diff --git a/.circleci/config.yml b/.circleci/config.yml
index d1440cf7a1..6e46d3fe33 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -2606,6 +2606,8 @@ jobs:
-e GEMINI_API_KEY=$GEMINI_API_KEY \
-e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
-e ASSEMBLYAI_API_KEY=$ASSEMBLYAI_API_KEY \
+ -e AZURE_API_KEY_PASSHROUGH=$AZURE_API_KEY_PASSHROUGH \
+ -e AZURE_API_BASE_PASSHROUGH=$AZURE_API_BASE_PASSHROUGH \
-e USE_DDTRACE=True \
-e DD_API_KEY=$DD_API_KEY \
-e DD_SITE=$DD_SITE \
diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md
index 35db752cbb..85147e12c6 100644
--- a/docs/my-website/docs/proxy/cost_tracking.md
+++ b/docs/my-website/docs/proxy/cost_tracking.md
@@ -8,6 +8,10 @@ Track spend for keys, users, and teams across 100+ LLMs.
LiteLLM automatically tracks spend for all known models. See our [model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json)
+:::tip Keep Pricing Data Updated
+[Sync model pricing data from GitHub](../sync_models_github.md) to ensure accurate cost tracking.
+:::
+
### How to Track Spend with LiteLLM
**Step 1**
diff --git a/docs/my-website/docs/proxy/model_management.md b/docs/my-website/docs/proxy/model_management.md
index a8cc66ae76..6a87dda2f4 100644
--- a/docs/my-website/docs/proxy/model_management.md
+++ b/docs/my-website/docs/proxy/model_management.md
@@ -19,6 +19,10 @@ model_list:
Retrieve detailed information about each model listed in the `/model/info` endpoint, including descriptions from the `config.yaml` file, and additional model info (e.g. max tokens, cost per input token, etc.) pulled from the model_info you set and the [litellm model cost map](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). Sensitive details like API keys are excluded for security purposes.
+:::tip Sync Model Data
+Keep your model pricing data up to date by [syncing models from GitHub](../sync_models_github.md).
+:::
+
+
```bash
curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
@@ -244,6 +246,34 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
}
}'
```
+
+
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "metadata": {
+ "foo": "hello world"
+ }
+}'
+```
+
+
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/team/new' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+ "metadata": {
+ "foo": "hello world"
+ }
+}'
+```
+
+
3. Check your `/metrics` endpoint for the custom metrics
diff --git a/docs/my-website/docs/proxy/security_encryption_faq.md b/docs/my-website/docs/proxy/security_encryption_faq.md
new file mode 100644
index 0000000000..690f67d79a
--- /dev/null
+++ b/docs/my-website/docs/proxy/security_encryption_faq.md
@@ -0,0 +1,354 @@
+# LiteLLM Self-Hosted Security & Encryption FAQ
+
+## Data in Transit Encryption
+
+### Does the product encrypt data in transit?
+
+**Yes**, LiteLLM encrypts data in transit using TLS/SSL.
+
+### Available in both OSS and Enterprise?
+
+**Yes**, TLS encryption is available in both Open Source and Enterprise versions.
+
+### In transit between the calling client and the product?
+
+**Yes**, HTTPS/TLS is supported through SSL certificate configuration.
+
+**Configuration:**
+```bash
+# CLI
+litellm --ssl_keyfile_path /path/to/key.pem --ssl_certfile_path /path/to/cert.pem
+
+# Environment Variables
+export SSL_KEYFILE_PATH="/path/to/key.pem"
+export SSL_CERTFILE_PATH="/path/to/cert.pem"
+```
+
+**Documentation Reference:** `docs/my-website/docs/guides/security_settings.md`
+
+### In transit between the product and the LLM providers?
+
+**Yes**, all connections to LLM providers use TLS encryption by default.
+
+**Implementation Details:**
+- Uses Python's `ssl.create_default_context()`
+- Leverages HTTPX and aiohttp libraries with SSL/TLS enabled
+- Uses certifi CA bundle by default for SSL verification
+
+**Code Reference:** `litellm/llms/custom_httpx/http_handler.py` (lines 43-105)
+
+### Are TCP sessions to the LLM providers shared?
+
+**Yes**, TCP connections are pooled and reused.
+
+**Details:**
+- Connection pooling is enabled by default
+- Default: 1000 max concurrent connections with keepalive
+- Sessions are maintained across requests to the same provider
+- Reduces overhead of TLS handshakes
+
+**Code Reference:** `litellm/llms/custom_httpx/http_handler.py` (lines 704-712)
+
+### Or does the product negotiate a new TLS session with the same LLM provider for every sequential call?
+
+**No**, TLS sessions are reused through connection pooling. New TLS handshakes are not performed for every request.
+
+### How is it encrypted?
+
+**TLS 1.2 and TLS 1.3**
+
+Uses Python's default SSL context which supports both TLS 1.2 and TLS 1.3. The specific version negotiated depends on:
+- Python version
+- System SSL library (typically OpenSSL)
+- Server capabilities
+
+**Implementation:** `ssl.create_default_context()` in Python
+
+### How are these added to the product's configuration?
+
+#### x.509 Certificate
+
+**Method 1: CLI Arguments**
+```bash
+litellm --ssl_certfile_path /path/to/certificate.pem
+```
+
+**Method 2: Environment Variable**
+```bash
+export SSL_CERTFILE_PATH="/path/to/certificate.pem"
+```
+
+#### Private Key
+
+**Method 1: CLI Arguments**
+```bash
+litellm --ssl_keyfile_path /path/to/private_key.pem
+```
+
+**Method 2: Environment Variable**
+```bash
+export SSL_KEYFILE_PATH="/path/to/private_key.pem"
+```
+
+#### Certificate Bundle/Chain
+
+**For client-to-proxy connections:**
+Use standard SSL certificate setup with intermediate certificates bundled in the certfile.
+
+**For proxy-to-LLM provider connections:**
+
+**Method 1: Config YAML**
+```yaml
+litellm_settings:
+ ssl_verify: "/path/to/ca_bundle.pem"
+```
+
+**Method 2: Environment Variable**
+```bash
+export SSL_CERT_FILE="/path/to/ca_bundle.pem"
+```
+
+**Method 3: Client Certificate Authentication**
+```yaml
+litellm_settings:
+ ssl_certificate: "/path/to/client_certificate.pem"
+```
+
+or
+
+```bash
+export SSL_CERTIFICATE="/path/to/client_certificate.pem"
+```
+
+### Documentation Coverage
+
+**Primary Documentation:**
+- `docs/my-website/docs/guides/security_settings.md` - SSL/TLS configuration guide
+
+**Additional References:**
+- `litellm/proxy/proxy_cli.py` (lines 455-467) - CLI options
+- `docs/my-website/docs/completion/http_handler_config.md` - Custom HTTP handler configuration
+
+---
+
+## Data at Rest Encryption
+
+### Does the product encrypt data at rest?
+
+**Partially**. Only specific sensitive data is encrypted at rest.
+
+### What data is stored in encrypted form?
+
+#### Encrypted Data:
+1. **LLM API Keys** - Model credentials in `LiteLLM_ProxyModelTable.litellm_params`
+2. **Provider Credentials** - Stored in `LiteLLM_CredentialsTable.credential_values`
+3. **Configuration Secrets** - Sensitive config values in `LiteLLM_Config` table
+4. **Virtual Keys** - When using secret managers (optional feature)
+
+#### NOT Encrypted:
+1. **Spend Logs** - Request/response data in `LiteLLM_SpendLogs`
+2. **Audit Logs** - Change history in `LiteLLM_AuditLog`
+3. **User/Team/Organization Data** - Metadata and configuration
+4. **Cached Prompts and Completions** - Cache data is stored in plaintext
+
+### Cached prompts and completions?
+
+**No**, cached prompts and completions are **NOT encrypted**.
+
+Cache backends (Redis, S3, local disk) store data as plaintext JSON.
+
+**Code References:**
+- `litellm/caching/redis_cache.py`
+- `litellm/caching/s3_cache.py`
+- `litellm/caching/caching.py`
+
+### Configuration data?
+
+**Partially encrypted**.
+
+#### What IS Encrypted:
+- LLM API keys and credentials in model configurations
+- Sensitive values in `LiteLLM_Config` table
+- Credential values in `LiteLLM_CredentialsTable`
+
+#### What is NOT Encrypted:
+- Model names and aliases
+- Rate limits and budget settings
+- User/team/organization metadata
+- Non-sensitive configuration parameters
+
+**Code Reference:** `litellm/proxy/management_endpoints/model_management_endpoints.py` (lines 275-308)
+
+### Log data?
+
+**No**, log data is **NOT encrypted**.
+
+Log data stored in database tables is in plaintext:
+- `LiteLLM_SpendLogs` - Contains request/response data, tokens, spend
+- `LiteLLM_ErrorLogs` - Error information
+- `LiteLLM_AuditLog` - Audit trail of changes
+
+**Note:** You can disable logging to avoid storing sensitive data:
+
+```yaml
+general_settings:
+ disable_spend_logs: True # Disable writing spend logs to DB
+ disable_error_logs: True # Disable writing error logs to DB
+```
+
+**Documentation:** `docs/my-website/docs/proxy/db_info.md` (lines 52-60)
+
+### Where is it stored?
+
+#### In the DB?
+
+**Yes**, encrypted data is stored in PostgreSQL database.
+
+**Key Tables with Encrypted Data:**
+- `LiteLLM_ProxyModelTable` - Model configurations with encrypted API keys
+- `LiteLLM_CredentialsTable` - Credential values
+- `LiteLLM_Config` - Configuration secrets
+
+**Schema Reference:** `schema.prisma`
+
+#### In the filesystem?
+
+**No**, encrypted data is not stored in the filesystem by default.
+
+**Note:** If using disk cache (`disk_cache_dir`), cached data is stored unencrypted.
+
+#### Somewhere else?
+
+**Optional:** When using secret managers (AWS Secrets Manager, Azure Key Vault, HashiCorp Vault), encrypted data can be stored externally.
+
+**Configuration:**
+```yaml
+general_settings:
+ key_management_system: "aws_secret_manager" # or "azure_key_vault", "hashicorp_vault"
+```
+
+**Documentation:** `docs/my-website/docs/secret.md`
+
+### How is it encrypted?
