optimclaw/src/llm/CLAUDE.md

LLM Module

Multi-provider LLM integration with circuit breaker, retry, failover, and response caching.

File Map

File	Role
mod.rs	Provider factory (create_llm_provider, build_provider_chain); LlmBackend enum
config.rs	LLM config types (LlmConfig, RegistryProviderConfig, NearAiConfig, BedrockConfig)
error.rs	LlmError enum used by all providers
provider.rs	LlmProvider trait, ChatMessage, ToolCall, CompletionRequest, sanitize_tool_messages
nearai_chat.rs	NEAR AI Chat Completions provider (dual auth: session token or API key)
codex_auth.rs	Reads Codex CLI auth.json, extracts tokens, refreshes ChatGPT OAuth access tokens
codex_chatgpt.rs	Custom Responses API provider for Codex ChatGPT backend (/backend-api/codex)
reasoning.rs	Reasoning struct, ReasoningContext, RespondResult, ActionPlan, ToolSelection; thinking-tag stripping; SILENT_REPLY_TOKEN
session.rs	NEAR AI session token management with disk + DB persistence, OAuth login flow
circuit_breaker.rs	Circuit breaker: Closed → Open → HalfOpen state machine
retry.rs	Exponential backoff retry wrapper; is_retryable() classification
failover.rs	FailoverProvider — tries providers in order with per-provider cooldown
response_cache.rs	In-memory LLM response cache with TTL and LRU eviction (keyed by SHA-256)
costs.rs	Static per-model cost table (OpenAI, Anthropic, local/Ollama heuristics)
rig_adapter.rs	Adapter bridging rig-core CompletionModel → LlmProvider; used by OpenAI, Anthropic, Ollama, Tinfoil
smart_routing.rs	SmartRoutingProvider — 13-dimension complexity scorer routes cheap vs primary model
recording.rs	RecordingLlm — trace capture for E2E replay testing (IRONCLAW_RECORD_TRACE)
bedrock.rs	AWS Bedrock provider via native Converse API (feature-gated: --features bedrock)

Provider Selection

Set via LLM_BACKEND env var:

Value	Provider	Key env vars
nearai (default)	NEAR AI Chat Completions	NEARAI_SESSION_TOKEN or NEARAI_API_KEY
openai	OpenAI	OPENAI_API_KEY
anthropic	Anthropic	ANTHROPIC_API_KEY
ollama	Ollama local	OLLAMA_BASE_URL
openai_compatible	Any OpenAI-compatible endpoint	LLM_BASE_URL, LLM_API_KEY, LLM_MODEL
tinfoil	Tinfoil TEE inference	TINFOIL_API_KEY, TINFOIL_MODEL
bedrock	AWS Bedrock (requires --features bedrock)	BEDROCK_REGION, BEDROCK_MODEL, AWS_PROFILE

Codex auth reuse:

• Set LLM_USE_CODEX_AUTH=true to load credentials from ~/.codex/auth.json (override with CODEX_AUTH_PATH).
• If Codex is logged in with API-key mode, IronClaw uses the standard OpenAI endpoint.
• If Codex is logged in with ChatGPT OAuth mode, IronClaw routes to the private chatgpt.com/backend-api/codex Responses API via codex_chatgpt.rs.
• ChatGPT mode supports one automatic 401 refresh using the refresh token persisted in auth.json.

AWS Bedrock Provider

Uses the native Converse API via aws-sdk-bedrockruntime (bedrock.rs). Requires --features bedrock at build time — not in default features due to heavy AWS SDK dependencies.

Auth: Standard AWS credential chain — IAM credentials (AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY), SSO profiles (AWS_PROFILE), or instance roles. The SDK resolves auth automatically from the environment.

Config:

• BEDROCK_REGION — AWS region (default: us-east-1)
• BEDROCK_MODEL — Required model ID (e.g., anthropic.claude-opus-4-6-v1)
• BEDROCK_CROSS_REGION — Optional cross-region inference prefix (us, eu, apac, global)

NEAR AI Provider Gotchas

Dual auth modes:

• Session token (default): NEARAI_SESSION_TOKEN=sess_..., base URL = https://private.near.ai. Tokens are persisted to ~/.ironclaw/session.json (mode 0600) and optionally to the DB settings table (nearai.session_token). On 401 responses where the body contains "session" + "expired"/"invalid", NearAiChatProvider calls session.handle_auth_failure() which triggers the interactive OAuth login flow and retries once. Plain AuthFailed 401s are not retried.
• API key: Set NEARAI_API_KEY (from cloud.near.ai), base URL defaults to https://cloud-api.near.ai. 401s with API key auth are immediately returned as LlmError::AuthFailed — no renewal.

Session renewal is interactive: When SessionExpired triggers renewal, it blocks and prompts the user in the terminal (GitHub/Google OAuth or manual API key entry). This is unsuitable for headless/hosted deployments — set NEARAI_SESSION_TOKEN env var instead.

