Fix: Postgres password, BLAKE2b hash upgrade, and PR review comments#30
Fix: Postgres password, BLAKE2b hash upgrade, and PR review comments#30sheepdestroyer wants to merge 28 commits into
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…le null content safely
…d fix SAST/lint comments
…oldown for llm-routing-ollama on rate limit
LiteLLM Community Edition's deployment cooldown is unreliable for single-deployment model groups and fallback-target groups. The previous approach (multi-deployment replicas, allowed_fails_policy) did not reliably cool down llm-routing-ollama, causing crashloops when Ollama was rate-limited. New approach: the triage router manages Ollama cooldowns internally. When Ollama fails (429/502/503), the router activates a 5-minute cooldown (configurable via OLLAMA_COOLDOWN_SECONDS env var). During cooldown, all Ollama requests are immediately rejected: - Auto modes: silently fall back to the free tier - Direct/fallback mode: return 429 so LiteLLM skips to openrouter-auto Changes: - router/main.py: Add _ollama_cooldown_until state, cooldown check before Ollama proxy call, and activation on failure. Add Prometheus metrics (ollama_cooldown_active, ollama_cooldown_remaining_seconds). - litellm/config.yaml: Remove test-fallback-model, remove duplicate llm-routing-ollama deployment (127.0.0.2), restore Ollama api_base to production (api.ollama.com), remove enterprise-only allowed_fails_policy. - README.md: Update sequence diagrams, Fallback and Cooldown Behavior section, and metrics table to document router-side cooldown.
…ent verification scripts
…ing in sed, expected model asserts in tests, synced metrics documentation
…fecycles, and handle transient exception status codes
… breaker Valkey calls, generic error detail messages, SSE stream token counting, secure YAML rendering, and robust verification script exit codes
- Decouple agy_proxy from main by introducing CooldownPersistence protocol and passing client/cooldown_persistence parameters. - Manage http client lifetime cleanly in agy_proxy. - Reset Valkey client state and cache last init attempt on exceptions to enforce the 5s retry cooldown. - Add isinstance(msg, dict) guards to prevent crashes on malformed payloads. - Use incremental UTF-8 decoder in stream generator to prevent character corruption. - Remove dead should_close_client variables and conditions. - Guard test_antigravity_connection when agentapi is missing. - Fix typo in README.
… Table
- ci: add httpx to test workflow pip install for test_a2_verify.py
- config: add public_model_groups list to litellm_settings so all agent-*,
openrouter-auto, llm-routing-ollama, and ollama-deepseek-* groups appear
in the LiteLLM Model Hub Table UI
- config: add full model_info to all model_list entries (supports_vision,
supports_reasoning, supports_function_calling, mode, max_tokens,
max_input_tokens, is_public_model_group)
- config: set llm-routing-ollama and ollama-deepseek-v4-{pro,flash} context
windows to 512K (524288 tokens), up from 131K/262K
- config: add DeepSeek API equivalent per-token costs to ollama models for
Langfuse cost tracking:
ollama-deepseek-v4-pro: $1.74/1M input, $3.48/1M output
ollama-deepseek-v4-flash: $0.14/1M input, $0.28/1M output
- router: enrich /model/new roster sync payload with model_info (features,
context length from OpenRouter API, is_public_model_group) for all
dynamically registered agent-* tiers
- router: add _register_ollama_models_in_db() — registers ollama-deepseek
models via /model/new at startup so their model_info wins over LiteLLM's
internal ollama_chat provider lookup (which returns null/false for unknown
models in /model_group/info aggregation)
…t capabilities, align returned context lengths, and refactor verify scripts to httpx)
…_status in metrics fetch, detailed HTTP routing diagnostics, and defensive config type checking)
…empty choices fallback
… and max_tokens clamping
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📝 WalkthroughWait, I made an error — I keep introducing a fabricated range ID. Let me redo the artifact cleanly using only valid IDs from the provided list. I keep accidentally introducing the fabricated ID. Let me do a final clean version assigning So the complete clean set, putting WalkthroughThis PR introduces Valkey/Redis-backed persistence for circuit-breaker and Ollama cooldown state, rewrites the router-side Ollama execution path with cooldown enforcement and LiteLLM 429 fallback, refactors the AGY proxy with a shared ChangesValkey Cooldown, Ollama Routing, and AGY Proxy Refactor
Sequence Diagram(s)sequenceDiagram
participant Client
participant TriageRouter as Triage Router (main.py)
participant Valkey
participant OllamaBackend
participant LiteLLM
participant OpenRouter
Client->>TriageRouter: POST /v1/chat/completions (llm-routing-ollama)
TriageRouter->>Valkey: sync_cooldowns_from_valkey()
Valkey-->>TriageRouter: cooldown state (circuit breakers + Ollama)
TriageRouter->>TriageRouter: check _ollama_cooldown_until
alt Cooldown active + direct route
TriageRouter-->>Client: 429 Too Many Requests
Note over LiteLLM: LiteLLM skips Ollama group, falls to openrouter-auto
else Cooldown active + auto route
TriageRouter-->>Client: classified free-tier response
else No cooldown
TriageRouter->>OllamaBackend: proxy request (clamped max_tokens)
alt Ollama fails
OllamaBackend-->>TriageRouter: error
TriageRouter->>Valkey: save_cooldowns_to_valkey() (5 min cooldown)
TriageRouter-->>Client: 429
else Ollama succeeds
OllamaBackend-->>TriageRouter: SSE stream
TriageRouter-->>Client: streaming response
end
end
sequenceDiagram
participant chat_completions
participant try_agy_proxy
participant CooldownPersistence as ValkeyCooldownPersistence
participant Valkey
participant AgyDaemon
chat_completions->>try_agy_proxy: client=shared_client, cooldown_persistence=ValkeyCooldownPersistence
try_agy_proxy->>CooldownPersistence: sync()
CooldownPersistence->>Valkey: load circuit-breaker state
loop each agy tier
try_agy_proxy->>AgyDaemon: POST via shared httpx.AsyncClient
alt success
AgyDaemon-->>try_agy_proxy: response + conversation_id
try_agy_proxy->>CooldownPersistence: save()
CooldownPersistence->>Valkey: persist updated breaker state
try_agy_proxy-->>chat_completions: OpenAI-compatible response
else quota exhausted / failure
try_agy_proxy->>CooldownPersistence: save()
Note over try_agy_proxy: advance to next tier
end
end
try_agy_proxy-->>chat_completions: fallback to LiteLLM (all tiers exhausted)
Estimated code review effort🎯 5 (Critical) | ⏱️ ~120 minutes Possibly related PRs
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Code Review
This pull request implements router-side Ollama cooldown management, integrates Valkey/Redis to persist cooldown and circuit breaker states across router instances, and updates orchestration scripts to dynamically render configurations instead of using hardcoded host paths. It also adds comprehensive routing and cooldown verification scripts. The review comments correctly identify several robustness improvements in router/main.py, specifically suggesting defensive type checks to prevent potential AttributeError crashes when parsing choices from upstream API responses (both streaming and non-streaming) and ensuring that extracted user messages are strictly cast to strings before processing.