+
+**Algorithm:** NaCl SecretBox (XSalsa20-Poly1305 AEAD)
+
+**NOT AES-256** - LiteLLM uses NaCl (Networking and Cryptography Library) which provides:
+- XSalsa20 stream cipher
+- Poly1305 MAC for authentication
+- Equivalent security to AES-256
+
+**Key Derivation:**
+1. Takes `LITELLM_SALT_KEY` (or `LITELLM_MASTER_KEY` if salt key not set)
+2. Hashes with SHA-256 to derive 256-bit encryption key
+3. Uses NaCl SecretBox for authenticated encryption
+
+**Code Reference:** `litellm/proxy/common_utils/encrypt_decrypt_utils.py` (lines 69-112)
+
+**Implementation:**
+```python
+import hashlib
+import nacl.secret
+
+# Derive 256-bit key from salt
+hash_object = hashlib.sha256(signing_key.encode())
+hash_bytes = hash_object.digest()
+
+# Create SecretBox and encrypt
+box = nacl.secret.SecretBox(hash_bytes)
+encrypted = box.encrypt(value_bytes)
+```
+
+### Setting the Encryption Key
+
+**Required Environment Variable:**
+```bash
+export LITELLM_SALT_KEY="your-strong-random-key-here"
+```
+
+**Important Notes:**
+- ⚠️ **Must be set before adding any models**
+- ⚠️ **Never change this key** - encrypted data becomes unrecoverable
+- ⚠️ Use a strong random key (recommended: https://1password.com/password-generator/)
+- If not set, falls back to `LITELLM_MASTER_KEY`
+
+**Documentation:** `docs/my-website/docs/proxy/prod.md` (section 8, lines 184-196)
+
+### Documentation Coverage
+
+**Primary Documentation:**
+- `docs/my-website/docs/proxy/prod.md` (section 8) - LITELLM_SALT_KEY setup
+- `docs/my-website/docs/secret.md` - Secret management systems
+- `docs/my-website/docs/proxy/db_info.md` - Database information
+
+**Additional References:**
+- `security.md` - General security measures
+- `docs/my-website/docs/data_security.md` - Data privacy overview
+- `schema.prisma` - Database schema with encrypted fields
+
+---
+
+## Summary of Security Features
+
+### ✅ Provided Out of the Box
+
+1. **TLS/SSL encryption** for client-to-proxy connections
+2. **TLS encryption** for proxy-to-LLM provider connections (with connection pooling)
+3. **Encrypted storage** of LLM API keys and credentials
+4. **Support for TLS 1.2 and TLS 1.3**
+5. **Connection pooling** to reduce TLS handshake overhead
+
+### ⚠️ Important Limitations
+
+1. **Cached data is NOT encrypted** (Redis, S3, disk cache)
+2. **Log data is NOT encrypted** (spend logs, audit logs)
+3. **Request/response payloads in logs are NOT encrypted**
+4. **Uses NaCl SecretBox, NOT AES-256** (equivalent security)
+5. **TLS version not explicitly configured** - uses Python/system defaults
+
+### 🔧 Configuration Requirements
+
+**For Production Deployments:**
+
+1. **Set LITELLM_SALT_KEY** before adding any models
+2. **Configure SSL certificates** for HTTPS client connections
+3. **Consider disabling logs** if they contain sensitive data
+4. **Use secret managers** for enhanced security (optional)
+5. **Configure CA bundles** if using custom certificates
+
+---
+
+## Quick Start Security Checklist
+
+```bash
+# 1. Generate a strong salt key
+export LITELLM_SALT_KEY="$(openssl rand -base64 32)"
+
+# 2. Set up SSL certificates (for HTTPS)
+export SSL_KEYFILE_PATH="/path/to/private_key.pem"
+export SSL_CERTFILE_PATH="/path/to/certificate.pem"
+
+# 3. Configure database
+export DATABASE_URL="postgresql://user:password@host:port/dbname"
+
+# 4. (Optional) Disable logs if they contain sensitive data
+# Add to config.yaml:
+# general_settings:
+# disable_spend_logs: True
+# disable_error_logs: True
+
+# 5. Start LiteLLM Proxy
+litellm --config config.yaml
+```
+
+---
+
+## Additional Resources
+
+- **LiteLLM Documentation:** https://docs.litellm.ai/
+- **Security Settings Guide:** https://docs.litellm.ai/docs/guides/security_settings
+- **Production Deployment:** https://docs.litellm.ai/docs/proxy/prod
+- **Secret Management:** https://docs.litellm.ai/docs/secret
+
+For security inquiries: support@berri.ai
+
diff --git a/docs/my-website/docs/proxy/sync_models_github.md b/docs/my-website/docs/proxy/sync_models_github.md
new file mode 100644
index 0000000000..d2f410e549
--- /dev/null
+++ b/docs/my-website/docs/proxy/sync_models_github.md
@@ -0,0 +1,61 @@
+# Syncing Models to GitHub model_context_window
+
+Sync model pricing data from GitHub's `model_prices_and_context_window.json` file outside of the LiteLLM UI.
+
+> **📹 Video Tutorial**: [Watch how to sync models via the Admin UI](https://www.loom.com/share/ba41acc1882d41b284bbddbb0e9c27ce?sid=bdae351e-2026-4e39-932b-fcb185ff612c)
+
+## Quick Start
+
+**Manual sync:**
+```bash
+curl -X POST "https://your-proxy-url/reload/model_cost_map" \
+ -H "Authorization: Bearer YOUR_ADMIN_TOKEN" \
+ -H "Content-Type: application/json"
+```
+
+**Automatic sync every 6 hours:**
+```bash
+curl -X POST "https://your-proxy-url/schedule/model_cost_map_reload?hours=6" \
+ -H "Authorization: Bearer YOUR_ADMIN_TOKEN" \
+ -H "Content-Type: application/json"
+```
+
+## API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/reload/model_cost_map` | POST | Manual sync |
+| `/schedule/model_cost_map_reload?hours={hours}` | POST | Schedule periodic sync |
+| `/schedule/model_cost_map_reload` | DELETE | Cancel scheduled sync |
+| `/schedule/model_cost_map_reload/status` | GET | Check sync status |
+
+**Authentication:** Requires admin role or master key
+
+## Python Example
+
+```python
+import requests
+
+def sync_models(proxy_url, admin_token):
+ response = requests.post(
+ f"{proxy_url}/reload/model_cost_map",
+ headers={"Authorization": f"Bearer {admin_token}"}
+ )
+ return response.json()
+
+# Usage
+result = sync_models("https://your-proxy-url", "your-admin-token")
+print(result['message'])
+```
+
+## Configuration
+
+**Custom model cost map URL:**
+```bash
+export LITELLM_MODEL_COST_MAP_URL="https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
+```
+
+**Use local model cost map:**
+```bash
+export LITELLM_LOCAL_MODEL_COST_MAP=True
+```
\ No newline at end of file
diff --git a/docs/my-website/docs/proxy/ui.md b/docs/my-website/docs/proxy/ui.md
index a093b226a2..f7419d2074 100644
--- a/docs/my-website/docs/proxy/ui.md
+++ b/docs/my-website/docs/proxy/ui.md
@@ -54,6 +54,20 @@ Allow others to create/delete their own keys.
[**Go Here**](./self_serve.md)
+## Model Management
+
+The Admin UI provides comprehensive model management capabilities:
+
+- **Add Models**: Add new models through the UI without restarting the proxy
+- **Model Hub**: Make models public for developers to discover available models
+- **Price Data Sync**: Keep model pricing data up to date by syncing from GitHub
+
+For detailed information on model management, see [Model Management](./model_management.md).
+
+:::tip Sync Model Pricing Data
+[Sync model pricing data from GitHub](./sync_models_github.md) to keep your model cost information current.
+:::
+
## Disable Admin UI
Set `DISABLE_ADMIN_UI="True"` in your environment to disable the Admin UI.
diff --git a/docs/my-website/img/release_notes/perf_77_5.png b/docs/my-website/img/release_notes/perf_77_5.png
new file mode 100644
index 0000000000..3aaebaf616
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diff --git a/docs/my-website/img/release_notes/perf_77_7.png b/docs/my-website/img/release_notes/perf_77_7.png
new file mode 100644
index 0000000000..bcf6a9afd5
Binary files /dev/null and b/docs/my-website/img/release_notes/perf_77_7.png differ
diff --git a/docs/my-website/img/release_notes/schedule_key_rotations.png b/docs/my-website/img/release_notes/schedule_key_rotations.png
new file mode 100644
index 0000000000..6ea7d8527d
Binary files /dev/null and b/docs/my-website/img/release_notes/schedule_key_rotations.png differ
diff --git a/docs/my-website/release_notes/v1.75.5-stable/index.md b/docs/my-website/release_notes/v1.75.5-stable/index.md
index da13906cd2..7035d28505 100644
--- a/docs/my-website/release_notes/v1.75.5-stable/index.md
+++ b/docs/my-website/release_notes/v1.75.5-stable/index.md
@@ -50,30 +50,6 @@ pip install litellm==1.75.5.post2
- **Oracle Cloud Infrastructure** - New LLM provider for calling models on Oracle Cloud Infrastructure.
- **Digital Ocean's Gradient AI** - New LLM provider for calling models on Digital Ocean's Gradient AI platform.
-
-### 54% RPS Improvement
-
-Throughput increased by 54% (1,040 → 1,602 RPS, aggregated) per instance while maintaining a 40 ms median overhead. The improvement comes from fixing major O(n²) inefficiencies in the router, primarily caused by repeated use of in statements inside loops over large arrays. Tests were run with a database-only setup (no cache hits). As a result, p95 latency improved by 30% (2,700 → 1,900 ms), enhancing overall stability and scalability under heavy load.
-
----
-
-### Test Setup
-
-All benchmarks were executed using Locust with 1,000 concurrent users and a ramp-up of 500. The environment was configured to stress the routing layer and eliminate caching as a variable.
-
-**System Specs**
-
-- **CPU:** 8 vCPUs
-- **Memory:** 32 GB RAM
-
-**Configuration (config.yaml)**
-
-View the complete configuration: [gist.github.com/AlexsanderHamir/config.yaml](https://gist.github.com/AlexsanderHamir/53f7d554a5d2afcf2c4edb5b6be68ff4)
-
-**Load Script (no_cache_hits.py)**
-
-View the complete load testing script: [gist.github.com/AlexsanderHamir/no_cache_hits.py](https://gist.github.com/AlexsanderHamir/42c33d7a4dc7a57f56a78b560dee3a42)
-
---
### Risk of Upgrade
diff --git a/docs/my-website/release_notes/v1.77.5-stable/index.md b/docs/my-website/release_notes/v1.77.5-stable/index.md
index ab0f4ac304..1e9807ee49 100644
--- a/docs/my-website/release_notes/v1.77.5-stable/index.md
+++ b/docs/my-website/release_notes/v1.77.5-stable/index.md
@@ -1,5 +1,5 @@
---
-title: "[Preview] v1.77.5-stable - MCP OAuth 2.0 Support"
+title: "v1.77.5-stable - MCP OAuth 2.0 Support"
slug: "v1-77-5"
date: 2025-09-29T10:00:00
authors:
@@ -11,6 +11,10 @@ authors:
title: CTO, LiteLLM
url: https://www.linkedin.com/in/reffajnaahsi/
image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+ - name: Alexsander Hamir
+ title: Backend Performance Engineer
+ url: https://www.linkedin.com/in/alexsander-baptista/
+ image_url: https://media.licdn.com/dms/image/v2/D5603AQGXnziu4kqNCQ/profile-displayphoto-crop_800_800/B56ZkxEcuOKEAI-/0/1757464874550?e=1762387200&v=beta&t=9SNXLsWhx8OnYPAMQ9fqAr02oevDYEAL2vMYg2f9ieg
hide_table_of_contents: false
---
@@ -28,7 +32,7 @@ import TabItem from '@theme/TabItem';
docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
-ghcr.io/berriai/litellm:v1.77.5.rc.1
+ghcr.io/berriai/litellm:v1.77.5-stable
```
@@ -49,7 +53,54 @@ pip install litellm==1.77.5
- **MCP OAuth 2.0 Support** - Enhanced authentication for Model Context Protocol integrations
- **Scheduled Key Rotations** - Automated key rotation capabilities for enhanced security
- **New Gemini 2.5 Flash & Flash-lite Models** - Latest September 2025 preview models with improved pricing and features
-- **Performance Improvements** - Critical InMemoryCache unbounded growth resolution
+- **Performance Improvements** - 54% RPS improvement
+
+---
+
+### Scheduled Key Rotations
+
+
+
+
+
+This release brings support for scheduling virtual key rotations on LiteLLM AI Gateway.
