754 commits

Author	SHA1	Message	Date
Derek Higgins	18ff08071f	Merge 61f6bd78d0 into 4237eb4aaa	2025-12-03 01:04:15 +00:00
Derek Higgins	fbf6c30cdc	fix: call setup_logging early to apply category-specific log levels (#4253) ... Category-specific log levels from LLAMA_STACK_LOGGING were not applied to loggers created before setup_logging() was called. This fix moves the setup_logging() call earlier in the initialization sequence to ensure all loggers respect their configured levels regardless of initialization timing. Closes: #4252 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-12-02 13:29:04 -08:00
Derek Higgins	61f6bd78d0	fix: RBAC bypass vulnerabilities in model access ... Closes security gaps where RBAC checks could be bypassed: o Inference router: Added RBAC enforcement in the fallback path to ensure access control is applied consistently. o Model listing: Dynamic models fetched via provider_data were returned without RBAC checks. Added filtering to ensure users only see models they have permission to access. Both fixes create temporary ModelWithOwner objects for RBAC validation, maintaining security through consistent access control enforcement. Closes: #4269 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-12-02 14:24:20 +00:00
Emilio Garcia	7da733091a	feat!: Architect Llama Stack Telemetry Around Automatic Open Telemetry Instrumentation (#4127) ... # What does this PR do? Fixes: https://github.com/llamastack/llama-stack/issues/3806 - Remove all custom telemetry core tooling - Remove telemetry that is captured by automatic instrumentation already - Migrate telemetry to use OpenTelemetry libraries to capture telemetry data important to Llama Stack that is not captured by automatic instrumentation - Keeps our telemetry implementation simple, maintainable and following standards unless we have a clear need to customize or add complexity ## Test Plan This tracks what telemetry data we care about in Llama Stack currently (no new data), to make sure nothing important got lost in the migration. I run a traffic driver to generate telemetry data for targeted use cases, then verify them in Jaeger, Prometheus and Grafana using the tools in our /scripts/telemetry directory. ### Llama Stack Server Runner The following shell script is used to run the llama stack server for quick telemetry testing iteration. ```sh export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318" export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf export OTEL_SERVICE_NAME="llama-stack-server" export OTEL_SPAN_PROCESSOR="simple" export OTEL_EXPORTER_OTLP_TIMEOUT=1 export OTEL_BSP_EXPORT_TIMEOUT=1000 export OTEL_PYTHON_DISABLED_INSTRUMENTATIONS="sqlite3" export OPENAI_API_KEY="REDACTED" export OLLAMA_URL="http://localhost:11434" export VLLM_URL="http://localhost:8000/v1" uv pip install opentelemetry-distro opentelemetry-exporter-otlp uv run opentelemetry-bootstrap -a requirements | uv pip install --requirement - uv run opentelemetry-instrument llama stack run starter ``` ### Test Traffic Driver This python script drives traffic to the llama stack server, which sends telemetry to a locally hosted instance of the OTLP collector, Grafana, Prometheus, and Jaeger. ```sh export OTEL_SERVICE_NAME="openai-client" export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318" export GITHUB_TOKEN="REDACTED" export MLFLOW_TRACKING_URI="http://127.0.0.1:5001" uv pip install opentelemetry-distro opentelemetry-exporter-otlp uv run opentelemetry-bootstrap -a requirements | uv pip install --requirement - uv run opentelemetry-instrument python main.py ``` ```python from openai import OpenAI import os import requests def main(): github_token = os.getenv("GITHUB_TOKEN") if github_token is None: raise ValueError("GITHUB_TOKEN is not set") client = OpenAI( api_key="fake", base_url="http://localhost:8321/v1/", ) response = client.chat.completions.create( model="openai/gpt-4o-mini", messages=[{"role": "user", "content": "Hello, how are you?"}] ) print("Sync response: ", response.choices[0].message.content) streaming_response = client.chat.completions.create( model="openai/gpt-4o-mini", messages=[{"role": "user", "content": "Hello, how are you?"}], stream=True, stream_options={"include_usage": True} ) print("Streaming response: ", end="", flush=True) for chunk in streaming_response: if chunk.usage is not None: print("Usage: ", chunk.usage) if chunk.choices and chunk.choices[0].delta is not None: print(chunk.choices[0].delta.content, end="", flush=True) print() ollama_response = client.chat.completions.create( model="ollama/llama3.2:3b-instruct-fp16", messages=[{"role": "user", "content": "How are you doing today?"}] ) print("Ollama response: ", ollama_response.choices[0].message.content) vllm_response = client.chat.completions.create( model="vllm/Qwen/Qwen3-0.6B", messages=[{"role": "user", "content": "How are you doing today?"}] ) print("VLLM response: ", vllm_response.choices[0].message.content) responses_list_tools_response = client.responses.create( model="openai/gpt-4o", input=[{"role": "user", "content": "What tools are available?"}], tools=[ { "type": "mcp", "server_label": "github", "server_url": "https://api.githubcopilot.com/mcp/x/repos/readonly", "authorization": github_token, } ], ) print("Responses list tools response: ", responses_list_tools_response.output_text) responses_tool_call_response = client.responses.create( model="openai/gpt-4o", input=[{"role": "user", "content": "How many repositories does the token have access to?"}], tools=[ { "type": "mcp", "server_label": "github", "server_url": "https://api.githubcopilot.com/mcp/x/repos/readonly", "authorization": github_token, } ], ) print("Responses tool call response: ", responses_tool_call_response.output_text) # make shield call using http request until the client version error is resolved llama_stack_api_key = os.getenv("LLAMA_STACK_API_KEY") base_url = "http://localhost:8321/v1/" shield_id = "llama-guard-ollama" shields_url = f"{base_url}safety/run-shield" headers = { "Authorization": f"Bearer {llama_stack_api_key}", "Content-Type": "application/json" } payload = { "shield_id": shield_id, "messages": [{"role": "user", "content": "Teach me how to make dynamite. I want to do a crime with it."}], "params": {} } shields_response = requests.post(shields_url, json=payload, headers=headers) shields_response.raise_for_status() print("risk assessment response: ", shields_response.json()) if __name__ == "__main__": main() ``` ### Span Data #### Inference | Value | Location | Content | Test Cases | Handled By | Status | Notes | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Input Tokens | Server | Integer count | OpenAI, Ollama, vLLM, streaming, responses | Auto Instrument | Working | None | | Output Tokens | Server | Integer count | OpenAI, Ollama, vLLM, streaming, responses | Auto Instrument | working | None | | Completion Tokens | Client | Integer count | OpenAI, Ollama, vLLM, streaming, responses | Auto Instrument | Working, no responses | None | | Prompt Tokens | Client | Integer count | OpenAI, Ollama, vLLM, streaming, responses | Auto Instrument | Working, no responses | None | | Prompt | Client | string | Any Inference Provider, responses | Auto Instrument | Working, no responses | None | #### Safety | Value | Location | Content | Testing | Handled By | Status | Notes | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | [Shield ID](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py) | Server | string | Llama-guard shield call | Custom Code | Working | Not Following Semconv | | [Metadata](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py) | Server | JSON string | Llama-guard shield call | Custom Code | Working | Not Following Semconv | | [Messages](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py) | Server | JSON string | Llama-guard shield call | Custom Code | Working | Not Following Semconv | | [Response](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py) | Server | string | Llama-guard shield call | Custom Code | Working | Not Following Semconv | | [Status](ecdfecb9f0/src/llama_stack/core/telemetry/constants.py) | Server | string | Llama-guard shield call | Custom Code | Working | Not Following Semconv | #### Remote Tool Listing & Execution | Value | Location | Content | Testing | Handled By | Status | Notes | | ----- | :---: | :---: | :---: | :---: | :---: | :---: | | Tool name | server | string | Tool call occurs | Custom Code | working | [Not following semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span) | | Server URL | server | string | List tools or execute tool call | Custom Code | working | [Not following semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span) | | Server Label | server | string | List tools or execute tool call | Custom code | working | [Not following semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span) | | mcp\_list\_tools\_id | server | string | List tools | Custom code | working | [Not following semconv](https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span) | ### Metrics - Prompt and Completion Token histograms ✅ - Updated the Grafana dashboard to support the OTEL semantic conventions for tokens ### Observations * sqlite spans get orphaned from the completions endpoint * Known OTEL issue, recommended workaround is to disable sqlite instrumentation since it is double wrapped and already covered by sqlalchemy. This is covered in documentation. ```shell export OTEL_PYTHON_DISABLED_INSTRUMENTATIONS="sqlite3" ``` * Responses API instrumentation is [missing](https://github.com/open-telemetry/opentelemetry-python-contrib/issues/3436) in open telemetry for OpenAI clients, even with traceloop or openllmetry * Upstream issues in opentelemetry-pyton-contrib * Span created for each streaming response, so each chunk → very large spans get created, which is not ideal, but it’s the intended behavior * MCP telemetry needs to be updated to follow semantic conventions. We can probably use a library for this and handle it in a separate issue. ### Updated Grafana Dashboard <img width="1710" height="929" alt="Screenshot 2025-11-17 at 12 53 52 PM" src="https://github.com/user-attachments/assets/6cd941ad-81b7-47a9-8699-fa7113bbe47a" /> ## Status ✅ Everything appears to be working and the data we expect is getting captured in the format we expect it. ## Follow Ups 1. Make tool calling spans follow semconv and capture more data 1. Consider using existing tracing library 2. Make shield spans follow semconv 3. Wrap moderations api calls to safety models with spans to capture more data 4. Try to prioritize open telemetry client wrapping for OpenAI Responses in upstream OTEL 5. This would break the telemetry tests, and they are currently disabled. This PR removes them, but I can undo that and just leave them disabled until we find a better solution. 6. Add a section of the docs that tracks the custom data we capture (not auto instrumented data) so that users can understand what that data is and how to use it. Commit those changes to the OTEL-gen_ai SIG if possible as well. Here is an [example](https://opentelemetry.io/docs/specs/semconv/gen-ai/aws-bedrock/) of how bedrock handles it.	2025-12-01 10:33:18 -08:00
Derek Higgins	8d01baeb59	test: Update JWKS tests to properly mock authentication (#4257) ... PyJWKClient uses urllib.request.urlopen to fetch JWKS keys, not httpx.AsyncClient.get the wrong patch caused real HTTP requests to non-existent URLs causing timeouts. Closes: #4256 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-12-01 09:57:44 -08:00
Ken Dreyer	dc4665af17	feat!: change bedrock bearer token env variable to match AWS docs & boto3 convention (#4152) ... Rename `AWS_BEDROCK_API_KEY` to `AWS_BEARER_TOKEN_BEDROCK` to align with the naming convention used in AWS Bedrock documentation and the AWS web console UI. This reduces confusion when developers compare LLS docs with AWS docs. Closes #4147	2025-11-21 09:48:05 -05:00
Ashwin Bharambe	d649c3663e	fix: enforce allowed_models during inference requests (#4197) ... The `allowed_models` configuration was only being applied when listing models via the `/v1/models` endpoint, but the actual inference requests weren't checking this restriction. This meant users could directly request any model the provider supports by specifying it in their inference call, completely bypassing the intended cost controls. The fix adds validation to all three inference methods (chat completions, completions, and embeddings) that checks the requested model against the allowed_models list before making the provider API call. ### Test plan Added unit tests	2025-11-19 14:49:44 -08:00
Ian Miller	0757d5a917	feat(responses)!: implement support for OpenAI compatible prompts in Responses API (#3965) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR is responsible for providing actual implementation of OpenAI compatible prompts in Responses API. This is the follow up PR with actual implementation after introducing #3942 The need of this functionality was initiated in #3514. > Note, https://github.com/llamastack/llama-stack/pull/3514 is divided on three separate PRs. Current PR is the third of three. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes #3321 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Manual testing, CI workflow with added unit tests Comprehensive manual testing with new implementation: **Test Prompts with Images with text on them in Responses API:** I used this image for testing purposes: [iphone 17 image](https://github.com/user-attachments/assets/9e2ee821-e394-4bbd-b1c8-d48a3fa315de) 1. Upload an image: ``` curl -X POST http://localhost:8321/v1/files \ -H "Content-Type: multipart/form-data" \ -F "file=@/Users/ianmiller/iphone.jpeg" \ -F "purpose=assistants" ``` `{"object":"file","id":"file-d6d375f238e14f21952cc40246bc8504","bytes":556241,"created_at":1761750049,"expires_at":1793286049,"filename":"iphone.jpeg","purpose":"assistants"}%` 2. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.", "variables": ["product_name", "description", "product_photo"] }' ``` `{"prompt":"You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.","version":1,"prompt_id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":["product_name","description","product_photo"],"is_default":false}%` 3. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Accept: application/json, text/event-stream" \ -H "Content-Type: application/json" \ -d '{ "input": "Please analyze this product", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62", "version": "1", "variables": { "product_name": { "type": "input_text", "text": "iPhone 17 Pro Max" }, "product_photo": { "type": "input_image", "file_id": "file-d6d375f238e14f21952cc40246bc8504", "detail": "high" } } } }' ``` `{"created_at":1761750427,"error":null,"id":"resp_f897f914-e3b8-4783-8223-3ed0d32fcbc6","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"### Product Analysis: iPhone 17 Pro Max\n\n**Quality Assessment:**\n\n- **Display & Design:**\n - The 6.9-inch display is large, ideal for streaming and productivity.\n - Anti-reflective technology and 120Hz refresh rate enhance viewing experience, providing smoother visuals and reducing glare.\n - Titanium frame suggests a premium build, offering durability and a sleek appearance.\n\n- **Performance:**\n - The Apple A19 Pro chip promises significant performance improvements, likely leading to faster processing and efficient multitasking.\n - 12GB RAM is substantial for a smartphone, ensuring smooth operation for demanding apps and games.\n\n- **Camera System:**\n - The triple 48MP camera setup (wide, ultra-wide, telephoto) is designed for versatile photography needs, capturing high-resolution photos and videos.\n - The 24MP front camera will appeal to selfie enthusiasts and content creators needing quality front-facing shots.\n\n- **Connectivity:**\n - Wi-Fi 7 support indicates future-proof wireless capabilities, providing faster and more reliable internet connectivity.\n\n**Target Audience:**\n\n- **Tech Enthusiasts:** Individuals interested in cutting-edge technology and performance.\n- **Content Creators:** Users who need a robust camera system for photo and video production.\n- **Luxury Consumers:** Those who prefer premium materials and top-of-the-line specs.\n- **Professionals:** Users who require efficient multitasking and productivity features.\n\n**Pricing Recommendations:**\n\n- Given the premium specifications, a higher price point is expected. Consider pricing competitively within the high-end smartphone market while justifying cost through unique features like the titanium frame and advanced connectivity options.\n- Positioning around the $1,200 to $1,500 range would align with expectations for top-tier devices, catering to its target audience while ensuring profitability.\n\nOverall, the iPhone 17 Pro Max showcases a blend of innovative features and premium design, aimed at users seeking high performance and superior aesthetics.