Tool message flattening: NEAR AI's API doesn't support role: "tool" messages in the standard format. nearai_chat.rs defaults flatten_tool_messages = true, converting tool results to user messages with [Tool result from <name>]: <content> format. Use NearAiChatProvider::new_with_flatten(..., false) to disable for compliant endpoints.

Pricing auto-fetch: On startup, NearAiChatProvider fires a background task to fetch per-model pricing from /v1/model/list. If the fetch fails, it silently falls back to costs::model_cost() / costs::default_cost(). Pricing is stored in-memory only.

HTTP request timeout: The NEAR AI HTTP client has a 120-second timeout per request. Rate limit Retry-After headers are parsed (both delay-seconds and HTTP-date formats) and forwarded as LlmError::RateLimited { retry_after } for the RetryProvider to honor.

Circuit Breaker

State machine in circuit_breaker.rs:

Closed (normal)
  → Open (after failure_threshold consecutive transient failures; default: 5)
    → HalfOpen (after recovery_timeout; default: 30s)
      → Closed (after half_open_successes_needed probe successes; default: 2)
      → Open (if any probe fails)

Transient vs non-transient errors: Only RequestFailed, RateLimited, InvalidResponse, SessionExpired, SessionRenewalFailed, Http, and Io count toward the threshold. AuthFailed, ContextLengthExceeded, ModelNotAvailable, and Json errors never trip the breaker — they indicate caller problems, not backend degradation.

Configure via NearAiConfig fields: circuit_breaker_threshold (None = disabled), circuit_breaker_recovery_secs (default: 30).

The circuit breaker wraps the entire provider chain. When open, it immediately returns LlmError::RequestFailed with a message including remaining cooldown seconds. The FailoverProvider sitting outside can then try a fallback model.

Failover Chain

FailoverProvider in failover.rs wraps a list of LlmProvider instances. On a retryable error, it tries the next provider in the list. Providers that fail repeatedly enter a cooldown period and are skipped (unless all providers are in cooldown, in which case the least-recently-cooled one is tried).

Cooldown defaults: failure_threshold = 3 consecutive retryable failures → cooldown for cooldown_duration = 300s. Configure via NearAiConfig fields: failover_cooldown_secs, failover_cooldown_threshold.

Current wiring: The failover is set up between primary model and NEARAI_FALLBACK_MODEL (a different model name on the same NEAR AI backend), not across different LLM provider types. Cross-provider failover (e.g., NEAR AI → Anthropic) requires manual construction.

Retry

RetryProvider in retry.rs wraps any LlmProvider with exponential backoff. Retries on: RequestFailed, RateLimited, InvalidResponse, SessionRenewalFailed, Http, Io. Does not retry: AuthFailed, SessionExpired, ContextLengthExceeded, ModelNotAvailable, Json.

Backoff schedule: base 1s doubled per attempt with ±25% jitter, minimum floor 100ms. Attempt 0: ~1s, attempt 1: ~2s, attempt 2: ~4s. For RateLimited, uses the retry_after duration from the error (provider-supplied) instead of backoff.

Configure via NearAiConfig.max_retries (env: NEARAI_MAX_RETRIES; default: 3). Set to 0 to disable.

LlmProvider Trait

The full trait (all methods must be implemented or rely on defaults):

#[async_trait]
pub trait LlmProvider: Send + Sync {
    // Required
    fn model_name(&self) -> &str;
    fn cost_per_token(&self) -> (Decimal, Decimal);  // (input, output) per token
    async fn complete(&self, request: CompletionRequest) -> Result<CompletionResponse, LlmError>;
    async fn complete_with_tools(&self, request: ToolCompletionRequest) -> Result<ToolCompletionResponse, LlmError>;

    // Optional (have defaults)
    async fn list_models(&self) -> Result<Vec<String>, LlmError> { Ok(vec![]) }
    async fn model_metadata(&self) -> Result<ModelMetadata, LlmError> { /* name only */ }
    fn effective_model_name(&self, requested_model: Option<&str>) -> String { /* uses active */ }
    fn active_model_name(&self) -> String { self.model_name().to_string() }
    fn set_model(&self, _model: &str) -> Result<(), LlmError> { /* Err: not supported */ }
    fn calculate_cost(&self, input_tokens: u32, output_tokens: u32) -> Decimal { /* uses cost_per_token */ }
}

Key notes:

• model_name() returns the configured model name; active_model_name() returns the currently active model (may differ if set_model() was called — only NearAiChatProvider supports this).
• cost_per_token() returns (Decimal, Decimal) using rust_decimal. Look up via costs::model_cost() in your constructor; fall back to costs::default_cost() for unknowns.
• RigAdapter ignores per-request model overrides (logs a warning). Only NearAiChatProvider supports per-request model overrides via CompletionRequest::model.
• complete_with_tools() is never cached (tool calls can have side effects) — CachedProvider always passes them through.