+
+This is great for Proxy Admins looking to enforce Enterprise Grade security for use cases going through LiteLLM AI Gateway.
+
+From this release you can enforce Virtual Keys to rotate on a schedule of your choice e.g every 15 days/30 days/60 days etc.
+
+---
+### Performance Improvements - 54% RPS Improvement
+
+
+
+
+
+This release brings a 54% RPS improvement (1,040 → 1,602 RPS, aggregated) per instance.
+
+The improvement comes from fixing O(n²) inefficiencies in the LiteLLM Router, primarily caused by repeated use of `in` statements inside loops over large arrays.
+
+Tests were run with a database-only setup (no cache hits).
+
+#### Test Setup
+
+All benchmarks were executed using Locust with 1,000 concurrent users and a ramp-up of 500. The environment was configured to stress the routing layer and eliminate caching as a variable.
+
+**System Specs**
+
+- **CPU:** 8 vCPUs
+- **Memory:** 32 GB RAM
+
+**Configuration (config.yaml)**
+
+View the complete configuration: [gist.github.com/AlexsanderHamir/config.yaml](https://gist.github.com/AlexsanderHamir/53f7d554a5d2afcf2c4edb5b6be68ff4)
+
+**Load Script (no_cache_hits.py)**
+
+View the complete load testing script: [gist.github.com/AlexsanderHamir/no_cache_hits.py](https://gist.github.com/AlexsanderHamir/42c33d7a4dc7a57f56a78b560dee3a42)
+
+---
+
## New Models / Updated Models
diff --git a/docs/my-website/release_notes/v1.77.7-stable/index.md b/docs/my-website/release_notes/v1.77.7-stable/index.md
new file mode 100644
index 0000000000..b5e53846b7
--- /dev/null
+++ b/docs/my-website/release_notes/v1.77.7-stable/index.md
@@ -0,0 +1,364 @@
+---
+title: "[Preview] v1.77.7-stable - Claude Sonnet 4.5"
+slug: "v1-77-7"
+date: 2025-10-04T10:00:00
+authors:
+ - name: Krrish Dholakia
+ title: CEO, LiteLLM
+ url: https://www.linkedin.com/in/krish-d/
+ image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+ - name: Ishaan Jaff
+ title: CTO, LiteLLM
+ url: https://www.linkedin.com/in/reffajnaahsi/
+ image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+ - name: Alexsander Hamir
+ title: Backend Performance Engineer
+ url: https://www.linkedin.com/in/alexsander-baptista/
+ image_url: https://media.licdn.com/dms/image/v2/D5603AQGXnziu4kqNCQ/profile-displayphoto-crop_800_800/B56ZkxEcuOKEAI-/0/1757464874550?e=1762387200&v=beta&t=9SNXLsWhx8OnYPAMQ9fqAr02oevDYEAL2vMYg2f9ieg
+ - name: Achintya Srivastava
+ title: Fullstack Engineer
+ url: https://www.linkedin.com/in/achintya-rajan/
+ image_url: https://media.licdn.com/dms/image/v2/D5603AQGdkEeyJTdljw/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1716271140869?e=1762387200&v=beta&t=9gOoLPeqR2E5z3KSX61EUj3HVZXmgo87vhVuSHeffjc
+ - name: Sameer Kankute
+ title: Backend Engineer (LLM Translation)
+ url: https://www.linkedin.com/in/sameer-kankute/
+ image_url: https://media.licdn.com/dms/image/v2/D4D03AQHB_loQYd5gjg/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1719137160975?e=1762387200&v=beta&t=0jbuX-f4eSnDxBY3olI6meuYr-LMbObhFmFbRcKF5mY
+
+hide_table_of_contents: false
+---
+
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+## Deploy this version
+
+
+
+
+``` showLineNumbers title="docker run litellm"
+docker run \
+-e STORE_MODEL_IN_DB=True \
+-p 4000:4000 \
+ghcr.io/berriai/litellm:v1.77.7.rc.1
+```
+
+
+
+
+
+``` showLineNumbers title="pip install litellm"
+pip install litellm==1.77.7.rc.1
+```
+
+
+
+
+---
+
+## Key Highlights
+
+- **Dynamic Rate Limiter v3** - Automatically maximizes throughput when capacity is available (< 80% saturation) by allowing lower-priority requests to use unused capacity, then switches to fair priority-based allocation under high load (≥ 80%) to prevent blocking
+- **Major Performance Improvements** - 2.9x lower median latency at 1,000 concurrent users.
+- **Claude Sonnet 4.5** - Support for Anthropic's new Claude Sonnet 4.5 model family with 200K+ context and tiered pricing
+- **MCP Gateway Enhancements** - Fine-grained tool control, server permissions, and forwardable headers
+- **AMD Lemonade & Nvidia NIM** - New provider support for AMD Lemonade and Nvidia NIM Rerank
+- **GitLab Prompt Management** - GitLab-based prompt management integration
+
+### Performance - 2.9x Lower Median Latency
+
+
+
+
+
+This update removes LiteLLM router inefficiencies, reducing complexity from O(M×N) to O(1). Previously, it built a new array and ran repeated checks like data["model"] in llm_router.get_model_ids(). Now, a direct ID-to-deployment map eliminates redundant allocations and scans.
+
+As a result, performance improved across all latency percentiles:
+
+- **Median latency:** 320 ms → **110 ms** (−65.6%)
+- **p95 latency:** 850 ms → **440 ms** (−48.2%)
+- **p99 latency:** 1,400 ms → **810 ms** (−42.1%)
+- **Average latency:** 864 ms → **310 ms** (−64%)
+
+
+#### Test Setup
+
+**Locust**
+
+- **Concurrent users:** 1,000
+- **Ramp-up:** 500
+
+**System Specs**
+
+- **CPU:** 4 vCPUs
+- **Memory:** 8 GB RAM
+- **LiteLLM Workers:** 4
+- **Instances**: 4
+
+**Configuration (config.yaml)**
+
+View the complete configuration: [gist.github.com/AlexsanderHamir/config.yaml](https://gist.github.com/AlexsanderHamir/53f7d554a5d2afcf2c4edb5b6be68ff4)
+
+**Load Script (no_cache_hits.py)**
+
+View the complete load testing script: [gist.github.com/AlexsanderHamir/no_cache_hits.py](https://gist.github.com/AlexsanderHamir/42c33d7a4dc7a57f56a78b560dee3a42)
+
+## New Models / Updated Models
+
+#### New Model Support
+
+| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
+| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
+| Anthropic | `claude-sonnet-4-5` | 200K | $3.00 | $15.00 | Chat, reasoning, vision, function calling, prompt caching |
+| Anthropic | `claude-sonnet-4-5-20250929` | 200K | $3.00 | $15.00 | Chat, reasoning, vision, function calling, prompt caching |
+| Bedrock | `eu.anthropic.claude-sonnet-4-5-20250929-v1:0` | 200K | $3.00 | $15.00 | Chat, reasoning, vision, function calling, prompt caching |
+| Azure AI | `azure_ai/grok-4` | 131K | $5.50 | $27.50 | Chat, reasoning, function calling, web search |
+| Azure AI | `azure_ai/grok-4-fast-reasoning` | 131K | $0.43 | $1.73 | Chat, reasoning, function calling, web search |
+| Azure AI | `azure_ai/grok-4-fast-non-reasoning` | 131K | $0.43 | $1.73 | Chat, function calling, web search |
+| Azure AI | `azure_ai/grok-code-fast-1` | 131K | $3.50 | $17.50 | Chat, function calling, web search |
+| Groq | `groq/moonshotai/kimi-k2-instruct-0905` | Context varies | Pricing varies | Pricing varies | Chat, function calling |
+| Ollama | Ollama Cloud models | Varies | Free | Free | Self-hosted models via Ollama Cloud |
+
+#### Features
+
+- **[Anthropic](../../docs/providers/anthropic)**
+ - Add new claude-sonnet-4-5 model family with tiered pricing above 200K tokens - [PR #15041](https://github.com/BerriAI/litellm/pull/15041)
+ - Add anthropic/claude-sonnet-4-5 to model price json with prompt caching support - [PR #15049](https://github.com/BerriAI/litellm/pull/15049)
+ - Add 200K prices for Sonnet 4.5 - [PR #15140](https://github.com/BerriAI/litellm/pull/15140)
+ - Add cost tracking for /v1/messages in streaming response - [PR #15102](https://github.com/BerriAI/litellm/pull/15102)
+ - Add /v1/messages/count_tokens to Anthropic routes for non-admin user access - [PR #15034](https://github.com/BerriAI/litellm/pull/15034)
+- **[Gemini](../../docs/providers/gemini)**
+ - Ignore type param for gemini tools - [PR #15022](https://github.com/BerriAI/litellm/pull/15022)
+- **[Vertex AI](../../docs/providers/vertex)**
+ - Add LiteLLM Overhead metric for VertexAI - [PR #15040](https://github.com/BerriAI/litellm/pull/15040)
+ - Support googlemap grounding in vertex ai - [PR #15179](https://github.com/BerriAI/litellm/pull/15179)
+- **[Azure](../../docs/providers/azure)**
+ - Add azure_ai grok-4 model family - [PR #15137](https://github.com/BerriAI/litellm/pull/15137)
+ - Use the `extra_query` parameter for GET requests in Azure Batch - [PR #14997](https://github.com/BerriAI/litellm/pull/14997)
+ - Use extra_query for download results (Batch API) - [PR #15025](https://github.com/BerriAI/litellm/pull/15025)
+ - Add support for Azure AD token-based authorization - [PR #14813](https://github.com/BerriAI/litellm/pull/14813)
+- **[Ollama](../../docs/providers/ollama)**
+ - Add ollama cloud models - [PR #15008](https://github.com/BerriAI/litellm/pull/15008)
+- **[Groq](../../docs/providers/groq)**
+ - Add groq/moonshotai/kimi-k2-instruct-0905 - [PR #15079](https://github.com/BerriAI/litellm/pull/15079)
+- **[OpenAI](../../docs/providers/openai)**
+ - Add support for GPT 5 codex models - [PR #14841](https://github.com/BerriAI/litellm/pull/14841)
+- **[DeepInfra](../../docs/providers/deepinfra)**
+ - Update DeepInfra model data refresh with latest pricing - [PR #14939](https://github.com/BerriAI/litellm/pull/14939)
+- **[Bedrock](../../docs/providers/bedrock)**
+ - Add JP Cross-Region Inference - [PR #15188](https://github.com/BerriAI/litellm/pull/15188)
+ - Add "eu.anthropic.claude-sonnet-4-5-20250929-v1:0" - [PR #15181](https://github.com/BerriAI/litellm/pull/15181)
+ - Add twelvelabs bedrock Async Invoke Support - [PR #14871](https://github.com/BerriAI/litellm/pull/14871)
+- **[Nvidia NIM](../../docs/providers/nvidia_nim)**
+ - Add Nvidia NIM Rerank Support - [PR #15152](https://github.com/BerriAI/litellm/pull/15152)
+
+### Bug Fixes
+
+- **[VLLM](../../docs/providers/vllm)**
+ - Fix response_format bug in hosted vllm audio_transcription - [PR #15010](https://github.com/BerriAI/litellm/pull/15010)
+ - Fix passthrough of atranscription into kwargs going to upstream provider - [PR #15005](https://github.com/BerriAI/litellm/pull/15005)