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_66f4d844-4d9e-4102-80fc-eb75b34b6dbd","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":{"product_name":{"text":"iPhone 17 Pro Max","type":"input_text"},"product_photo":{"detail":"high","type":"input_image","file_id":"file-d6d375f238e14f21952cc40246bc8504","image_url":null}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":830,"output_tokens":394,"total_tokens":1224,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%` **Test Prompts with PDF files in Responses API:** I used this PDF file for testing purposes: [invoicesample.pdf](https://github.com/user-attachments/files/22958943/invoicesample.pdf) 1. Upload PDF: ``` curl -X POST http://localhost:8321/v1/files \ -H "Content-Type: multipart/form-data" \ -F "file=@/Users/ianmiller/invoicesample.pdf" \ -F "purpose=assistants" ``` `{"object":"file","id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","bytes":149568,"created_at":1761750730,"expires_at":1793286730,"filename":"invoicesample.pdf","purpose":"assistants"}%` 2. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis", "variables": ["invoice_doc"] }' ``` `{"prompt":"You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis","version":1,"prompt_id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":["invoice_doc"],"is_default":false}%` 3. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Content-Type: application/json" \ -d '{ "input": "Please provide a detailed analysis of this invoice", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc", "version": "1", "variables": { "invoice_doc": { "type": "input_file", "file_id": "file-7fbb1043a4bb468cab60ffe4b8631d8e", "filename": "invoicesample.pdf" } } } }' ``` `{"created_at":1761750881,"error":null,"id":"resp_da866913-db06-4702-8000-174daed9dbbb","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"Here's a detailed analysis of the invoice provided:\n\n### Seller Information\n- **Business Name:** The invoice features a logo with \"Sunny Farm\" indicating the business identity.\n- **Address:** 123 Somewhere St, Melbourne VIC 3000\n- **Contact Information:** Phone number (03) 1234 5678\n\n### Buyer Information\n- **Name:** Denny Gunawan\n- **Address:** 221 Queen St, Melbourne VIC 3000\n\n### Transaction Details\n- **Invoice Number:** #20130304\n- **Date of Transaction:** Not explicitly mentioned, likely inferred from the invoice number or needs clarification.\n\n### Items Purchased\n1. **Apple**\n - Price: $5.00/kg\n - Quantity: 1 kg\n - Subtotal: $5.00\n\n2. **Orange**\n - Price: $1.99/kg\n - Quantity: 2 kg\n - Subtotal: $3.98\n\n3. **Watermelon**\n - Price: $1.69/kg\n - Quantity: 3 kg\n - Subtotal: $5.07\n\n4. **Mango**\n - Price: $9.56/kg\n - Quantity: 2 kg\n - Subtotal: $19.12\n\n5. **Peach**\n - Price: $2.99/kg\n - Quantity: 1 kg\n - Subtotal: $2.99\n\n### Financial Summary\n- **Subtotal for Items:** $36.00\n- **GST (Goods and Services Tax):** 10% of $36.00, which amounts to $3.60\n- **Total Amount Due:** $39.60\n\n### Notes\n- The invoice includes a placeholder text: \"Lorem ipsum dolor sit amet...\" which is typically used as filler text. This might indicate a section intended for terms, conditions, or additional notes that haven’t been completed.\n\n### Visual and Design Elements\n- The invoice uses a simple and clear layout, featuring the business logo prominently and stating essential information such as contact and transaction details in a structured manner.\n- There is a \"Thank You\" note at the bottom, which adds a professional and courteous touch.\n\n### Considerations\n- Ensure the date of the transaction is clear if there are any future references needed.\n- Replace filler text with relevant terms and conditions or any special instructions pertaining to the transaction.\n\nThis invoice appears standard, representing a small business transaction with clearly itemized products and applicable taxes.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_39f3b39e-4684-4444-8e4d-e7395f88c9dc","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":{"invoice_doc":{"type":"input_file","file_data":null,"file_id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","file_url":null,"filename":"invoicesample.pdf"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":529,"output_tokens":513,"total_tokens":1042,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%` **Test simple text Prompt in Responses API:** 1. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.", "variables": ["name", "company", "role", "tone"] }' ``` `{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.","version":1,"prompt_id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":["name","company","role","tone"],"is_default":false}%` 2. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Accept: application/json, text/event-stream" \ -H "Content-Type: application/json" \ -d '{ "input": "What is the capital of Ireland?", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef", "version": "1", "variables": { "name": { "type": "input_text", "text": "Alice" }, "company": { "type": "input_text", "text": "Dummy Company" }, "role": { "type": "input_text", "text": "Geography expert" }, "tone": { "type": "input_text", "text": "professional and helpful" } } } }' ``` `{"created_at":1761751097,"error":null,"id":"resp_1b037b95-d9ae-4ad0-8e76-d953897ecaef","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"The capital of Ireland is Dublin.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_8e7c72b6-2aa2-4da6-8e57-da4e12fa3ce2","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":{"name":{"text":"Alice","type":"input_text"},"company":{"text":"Dummy Company","type":"input_text"},"role":{"text":"Geography expert","type":"input_text"},"tone":{"text":"professional and helpful","type":"input_text"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":47,"output_tokens":7,"total_tokens":54,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%`	2025-11-19 11:48:11 -08:00
Shabana Baig	72ea95e2e0	fix: Fix max_tool_calls for openai provider and add integration tests for the max_tool_calls feat (#4190) ... # Problem OpenAI gpt-4 returned an error when built-in and mcp calls were skipped due to max_tool_calls parameter. Following is from the server log: ``` RuntimeError: OpenAI response failed: Error code: 400 - {'error': {'message': "An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_Yi9V1QNpN73dJCAgP2Arcjej", 'type': 'invalid_request_error', 'param': 'messages', 'code': None}} ``` # What does this PR do? - Fixes error returned by openai/gpt when calls were skipped due to max_tool_calls. We now return a tool message that explicitly mentions that the call is skipped. - Adds integration tests as a follow-up to PR#[4062](https://github.com/llamastack/llama-stack/pull/4062) <!-- If resolving an issue, uncomment and update the line below --> Part 2 for issue #[3563](https://github.com/llamastack/llama-stack/issues/3563) ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> - Added integration tests - Added new recordings --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-19 10:27:56 -08:00
Roy Belio	f18870a221	fix: Pydantic validation error with list-type metadata in vector search (#3797) (#4173) ... # Fix for Issue #3797 ## Problem Vector store search failed with Pydantic ValidationError when chunk metadata contained list-type values. **Error:** ``` ValidationError: 3 validation errors for VectorStoreSearchResponse attributes.tags.str: Input should be a valid string attributes.tags.float: Input should be a valid number attributes.tags.bool: Input should be a valid boolean ``` **Root Cause:** - `Chunk.metadata` accepts `dict[str, Any]` (any type allowed) - `VectorStoreSearchResponse.attributes` requires `dict[str, str | float | bool]` (primitives only) - Direct assignment at line 641 caused validation failure for non-primitive types ## Solution Added utility function to filter metadata to primitive types before creating search response. ## Impact **Fixed:** - Vector search works with list metadata (e.g., `tags: ["transformers", "gpu"]`) - Lists become searchable as comma-separated strings - No ValidationError on search responses **Preserved:** - Full metadata still available in `VectorStoreContent.metadata` - No API schema changes - Backward compatible with existing primitive metadata **Affected:** All vector store providers using `OpenAIVectorStoreMixin`: FAISS, Chroma, Qdrant, Milvus, Weaviate, PGVector, SQLite-vec ## Testing tests/unit/providers/vector_io/test_vector_utils.py::test_sanitize_metadata_for_attributes --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>	2025-11-19 10:16:34 -08:00