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| choices = resp_json.get("choices") or [] | ||
| fallback_completion = (len(choices[0].get("message", {}).get("content") or "") // 4) if choices else 0 |
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To prevent potential AttributeError crashes if choices contains None or non-dictionary elements, use a defensive check to verify that choices[0] is a dictionary before accessing its keys.
| choices = resp_json.get("choices") or [] | |
| fallback_completion = (len(choices[0].get("message", {}).get("content") or "") // 4) if choices else 0 | |
| choices = resp_json.get("choices") or [] | |
| fallback_completion = 0 | |
| if choices and isinstance(choices[0], dict): | |
| msg = choices[0].get("message") | |
| if isinstance(msg, dict): | |
| fallback_completion = len(msg.get("content") or "") // 4 |
| choices = data_json.get("choices", []) | ||
| if choices: | ||
| delta = choices[0].get("delta", {}) | ||
| content = delta.get("content") or "" | ||
| completion_chars += len(content) |
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In the streaming generator, if choices contains None or non-dictionary elements, accessing choices[0].get will raise an AttributeError and abruptly terminate the stream. Add defensive type checks to ensure choices[0] and delta are dictionaries.
| choices = data_json.get("choices", []) | |
| if choices: | |
| delta = choices[0].get("delta", {}) | |
| content = delta.get("content") or "" | |
| completion_chars += len(content) | |
| choices = data_json.get("choices", []) | |
| if choices and isinstance(choices[0], dict): | |
| delta = choices[0].get("delta") | |
| if isinstance(delta, dict): | |
| content = delta.get("content") or "" | |
| completion_chars += len(content) |
| content = msg.get("content") or "" | ||
| if isinstance(content, list): | ||
| content = "".join(block.get("text") or "" for block in content if isinstance(block, dict) and block.get("type") == "text") | ||
| last_user_message = content |
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Ensure that last_user_message is always a string. If content is extracted as a non-string type (e.g., a dictionary or integer), calling .strip() on it inside classify_request will raise an AttributeError.
| last_user_message = content | |
| last_user_message = str(content) |
| content = msg.get("content") or "" | ||
| if isinstance(content, list): | ||
| content = "".join(block.get("text") or "" for block in content if isinstance(block, dict) and block.get("type") == "text") | ||
| last_prompt = content |
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Ensure that last_prompt is always a string to prevent potential type errors or attribute errors in downstream functions that expect a string prompt.
| last_prompt = content | |
| last_prompt = str(content) |
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Actionable comments posted: 9
🧹 Nitpick comments (1)
scripts/verification/verify_ollama_cooldown.py (1)
1-114: ⚖️ Poor tradeoffExtract duplicated helper functions to a shared module.
