_uncached) / 1M.
* fix(brightstaff): bound pricing-feed fetch with timeouts and warn when session state runs without a tenant header
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(routing): validate the session overhead cap end-to-end across a multi-turn session
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(demos): fix stale pinning semantics and add routing_budget demo script
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Adil Hafeez <adil.hafeez@gmail.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
|
||
|---|---|---|
| .. | ||
| src | ||
| Cargo.toml | ||
| README.md | ||
hermesllm
A Rust library for handling LLM (Large Language Model) API requests and responses with unified abstractions across multiple providers.
Features
- Unified request/response types with provider-specific parsing
- Support for both streaming and non-streaming responses
- Type-safe provider identification
- OpenAI-compatible API structure with extensible provider support
Supported Providers
- OpenAI
- Mistral
- Groq
- Deepseek
- Gemini
- Claude
- GitHub
Installation
Add to your Cargo.toml:
[dependencies]
hermesllm = { path = "../hermesllm" } # or appropriate path in workspace
Usage
Basic Request Parsing
use hermesllm::providers::{ProviderRequestType, ProviderRequest, ProviderId};
// Parse request from JSON bytes
let request_bytes = r#"{"model": "gpt-4", "messages": [{"role": "user", "content": "Hello!"}]}"#;
// Parse with provider context
let request = ProviderRequestType::try_from((request_bytes.as_bytes(), &ProviderId::OpenAI))?;
// Access request properties
println!("Model: {}", request.model());
println!("User message: {:?}", request.get_recent_user_message());
println!("Is streaming: {}", request.is_streaming());
Working with Responses
use hermesllm::providers::{ProviderResponseType, ProviderResponse};
// Parse response from provider
let response_bytes = /* JSON response from LLM */;
let response = ProviderResponseType::try_from((response_bytes, ProviderId::OpenAI))?;
// Extract token usage
if let Some((prompt, completion, total)) = response.extract_usage_counts() {
println!("Tokens used: {}/{}/{}", prompt, completion, total);
}
Handling Streaming Responses
use hermesllm::providers::{ProviderStreamResponseIter, ProviderStreamResponse};
// Create streaming iterator from SSE data
let sse_data = /* Server-Sent Events data */;
let mut stream = ProviderStreamResponseIter::try_from((sse_data, &ProviderId::OpenAI))?;
// Process streaming chunks
for chunk_result in stream {
match chunk_result {
Ok(chunk) => {
if let Some(content) = chunk.content_delta() {
print!("{}", content);
}
if chunk.is_final() {
break;
}
}
Err(e) => eprintln!("Stream error: {}", e),
}
}
Provider Compatibility
use hermesllm::providers::{ProviderId, has_compatible_api, supported_apis};
// Check API compatibility
let provider = ProviderId::Groq;
if has_compatible_api(&provider, "/v1/chat/completions") {
println!("Provider supports chat completions");
}
// List supported APIs
let apis = supported_apis(&provider);
println!("Supported APIs: {:?}", apis);
Core Types
Provider Types
ProviderId- Enum identifying supported providers (OpenAI, Mistral, Groq, etc.)ProviderRequestType- Enum wrapping provider-specific request typesProviderResponseType- Enum wrapping provider-specific response typesProviderStreamResponseIter- Iterator for streaming response chunks
Traits
ProviderRequest- Common interface for all request typesProviderResponse- Common interface for all response typesProviderStreamResponse- Interface for streaming response chunksTokenUsage- Interface for token usage information
OpenAI API Types
ChatCompletionsRequest- Chat completion request structureChatCompletionsResponse- Chat completion response structureMessage,Role,MessageContent- Message building blocks
Architecture
The library uses a type-safe enum-based approach that:
- Provides Type Safety: All provider operations are checked at compile time
- Enables Runtime Provider Selection: Provider can be determined from request headers or config
- Maintains Clean Abstractions: Common traits hide provider-specific details
- Supports Extensibility: New providers can be added by extending the enums
All requests are parsed into a common ProviderRequestType enum which implements the ProviderRequest trait, allowing uniform access to request properties regardless of the underlying provider format.
Examples
See the src/lib.rs tests for complete working examples of:
- Parsing requests with provider context
- Handling streaming responses
- Working with token usage information
License
This project is licensed under the MIT License.