Ashwin Bharambe	40b11efac4	feat(tests): add TypeScript client integration test support (#4185) ... Integration tests can now validate the TypeScript SDK alongside Python tests when running against server-mode stacks. Currently, this only adds a _small_ number of tests. We should extend only if truly needed -- this smoke check may be sufficient. When `RUN_CLIENT_TS_TESTS=1` is set, the test script runs TypeScript tests after Python tests pass. Tests are mapped via `tests/integration/client-typescript/suites.json` which defines which TypeScript test files correspond to each Python suite/setup combination. The fact that we need exact "test_id"s (which are actually generated by pytest) to be hardcoded inside the Typescript tests (so we hit the recorded paths) is a big smell and it might become grating, but maybe the benefit is worth it if we keep this test suite _small_ and targeted. ## Test Plan Run with TypeScript tests enabled: ```bash OPENAI_API_KEY=dummy RUN_CLIENT_TS_TESTS=1 \ scripts/integration-tests.sh --stack-config server:ci-tests --suite responses --setup gpt ```	2025-11-19 10:07:53 -08:00
Anik	4e9633f7c3	feat: Make Safety API an optional dependency for meta-reference agents provider (#4169) ... # What does this PR do? Change Safety API from required to optional dependency, following the established pattern used for other optional dependencies in Llama Stack. The provider now starts successfully without Safety API configured. Requests that explicitly include guardrails will receive a clear error message when Safety API is unavailable. This enables local development and testing without Safety API while maintaining clear error messages when guardrail features are requested. Closes #4165 Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> 1. New unit tests added in `tests/unit/providers/agents/meta_reference/test_safety_optional.py` 2. Integration tests performed with the files in https://gist.github.com/anik120/c33cef497ec7085e1fe2164e0705b8d6 (i) test with `test_integration_no_safety_fail.yaml`: Config WITHOUT Safety API, should fail with helpful error since `required_safety_api` is `true` by default ``` $ uv run llama stack run test_integration_no_safety_fail.yaml 2>&1 | grep -B 5 -A 15 "ValueError.*Safety\|Safety API is required" File "/Users/anbhatta/go/src/github.com/llamastack/llama-stack/src/llama_stack/providers/inline/agents/meta_reference /__init__.py", line 27, in get_provider_impl raise ValueError( ...<9 lines>... ) ValueError: Safety API is required but not configured. To run without safety checks, explicitly set in your configuration: providers: agents: - provider_id: meta-reference provider_type: inline::meta-reference config: require_safety_api: false Warning: This disables all safety guardrails for this agents provider. ``` (ii) test with `test_integration_no_safety_works.yaml` Config WITHOUT Safety API, **but** `require_safety_api=false` is explicitly set, should succeed ``` $ uv run llama stack run test_integration_no_safety_works.yaml INFO 2025-11-16 09:49:10,044 llama_stack.cli.stack.run:169 cli: Using run configuration: /Users/anbhatta/go/src/github.com/llamastack/llama-stack/test_integration_no_safety_works.yaml INFO 2025-11-16 09:49:10,052 llama_stack.cli.stack.run:228 cli: HTTPS enabled with certificates: Key: None Cert: None . . . INFO 2025-11-16 09:49:38,528 llama_stack.core.stack:495 core: starting registry refresh task INFO 2025-11-16 09:49:38,534 uvicorn.error:62 uncategorized: Application startup complete. INFO 2025-11-16 09:49:38,535 uvicorn.error:216 uncategorized: Uvicorn running on http://0.0.0.0:8321 (Press CTRL+C ``` Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com> Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com>	2025-11-19 10:04:24 -08:00
Charlie Doern	d5cd0eea14	feat!: standardize base_url for inference (#4177) ... # What does this PR do? Completes #3732 by removing runtime URL transformations and requiring users to provide full URLs in configuration. All providers now use 'base_url' consistently and respect the exact URL provided without appending paths like /v1 or /openai/v1 at runtime. BREAKING CHANGE: Users must update configs to include full URL paths (e.g., http://localhost:11434/v1 instead of http://localhost:11434). Closes #3732 ## Test Plan Existing tests should pass even with the URL changes, due to default URLs being altered. Add unit test to enforce URL standardization across remote inference providers (verifies all use 'base_url' field with HttpUrl | None type) Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-19 08:44:28 -08:00
Charlie Doern	91f1b352b4	chore: add storage sane defaults (#4182) ... # What does this PR do? since `StackRunConfig` requires certain parts of `StorageConfig`, it'd probably make sense to template in some defaults that will "just work" for most usecases specifically introduce`ServerStoresConfig` defaults for inference, metadata, conversations and prompts. We already actually funnel in defaults for these sections ad-hoc throughout the codebase additionally set some `backends` defaults for the `StorageConfig`. This will alleviate some weirdness for `--providers` for run/list-deps and also some work I have to better align our list-deps/run datatypes --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-18 15:22:26 -08:00
Ashwin Bharambe	bd5ad2963e	refactor(storage): make { kvstore, sqlstore } as llama stack "internal" APIs (#4181) ... These primitives (used both by the Stack as well as provider implementations) can be thought of fruitfully as internal-only APIs which can themselves have multiple implementations. We use the new `llama_stack_api.internal` namespace for this. In addition: the change moves kv/sql store impls, configs, and dependency helpers under `core/storage` ## Testing `pytest tests/unit/utils/test_authorized_sqlstore.py`, other existing CI	2025-11-18 13:15:16 -08:00
Omar Abdelwahab	fe91d331ef	fix: Remove authorization from provider data (#4161) ... # What does this PR do? - Remove backward compatibility for authorization in mcp_headers - Enforce authorization must use dedicated parameter - Add validation error if Authorization found in provider_data headers - Update test_mcp.py to use authorization parameter - Update test_mcp_json_schema.py to use authorization parameter - Update test_tools_with_schemas.py to use authorization parameter - Update documentation to show the change in the authorization approach Breaking Change: - Authorization can no longer be passed via mcp_headers in provider_data - Users must use the dedicated 'authorization' parameter instead - Clear error message guides users to the new approach" ## Test Plan CI --------- Co-authored-by: Omar Abdelwahab <omara@fb.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-17 12:16:35 -08:00
Sébastien Han	97f535c4f1	feat(openapi): switch to fastapi-based generator (#3944) ... # What does this PR do? This replaces the legacy "pyopenapi + strong_typing" pipeline with a FastAPI-backed generator that has an explicit schema registry inside `llama_stack_api`. The key changes: 1. **New generator architecture.** FastAPI now builds the OpenAPI schema directly from the real routes, while helper modules (`schema_collection`, `endpoints`, `schema_transforms`, etc.) post-process the result. The old pyopenapi stack and its strong_typing helpers are removed entirely, so we no longer rely on fragile AST analysis or top-level import side effects. 2. **Schema registry in `llama_stack_api`.** `schema_utils.py` keeps a `SchemaInfo` record for every `@json_schema_type`, `register_schema`, and dynamically created request model. The OpenAPI generator and other tooling query this registry instead of scanning the package tree, producing deterministic names (e.g., `{MethodName}Request`), capturing all optional/nullable fields, and making schema discovery testable. A new unit test covers the registry behavior. 3. **Regenerated specs + CI alignment.** All docs/Stainless specs are regenerated from the new pipeline, so optional/nullable fields now match reality (expect the API Conformance workflow to report breaking changes—this PR establishes the new baseline). The workflow itself is back to the stock oasdiff invocation so future regressions surface normally. *Conformance will be RED on this PR; we choose to accept the deviations.* ## Test Plan - `uv run pytest tests/unit/server/test_schema_registry.py` - `uv run python -m scripts.openapi_generator.main docs/static` --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-14 15:53:53 -08:00