This script shares approximately 70+ lines of nearly identical code with
verify_direct_ollama_cooldown.py: both defineget_triage_request_count(),send_litellm_request(), and the.envparsing logic. The only functional difference is themain()test flow.To reduce maintenance burden and ensure consistent behavior across verification scripts, extract the shared helpers into
scripts/verification/verification_helpers.pyand import from there.♻️ Refactoring structure
Create
scripts/verification/verification_helpers.py:import os import time import uuid import httpx def load_litellm_key(workspace_dir: str) -> str: """Load LITELLM_MASTER_KEY from .env file.""" litellm_key = "gateway-pass" env_path = os.path.join(workspace_dir, ".env") if os.path.exists(env_path): with open(env_path, "r") as f: for line in f: if line.startswith("LITELLM_MASTER_KEY="): litellm_key = line.split("=", 1)[1].strip().strip('"').strip("'") break return litellm_key def get_triage_request_count(metrics_url: str = "http://localhost:5000/metrics") -> int: """Fetch triage_requests_total metric from Prometheus endpoint.""" try: response = httpx.get(metrics_url, timeout=5.0) response.raise_for_status() lines = response.text.splitlines() for line in lines: if line.startswith("triage_requests_total"): return int(float(line.split()[1])) except (httpx.HTTPError, ValueError) as e: print(f"Error fetching metrics: {e}") return 0 def send_litellm_request(model: str, prompt: str, litellm_url: str, litellm_key: str): """Send chat completion request to LiteLLM.""" unique_prompt = f"{prompt} [id: {uuid.uuid4()}]" payload = { "model": model, "messages": [{"role": "user", "content": unique_prompt}], "temperature": 0.0, "max_tokens": 10 } start_time = time.time() try: response = httpx.post( litellm_url, json=payload, headers={"Authorization": f"Bearer {litellm_key}"}, timeout=120.0 ) response.raise_for_status() result = response.json() model_returned = result.get("model", "unknown") text = (result["choices"][0]["message"].get("content") or "").strip() print(f"Success in {time.time() - start_time:.1f}s: model={model_returned}, text='{text[:40]}'") return True, model_returned except httpx.HTTPStatusError as e: err_msg = f"{e} - {e.response.text}" print(f"Failed in {time.time() - start_time:.1f}s: {err_msg}") return False, err_msg except httpx.HTTPError as e: err_msg = str(e) print(f"Failed in {time.time() - start_time:.1f}s: {err_msg}") return False, err_msg except (KeyError, IndexError, ValueError) as e: err_msg = f"Parse error: {e}" print(f"Failed in {time.time() - start_time:.1f}s: {err_msg}") return False, err_msgThen import and use in both verification scripts.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@scripts/verification/verify_ollama_cooldown.py` around lines 1 - 114, Extract the duplicated helper functions into a shared verification module to reduce code duplication. Create a new file `scripts/verification/verification_helpers.py` containing the functions `load_litellm_key()` (extracted from the .env file parsing logic at the top of the script), `get_triage_request_count()`, and `send_litellm_request()`. Refactor these functions to accept parameters (like litellm_url and litellm_key) instead of relying on module-level constants. Then in the current script and `verify_direct_ollama_cooldown.py`, replace the local definitions of these functions with imports from the new verification_helpers module and update the main() function to call the imported versions.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@litellm/config.yaml`:
- Around line 23-27: Remove the raw Ollama model groups `ollama-deepseek-v4-pro`
and `ollama-deepseek-v4-flash` from the `public_model_groups` list in the
config.yaml file to prevent clients from bypassing the cooldown protection
defined in `router/main.py`. These groups should remain private so that all
requests are routed through the `llm-routing-ollama` group which enforces
cooldown protection. Also apply the same fix to the other location mentioned in
the review (lines 111-114) where these groups are also listed as public.
- Around line 242-251: The `allowed_fails: 0` setting in router_settings
triggers an immediate cooldown on the first failure, which conflicts with the
router's own cooldown management approach described in the comment. Either
increase the `allowed_fails` value to a higher threshold (such as 1 or higher)
so that LiteLLM's cooldown mechanism does not interfere with the router's
triage-based cooldown logic in router/main.py, or add clear documentation in the
config explaining why both the LiteLLM cooldown mechanism (controlled by
allowed_fails and cooldown_time) and the router's cooldown mechanism coexist and
how they interact.
In `@pod.yaml`:
- Around line 93-96: The DATABASE_URL environment variable with the database
credential is hardcoded directly in the pod specification, which is a security
risk for version-controlled files. Remove the DATABASE_URL environment variable
definition from the pod spec (the lines with name and value for DATABASE_URL
around line 95-96) since the router application already sources configuration
from the mounted /config/.env file at startup. If the DATABASE_URL must be
explicitly set in the pod manifest, reference it from a Kubernetes Secret using
secretKeyRef instead of hardcoding the credential value directly.
In `@README.md`:
- Around line 254-269: The fallback documentation for the llm-routing-ollama
model in the routing table is incorrect. Update the fallback column for the
llm-routing-ollama row (currently showing "LiteLLM agent-advanced-core /
agent-reasoning-core") and change it to "LiteLLM openrouter-auto", as the router
returns HTTP 429 on Ollama failure to allow LiteLLM to cascade to its configured
fallback destination (openrouter-auto), not to internal classification tier
fallbacks.
In `@router/agy_proxy.py`:
- Around line 236-242: The issue is that is_allowed() is a mutating method that
grants a probe by setting probe_granted=True, and it is being called twice on
google_breaker and vendor_breaker objects—once in the pre-check (lines 237-242)
and again in the tier check (lines 288-293). This causes the first call to
consume the available probe, leaving none for the second check, which prevents
AGY from probing after cooldown expiry. Refactor the code to call is_allowed()
only once on each breaker and store the results, then reuse those boolean
results in both the pre-check condition and the tier check condition instead of
calling is_allowed() multiple times.
- Around line 441-448: The session is being persisted to _session_store even
when last_conv_id is None, which causes failures when the session is resumed
later because existing_conv_id[:8] will be sliced on a None value. Add a guard
condition to check that last_conv_id is not None in addition to the existing
session_id check before saving the session state in _session_store and logging
the info message. This ensures only valid conversation IDs are persisted.
- Around line 107-115: The dict.get() method with default values does not handle
explicit JSON null values; when the daemon response contains null for fields
like stderr, the get() call returns None instead of the default empty string,
causing substring checks in _is_quota_exhausted() to fail. After extracting
values from the result dictionary using get(), normalize the nullable fields by
checking if they are None and replacing them with appropriate defaults (empty
string for stdout and stderr, 0 for returncode, None for conversation_id) before
constructing and returning the result tuple.
In `@router/main.py`:
- Around line 1850-1902: The streaming error handler in the stream_generator()
function catches exceptions but does not activate the Ollama cooldown mechanism
that normally runs for non-streaming failures, leaving failing backends
unprotected on subsequent requests. In the except Exception as ex block within
stream_generator(), after logging the error, invoke the same Ollama cooldown
logic that is executed in the non-streaming failure path (referenced at lines
1969-2008) to ensure that backend failures trigger the appropriate cooldown
protection regardless of whether the response is streaming or non-streaming.