+- **[OCI](../../docs/providers/oci)**
+ - Fix OCI Generative AI Integration when using Proxy - [PR #15072](https://github.com/BerriAI/litellm/pull/15072)
+- **General**
+ - Fix: Authorization header to use correct "Bearer" capitalization - [PR #14764](https://github.com/BerriAI/litellm/pull/14764)
+ - Bug fix: gpt-5-chat-latest has incorrect max_input_tokens value - [PR #15116](https://github.com/BerriAI/litellm/pull/15116)
+ - Update request handling for original exceptions - [PR #15013](https://github.com/BerriAI/litellm/pull/15013)
+
+#### New Provider Support
+
+- **[AMD Lemonade](../../docs/providers/lemonade)**
+ - Add AMD Lemonade provider support - [PR #14840](https://github.com/BerriAI/litellm/pull/14840)
+
+---
+
+## LLM API Endpoints
+
+#### Features
+
+- **[Responses API](../../docs/response_api)**
+ - Return Cost for Responses API Streaming requests - [PR #15053](https://github.com/BerriAI/litellm/pull/15053)
+
+- **[/generateContent](../../docs/providers/gemini)**
+ - Add full support for native Gemini API translation - [PR #15029](https://github.com/BerriAI/litellm/pull/15029)
+
+- **Passthrough Gemini Routes**
+ - Add Gemini generateContent passthrough cost tracking - [PR #15014](https://github.com/BerriAI/litellm/pull/15014)
+ - Add streamGenerateContent cost tracking in passthrough - [PR #15199](https://github.com/BerriAI/litellm/pull/15199)
+
+- **Passthrough Vertex AI Routes**
+ - Add cost tracking for Vertex AI Passthrough `/predict` endpoint - [PR #15019](https://github.com/BerriAI/litellm/pull/15019)
+ - Add cost tracking for Vertex AI Live API WebSocket Passthrough - [PR #14956](https://github.com/BerriAI/litellm/pull/14956)
+
+- **General**
+ - Preserve Whitespace Characters in Model Response Streams - [PR #15160](https://github.com/BerriAI/litellm/pull/15160)
+ - Add provider name to payload specification - [PR #15130](https://github.com/BerriAI/litellm/pull/15130)
+ - Ensure query params are forwarded from origin url to downstream request - [PR #15087](https://github.com/BerriAI/litellm/pull/15087)
+
+---
+
+## Management Endpoints / UI
+
+#### Features
+
+- **Virtual Keys**
+ - Ensure LLM_API_KEYs can access pass through routes - [PR #15115](https://github.com/BerriAI/litellm/pull/15115)
+ - Support 'guaranteed_throughput' when setting limits on keys belonging to a team - [PR #15120](https://github.com/BerriAI/litellm/pull/15120)
+
+- **Models + Endpoints**
+ - Ensure OCI secret fields not shared on /models and /v1/models endpoints - [PR #15085](https://github.com/BerriAI/litellm/pull/15085)
+ - Add snowflake on UI - [PR #15083](https://github.com/BerriAI/litellm/pull/15083)
+ - Make UI theme settings publicly accessible for custom branding - [PR #15074](https://github.com/BerriAI/litellm/pull/15074)
+
+- **Admin Settings**
+ - Ensure OTEL settings are saved in DB after set on UI - [PR #15118](https://github.com/BerriAI/litellm/pull/15118)
+ - Top api key tags - [PR #15151](https://github.com/BerriAI/litellm/pull/15151), [PR #15156](https://github.com/BerriAI/litellm/pull/15156)
+
+- **MCP**
+ - show health status of MCP servers - [PR #15185](https://github.com/BerriAI/litellm/pull/15185)
+ - allow setting extra headers on the UI - [PR #15185](https://github.com/BerriAI/litellm/pull/15185)
+ - allow editing allowed tools on the UI - [PR #15185](https://github.com/BerriAI/litellm/pull/15185)
+
+### Bug Fixes
+
+- **Virtual Keys**
+ - (security) prevent user key from updating other user keys - [PR #15201](https://github.com/BerriAI/litellm/pull/15201)
+ - (security) don't return all keys with blank key alias on /v2/key/info - [PR #15201](https://github.com/BerriAI/litellm/pull/15201)
+ - Fix Session Token Cookie Infinite Logout Loop - [PR #15146](https://github.com/BerriAI/litellm/pull/15146)
+
+- **Models + Endpoints**
+ - Make UI theme settings publicly accessible for custom branding - [PR #15074](https://github.com/BerriAI/litellm/pull/15074)
+
+- **Teams**
+ - fix failed copy to clipboard for http ui - [PR #15195](https://github.com/BerriAI/litellm/pull/15195)
+
+- **Logs**
+ - fix logs page render logs on filter lookup - [PR #15195](https://github.com/BerriAI/litellm/pull/15195)
+ - fix lookup list of end users (migrate to more efficient /customers/list lookup) - [PR #15195](https://github.com/BerriAI/litellm/pull/15195)
+
+- **Test key**
+ - update selected model on key change - [PR #15197](https://github.com/BerriAI/litellm/pull/15197)
+
+- **Dashboard**
+ - Fix LiteLLM model name fallback in dashboard overview - [PR #14998](https://github.com/BerriAI/litellm/pull/14998)
+
+
+---
+
+## Logging / Guardrail / Prompt Management Integrations
+
+#### Features
+
+- **[OpenTelemetry](../../docs/observability/otel)**
+ - Use generation_name for span naming in logging method - [PR #14799](https://github.com/BerriAI/litellm/pull/14799)
+- **[Langfuse](../../docs/proxy/logging#langfuse)**
+ - Handle non-serializable objects in Langfuse logging - [PR #15148](https://github.com/BerriAI/litellm/pull/15148)
+ - Set usage_details.total in langfuse integration - [PR #15015](https://github.com/BerriAI/litellm/pull/15015)
+- **[Prometheus](../../docs/proxy/prometheus)**
+ - support custom metadata labels on key/team - [PR #15094](https://github.com/BerriAI/litellm/pull/15094)
+
+
+#### Guardrails
+
+- **[Javelin](../../docs/proxy/guardrails)**
+ - Add Javelin standalone guardrails integration for LiteLLM Proxy - [PR #14983](https://github.com/BerriAI/litellm/pull/14983)
+ - Add logging for important status fields in guardrails - [PR #15090](https://github.com/BerriAI/litellm/pull/15090)
+ - Don't run post_call guardrail if no text returned from Bedrock - [PR #15106](https://github.com/BerriAI/litellm/pull/15106)
+
+#### Prompt Management
+
+- **[GitLab](../../docs/proxy/prompt_management)**
+ - GitLab based Prompt manager - [PR #14988](https://github.com/BerriAI/litellm/pull/14988)
+
+---
+
+## Spend Tracking, Budgets and Rate Limiting
+
+- **Cost Tracking**
+ - Proxy: end user cost tracking in the responses API - [PR #15124](https://github.com/BerriAI/litellm/pull/15124)
+- **Parallel Request Limiter v3**
+ - Use well known redis cluster hashing algorithm - [PR #15052](https://github.com/BerriAI/litellm/pull/15052)
+ - Fixes to dynamic rate limiter v3 - add saturation detection - [PR #15119](https://github.com/BerriAI/litellm/pull/15119)
+ - Dynamic Rate Limiter v3 - fixes for detecting saturation + fixes for post saturation behavior - [PR #15192](https://github.com/BerriAI/litellm/pull/15192)
+- **Teams**
+ - Add model specific tpm/rpm limits to teams on LiteLLM - [PR #15044](https://github.com/BerriAI/litellm/pull/15044)
+
+---
+
+## MCP Gateway
+
+- **Server Configuration**
+ - Specify forwardable headers, specify allowed/disallowed tools for MCP servers - [PR #15002](https://github.com/BerriAI/litellm/pull/15002)
+ - Enforce server permissions on call tools - [PR #15044](https://github.com/BerriAI/litellm/pull/15044)
+ - MCP Gateway Fine-grained Tools Addition - [PR #15153](https://github.com/BerriAI/litellm/pull/15153)
+- **Bug Fixes**
+ - Remove servername prefix mcp tools tests - [PR #14986](https://github.com/BerriAI/litellm/pull/14986)
+ - Resolve regression with duplicate Mcp-Protocol-Version header - [PR #15050](https://github.com/BerriAI/litellm/pull/15050)
+ - Fix test_mcp_server.py - [PR #15183](https://github.com/BerriAI/litellm/pull/15183)
+
+---
+
+## Performance / Loadbalancing / Reliability improvements
+
+- **Router Optimizations**
+ - **+62.5% P99 Latency Improvement** - Remove router inefficiencies (from O(M*N) to O(1)) - [PR #15046](https://github.com/BerriAI/litellm/pull/15046)
+ - Remove hasattr checks in Router - [PR #15082](https://github.com/BerriAI/litellm/pull/15082)
+ - Remove Double Lookups - [PR #15084](https://github.com/BerriAI/litellm/pull/15084)
+ - Optimize _filter_cooldown_deployments from O(n×m + k×n) to O(n) - [PR #15091](https://github.com/BerriAI/litellm/pull/15091)
+ - Optimize unhealthy deployment filtering in retry path (O(n*m) → O(n+m)) - [PR #15110](https://github.com/BerriAI/litellm/pull/15110)
+- **Cache Optimizations**
+ - Reduce complexity of InMemoryCache.evict_cache from O(n*log(n)) to O(log(n)) - [PR #15000](https://github.com/BerriAI/litellm/pull/15000)
+ - Avoiding expensive operations when cache isn't available - [PR #15182](https://github.com/BerriAI/litellm/pull/15182)
+- **Worker Management**
+ - Add proxy CLI option to recycle workers after N requests - [PR #15007](https://github.com/BerriAI/litellm/pull/15007)
+- **Metrics & Monitoring**
+ - LiteLLM Overhead metric tracking - Add support for tracking litellm overhead on cache hits - [PR #15045](https://github.com/BerriAI/litellm/pull/15045)
+
+---
+
+## Documentation Updates
+
+- **Provider Documentation**
+ - Update litellm docs from latest release - [PR #15004](https://github.com/BerriAI/litellm/pull/15004)
+ - Add missing api_key parameter - [PR #15058](https://github.com/BerriAI/litellm/pull/15058)
+- **General Documentation**
+ - Use docker compose instead of docker-compose - [PR #15024](https://github.com/BerriAI/litellm/pull/15024)
+ - Add railtracks to projects that are using litellm - [PR #15144](https://github.com/BerriAI/litellm/pull/15144)
+ - Perf: Last week improvement - [PR #15193](https://github.com/BerriAI/litellm/pull/15193)
+ - Sync models GitHub documentation with Loom video and cross-reference - [PR #15191](https://github.com/BerriAI/litellm/pull/15191)
+
+---
+
+## Security Fixes
+
+- **JWT Token Security** - Don't log JWT SSO token on .info() log - [PR #15145](https://github.com/BerriAI/litellm/pull/15145)