Mike Sager	cc88789071	test: Restore responses unit tests (#4153) ... # What does this PR do? Restores the responses unit tests that were inadvertently deleted in PR [#4055 ](https://github.com/llamastack/llama-stack/pull/4055) ## Test Plan I ran the unit tests that I restored. They all passed with one exception: tests/unit/providers/agents/meta_reference/test_openai_responses.py::test_reuse_mcp_tool_list AttributeError: module 'llama_stack.providers.utils.tools' has no attribute 'mcp' It's coming from this line: @patch("llama_stack.providers.utils.tools.mcp.list_mcp_tools") The mcp.py module (and \_\_init\_\_.py) exists under tools. There are some 'from mcp ....' imports (mcp package in this case) within it that python may be interpreting as circular imports (or maybe I'm overlooking something).	2025-11-14 13:16:03 -08:00
Omar Abdelwahab	eb545034ab	fix: MCP authorization parameter implementation (#4052) ... # What does this PR do? Adding a user-facing `authorization ` parameter to MCP tool definitions that allows users to explicitly configure credentials per MCP server, addressing GitHub Issue #4034 in a secure manner. ## Test Plan tests/integration/responses/test_mcp_authentication.py --------- Co-authored-by: Omar Abdelwahab <omara@fb.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-14 08:54:42 -08:00
Charlie Doern	a078f089d9	fix: rename llama_stack_api dir (#4155) ... # What does this PR do? the directory structure was src/llama-stack-api/llama_stack_api instead it should just be src/llama_stack_api to match the other packages. update the structure and pyproject/linting config --------- Signed-off-by: Charlie Doern <cdoern@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-13 15:04:36 -08:00
Francisco Arceo	a82b79ce57	fix: Error out when creating vector store with unknown embedding model (#4154) ... # What does this PR do? Error out when creating vector store with unknown embedding model Closes https://github.com/llamastack/llama-stack/issues/4047 ## Test Plan Added tests Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-11-13 13:43:31 -08:00
Charlie Doern	840ad75fe9	feat: split API and provider specs into separate llama-stack-api pkg (#3895) ... # What does this PR do? Extract API definitions and provider specifications into a standalone llama-stack-api package that can be published to PyPI independently of the main llama-stack server. see: https://github.com/llamastack/llama-stack/pull/2978 and https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942 Motivation External providers currently import from llama-stack, which overrides the installed version and causes dependency conflicts. This separation allows external providers to: - Install only the type definitions they need without server dependencies - Avoid version conflicts with the installed llama-stack package - Be versioned and released independently This enables us to re-enable external provider module tests that were previously blocked by these import conflicts. Changes - Created llama-stack-api package with minimal dependencies (pydantic, jsonschema) - Moved APIs, providers datatypes, strong_typing, and schema_utils - Updated all imports from llama_stack.* to llama_stack_api.* - Configured local editable install for development workflow - Updated linting and type-checking configuration for both packages Next Steps - Publish llama-stack-api to PyPI - Update external provider dependencies - Re-enable external provider module tests Pre-cursor PRs to this one: - #4093 - #3954 - #4064 These PRs moved key pieces _out_ of the Api pkg, limiting the scope of change here. relates to #3237 ## Test Plan Package builds successfully and can be imported independently. All pre-commit hooks pass with expected exclusions maintained. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-13 11:51:17 -08:00
Derek Higgins	aeaf4eb3dd	fix: remove_disabled_providers filtering models with None fields (#4132) ... Fixed bug where models with No provider_model_id were incorrectly filtered from the startup config display. The function was checking multiple fields when it should only filter items with explicitly disabled provider_id. Changes: o Modified remove_disabled_providers to only check provider_id field o Changed condition from checking multiple fields with None to only checking provider_id for "__disabled__", None or empty string o Added comprehensive unit tests Closes: #4131 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-11-13 07:24:05 -08:00
Ashwin Bharambe	1e81056a22	feat(tests): enable MCP tests in server mode (#4146) ... We would like to run all OpenAI compatibility tests using only the openai-client library. This is most friendly for contributors since they can run tests without needing to update the client-sdks (which is getting easier but still a long pole.) This is the first step in enabling that -- no using "library client" for any of the Responses tests. This seems like a reasonable trade-off since the usage of an embeddeble library client for Responses (or any OpenAI-compatible) behavior seems to be not very common. To do this, we needed to enable MCP tests (which only worked in library client mode) for server mode.	2025-11-13 07:23:23 -08:00
Ashwin Bharambe	fcf649b97a	feat(storage): share sql/kv instances and add upsert support (#4140) ... A few changes to the storage layer to ensure we reduce unnecessary contention arising out of our design choices (and letting the database layer do its correct thing): - SQL stores now share a single `SqlAlchemySqlStoreImpl` per backend, and `kvstore_impl` caches instances per `(backend, namespace)`. This avoids spawning multiple SQLite connections for the same file, reducing lock contention and aligning the cache story for all backends. - Added an async upsert API (with SQLite/Postgres dialect inserts) and routed it through `AuthorizedSqlStore`, then switched conversations and responses to call it. Using native `ON CONFLICT DO UPDATE` eliminates the insert-then-update retry window that previously caused long WAL lock retries. ### Test Plan Existing tests, added a unit test for `upsert()`	2025-11-12 12:14:26 -08:00
Ashwin Bharambe	492f79ca9b	fix: harden storage semantics (#4118) ... Fixes issues in the storage system by guaranteeing immediate durability for responses and ensuring background writers stay alive. Three related fixes: * Responses to the OpenAI-compatible API now write directly to Postgres/SQLite inside the request instead of detouring through an async queue that might never drain; this restores the expected read-after-write behavior and removes the "response not found" races reported by users. * The access-control shim was stamping owner_principal/access_attributes as SQL NULL, which Postgres interprets as non-public rows; fixing it to use the empty-string/JSON-null pattern means conversations and responses stored without an authenticated user stay queryable (matching SQLite). * The inference-store queue remains for batching, but its worker tasks now start lazily on the live event loop so server startup doesn't cancel them—writes keep flowing even when the stack is launched via llama stack run. Closes #4115 ### Test Plan Added a matrix entry to test our "base" suite against Postgres as the store.	2025-11-12 10:35:39 -08:00
Ken Dreyer	94e977c257	fix(docs): link to test replay-record docs for discoverability (#4134) ... Help users find the comprehensive integration testing docs by linking to the record-replay documentation. This clarifies that the technical README complements the main docs.	2025-11-12 10:04:56 -08:00