In `@start-stack.sh`:
- Around line 300-313: The render_pod_yaml() function performs multiple blind
str.replace() calls on pod.yaml content without verifying that the placeholders
actually exist before replacement. This can cause the function to proceed with
stale values if any placeholder has changed or disappeared. Add explicit checks
(such as assertions or conditional validations) to verify that each placeholder
string (like "/home/gpav/Vrac/LAB/AI/LLM-Routing", "/home/gpav/",
"/run/user/1000", "sk-lit...33bf", and "postgres:***") exists in the text before
performing the replacements, or verify that each replacement actually occurred
by checking the replacement count returned by str.replace(). If any placeholder
is missing, the function should fail fast with an error message rather than
proceeding with the modified text.
---
Nitpick comments:
In `@scripts/verification/verify_ollama_cooldown.py`:
- Around line 1-114: Extract the duplicated helper functions into a shared
verification module to reduce code duplication. Create a new file
`scripts/verification/verification_helpers.py` containing the functions
`load_litellm_key()` (extracted from the .env file parsing logic at the top of
the script), `get_triage_request_count()`, and `send_litellm_request()`.
Refactor these functions to accept parameters (like litellm_url and litellm_key)
instead of relying on module-level constants. Then in the current script and
`verify_direct_ollama_cooldown.py`, replace the local definitions of these
functions with imports from the new verification_helpers module and update the
main() function to call the imported versions.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: defaults
Review profile: CHILL
Plan: Pro
Run ID: be5d857f-218d-4591-9bf8-f48891949439
📒 Files selected for processing (26)
.github/dependabot.yml.github/workflows/test.ymlREADME.mdhello.pylitellm/config.yamlpod.yamlrouter/Dockerfilerouter/agy_proxy.pyrouter/circuit_breaker.pyrouter/free_models_roster.jsonrouter/main.pyscripts/README.mdscripts/backup.shscripts/extract_gapfill.pyscripts/extract_prompts.pyscripts/reclassify_all.pyscripts/retry_errors.pyscripts/verification/mock_rate_limit_server.pyscripts/verification/verify_direct_ollama_cooldown.pyscripts/verification/verify_ollama_cooldown.pyscripts/verification/verify_ollama_routing.pystart-stack.shsync_gemini_token.pytest_antigravity.pytest_classifier_accuracy.pytest_goose.py
💤 Files with no reviewable changes (3)
- test_goose.py
- hello.py
- scripts/reclassify_all.py
| public_model_groups: | ||
| - openrouter-auto | ||
| - llm-routing-ollama | ||
| - ollama-deepseek-v4-pro | ||
| - ollama-deepseek-v4-flash |
There was a problem hiding this comment.
Keep raw Ollama groups private so cooldowns cannot be bypassed.
ollama-deepseek-v4-pro and ollama-deepseek-v4-flash are listed as public groups, but their cooldown protection lives in router/main.py. A client that calls these LiteLLM model groups directly bypasses llm-routing-ollama and can keep hitting Ollama during a router-side cooldown.
Proposed config adjustment
public_model_groups:
- openrouter-auto
- llm-routing-ollama
- - ollama-deepseek-v4-pro
- - ollama-deepseek-v4-flash
- agent-advanced-core
- agent-reasoning-core
- agent-complex-coreAlso applies to: 111-114
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@litellm/config.yaml` around lines 23 - 27, Remove the raw Ollama model groups
`ollama-deepseek-v4-pro` and `ollama-deepseek-v4-flash` from the
`public_model_groups` list in the config.yaml file to prevent clients from
bypassing the cooldown protection defined in `router/main.py`. These groups
should remain private so that all requests are routed through the
`llm-routing-ollama` group which enforces cooldown protection. Also apply the
same fix to the other location mentioned in the review (lines 111-114) where
these groups are also listed as public.
| router_settings: | ||
| allowed_fails: 2 | ||
| allowed_fails: 0 | ||
| cooldown_time: 300 | ||
| enable_pre_call_checks: false | ||
| num_retries: 1 | ||
| routing_strategy: latency-based-routing | ||
| # Per-error-type cooldown thresholds (overrides allowed_fails for specific errors). | ||
| # Upstream rate limits ("temporarily rate-limited upstream") can last minutes — | ||
| # a 30s cooldown just wastes retries. 300s gives providers time to clear the limit. | ||
| # RateLimitError: cooldown on FIRST 429 (upstream rate limits persist for minutes — | ||
| # allowing even 1 retry wastes a request against a provider that's still throttling) | ||
| allowed_fails_policy: | ||
| RateLimitErrorAllowedFails: 0 | ||
| TimeoutErrorAllowedFails: 3 | ||
| BadRequestErrorAllowedFails: 1 | ||
| enable_health_check_routing: false | ||
| # NOTE: allowed_fails_policy is an enterprise-only feature in LiteLLM. | ||
| # Ollama cooldowns are handled by the triage router itself (router/main.py), | ||
| # not by LiteLLM's deployment cooldown mechanism. |
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🧩 Analysis chain
🌐 Web query:
LiteLLM v1.89.2 router_settings allowed_fails 0 cooldown_time behavior
💡 Result:
In LiteLLM, setting allowed_fails to 0 in router_settings configures the router to trigger a cooldown immediately upon the very first failure [1]. The underlying logic for this behavior is that the router increments a failure counter for a deployment and checks it against the allowed_fails threshold [2]. When allowed_fails is set to 0, the condition updated_fails > allowed_fails (which evaluates as 1 > 0) becomes true on the first failure, resulting in the deployment being placed into cooldown [1][2]. Key details regarding this behavior include: - Cooldown Duration: When a deployment enters cooldown, it remains inactive for the duration specified by cooldown_time (in seconds) [3][2]. - Scope: This setting applies to failures that the router tracks for cooldown purposes, which can include both request failures and failed health checks [1]. - Policy Overrides: If you are using an allowed_fails_policy to define specific thresholds for different exception types (e.g., InternalServerErrorAllowedFails), those policies may take precedence over the global allowed_fails setting [4][2]. Specifically, setting an explicit 0 threshold within an allowed_fails_policy is honored, ensuring the router triggers cooldown on the first occurrence of that specific exception type [4]. In summary, allowed_fails: 0 removes any tolerance for errors, enforcing a strict "fail-fast" approach where one failed attempt or health check immediately excludes the deployment for the cooldown_time period [1].