+
+---
+
+## New Contributors
+
+* @herve-ves made their first contribution in [PR #14998](https://github.com/BerriAI/litellm/pull/14998)
+* @wenxi-onyx made their first contribution in [PR #15008](https://github.com/BerriAI/litellm/pull/15008)
+* @jpetrucciani made their first contribution in [PR #15005](https://github.com/BerriAI/litellm/pull/15005)
+* @abhijitjavelin made their first contribution in [PR #14983](https://github.com/BerriAI/litellm/pull/14983)
+* @ZeroClover made their first contribution in [PR #15039](https://github.com/BerriAI/litellm/pull/15039)
+* @cedarm made their first contribution in [PR #15043](https://github.com/BerriAI/litellm/pull/15043)
+* @Isydmr made their first contribution in [PR #15025](https://github.com/BerriAI/litellm/pull/15025)
+* @serializer made their first contribution in [PR #15013](https://github.com/BerriAI/litellm/pull/15013)
+* @eddierichter-amd made their first contribution in [PR #14840](https://github.com/BerriAI/litellm/pull/14840)
+* @malags made their first contribution in [PR #15000](https://github.com/BerriAI/litellm/pull/15000)
+* @henryhwang made their first contribution in [PR #15029](https://github.com/BerriAI/litellm/pull/15029)
+* @plafleur made their first contribution in [PR #15111](https://github.com/BerriAI/litellm/pull/15111)
+* @tyler-liner made their first contribution in [PR #14799](https://github.com/BerriAI/litellm/pull/14799)
+* @Amir-R25 made their first contribution in [PR #15144](https://github.com/BerriAI/litellm/pull/15144)
+* @georg-wolflein made their first contribution in [PR #15124](https://github.com/BerriAI/litellm/pull/15124)
+* @niharm made their first contribution in [PR #15140](https://github.com/BerriAI/litellm/pull/15140)
+* @anthony-liner made their first contribution in [PR #15015](https://github.com/BerriAI/litellm/pull/15015)
+* @rishiganesh2002 made their first contribution in [PR #15153](https://github.com/BerriAI/litellm/pull/15153)
+* @danielaskdd made their first contribution in [PR #15160](https://github.com/BerriAI/litellm/pull/15160)
+* @JVenberg made their first contribution in [PR #15146](https://github.com/BerriAI/litellm/pull/15146)
+* @speglich made their first contribution in [PR #15072](https://github.com/BerriAI/litellm/pull/15072)
+* @daily-kim made their first contribution in [PR #14764](https://github.com/BerriAI/litellm/pull/14764)
+
+---
+
+## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.5.rc.4...v1.77.7.rc.1)**
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 7acd92240c..12fb2a681f 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -674,6 +674,7 @@ const sidebars = {
items: [
"data_security",
"data_retention",
+ "proxy/security_encryption_faq",
"migration_policy",
{
type: "category",
diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
index d4964b9667..8db0fcf752 100644
--- a/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
+++ b/enterprise/litellm_enterprise/enterprise_callbacks/pagerduty/pagerduty.py
@@ -119,6 +119,7 @@ class PagerDutyAlerting(SlackAlerting):
user_api_key_end_user_id=_meta.get("user_api_key_end_user_id"),
user_api_key_user_email=_meta.get("user_api_key_user_email"),
user_api_key_request_route=_meta.get("user_api_key_request_route"),
+ user_api_key_auth_metadata=_meta.get("user_api_key_auth_metadata"),
)
)
@@ -196,7 +197,11 @@ class PagerDutyAlerting(SlackAlerting):
user_api_key_alias=user_api_key_dict.key_alias,
user_api_key_spend=user_api_key_dict.spend,
user_api_key_max_budget=user_api_key_dict.max_budget,
- user_api_key_budget_reset_at=user_api_key_dict.budget_reset_at.isoformat() if user_api_key_dict.budget_reset_at else None,
+ user_api_key_budget_reset_at=(
+ user_api_key_dict.budget_reset_at.isoformat()
+ if user_api_key_dict.budget_reset_at
+ else None
+ ),
user_api_key_org_id=user_api_key_dict.org_id,
user_api_key_team_id=user_api_key_dict.team_id,
user_api_key_user_id=user_api_key_dict.user_id,
@@ -204,6 +209,7 @@ class PagerDutyAlerting(SlackAlerting):
user_api_key_end_user_id=user_api_key_dict.end_user_id,
user_api_key_user_email=user_api_key_dict.user_email,
user_api_key_request_route=user_api_key_dict.request_route,
+ user_api_key_auth_metadata=user_api_key_dict.metadata,
)
)
diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py
index d3b0aefb86..3b37e14b89 100644
--- a/enterprise/litellm_enterprise/integrations/prometheus.py
+++ b/enterprise/litellm_enterprise/integrations/prometheus.py
@@ -21,6 +21,7 @@ from litellm._logging import print_verbose, verbose_logger
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import LiteLLM_TeamTable, UserAPIKeyAuth
from litellm.types.integrations.prometheus import *
+from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name
from litellm.types.utils import StandardLoggingPayload
from litellm.utils import get_end_user_id_for_cost_tracking
@@ -794,9 +795,16 @@ class PrometheusLogger(CustomLogger):
output_tokens = standard_logging_payload["completion_tokens"]
tokens_used = standard_logging_payload["total_tokens"]
response_cost = standard_logging_payload["response_cost"]
- _requester_metadata = standard_logging_payload["metadata"].get(
+ _requester_metadata: Optional[dict] = standard_logging_payload["metadata"].get(
"requester_metadata"
)
+ user_api_key_auth_metadata: Optional[dict] = standard_logging_payload[
+ "metadata"
+ ].get("user_api_key_auth_metadata")
+ combined_metadata: Dict[str, Any] = {
+ **(_requester_metadata if _requester_metadata else {}),
+ **(user_api_key_auth_metadata if user_api_key_auth_metadata else {}),
+ }
if standard_logging_payload is not None and isinstance(
standard_logging_payload, dict
):
@@ -828,8 +836,7 @@ class PrometheusLogger(CustomLogger):
exception_status=None,
exception_class=None,
custom_metadata_labels=get_custom_labels_from_metadata(
- metadata=standard_logging_payload["metadata"].get("requester_metadata")
- or {}
+ metadata=combined_metadata
),
route=standard_logging_payload["metadata"].get(
"user_api_key_request_route"
@@ -1649,9 +1656,22 @@ class PrometheusLogger(CustomLogger):
api_base: Optional[str],
api_provider: str,
):
- self.litellm_deployment_state.labels(
- litellm_model_name, model_id, api_base, api_provider
- ).set(state)
+ """
+ Set the deployment state.
+ """
+ ### get labels
+ _labels = prometheus_label_factory(
+ supported_enum_labels=self.get_labels_for_metric(
+ metric_name="litellm_deployment_state"
+ ),
+ enum_values=UserAPIKeyLabelValues(
+ litellm_model_name=litellm_model_name,
+ model_id=model_id,
+ api_base=api_base,
+ api_provider=api_provider,
+ ),
+ )
+ self.litellm_deployment_state.labels(**_labels).set(state)
def set_deployment_healthy(
self,
@@ -2228,8 +2248,10 @@ def prometheus_label_factory(
if enum_values.custom_metadata_labels is not None:
for key, value in enum_values.custom_metadata_labels.items():
- if key in supported_enum_labels:
- filtered_labels[key] = value
+ # check sanitized key
+ sanitized_key = _sanitize_prometheus_label_name(key)
+ if sanitized_key in supported_enum_labels:
+ filtered_labels[sanitized_key] = value
# Add custom tags if configured
if enum_values.tags is not None:
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.23-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.23-py3-none-any.whl
new file mode 100644
index 0000000000..4220fad36c
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.23-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.23.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.23.tar.gz
new file mode 100644
index 0000000000..ceccaacda4
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.23.tar.gz differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.25-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.25-py3-none-any.whl
new file mode 100644
index 0000000000..8e0f50c212
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.25-py3-none-any.whl differ
diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.25.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.25.tar.gz
new file mode 100644
index 0000000000..2565f68a2b
Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.2.25.tar.gz differ
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251003165142_add_allowed_tools_to_mcp/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251003165142_add_allowed_tools_to_mcp/migration.sql
new file mode 100644
index 0000000000..bdac1e42bc
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251003165142_add_allowed_tools_to_mcp/migration.sql
@@ -0,0 +1,3 @@
+-- AlterTable
+ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN "allowed_tools" TEXT[] DEFAULT ARRAY[]::TEXT[];
+
diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251003190954_extra_headers_to_mcp_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251003190954_extra_headers_to_mcp_table/migration.sql
new file mode 100644
index 0000000000..1cfcf062eb
--- /dev/null
+++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251003190954_extra_headers_to_mcp_table/migration.sql
@@ -0,0 +1,3 @@
+-- AlterTable
+ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN "extra_headers" TEXT[] DEFAULT ARRAY[]::TEXT[];
+
diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
index 766625145f..ff5d7d6b9f 100644
--- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
+++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma
@@ -178,6 +178,8 @@ model LiteLLM_MCPServerTable {
updated_by String?
mcp_info Json? @default("{}")
mcp_access_groups String[]
+ allowed_tools String[] @default([])
+ extra_headers String[] @default([])
// Health check status
status String? @default("unknown")
last_health_check DateTime?
diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml
index 94e7f59bfa..fbd8167e79 100644
--- a/litellm-proxy-extras/pyproject.toml
+++ b/litellm-proxy-extras/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm-proxy-extras"
-version = "0.2.22"
+version = "0.2.25"
description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
authors = ["BerriAI"]
readme = "README.md"
@@ -22,7 +22,7 @@ requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
-version = "0.2.22"
+version = "0.2.25"
version_files = [
"pyproject.toml:version",
"../requirements.txt:litellm-proxy-extras==",
diff --git a/litellm/__init__.py b/litellm/__init__.py
index fbc3c79508..60cf327c26 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -290,7 +290,7 @@ banned_keywords_list: Optional[Union[str, List]] = None
llm_guard_mode: Literal["all", "key-specific", "request-specific"] = "all"
guardrail_name_config_map: Dict[str, GuardrailItem] = {}
include_cost_in_streaming_usage: bool = False
-### PROMPTS ###
+### PROMPTS ####
from litellm.types.prompts.init_prompts import PromptSpec
prompt_name_config_map: Dict[str, PromptSpec] = {}
diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py
index b5ab5aeefe..696c67c44d 100644
--- a/litellm/litellm_core_utils/litellm_logging.py
+++ b/litellm/litellm_core_utils/litellm_logging.py
@@ -4040,6 +4040,7 @@ class StandardLoggingPayloadSetup:
usage_object=usage_object,
requester_custom_headers=None,
cold_storage_object_key=None,
+ user_api_key_auth_metadata=None,
)
if isinstance(metadata, dict):
# Filter the metadata dictionary to include only the specified keys
@@ -4755,6 +4756,7 @@ def get_standard_logging_metadata(
requester_custom_headers=None,
user_api_key_request_route=None,
cold_storage_object_key=None,
+ user_api_key_auth_metadata=None,
)
if isinstance(metadata, dict):
# Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields
diff --git a/litellm/llms/snowflake/chat/transformation.py b/litellm/llms/snowflake/chat/transformation.py
index 2b92911b05..4c0258d9f4 100644
--- a/litellm/llms/snowflake/chat/transformation.py
+++ b/litellm/llms/snowflake/chat/transformation.py
@@ -1,14 +1,15 @@
"""
-Support for Snowflake REST API
+Support for Snowflake REST API
"""
-from typing import TYPE_CHECKING, Any, List, Optional, Tuple
+import json
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import httpx
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllMessageValues
-from litellm.types.utils import ModelResponse
+from litellm.types.utils import ChatCompletionMessageToolCall, Function, ModelResponse
from ...openai_like.chat.transformation import OpenAIGPTConfig
@@ -22,15 +23,25 @@ else:
class SnowflakeConfig(OpenAIGPTConfig):
"""
- source: https://docs.snowflake.com/en/sql-reference/functions/complete-snowflake-cortex
+ Reference: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-llm-rest-api
+
+ Snowflake Cortex LLM REST API supports function calling with specific models (e.g., Claude 3.5 Sonnet).
+ This config handles transformation between OpenAI format and Snowflake's tool_spec format.
"""
@classmethod
def get_config(cls):
return super().get_config()
- def get_supported_openai_params(self, model: str) -> List:
- return ["temperature", "max_tokens", "top_p", "response_format"]
+ def get_supported_openai_params(self, model: str) -> List[str]:
+ return [
+ "temperature",
+ "max_tokens",
+ "top_p",
+ "response_format",
+ "tools",
+ "tool_choice",
+ ]
def map_openai_params(
self,
@@ -56,6 +67,57 @@ class SnowflakeConfig(OpenAIGPTConfig):
optional_params[param] = value
return optional_params
+ def _transform_tool_calls_from_snowflake_to_openai(
+ self, content_list: List[Dict[str, Any]]
+ ) -> Tuple[str, Optional[List[ChatCompletionMessageToolCall]]]:
+ """
+ Transform Snowflake tool calls to OpenAI format.
+
+ Args:
+ content_list: Snowflake's content_list array containing text and tool_use items
+
+ Returns:
+ Tuple of (text_content, tool_calls)
+
+ Snowflake format in content_list:
+ {
+ "type": "tool_use",
+ "tool_use": {
+ "tool_use_id": "tooluse_...",
+ "name": "get_weather",
+ "input": {"location": "Paris"}
+ }
+ }
+
+ OpenAI format (returned tool_calls):
+ ChatCompletionMessageToolCall(
+ id="tooluse_...",
+ type="function",
+ function=Function(name="get_weather", arguments='{"location": "Paris"}')
+ )
+ """
+ text_content = ""
+ tool_calls: List[ChatCompletionMessageToolCall] = []
+
+ for idx, content_item in enumerate(content_list):
+ if content_item.get("type") == "text":
+ text_content += content_item.get("text", "")
+
+ ## TOOL CALLING
+ elif content_item.get("type") == "tool_use":
+ tool_use_data = content_item.get("tool_use", {})
+ tool_call = ChatCompletionMessageToolCall(
+ id=tool_use_data.get("tool_use_id", ""),
+ type="function",
+ function=Function(
+ name=tool_use_data.get("name", ""),
+ arguments=json.dumps(tool_use_data.get("input", {})),
+ ),
+ )
+ tool_calls.append(tool_call)
+
+ return text_content, tool_calls if tool_calls else None
+
def transform_response(
self,
model: str,
@@ -71,6 +133,7 @@ class SnowflakeConfig(OpenAIGPTConfig):
json_mode: Optional[bool] = None,
) -> ModelResponse:
response_json = raw_response.json()
+
logging_obj.post_call(
input=messages,
api_key="",
@@ -78,6 +141,26 @@ class SnowflakeConfig(OpenAIGPTConfig):
additional_args={"complete_input_dict": request_data},
)
+ ## RESPONSE TRANSFORMATION
+ # Snowflake returns content_list (not content) with tool_use objects
+ # We need to transform this to OpenAI's format with content + tool_calls
+ if "choices" in response_json and len(response_json["choices"]) > 0:
+ choice = response_json["choices"][0]
+ if "message" in choice and "content_list" in choice["message"]:
+ content_list = choice["message"]["content_list"]
+ (
+ text_content,
+ tool_calls,
+ ) = self._transform_tool_calls_from_snowflake_to_openai(content_list)
+
+ # Update the choice message with OpenAI format
+ choice["message"]["content"] = text_content
+ if tool_calls:
+ choice["message"]["tool_calls"] = tool_calls
+
+ # Remove Snowflake-specific content_list
+ del choice["message"]["content_list"]
+
returned_response = ModelResponse(**response_json)
returned_response.model = "snowflake/" + (returned_response.model or "")
@@ -150,6 +233,95 @@ class SnowflakeConfig(OpenAIGPTConfig):
return api_base
+ def _transform_tools(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+ """
+ Transform OpenAI tool format to Snowflake tool format.
+
+ Args:
+ tools: List of tools in OpenAI format
+
+ Returns:
+ List of tools in Snowflake format
+
+ OpenAI format:
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "...",
+ "parameters": {...}
+ }
+ }
+
+ Snowflake format:
+ {
+ "tool_spec": {
+ "type": "generic",
+ "name": "get_weather",
+ "description": "...",
+ "input_schema": {...}
+ }
+ }
+ """
+ snowflake_tools: List[Dict[str, Any]] = []
+ for tool in tools:
+ if tool.get("type") == "function":
+ function = tool.get("function", {})
+ snowflake_tool: Dict[str, Any] = {
+ "tool_spec": {
+ "type": "generic",
+ "name": function.get("name"),
+ "input_schema": function.get(
+ "parameters",
+ {"type": "object", "properties": {}},
+ ),
+ }
+ }
+ # Add description if present
+ if "description" in function:
+ snowflake_tool["tool_spec"]["description"] = function[
+ "description"
+ ]
+
+ snowflake_tools.append(snowflake_tool)
+
+ return snowflake_tools
+
+ def _transform_tool_choice(
+ self, tool_choice: Union[str, Dict[str, Any]]
+ ) -> Union[str, Dict[str, Any]]:
+ """
+ Transform OpenAI tool_choice format to Snowflake format.
+
+ Args:
+ tool_choice: Tool choice in OpenAI format (str or dict)
+
+ Returns:
+ Tool choice in Snowflake format
+
+ OpenAI format:
+ {"type": "function", "function": {"name": "get_weather"}}
+
+ Snowflake format:
+ {"type": "tool", "name": ["get_weather"]}
+
+ Note: String values ("auto", "required", "none") pass through unchanged.
+ """
+ if isinstance(tool_choice, str):
+ # "auto", "required", "none" pass through as-is
+ return tool_choice
+
+ if isinstance(tool_choice, dict):
+ if tool_choice.get("type") == "function":
+ function_name = tool_choice.get("function", {}).get("name")
+ if function_name:
+ return {
+ "type": "tool",
+ "name": [function_name], # Snowflake expects array
+ }
+
+ return tool_choice
+
def transform_request(
self,
model: str,
@@ -160,6 +332,18 @@ class SnowflakeConfig(OpenAIGPTConfig):
) -> dict:
stream: bool = optional_params.pop("stream", None) or False
extra_body = optional_params.pop("extra_body", {})
+
+ ## TOOL CALLING
+ # Transform tools from OpenAI format to Snowflake's tool_spec format
+ tools = optional_params.pop("tools", None)
+ if tools:
+ optional_params["tools"] = self._transform_tools(tools)
+
+ # Transform tool_choice from OpenAI format to Snowflake's tool name array format
+ tool_choice = optional_params.pop("tool_choice", None)
+ if tool_choice:
+ optional_params["tool_choice"] = self._transform_tool_choice(tool_choice)
+
return {
"model": model,
"messages": messages,
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index cc50bc9954..d3f6796721 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -415,8 +415,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
googleSearchRetrieval = self.get_tool_value(tool, VertexToolName.GOOGLE_SEARCH_RETRIEVAL.value)
elif tool_name and tool_name == VertexToolName.ENTERPRISE_WEB_SEARCH.value:
enterpriseWebSearch = self.get_tool_value(tool, VertexToolName.ENTERPRISE_WEB_SEARCH.value)
- elif tool_name and tool_name == VertexToolName.URL_CONTEXT.value:
- urlContext = self.get_tool_value(tool, VertexToolName.URL_CONTEXT.value)
+ elif tool_name and (tool_name == VertexToolName.URL_CONTEXT.value or tool_name == "urlContext"):
+ urlContext = self.get_tool_value(tool, tool_name)
elif tool_name and (
tool_name == VertexToolName.GOOGLE_MAPS.value or tool_name == "google_maps"
):
@@ -448,9 +448,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
"Invalid tool={}. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request."