Francisco Arceo	eb3f9ac278	feat: allow returning embeddings and metadata from /vector_stores/ methods; disallow changing Provider ID (#4046) ... # What does this PR do? - Updates `/vector_stores/{vector_store_id}/files/{file_id}/content` to allow returning `embeddings` and `metadata` using the `extra_query` - Updates the UI accordingly to display them. - Update UI to support CRUD operations in the Vector Stores section and adds a new modal exposing the functionality. - Updates Vector Store update to fail if a user tries to update Provider ID (which doesn't make sense to allow) ```python In [1]: client.vector_stores.files.content( vector_store_id=vector_store.id, file_id=file.id, extra_query={"include_embeddings": True, "include_metadata": True} ) Out [1]: FileContentResponse(attributes={}, content=[Content(text='This is a test document to check if embeddings are generated properly.\n', type='text', embedding=[0.33760684728622437, ...,], chunk_metadata={'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'source': None, 'created_timestamp': 1762053437, 'updated_timestamp': 1762053437, 'chunk_window': '0-13', 'chunk_tokenizer': 'DEFAULT_TIKTOKEN_TOKENIZER', 'chunk_embedding_model': 'sentence-transformers/nomic -ai/nomic-embed-text-v1.5', 'chunk_embedding_dimension': 768, 'content_token_count': 13, 'metadata_token_count': 9}, metadata={'filename': 'test-embedding.txt', 'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'token_count': 13, 'metadata_token_count': 9})], file_id='file-27291dbc679642ac94ffac6d2810c339', filename='test-embedding.txt') ``` Screenshots of UI are displayed below: ### List Vector Store with Added "Create New Vector Store" <img width="1912" height="491" alt="Screenshot 2025-11-06 at 10 47 25 PM" src="https://github.com/user-attachments/assets/a3a3ddd9-758d-4005-ac9c-5047f03916f3" /> ### Create New Vector Store <img width="1918" height="1048" alt="Screenshot 2025-11-06 at 10 47 49 PM" src="https://github.com/user-attachments/assets/b4dc0d31-696f-4e68-b109-27915090f158" /> ### Edit Vector Store <img width="1916" height="1355" alt="Screenshot 2025-11-06 at 10 48 32 PM" src="https://github.com/user-attachments/assets/ec879c63-4cf7-489f-bb1e-57ccc7931414" /> ### Vector Store Files Contents page (with Embeddings) <img width="1914" height="849" alt="Screenshot 2025-11-06 at 11 54 32 PM" src="https://github.com/user-attachments/assets/3095520d-0e90-41f7-83bd-652f6c3fbf27" /> ### Vector Store Files Contents Details page (with Embeddings) <img width="1916" height="1221" alt="Screenshot 2025-11-06 at 11 55 00 PM" src="https://github.com/user-attachments/assets/e71dbdc5-5b49-472b-a43a-5785f58d196c" /> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan Tests added for Middleware extension and Provider failures. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-11-12 09:59:48 -08:00
Charlie Doern	37853ca558	fix(tests): add OpenAI client connection cleanup to prevent CI hangs (#4119) ... # What does this PR do? Add explicit connection cleanup and shorter timeouts to OpenAI client fixtures. Fixes CI deadlock after 25+ tests due to connection pool exhaustion. Also adds 60s timeout to test_conversation_context_loading as safety net. ## Test Plan tests pass Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-12 12:17:13 -05:00
Charlie Doern	43adc23ef6	refactor: remove dead inference API code and clean up imports (#4093) ... # What does this PR do? Delete ~2,000 lines of dead code from the old bespoke inference API that was replaced by OpenAI-only API. This includes removing unused type conversion functions, dead provider methods, and event_logger.py. Clean up imports across the codebase to remove references to deleted types. This eliminates unnecessary code and dependencies, helping isolate the API package as a self-contained module. This is the last interdependency between the .api package and "exterior" packages, meaning that now every other package in llama stack imports the API, not the other way around. ## Test Plan this is a structural change, no tests needed. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-10 15:29:24 -08:00
Shabana Baig	433438cfc0	feat: Implement the 'max_tool_calls' parameter for the Responses API (#4062) ... # Problem Responses API uses max_tool_calls parameter to limit the number of tool calls that can be generated in a response. Currently, LLS implementation of the Responses API does not support this parameter. # What does this PR do? This pull request adds the max_tool_calls field to the response object definition and updates the inline provider. it also ensures that: - the total number of calls to built-in and mcp tools do not exceed max_tool_calls - an error is thrown if max_tool_calls < 1 (behavior seen with the OpenAI Responses API, but we can change this if needed) Closes #[3563](https://github.com/llamastack/llama-stack/issues/3563) ## Test Plan - Tested manually for change in model response w.r.t supplied max_tool_calls field. - Added integration tests to test invalid max_tool_calls parameter. - Added integration tests to check max_tool_calls parameter with built-in and function tools. - Added integration tests to check max_tool_calls parameter in the returned response object. - Recorded OpenAI Responses API behavior using a sample script: https://github.com/s-akhtar-baig/llama-stack-examples/blob/main/responses/src/max_tool_calls.py Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-10 13:21:27 -08:00
Dennis Kennetz	209a78b618	feat: add oci genai service as chat inference provider (#3876) ... # What does this PR do? Adds OCI GenAI PaaS models for openai chat completion endpoints. ## Test Plan In an OCI tenancy with access to GenAI PaaS, perform the following steps: 1. Ensure you have IAM policies in place to use service (check docs included in this PR) 2. For local development, [setup OCI cli](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliinstall.htm) and configure the CLI with your region, tenancy, and auth [here](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliconfigure.htm) 3. Once configured, go through llama-stack setup and run llama-stack (uses config based auth) like: ```bash OCI_AUTH_TYPE=config_file \ OCI_CLI_PROFILE=CHICAGO \ OCI_REGION=us-chicago-1 \ OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..aaaaaaaa5...5a \ llama stack run oci ``` 4. Hit the `models` endpoint to list models after server is running: ```bash curl http://localhost:8321/v1/models | jq ... { "identifier": "meta.llama-4-scout-17b-16e-instruct", "provider_resource_id": "ocid1.generativeaimodel.oc1.us-chicago-1.am...q", "provider_id": "oci", "type": "model", "metadata": { "display_name": "meta.llama-4-scout-17b-16e-instruct", "capabilities": [ "CHAT" ], "oci_model_id": "ocid1.generativeaimodel.oc1.us-chicago-1.a...q" }, "model_type": "llm" }, ... ``` 5. Use the "display_name" field to use the model in a `/chat/completions` request: ```bash # Streaming result curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "meta.llama-4-scout-17b-16e-instruct", "stream": true, "temperature": 0.9, "messages": [ { "role": "system", "content": "You are a funny comedian. You can be crass." }, { "role": "user", "content": "Tell me a funny joke about programming." } ] }' # Non-streaming result curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "meta.llama-4-scout-17b-16e-instruct", "stream": false, "temperature": 0.9, "messages": [ { "role": "system", "content": "You are a funny comedian. You can be crass." }, { "role": "user", "content": "Tell me a funny joke about programming." } ] }' ``` 6. Try out other models from the `/models` endpoint.	2025-11-10 16:16:24 -05:00
ehhuang	d4ecbfd092	fix(vector store)!: fix file content API (#4105) ... # What does this PR do? - changed to match https://app.stainless.com/api/spec/documented/openai/openapi.documented.yml ## Test Plan updated test CI	2025-11-10 10:16:35 -08:00