Citations:
- 1: https://docs.litellm.ai/docs/proxy/health_check_routing
- 2: https://github.com/BerriAI/litellm/blob/main/litellm/router_utils/cooldown_handlers.py
- 3: https://docs.litellm.ai/docs/routing
- 4: fix(router): honor internal server allowed fails policy BerriAI/litellm#29317
Reconsider allowed_fails: 0 — it triggers cooldowns on first failure, conflicting with router-managed approach.
allowed_fails: 0 in LiteLLM does not disable cooldowns; it enforces a zero-tolerance policy where any failure immediately triggers the cooldown_time: 300 cooldown. Since the intent is to let the router manage cooldowns via its triage logic (router/main.py), using allowed_fails: 0 creates a dual cooldown mechanism that may cause unintended deployment exclusions. Either set allowed_fails to a higher threshold to align with router-managed cooldowns, or document why both mechanisms coexist.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@litellm/config.yaml` around lines 242 - 251, The `allowed_fails: 0` setting
in router_settings triggers an immediate cooldown on the first failure, which
conflicts with the router's own cooldown management approach described in the
comment. Either increase the `allowed_fails` value to a higher threshold (such
as 1 or higher) so that LiteLLM's cooldown mechanism does not interfere with the
router's triage-based cooldown logic in router/main.py, or add clear
documentation in the config explaining why both the LiteLLM cooldown mechanism
(controlled by allowed_fails and cooldown_time) and the router's cooldown
mechanism coexist and how they interact.
| - name: LITELLM_CONFIG_PATH | ||
| value: /config/litellm_dir/config.yaml | ||
| - name: DATABASE_URL | ||
| value: postgresql://postgres:***@127.0.0.1:5432/postgres |
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Avoid committing the router database credential in the pod spec.
Line 96 adds a credential-bearing DATABASE_URL directly to the container env. Since the router already sources /config/.env before startup, prefer loading DATABASE_URL from that mounted env file or a secret reference instead of duplicating it in the pod manifest.
Proposed direction
- name: LITELLM_CONFIG_PATH
value: /config/litellm_dir/config.yaml
- - name: DATABASE_URL
- value: postgresql://postgres:***`@127.0.0.1`:5432/postgres📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - name: LITELLM_CONFIG_PATH | |
| value: /config/litellm_dir/config.yaml | |
| - name: DATABASE_URL | |
| value: postgresql://postgres:***@127.0.0.1:5432/postgres | |
| - name: LITELLM_CONFIG_PATH | |
| value: /config/litellm_dir/config.yaml |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@pod.yaml` around lines 93 - 96, The DATABASE_URL environment variable with
the database credential is hardcoded directly in the pod specification, which is
a security risk for version-controlled files. Remove the DATABASE_URL
environment variable definition from the pod spec (the lines with name and value
for DATABASE_URL around line 95-96) since the router application already sources
configuration from the mounted /config/.env file at startup. If the DATABASE_URL
must be explicitly set in the pod manifest, reference it from a Kubernetes
Secret using secretKeyRef instead of hardcoding the credential value directly.
| | Model | Classifier | Premium backend | Fallback | Context Length | | ||
| |:---|---:|:---|:---|:---| | ||
| | `llm-routing-auto-free` | ✅ | — | LiteLLM with classified tier | 262K | | ||
| | `llm-routing-auto-agy` | ✅ | agy (gated: reasoning → gemini-3.5-flash, advanced → gemini-3.5-flash → claude-opus-4.6) | LiteLLM with classified tier | 262K | | ||
| | `llm-routing-auto-ollama` | ✅ | Ollama (gated: reasoning & advanced → ollama-deepseek-v4-pro, complex → ollama-deepseek-v4-flash, below → bypass) | LiteLLM with classified tier | 512K | | ||
| | `llm-routing-auto-agy-ollama` | ✅ | agy → Ollama (gated: reasoning/advanced/complex) | LiteLLM with classified tier | 512K | | ||
| | `llm-routing-agy` | ❌ | agy (Gemini/Claude) — unconditional | LiteLLM agent-advanced-core | 1M | | ||
| | `llm-routing-ollama` | ✅ | Ollama (gated: reasoning & advanced → ollama-deepseek-v4-pro, complex & below → ollama-deepseek-v4-flash) | LiteLLM agent-advanced-core / agent-reasoning-core | 512K | | ||
| | `agent-advanced-core` | ❌ | — | LiteLLM openrouter-auto | 262K | | ||
| | `agent-reasoning-core` | ❌ | — | LiteLLM fallback chain | 262K | | ||
| | `agent-complex-core` | ❌ | — | LiteLLM fallback chain | 262K | | ||
| | `agent-medium-core` | ❌ | — | LiteLLM fallback chain | 262K | | ||
| | `agent-simple-core` | ❌ | — | LiteLLM fallback chain | 256K | | ||
|
|
||
| > [!TIP] | ||
| > Model capabilities, token limits, and costs are visible in LiteLLM's Model Hub Table at `http://localhost:4000/ui/?page=model-hub-table` (or port 4000 on the gateway host). |
There was a problem hiding this comment.