)
- _tools = Tools(
- function_declarations=gtool_func_declarations,
- )
+ # Only include function_declarations if there are actual functions
+ _tools = Tools()
+ if gtool_func_declarations:
+ _tools["function_declarations"] = gtool_func_declarations
if googleSearch is not None:
_tools[VertexToolName.GOOGLE_SEARCH.value] = googleSearch
if googleSearchRetrieval is not None:
diff --git a/litellm/main.py b/litellm/main.py
index cfb0bef079..46a024a763 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -2872,7 +2872,7 @@ def completion( # type: ignore # noqa: PLR0915
custom_llm_provider=custom_llm_provider, # type: ignore
client=client,
api_base=api_base,
- extra_headers=extra_headers,
+ extra_headers=headers,
)
elif custom_llm_provider == "vertex_ai":
@@ -2941,7 +2941,7 @@ def completion( # type: ignore # noqa: PLR0915
custom_llm_provider=custom_llm_provider, # type: ignore
client=client,
api_base=api_base,
- extra_headers=extra_headers,
+ extra_headers=headers,
)
elif "openai" in model:
# Vertex Model Garden - OpenAI compatible models
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 72b9c551d7..fd09aca4c1 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -3324,28 +3324,27 @@
"supports_web_search": true
},
"azure_ai/grok-4-fast-non-reasoning": {
- "input_cost_per_token": 5e-06,
+ "input_cost_per_token": 0.43e-06,
+ "output_cost_per_token": 1.73e-06,
"litellm_provider": "azure_ai",
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 2.5e-03,
- "source": "https://azure.microsoft.com/en-us/blog/grok-4-is-now-available-in-azure-ai-foundry-unlock-frontier-intelligence-and-business-ready-capabilities/",
"supports_function_calling": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_web_search": true
},
"azure_ai/grok-4-fast-reasoning": {
- "input_cost_per_token": 5.8e-06,
+ "input_cost_per_token": 0.43e-06,
+ "output_cost_per_token": 1.73e-06,
"litellm_provider": "azure_ai",
"max_input_tokens": 131072,
"max_output_tokens": 131072,
"max_tokens": 131072,
"mode": "chat",
- "output_cost_per_token": 2.9e-03,
- "source": "https://azure.microsoft.com/en-us/blog/grok-4-is-now-available-in-azure-ai-foundry-unlock-frontier-intelligence-and-business-ready-capabilities/",
+ "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-the-grok-4-fast-models-from-xai-now-available-in-azure-ai-foundry/4456701",
"supports_function_calling": true,
"supports_reasoning": true,
"supports_response_schema": true,
@@ -22174,6 +22173,307 @@
"supports_tool_choice": true,
"supports_vision": false
},
+ "watsonx/bigscience/mt0-xxl-13b": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0005,
+ "output_cost_per_token": 0.002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/core42/jais-13b-chat": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0005,
+ "output_cost_per_token": 0.002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/google/flan-t5-xl-3b": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0001,
+ "output_cost_per_token": 0.00025,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-13b-chat-v2": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0005,
+ "output_cost_per_token": 0.002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-13b-instruct-v2": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0005,
+ "output_cost_per_token": 0.002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-3-3-8b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00025,
+ "output_cost_per_token": 0.001,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-4-h-small": {
+ "max_tokens": 20480,
+ "max_input_tokens": 20480,
+ "max_output_tokens": 20480,
+ "input_cost_per_token": 0.000625,
+ "output_cost_per_token": 0.0025,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-guardian-3-2-2b": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00015,
+ "output_cost_per_token": 0.0006,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-guardian-3-3-8b": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00025,
+ "output_cost_per_token": 0.001,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-ttm-1024-96-r2": {
+ "max_tokens": 512,
+ "max_input_tokens": 512,
+ "max_output_tokens": 512,
+ "input_cost_per_token": 0.000625,
+ "output_cost_per_token": 0.000625,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-ttm-1536-96-r2": {
+ "max_tokens": 512,
+ "max_input_tokens": 512,
+ "max_output_tokens": 512,
+ "input_cost_per_token": 0.000625,
+ "output_cost_per_token": 0.000625,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-ttm-512-96-r2": {
+ "max_tokens": 512,
+ "max_input_tokens": 512,
+ "max_output_tokens": 512,
+ "input_cost_per_token": 0.000625,
+ "output_cost_per_token": 0.000625,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/ibm/granite-vision-3-2-2b": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.00015,
+ "output_cost_per_token": 0.0006,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": true
+ },
+ "watsonx/meta-llama/llama-3-2-11b-vision-instruct": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.00025,
+ "output_cost_per_token": 0.001,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": true
+ },
+ "watsonx/meta-llama/llama-3-2-1b-instruct": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.0001,
+ "output_cost_per_token": 0.0002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/meta-llama/llama-3-2-3b-instruct": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.00015,
+ "output_cost_per_token": 0.0006,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/meta-llama/llama-3-2-90b-vision-instruct": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.002,
+ "output_cost_per_token": 0.008,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": true
+ },
+ "watsonx/meta-llama/llama-3-3-70b-instruct": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.002,
+ "output_cost_per_token": 0.006,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/meta-llama/llama-4-maverick-17b": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.0005,
+ "output_cost_per_token": 0.002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/meta-llama/llama-guard-3-11b-vision": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.00025,
+ "output_cost_per_token": 0.001,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": true
+ },
+ "watsonx/mistralai/mistral-medium-2505": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.00225,
+ "output_cost_per_token": 0.00675,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/mistralai/mistral-small-2503": {
+ "max_tokens": 32000,
+ "max_input_tokens": 32000,
+ "max_output_tokens": 32000,
+ "input_cost_per_token": 0.0002,
+ "output_cost_per_token": 0.0006,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": true,
+ "supports_parallel_function_calling": true,
+ "supports_vision": false
+ },
+ "watsonx/mistralai/pixtral-12b-2409": {
+ "max_tokens": 128000,
+ "max_input_tokens": 128000,
+ "max_output_tokens": 128000,
+ "input_cost_per_token": 0.00015,
+ "output_cost_per_token": 0.00015,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": true
+ },
+ "watsonx/openai/gpt-oss-120b": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.004,
+ "output_cost_per_token": 0.016,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+ "watsonx/sdaia/allam-1-13b-instruct": {
+ "max_tokens": 8192,
+ "max_input_tokens": 8192,
+ "max_output_tokens": 8192,
+ "input_cost_per_token": 0.0005,
+ "output_cost_per_token": 0.002,
+ "litellm_provider": "watsonx",
+ "mode": "chat",
+ "supports_function_calling": false,
+ "supports_parallel_function_calling": false,
+ "supports_vision": false
+ },
+
"whisper-1": {
"input_cost_per_second": 0.0001,
"litellm_provider": "openai",
diff --git a/litellm/proxy/_experimental/mcp_server/db.py b/litellm/proxy/_experimental/mcp_server/db.py
index 79e3b0f762..2269548574 100644
--- a/litellm/proxy/_experimental/mcp_server/db.py
+++ b/litellm/proxy/_experimental/mcp_server/db.py
@@ -1,7 +1,7 @@
-from litellm._uuid import uuid
from typing import Any, Dict, Iterable, List, Optional, Set, Union
from litellm._logging import verbose_proxy_logger
+from litellm._uuid import uuid
from litellm.proxy._types import (
LiteLLM_MCPServerTable,
LiteLLM_ObjectPermissionTable,
@@ -30,7 +30,7 @@ def _prepare_mcp_server_data(
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
# Convert model to dict
- data_dict = data.model_dump()
+ data_dict = data.model_dump(exclude_none=True)
# Ensure alias is always present in the dict (even if None)
if "alias" not in data_dict:
data_dict["alias"] = getattr(data, "alias", None)
diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
index 9172568f30..2c46f9561d 100644
--- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
+++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
@@ -10,7 +10,7 @@ import asyncio
import datetime
import hashlib
import json
-from typing import Any, Dict, List, Optional, Union, cast
+from typing import Any, Dict, List, Optional, Set, Union, cast
from fastapi import HTTPException
from mcp.types import CallToolRequestParams as MCPCallToolRequestParams
@@ -240,50 +240,64 @@ class MCPServerManager:
)
def add_update_server(self, mcp_server: LiteLLM_MCPServerTable):
- if mcp_server.server_id not in self.get_registry():
- _mcp_info: MCPInfo = mcp_server.mcp_info or {}
- # Use helper to deserialize environment dictionary
- # Safely access env field which may not exist on Prisma model objects
- env_data = getattr(mcp_server, "env", None)
- env_dict = _deserialize_env_dict(env_data)
- # Use alias for name if present, else server_name
- name_for_prefix = (
- mcp_server.alias or mcp_server.server_name or mcp_server.server_id
- )
- # Preserve all custom fields from database while setting defaults for core fields
- mcp_info: MCPInfo = _mcp_info.copy()
- # Set default values for core fields if not present
- if "server_name" not in mcp_info:
- mcp_info["server_name"] = mcp_server.server_name or mcp_server.server_id
- if "description" not in mcp_info and mcp_server.description:
- mcp_info["description"] = mcp_server.description
+ try:
+ if mcp_server.server_id not in self.get_registry():
+ _mcp_info: MCPInfo = mcp_server.mcp_info or {}
+ # Use helper to deserialize environment dictionary
+ # Safely access env field which may not exist on Prisma model objects
+ env_data = getattr(mcp_server, "env", None)
+ env_dict = _deserialize_env_dict(env_data)
+ # Use alias for name if present, else server_name
+ name_for_prefix = (
+ mcp_server.alias or mcp_server.server_name or mcp_server.server_id
+ )
+ # Preserve all custom fields from database while setting defaults for core fields
+ mcp_info: MCPInfo = _mcp_info.copy()
+ # Set default values for core fields if not present
+ if "server_name" not in mcp_info:
+ mcp_info["server_name"] = (
+ mcp_server.server_name or mcp_server.server_id
+ )
+ if "description" not in mcp_info and mcp_server.description:
+ mcp_info["description"] = mcp_server.description
- new_server = MCPServer(
- server_id=mcp_server.server_id,
- name=name_for_prefix,
- alias=getattr(mcp_server, "alias", None),
- server_name=getattr(mcp_server, "server_name", None),
- url=mcp_server.url,
- transport=cast(MCPTransportType, mcp_server.transport),
- auth_type=cast(MCPAuthType, mcp_server.auth_type),
- mcp_info=mcp_info,
- extra_headers=getattr(mcp_server, "extra_headers", None),
- # oauth specific fields
- client_id=getattr(mcp_server, "client_id", None),
- client_secret=getattr(mcp_server, "client_secret", None),
- scopes=getattr(mcp_server, "scopes", None),
- authorization_url=getattr(mcp_server, "authorization_url", None),