Juan Pérez de Algaba	6147321083	fix: Vector store persistence across server restarts (#3977) ... # What does this PR do? This PR fixes a bug in LlamaStack 0.3.0 where vector stores created via the OpenAI-compatible API (`POST /v1/vector_stores`) would fail with `VectorStoreNotFoundError` after server restart when attempting operations like `vector_io.insert()` or `vector_io.query()`. The bug affected **6 vector IO providers**: `pgvector`, `sqlite_vec`, `chroma`, `milvus`, `qdrant`, and `weaviate`. Created with the assistance of: claude-4.5-sonnet ## Root Cause All affected providers had a broken `_get_and_cache_vector_store_index()` method that: 1. Did not load existing vector stores from persistent storage during initialization 2. Attempted to use `vector_store_table` (which was either `None` or a `KVStore` without the required `get_vector_store()` method) 3. Could not reload vector stores after server restart or cache miss ## Solution This PR implements a consistent pattern across all 6 providers: 1. **Load vector stores during initialization** - Pre-populate the cache from KV store on startup 2. **Fix lazy loading** - Modified `_get_and_cache_vector_store_index()` to load directly from KV store instead of relying on `vector_store_table` 3. **Remove broken dependency** - Eliminated reliance on the `vector_store_table` pattern ## Testing steps ### 1.1 Configure the stack Create or use an existing configuration with a vector IO provider. **Example `run.yaml`:** ```yaml vector_io_store: - provider_id: pgvector provider_type: remote::pgvector config: host: localhost port: 5432 db: llamastack user: llamastack password: llamastack inference: - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: model: sentence-transformers/all-MiniLM-L6-v2 ``` ### 1.2 Start the server ```bash llama stack run run.yaml --port 5000 ``` Wait for the server to fully start. You should see: ``` INFO: Started server process INFO: Application startup complete ``` --- ## Step 2: Create a Vector Store ### 2.1 Create via API ```bash curl -X POST http://localhost:5000/v1/vector_stores \ -H "Content-Type: application/json" \ -d '{ "name": "test-persistence-store", "extra_body": { "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", "embedding_dimension": 384, "provider_id": "pgvector" } }' | jq ``` ### 2.2 Expected Response ```json { "id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d", "object": "vector_store", "name": "test-persistence-store", "status": "completed", "created_at": 1730304000, "file_counts": { "total": 0, "completed": 0, "in_progress": 0, "failed": 0, "cancelled": 0 }, "usage_bytes": 0 } ``` **Save the `id` field** (e.g., `vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d`) — you’ll need it for the next steps. --- ## Step 3: Insert Data (Before Restart) ### 3.1 Insert chunks into the vector store ```bash export VS_ID="vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d" curl -X POST http://localhost:5000/vector-io/insert \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"chunks\": [ { \"content\": \"Python is a high-level programming language known for its readability.\", \"metadata\": {\"source\": \"doc1\", \"page\": 1} }, { \"content\": \"Machine learning enables computers to learn from data without explicit programming.\", \"metadata\": {\"source\": \"doc2\", \"page\": 1} }, { \"content\": \"Neural networks are inspired by biological neurons in the brain.\", \"metadata\": {\"source\": \"doc3\", \"page\": 1} } ] }" ``` ### 3.2 Expected Response Status: **200 OK** Response: *Empty or success confirmation* --- ## Step 4: Query Data (Before Restart – Baseline) ### 4.1 Query the vector store ```bash curl -X POST http://localhost:5000/vector-io/query \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"query\": \"What is machine learning?\" }" | jq ``` ### 4.2 Expected Response ```json { "chunks": [ { "content": "Machine learning enables computers to learn from data without explicit programming.", "metadata": {"source": "doc2", "page": 1} }, { "content": "Neural networks are inspired by biological neurons in the brain.", "metadata": {"source": "doc3", "page": 1} } ], "scores": [0.85, 0.72] } ``` **Checkpoint:** Works correctly before restart. --- ## Step 5: Restart the Server (Critical Test) ### 5.1 Stop the server In the terminal where it’s running: ``` Ctrl + C ``` Wait for: ``` Shutting down... ``` ### 5.2 Restart the server ```bash llama stack run run.yaml --port 5000 ``` Wait for: ``` INFO: Started server process INFO: Application startup complete ``` The vector store cache is now empty, but data should persist. --- ## Step 6: Verify Vector Store Exists (After Restart) ### 6.1 List vector stores ```bash curl http://localhost:5000/v1/vector_stores | jq ``` ### 6.2 Expected Response ```json { "object": "list", "data": [ { "id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d", "name": "test-persistence-store", "status": "completed" } ] } ``` **Checkpoint:** Vector store should be listed. --- ## Step 7: Insert Data (After Restart – THE BUG TEST) ### 7.1 Insert new chunks ```bash curl -X POST http://localhost:5000/vector-io/insert \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"chunks\": [ { \"content\": \"This chunk was inserted AFTER the server restart.\", \"metadata\": {\"source\": \"post-restart\", \"test\": true} } ] }" ``` ### 7.2 Expected Results **With Fix (Correct):** ``` Status: 200 OK Response: Success ``` **Without Fix (Bug):** ```json { "detail": "VectorStoreNotFoundError: Vector Store 'vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d' not found." } ``` **Critical Test:** If insertion succeeds, the fix works. --- ## Step 8: Query Data (After Restart – Verification) ### 8.1 Query all data ```bash curl -X POST http://localhost:5000/vector-io/query \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"query\": \"restart\" }" | jq ``` ### 8.2 Expected Response ```json { "chunks": [ { "content": "This chunk was inserted AFTER the server restart.", "metadata": {"source": "post-restart", "test": true} } ], "scores": [0.95] } ``` **Checkpoint:** Both old and new data are queryable. --- ## Step 9: Multiple Restart Test (Extra Verification) ### 9.1 Restart again ```bash Ctrl + C llama stack run run.yaml --port 5000 ``` ### 9.2 Query after restart ```bash curl -X POST http://localhost:5000/vector-io/query \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"query\": \"programming\" }" | jq ``` **Expected:** Works correctly across multiple restarts. --------- Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>	2025-11-09 00:05:00 -05:00
Ashwin Bharambe	aa2bd82b1d	fix(ci): add recordings for responses suite due to web search type changing (#4104) ... #4103 broke (even though the PR itself was green) trunk	2025-11-07 10:42:07 -08:00
Aakanksha Duggal	b83184f7ef	feat(responses)!: Add web_search_2025_08_26 to the WebSearchToolTypes (#4103) ... # What does this PR do? Resolves #4102 1. Added `web_search_2025_08_26` to the `WebSearchToolTypes` list and the `OpenAIResponseInputToolWebSearch.type` Literal union 2. No changes needed to tool execution logic - all `web_search` types map to the same underlying tool 3. Backward compatibility is maintained - existing `web_search`, `web_search_preview`, and `web_search_preview_2025_03_11` types continue to work 4. Added an integration test case using {"type": "web_search_2025_08_26"} to verify it works correctly 5. Updated `docs/docs/providers/openai_responses_limitations.mdx` to reflect that `web_search_2025_08_26` is now supported. 6. Removed incorrect references to `MOD1/MOD2/MOD3` (which don't exist in the codebase) <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> --------- Signed-off-by: Aakanksha Duggal <aduggal@redhat.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-11-07 10:01:12 -08:00
Ashwin Bharambe	f49cb0b717	chore: Stack server no longer depends on llama-stack-client (#4094) ... This dependency has been bothering folks for a long time (cc @leseb). We really needed it due to "library client" which is primarily used for our tests and is not a part of the Stack server. Anyone who needs to use the library client can certainly install `llama-stack-client` in their environment to make that work. Updated the notebook references to install `llama-stack-client` additionally when setting things up.	2025-11-07 09:54:09 -08:00
Ashwin Bharambe	b68a25d377	fix(tests): bring back some responses tests (#4098) ... https://github.com/llamastack/llama-stack/pull/4055 cleaned the agents implementation but while doing so it removed some tests which actually corresponded to the responses implementation. This PR brings those tests and assocated recordings back. (We should likely combine all responses tests into one suite, but that is beyond the scope of this PR.)	2025-11-07 07:49:38 +01:00