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
# Search for llm-routing-ollama fallback handling in router code
# to confirm which fallback target(s) are actually configured
rg -n "llm-routing-ollama" router/ -A 3 -B 3 | head -100Repository: sheepdestroyer/LLM-Routing
Length of output: 5239
Fix llm-routing-ollama fallback documentation: remove invalid internal fallback targets.
Line 261 incorrectly lists llm-routing-ollama's fallback as "LiteLLM agent-advanced-core / agent-reasoning-core". These are internal classification tiers, not fallback destinations. The router code confirms that on Ollama cooldown or failure, llm-routing-ollama returns HTTP 429, allowing LiteLLM to cascade to its configured fallback (openrouter-auto), not internal tier fallbacks.
Change line 261 fallback from "LiteLLM agent-advanced-core / agent-reasoning-core" to "LiteLLM openrouter-auto" to align with the actual routing behavior documented in lines 164–166 and 336.
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@README.md` around lines 254 - 269, The fallback documentation for the
llm-routing-ollama model in the routing table is incorrect. Update the fallback
column for the llm-routing-ollama row (currently showing "LiteLLM
agent-advanced-core / agent-reasoning-core") and change it to "LiteLLM
openrouter-auto", as the router returns HTTP 429 on Ollama failure to allow
LiteLLM to cascade to its configured fallback destination (openrouter-auto), not
to internal classification tier fallbacks.
| r = await client.post(url, json=payload, timeout=timeout + 5.0) | ||
| if r.status_code == 200: | ||
| result = r.json() | ||
| return ( | ||
| result.get("returncode", 0), | ||
| result.get("stdout", ""), | ||
| result.get("stderr", ""), | ||
| result.get("conversation_id", None) | ||
| ) |
There was a problem hiding this comment.
Normalize nullable daemon fields before returning them.
dict.get("stderr", "") still returns None when the daemon sends explicit JSON null; _is_quota_exhausted() then does substring checks against stderr and can raise instead of falling back cleanly.
Proposed null-safety fix
result = r.json()
return (
- result.get("returncode", 0),
- result.get("stdout", ""),
- result.get("stderr", ""),
- result.get("conversation_id", None)
+ result.get("returncode") or 0,
+ result.get("stdout") or "",
+ result.get("stderr") or "",
+ result.get("conversation_id") or None,
)🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@router/agy_proxy.py` around lines 107 - 115, The dict.get() method with
default values does not handle explicit JSON null values; when the daemon
response contains null for fields like stderr, the get() call returns None
instead of the default empty string, causing substring checks in
_is_quota_exhausted() to fail. After extracting values from the result
dictionary using get(), normalize the nullable fields by checking if they are
None and replacing them with appropriate defaults (empty string for stdout and
stderr, 0 for returncode, None for conversation_id) before constructing and
returning the result tuple.
| # Check if ANY model path is available | ||
| if not google_breaker.is_allowed() and not vendor_breaker.is_allowed(): | ||
| logger.info( | ||
| f"agy proxy: both circuit breakers open (google tier={google_breaker.tier}, " | ||
| f"vendor tier={vendor_breaker.tier}) — skipping agy, falling through to LiteLLM" | ||
| ) | ||
| return None |
There was a problem hiding this comment.
Do not call the mutating breaker check twice before a probe.
is_allowed() grants a probe by setting probe_granted=True. The pre-check at Lines 237-242 can consume that probe, then the tier check at Lines 288-293 sees the probe already granted and skips the request, so AGY never probes after cooldown expiry.
Proposed fix direction
- # Check if ANY model path is available
- if not google_breaker.is_allowed() and not vendor_breaker.is_allowed():
- logger.info(
- f"agy proxy: both circuit breakers open (google tier={google_breaker.tier}, "
- f"vendor tier={vendor_breaker.tier}) — skipping agy, falling through to LiteLLM"
- )
- return None
-
# Build context-aware prompt from message historyAlso applies to: 288-293
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@router/agy_proxy.py` around lines 236 - 242, The issue is that is_allowed()
is a mutating method that grants a probe by setting probe_granted=True, and it
is being called twice on google_breaker and vendor_breaker objects—once in the
pre-check (lines 237-242) and again in the tier check (lines 288-293). This
causes the first call to consume the available probe, leaving none for the
second check, which prevents AGY from probing after cooldown expiry. Refactor
the code to call is_allowed() only once on each breaker and store the results,
then reuse those boolean results in both the pre-check condition and the tier
check condition instead of calling is_allowed() multiple times.
| # Save session state for continuation | ||
| if session_id: | ||
| _session_store[session_id] = { | ||
| "conversation_id": last_conv_id, | ||
| "current_tier_index": actual_tier_idx, | ||
| } | ||
| logger.info(f"agy proxy: saved session {session_id[:8]}..." | ||
| f" → conversation={last_conv_id[:8]}..., tier={tier['model_name']}") |
There was a problem hiding this comment.
Only persist AGY sessions when a conversation ID exists.
last_conv_id can remain None when the daemon returns a successful response without conversation_id; storing that session makes the next resume path slice existing_conv_id[:8] and fail before AGY can run.