To add a new provider:

1. Create src/llm/myprovider.rs implementing LlmProvider
2. Add variant to LlmBackend in mod.rs
3. Wire into the factory match in mod.rs
4. Add env vars to config/llm.rs and .env.example

Response Cache

CachedProvider in response_cache.rs caches complete() responses. complete_with_tools() is never cached (side effects). Cache key is SHA-256 of (model_name, messages_json, max_tokens, temperature, stop_sequences). LRU eviction when max_entries is reached; TTL-based expiry on access.

Defaults: TTL = 1 hour, max entries = 1000. Configure via NearAiConfig fields: response_cache_enabled (env: NEARAI_RESPONSE_CACHE_ENABLED), response_cache_ttl_secs, response_cache_max_entries. Cache is in-memory only — evicted on restart.

OpenAI-Compatible Custom Headers

Set LLM_EXTRA_HEADERS=Key:Value,Key2:Value2 to inject headers into every request. Useful for OpenRouter attribution (HTTP-Referer, X-Title). Invalid header names/values are skipped with a warning (not a fatal error).

Provider Chain Construction

build_provider_chain() in mod.rs is the single source of truth for assembling decorators. The chain is:

Raw provider
  → RetryProvider           (per-provider backoff; wraps both primary and fallback)
  → SmartRoutingProvider    (cheap/primary split when NEARAI_CHEAP_MODEL is set)
  → FailoverProvider        (fallback model; only when NEARAI_FALLBACK_MODEL is set)
  → CircuitBreakerProvider  (fast-fail; only when NEARAI_CIRCUIT_BREAKER_THRESHOLD is set)
  → CachedProvider          (response cache; only when NEARAI_RESPONSE_CACHE_ENABLED=true)
  → RecordingLlm            (trace capture; only when IRONCLAW_RECORD_TRACE is set)

build_provider_chain() also returns a separate standalone cheap LLM provider (for heartbeat/evaluation tasks — not part of the decorator chain).

reasoning.rs Contents

reasoning.rs does not contain an IntentClassifier. It contains:

• Reasoning struct — the main reasoning engine used by the agent worker; calls complete_with_tools() and handles tool dispatch
• ReasoningContext — carries messages, available tools, job description, and metadata into a reasoning call
• RespondResult, ActionPlan, ToolSelection — output types from the reasoning engine
• TokenUsage — input/output token counts
• SILENT_REPLY_TOKEN ("NO_REPLY") and is_silent_reply() — used by the dispatcher to suppress empty responses in group chats
• Thinking-tag stripping — regex-based removal of <thinking>, <reflection>, <scratchpad>, <|think|>, <final>, etc. from model responses before returning to the user

costs.rs Details

costs.rs provides a static lookup table (model_cost(model_id)) returning (input_cost, output_cost) per token as rust_decimal::Decimal. Provider prefixes like "openai/gpt-4o" are stripped before lookup. Returns None for unknown models — callers should fall back to default_cost() (roughly GPT-4o pricing). Local model heuristic (is_local_model()) returns zero cost for Ollama-style identifiers (llama*, mistral*, :latest, :instruct, etc.).

rig_adapter.rs Details

RigAdapter<M> bridges any rig-core CompletionModel to LlmProvider. It is actively used in production for all non-NEAR AI providers (OpenAI, Anthropic, Ollama, Tinfoil, OpenAI-compatible). Key behaviors:

• Per-request model overrides are silently ignored (warning logged); the model is baked at construction time.
• OpenAI strict-mode schema normalization is applied to all tool definitions: additionalProperties: false, all properties added to required, optional fields made nullable via "type": ["T", "null"]. This happens transparently at the provider boundary.
• System messages are extracted into the rig-core preamble field (concatenated with newlines if multiple).
• Tool call IDs are generated (generated_tool_call_{seed}) if the provider returns empty/whitespace IDs.
• Tool name normalization: strips proxy_ prefix if it matches a known tool (handles some proxy implementations).
• OpenAI uses Chat Completions API (completions_api()), not the newer Responses API — the Responses API path panics when tool results are sent back (rig-core doesn't thread call_id through ToolCall).

Streaming Support

No streaming support. All providers use non-streaming (blocking) Chat Completions requests. The complete() and complete_with_tools() methods return only after the full response is available.

Trace Recording

Set IRONCLAW_RECORD_TRACE=1 to enable live trace recording via RecordingLlm. Traces are JSON files containing: memory snapshot, HTTP exchanges from tools, and LLM steps (user inputs, text responses, tool call responses). Replay these in E2E tests via TraceLlm. Configure output path with IRONCLAW_TRACE_OUTPUT (default: trace_{timestamp}.json).