- token_url=getattr(mcp_server, "token_url", None),
- # Stdio-specific fields
- command=getattr(mcp_server, "command", None),
- args=getattr(mcp_server, "args", None) or [],
- env=env_dict,
- access_groups=getattr(mcp_server, "mcp_access_groups", None),
- allowed_tools=getattr(mcp_server, "allowed_tools", None),
- disallowed_tools=getattr(mcp_server, "disallowed_tools", None),
- )
- self.registry[mcp_server.server_id] = new_server
- verbose_logger.debug(f"Added MCP Server: {name_for_prefix}")
+ new_server = MCPServer(
+ server_id=mcp_server.server_id,
+ name=name_for_prefix,
+ alias=getattr(mcp_server, "alias", None),
+ server_name=getattr(mcp_server, "server_name", None),
+ url=mcp_server.url,
+ transport=cast(MCPTransportType, mcp_server.transport),
+ auth_type=cast(MCPAuthType, mcp_server.auth_type),
+ mcp_info=mcp_info,
+ extra_headers=getattr(mcp_server, "extra_headers", None),
+ # oauth specific fields
+ client_id=getattr(mcp_server, "client_id", None),
+ client_secret=getattr(mcp_server, "client_secret", None),
+ scopes=getattr(mcp_server, "scopes", None),
+ authorization_url=getattr(mcp_server, "authorization_url", None),
+ token_url=getattr(mcp_server, "token_url", None),
+ # Stdio-specific fields
+ command=getattr(mcp_server, "command", None),
+ args=getattr(mcp_server, "args", None) or [],
+ env=env_dict,
+ access_groups=getattr(mcp_server, "mcp_access_groups", None),
+ allowed_tools=getattr(mcp_server, "allowed_tools", None),
+ disallowed_tools=getattr(mcp_server, "disallowed_tools", None),
+ )
+ self.registry[mcp_server.server_id] = new_server
+ verbose_logger.debug(f"Added MCP Server: {name_for_prefix}")
+
+ except Exception as e:
+ verbose_logger.debug(f"Failed to add MCP server: {str(e)}")
+ raise e
+
+ def get_all_mcp_server_ids(self) -> Set[str]:
+ """
+ Get all MCP server IDs
+ """
+ all_servers = list(self.get_registry().values())
+ return {server.server_id for server in all_servers}
async def get_allowed_mcp_servers(
self, user_api_key_auth: Optional[UserAPIKeyAuth] = None
@@ -1118,25 +1132,23 @@ class MCPServerManager:
if _server_id in allowed_server_ids:
list_mcp_servers.append(
LiteLLM_MCPServerTable(
- server_id=_server_id,
- server_name=_server_config.name,
- alias=_server_config.alias,
- url=_server_config.url,
- transport=_server_config.transport,
- auth_type=_server_config.auth_type,
- created_at=datetime.datetime.now(),
- updated_at=datetime.datetime.now(),
- description=(
- _server_config.mcp_info.get("description")
- if _server_config.mcp_info
- else None
- ),
- mcp_info=_server_config.mcp_info,
- mcp_access_groups=_server_config.access_groups or [],
- # Stdio-specific fields
- command=getattr(_server_config, "command", None),
- args=getattr(_server_config, "args", None) or [],
- env=getattr(_server_config, "env", None) or {},
+ **{
+ **_server_config.model_dump(),
+ "created_at": datetime.datetime.now(),
+ "updated_at": datetime.datetime.now(),
+ "description": (
+ _server_config.mcp_info.get("description")
+ if _server_config.mcp_info
+ else None
+ ),
+ "allowed_tools": _server_config.allowed_tools or [],
+ "mcp_info": _server_config.mcp_info,
+ "mcp_access_groups": _server_config.access_groups or [],
+ "extra_headers": _server_config.extra_headers or [],
+ "command": getattr(_server_config, "command", None),
+ "args": getattr(_server_config, "args", None) or [],
+ "env": getattr(_server_config, "env", None) or {},
+ }
)
)
@@ -1176,44 +1188,19 @@ class MCPServerManager:
}
)
- # Map servers to their teams and return with health data
- from typing import cast
+ ## mark invalid servers w/ reason for being invalid
+ valid_server_ids = self.get_all_mcp_server_ids()
+ for server in list_mcp_servers:
+ if server.server_id not in valid_server_ids:
+ server.status = "unhealthy"
+ ## try adding server to registry to get error
+ try:
+ self.add_update_server(server)
+ except Exception as e:
+ server.health_check_error = str(e)
+ server.health_check_error = "Server is not in in memory registry yet. This could be a temporary sync issue."
- return [
- LiteLLM_MCPServerTable(
- server_id=server.server_id,
- server_name=server.server_name,
- alias=server.alias,
- description=server.description,
- url=server.url,
- transport=server.transport,
- auth_type=server.auth_type,
- created_at=server.created_at,
- created_by=server.created_by,
- updated_at=server.updated_at,
- updated_by=server.updated_by,
- mcp_access_groups=(
- server.mcp_access_groups
- if server.mcp_access_groups is not None
- else []
- ),
- allowed_tools=(
- server.allowed_tools
- if server.allowed_tools is not None
- else []
- ),
- mcp_info=server.mcp_info,
- teams=cast(
- List[Dict[str, str | None]],
- server_to_teams_map.get(server.server_id, []),
- ),
- # Stdio-specific fields
- command=getattr(server, "command", None),
- args=getattr(server, "args", None) or [],
- env=getattr(server, "env", None) or {},
- )
- for server in list_mcp_servers
- ]
+ return list_mcp_servers
async def reload_servers_from_database(self):
"""
diff --git a/litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/_S0i_Y-CCoYQWc9dIuLxF/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/_S0i_Y-CCoYQWc9dIuLxF/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/_S0i_Y-CCoYQWc9dIuLxF/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/WkpkdsewrdPMuTzVGS_5j/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/_S0i_Y-CCoYQWc9dIuLxF/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/154-f87cf692dcea3018.js b/litellm/proxy/_experimental/out/_next/static/chunks/154-f87cf692dcea3018.js
deleted file mode 100644
index 16755ff73c..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/154-f87cf692dcea3018.js
+++ /dev/null
@@ -1 +0,0 @@
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n={method:"GET",headers:{[f]:"Bearer ".concat(e)}},l=await fetch(r,n);if(!l.ok){let e=await l.json(),t=ov(e);throw m(t),Error(t)}let c=await l.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eI=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/global/activity/exceptions/deployment"):"/global/activity/exceptions/deployment";t&&o&&(r+="?start_date=".concat(t,"&end_date=").concat(o)),a&&(r+="&model_group=".concat(a));let n={method:"GET",headers:{[f]:"Bearer ".concat(e)}},l=await fetch(r,n);if(!l.ok){let e=await l.json(),t=ov(e);throw m(t),Error(t)}let c=await l.json();return console.log(c),c}catch(e){throw console.error("Failed to fetch spend data:",e),e}},eM=async e=>{try{let t=s?"".concat(s,"/global/spend/models?limit=5"):"/global/spend/models?limit=5",o=await fetch(t,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok){let e=await o.json(),t=ov(e);throw m(t),Error(t)}let a=await o.json();return 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a=s?"".concat(s,"/config/pass_through_endpoint/").concat(encodeURIComponent(t)):"/config/pass_through_endpoint/".concat(encodeURIComponent(t)),r=await fetch(a,{method:"POST",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"},body:JSON.stringify(o)});if(!r.ok){let e=await r.json(),t=ov(e);throw m(t),Error(t)}let l=await r.json();return n.Z.success("Pass through endpoint updated successfully"),l}catch(e){throw console.error("Failed to update pass through endpoint:",e),e}},od=async(e,t)=>{try{let o=s?"".concat(s,"/config/pass_through_endpoint?endpoint_id=").concat(encodeURIComponent(t)):"/config/pass_through_endpoint?endpoint_id=".concat(encodeURIComponent(t)),a=await fetch(o,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!a.ok){let e=await a.json(),t=ov(e);throw m(t),Error(t)}let r=(await a.json()).endpoints;if(!r||0===r.length)throw Error("Pass through endpoint not found");return r[0]}catch(e){throw console.error("Failed to get pass through endpoint info:",e),e}},ou=async(e,t)=>{try{let o=s?"".concat(s,"/config/callback/delete"):"/config/callback/delete",a=await fetch(o,{method:"POST",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"},body:JSON.stringify({callback_name:t})});if(!a.ok){let e=await a.json(),t=ov(e);throw m(t),Error(t)}return await a.json()}catch(e){throw console.error("Failed to delete specific callback:",e),e}},op=async e=>{let t=u(),o=await fetch("".concat(t,"/v1/mcp/tools"),{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!o.ok)throw Error("HTTP error! status: ".concat(o.status));return await o.json()},oh=async(e,t)=>{try{console.log("Testing MCP connection with config:",JSON.stringify(t));let a=s?"".concat(s,"/mcp-rest/test/connection"):"/mcp-rest/test/connection",r=await fetch(a,{method:"POST",headers:{"Content-Type":"application/json",[f]:"Bearer ".concat(e)},body:JSON.stringify(t)}),n=r.headers.get("content-type");if(!n||!n.includes("application/json")){let e=await r.text();throw console.error("Received non-JSON response:",e),Error("Received non-JSON response (".concat(r.status,": ").concat(r.statusText,"). Check network tab for details."))}let l=await r.json();if(!r.ok||"error"===l.status){if("error"===l.status);else{var o;return{status:"error",message:(null===(o=l.error)||void 0===o?void 0:o.message)||"MCP connection test failed: ".concat(r.status," ").concat(r.statusText)}}}return l}catch(e){throw console.error("MCP connection test error:",e),e}},og=async(e,t)=>{try{console.log("Testing MCP tools list with config:",JSON.stringify(t));let o=s?"".concat(s,"/mcp-rest/test/tools/list"):"/mcp-rest/test/tools/list",a=await fetch(o,{method:"POST",headers:{"Content-Type":"application/json",[f]:"Bearer ".concat(e)},body:JSON.stringify(t)}),r=a.headers.get("content-type");if(!r||!r.includes("application/json")){let e=await a.text();throw console.error("Received non-JSON response:",e),Error("Received non-JSON response (".concat(a.status,": ").concat(a.statusText,"). 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c=await fetch(r,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!c.ok){let e=await c.json(),t=ov(e);throw m(t),Error(t)}return await c.json()}catch(e){throw console.error("Failed to fetch DAU:",e),e}},oy=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/tag/wau"):"/tag/wau",n=new URLSearchParams;n.append("end_date",(e=>{let t=e.getFullYear(),o=String(e.getMonth()+1).padStart(2,"0"),a=String(e.getDate()).padStart(2,"0");return"".concat(t,"-").concat(o,"-").concat(a)})(t)),a&&a.length>0?a.forEach(e=>{n.append("tag_filters",e)}):o&&n.append("tag_filter",o);let l=n.toString();l&&(r+="?".concat(l));let c=await fetch(r,{method:"GET",headers:{[f]:"Bearer ".concat(e),"Content-Type":"application/json"}});if(!c.ok){let e=await c.json(),t=ov(e);throw m(t),Error(t)}return await c.json()}catch(e){throw console.error("Failed to fetch WAU:",e),e}},oj=async(e,t,o,a)=>{try{let r=s?"".concat(s,"/tag/mau"):"/tag/mau",n=new URLSearchParams;n.append("end_date",(e=>{let 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js b/litellm/proxy/_experimental/out/_next/static/chunks/162-ffcd7d9fbb033bdf.js
similarity index 50%
rename from litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/162-ffcd7d9fbb033bdf.js
index 6960e05b16..8b73990ae4 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/162-dd6427ff1a4ad9f4.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/162-ffcd7d9fbb033bdf.js
@@ -1 +1 @@
-"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(h,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(j,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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+"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(h,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(j,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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