Proposed guard
- if session_id:
+ if session_id and last_conv_id:
_session_store[session_id] = {
"conversation_id": last_conv_id,
"current_tier_index": actual_tier_idx,
}
logger.info(f"agy proxy: saved session {session_id[:8]}..."
f" → conversation={last_conv_id[:8]}..., tier={tier['model_name']}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Save session state for continuation | |
| if session_id: | |
| _session_store[session_id] = { | |
| "conversation_id": last_conv_id, | |
| "current_tier_index": actual_tier_idx, | |
| } | |
| logger.info(f"agy proxy: saved session {session_id[:8]}..." | |
| f" → conversation={last_conv_id[:8]}..., tier={tier['model_name']}") | |
| # Save session state for continuation | |
| if session_id and last_conv_id: | |
| _session_store[session_id] = { | |
| "conversation_id": last_conv_id, | |
| "current_tier_index": actual_tier_idx, | |
| } | |
| logger.info(f"agy proxy: saved session {session_id[:8]}..." | |
| f" → conversation={last_conv_id[:8]}..., tier={tier['model_name']}") |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@router/agy_proxy.py` around lines 441 - 448, The session is being persisted
to _session_store even when last_conv_id is None, which causes failures when the
session is resumed later because existing_conv_id[:8] will be sliced on a None
value. Add a guard condition to check that last_conv_id is not None in addition
to the existing session_id check before saving the session state in
_session_store and logging the info message. This ensures only valid
conversation IDs are persisted.
| if body.get("stream", False): | ||
| logger.info(f"Proxying streaming to LiteLLM as model={model_name}") | ||
| req = client.build_request("POST", f"{backend_api_base}/chat/completions", json=body_to_send, headers=headers) | ||
| r = await client.send(req, stream=True) | ||
| if r.status_code == 200: | ||
| async def stream_generator(): | ||
| """Asynchronous generator that yields streaming chunks from LiteLLM completions response and logs usage stats on completion.""" | ||
| import codecs | ||
| completion_chars = 0 | ||
| request_tokens = estimate_prompt_tokens(body_to_send) | ||
| sse_buffer = "" | ||
| decoder = codecs.getincrementaldecoder("utf-8")() | ||
| try: | ||
| async for chunk in r.aiter_bytes(): | ||
| yield chunk | ||
| try: | ||
| sse_buffer += decoder.decode(chunk) | ||
| while "\n" in sse_buffer: | ||
| line, sse_buffer = sse_buffer.split("\n", 1) | ||
| line = line.strip() | ||
| if line.startswith("data:"): | ||
| data_str = line[5:].strip() | ||
| if data_str == "[DONE]": | ||
| continue | ||
| try: | ||
| data_json = json.loads(data_str) | ||
| choices = data_json.get("choices", []) | ||
| if choices: | ||
| delta = choices[0].get("delta", {}) | ||
| content = delta.get("content") or "" | ||
| completion_chars += len(content) | ||
| except Exception: | ||
| pass | ||
| except Exception: | ||
| pass | ||
| proxy_latency = (time.time() - proxy_start) * 1000.0 | ||
| stats["total_proxy_time_ms"] += proxy_latency | ||
| stats["avg_proxy_latency_ms"] = stats["total_proxy_time_ms"] / stats["total_requests"] | ||
| record_tool_usage(active_tool, request_tokens, completion_chars // 4, model_name, proxy_latency, route="litellm_fallback") | ||
| # Finalize LiteLLM span (streaming path) | ||
| if litellm_span_obj: | ||
| try: | ||
| litellm_span_obj.end( | ||
| output={"model": model_name, "stream": True}, | ||
| metadata={"latency_ms": proxy_latency, "tokens": completion_chars // 4}, | ||
| ) | ||
| except Exception: | ||
| pass | ||
| except Exception as ex: | ||
| logger.error(f"Stream error: {ex}") | ||
| finally: | ||
| await r.aclose() | ||
| return StreamingResponse(stream_generator(), media_type="text/event-stream") |
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Activate Ollama cooldowns from the streaming generator too.
For streaming responses, execute_proxy() returns StreamingResponse before the upstream body is consumed. If r.aiter_bytes() fails later, the exception is only logged inside stream_generator, so the Ollama cooldown path at Lines 1969-2008 never runs and subsequent requests keep hitting the failing backend.
Proposed fix direction
except Exception as ex:
logger.error(f"Stream error: {ex}")
+ if model_name.startswith("ollama-deepseek-"):
+ global _ollama_cooldown_until
+ _ollama_cooldown_until = time.monotonic() + OLLAMA_COOLDOWN_SECONDS
+ await save_cooldowns_to_valkey()
+ logger.error(
+ f"🧊 Ollama stream failed ({ex}), activating "
+ f"{OLLAMA_COOLDOWN_SECONDS}s cooldown"
+ )
+ raise
finally:
await r.aclose()Also applies to: 1966-2008
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🪛 Ruff (0.15.17)
[error] 1881-1882: try-except-pass detected, consider logging the exception
(S110)
[warning] 1881-1881: Do not catch blind exception: Exception
(BLE001)
[error] 1883-1884: try-except-pass detected, consider logging the exception
(S110)
[warning] 1883-1883: Do not catch blind exception: Exception
(BLE001)
[error] 1896-1897: try-except-pass detected, consider logging the exception
(S110)
[warning] 1896-1896: Do not catch blind exception: Exception
(BLE001)
[warning] 1898-1898: Do not catch blind exception: Exception
(BLE001)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@router/main.py` around lines 1850 - 1902, The streaming error handler in the
stream_generator() function catches exceptions but does not activate the Ollama
cooldown mechanism that normally runs for non-streaming failures, leaving
failing backends unprotected on subsequent requests. In the except Exception as
ex block within stream_generator(), after logging the error, invoke the same
Ollama cooldown logic that is executed in the non-streaming failure path
(referenced at lines 1969-2008) to ensure that backend failures trigger the
appropriate cooldown protection regardless of whether the response is streaming
or non-streaming.
| render_pod_yaml() { | ||
| export WORKDIR HOME LITELLM_MASTER_KEY | ||
| python3 - "$WORKDIR/pod.yaml" <<'PY' | ||
| import os, sys | ||
| uid = os.getuid() | ||
| with open(sys.argv[1], "r", encoding="utf-8") as f: | ||
| text = f.read() | ||
| text = text.replace("/home/gpav/Vrac/LAB/AI/LLM-Routing", os.environ["WORKDIR"]) | ||
| text = text.replace("/home/gpav/", os.environ["HOME"] + "/") | ||
| text = text.replace("/run/user/1000", f"/run/user/{uid}") | ||
| text = text.replace("sk-lit...33bf", os.environ["LITELLM_MASTER_KEY"]) | ||
| text = text.replace("postgres:***", "postgres:postgres-local-pw-2026") | ||
| sys.stdout.write(text) | ||
| PY |
There was a problem hiding this comment.
🛠️ Refactor suggestion | 🟠 Major | ⚡ Quick win
Fail fast when pod.yaml placeholders are missing before replacement.
render_pod_yaml() currently does blind str.replace() calls. If any placeholder disappears/changes in pod.yaml, deploy proceeds with stale values and can fail at runtime. Add explicit placeholder presence checks (or replacement-count checks) before writing output.
Proposed hardening
render_pod_yaml() {
export WORKDIR HOME LITELLM_MASTER_KEY
python3 - "$WORKDIR/pod.yaml" <<'PY'
import os, sys
uid = os.getuid()
with open(sys.argv[1], "r", encoding="utf-8") as f:
text = f.read()
+required = [
+ "/home/gpav/Vrac/LAB/AI/LLM-Routing",
+ "/home/gpav/",
+ "/run/user/1000",
+ "sk-lit...33bf",
+ "postgres:***",
+]
+missing = [p for p in required if p not in text]
+if missing:
+ raise SystemExit(f"pod.yaml template drift: missing placeholders: {missing}")
text = text.replace("/home/gpav/Vrac/LAB/AI/LLM-Routing", os.environ["WORKDIR"])
text = text.replace("/home/gpav/", os.environ["HOME"] + "/")
text = text.replace("/run/user/1000", f"/run/user/{uid}")
text = text.replace("sk-lit...33bf", os.environ["LITELLM_MASTER_KEY"])
text = text.replace("postgres:***", "postgres:postgres-local-pw-2026")
sys.stdout.write(text)
PY
}🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@start-stack.sh` around lines 300 - 313, The render_pod_yaml() function
performs multiple blind str.replace() calls on pod.yaml content without
verifying that the placeholders actually exist before replacement. This can
cause the function to proceed with stale values if any placeholder has changed
or disappeared. Add explicit checks (such as assertions or conditional
validations) to verify that each placeholder string (like
"/home/gpav/Vrac/LAB/AI/LLM-Routing", "/home/gpav/", "/run/user/1000",
"sk-lit...33bf", and "postgres:***") exists in the text before performing the
replacements, or verify that each replacement actually occurred by checking the
replacement count returned by str.replace(). If any placeholder is missing, the
function should fail fast with an error message rather than proceeding with the
modified text.
1 participant
Summary of Changes
This pull request addresses the open code reviews, security/CodeQL alerts, and startup connection issues from the previous PR stack.
Key Modifications
1. PostgreSQL Password Dynamic Interpolation & Config
start-stack.sh: Updatedrender_pod_yaml()to dynamically substitute thepostgres:***secrets placeholder with the correct local passwordpostgres:postgres-local-pw-2026at deploy time.litellm/config.yaml: Refactored the vector storeconnection_stringfrom the hardcoded***placeholder to the dynamic env referenceos.environ/DATABASE_URLto prevent initialization connection failures.2. SOTA Performance Session Fingerprinting (CodeQL Fix)
router/main.py: Refactored the session tracking fingerprint to extract the first user message instead of slicingmessages[:4](which drifted over subsequent message turns).hashlib.md5hashing algorithm withhashlib.blake2b(..., digest_size=32)to satisfy CodeQL cryptographic rules while ensuring optimal 64-bit performance.3. Null-Safety for Upstream API Usage Fields
router/main.py: Fixed potentialAttributeErrorcrashes. Sourcingusage = response.get("usage", {})evaluates toNoneif the key exists but its value is explicitly returned asnullby the upstream API. Resolved by using theor {}fallback (response.get("usage") or {}), which safely guarantees a dictionary.4. More Accurate Fallback Completion Token Estimation
router/main.py: Replaced the naivelen(json.dumps(resp_json)) // 4completion token fallback with a choice-content-based estimation logic ((len(choices[0].get("message", {}).get("content") or "") // 4) if choices else 0). This avoids severely overestimating token counts by excluding response wrapper metadata.5. Direct Python Execution of
test_antigravity.pytest_antigravity.py: Wrappedpytest.skipwith anif __name__ != "__main__"check to allow running the test script directly outside pytest. Updated the invocation syntax from the deprecated--printoption to the correctnew-conversationcommand structure.Verification Details
./start-stack.sh --full-rebuild.test_circuit_breaker.py,verify_breaker.py, andtest_a2_verify.pypassed.test_antigravity.pypassed.test_classifier_accuracy.pyandtest_agy_tiers.pypassed.Summary by CodeRabbit
Release Notes
New Features
Documentation
Infrastructure
Bug Fixes