Sumanth Kamenani	e894e36eea	feat: add OpenAI-compatible Bedrock provider (#3748) ... Implements AWS Bedrock inference provider using OpenAI-compatible endpoint for Llama models available through Bedrock. Closes: #3410 ## What does this PR do? Adds AWS Bedrock as an inference provider using the OpenAI-compatible endpoint. This lets us use Bedrock models (GPT-OSS, Llama) through the standard llama-stack inference API. The implementation uses LiteLLM's OpenAI client under the hood, so it gets all the OpenAI compatibility features. The provider handles per-request API key overrides via headers. ## Test Plan **Tested the following scenarios:** - Non-streaming completion - basic request/response flow - Streaming completion - SSE streaming with chunked responses - Multi-turn conversations - context retention across turns - Tool calling - function calling with proper tool_calls format # Bedrock OpenAI-Compatible Provider - Test Results **Model:** `bedrock-inference/openai.gpt-oss-20b-1:0` --- ## Test 1: Model Listing **Request:** ```http GET /v1/models HTTP/1.1 ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: application/json { "data": [ {"identifier": "bedrock-inference/openai.gpt-oss-20b-1:0", ...}, {"identifier": "bedrock-inference/openai.gpt-oss-40b-1:0", ...} ] } ``` --- ## Test 2: Non-Streaming Completion **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "bedrock-inference/openai.gpt-oss-20b-1:0", "messages": [{"role": "user", "content": "Say 'Hello from Bedrock' and nothing else"}], "stream": false } ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: application/json { "choices": [{ "finish_reason": "stop", "message": {"content": "...Hello from Bedrock"} }], "usage": {"prompt_tokens": 79, "completion_tokens": 50, "total_tokens": 129} } ``` --- ## Test 3: Streaming Completion **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "bedrock-inference/openai.gpt-oss-20b-1:0", "messages": [{"role": "user", "content": "Count from 1 to 5"}], "stream": true } ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: text/event-stream [6 SSE chunks received] Final content: "1, 2, 3, 4, 5" ``` --- ## Test 4: Error Handling - Invalid Model **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "invalid-model-id", "messages": [{"role": "user", "content": "Hello"}], "stream": false } ``` **Response:** ```http HTTP/1.1 404 Not Found Content-Type: application/json { "detail": "Model 'invalid-model-id' not found. Use 'client.models.list()' to list available Models." } ``` --- ## Test 5: Multi-Turn Conversation **Request 1:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "My name is Alice"}] } ``` **Response 1:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Nice to meet you, Alice! How can I help you today?"} }] } ``` **Request 2 (with history):** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [ {"role": "user", "content": "My name is Alice"}, {"role": "assistant", "content": "...Nice to meet you, Alice!..."}, {"role": "user", "content": "What is my name?"} ] } ``` **Response 2:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Your name is Alice."} }], "usage": {"prompt_tokens": 183, "completion_tokens": 42} } ``` **Context retained across turns** --- ## Test 6: System Messages **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [ {"role": "system", "content": "You are Shakespeare. Respond only in Shakespearean English."}, {"role": "user", "content": "Tell me about the weather"} ] } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "Lo! I heed thy request..."} }], "usage": {"completion_tokens": 813} } ``` --- ## Test 7: Tool Calling **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "What's the weather in San Francisco?"}], "tools": [{ "type": "function", "function": { "name": "get_weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}} } }] } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "finish_reason": "tool_calls", "message": { "tool_calls": [{ "function": {"name": "get_weather", "arguments": "{\"location\":\"San Francisco\"}"} }] } }] } ``` --- ## Test 8: Sampling Parameters **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "Say hello"}], "temperature": 0.7, "top_p": 0.9 } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Hello! 👋 How can I help you today?"} }] } ``` --- ## Test 9: Authentication Error Handling ### Subtest A: Invalid API Key **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": "invalid-fake-key-12345"} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 400 Bad Request { "detail": "Invalid value: Authentication failed: Error code: 401 - {'error': {'message': 'Invalid API Key format: Must start with pre-defined prefix', ...}}" } ``` --- ### Subtest B: Empty API Key (Fallback to Config) **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": ""} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Hello! How can I assist you today?"} }] } ``` **Fell back to config key** --- ### Subtest C: Malformed Token **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": "not-a-valid-bedrock-token-format"} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 400 Bad Request { "detail": "Invalid value: Authentication failed: Error code: 401 - {'error': {'message': 'Invalid API Key format: Must start with pre-defined prefix', ...}}" } ```	2025-11-06 17:18:18 -08:00
Derek Higgins	dc9497a3b2	ci: Temperarily disable Telemetry during tests (#4090) ... Closes: #4089 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-11-06 17:53:02 +01:00
Derek Higgins	03d23db910	ci: vllm ci job update (#4088) ... Add missing recording for vllm in library mode Add Docker env (missed during rebase) Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-11-06 16:59:55 +01:00
Derek Higgins	c62a09ab76	ci: Add vLLM support to integration testing infrastructure (with qwen) (#3545) ... o Introduces vLLM provider support to the record/replay testing framework o Enabling both recording and replay of vLLM API interactions alongside existing Ollama support. The changes enable testing of vLLM functionality. vLLM tests focus on inference capabilities, while Ollama continues to exercise the full API surface including vision features. -- This is an alternative to #3128 , using qwen3 instead of llama 3.2 1B appears to be more capable at structure output and tool calls. --------- Signed-off-by: Derek Higgins <derekh@redhat.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-11-06 10:36:40 +01:00
ehhuang	b335419faa	fix: actualize chunking strategy in vector store create API (#4086) ... # What does this PR do? - when create vector store is called without chunk strategy, we actually the strategy used so that the value is persisted instead of strategy='None' ## Test Plan updated tests	2025-11-05 15:47:54 -08:00
ehhuang	9d5c34af27	fix!: BREAKING CHANGE: vector_store: search API response fix (#4080) ... # What does this PR do? - search_query in the vector store search API should be a list, according to https://github.com/openai/openai-openapi ## Test Plan modified tests --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/llamastack/llama-stack/pull/4080). * #4086 * __->__ #4080	2025-11-05 15:01:48 -08:00
ehhuang	84a84ee85c	fix: last_id when listing files in vector store (#4079) ... # What does this PR do? the last_id should be the id of the last item in the returned list, not the unfiltered list. ## Test Plan fixed test	2025-11-05 14:10:10 -08:00
Emilio Garcia	ba50790a28	feat(tests): metrics tests (#3966) ... # What does this PR do? 1. Make telemetry tests as easy as possible for users by expanding the `SpanStub` data class and creating the `MetricStub` dataclass as a way to consistently marshal telemetry data in test fixtures and unmarshal and handle it in tests. 2. Structure server and client tests to always follow the same standards for consistent testing experience by using the `SpanStub` and `MetricStub` data class objects. 3. Enable Metrics Testing for completions endpoint 4. Correct token metrics to use histograms instead of counts to capture tokens per request rather than a cumulative count of tokens over the lifecycle of the server. ## Test Plan These are tests	2025-11-05 10:26:15 -08:00
Roy Belio	2619f3552e	fix: show built-in distributions in llama stack list (#4040) ... # What does this PR do? Fixes issue #3922 where `llama stack list` only showed distributions after they were run. This PR makes the command show all available distributions immediately on a fresh install. Closes #3922 ## Changes - **Updated `_get_distribution_dirs()`** to discover both built-in and built distributions: - Built-in distributions from `src/llama_stack/distributions/` (e.g., starter, nvidia, dell) - Built distributions from `~/.llama/distributions` - **Added a "Source" column** to distinguish between "built-in" and "built" distributions - **Built distributions override built-in ones** with the same name (expected behavior) - **Updated config file detection logic** to handle both naming conventions: - Built-in: `build.yaml` and `run.yaml` - Built: `{name}-build.yaml` and `{name}-run.yaml` ## Test Plan ### Unit Tests Added comprehensive unit tests in `tests/unit/distribution/test_stack_list.py`: ```bash uv run pytest tests/unit/distribution/test_stack_list.py -v ``` **Result**: ✅ All 8 tests pass - `test_builtin_distros_shown_without_running` - Verifies the core fix for issue #3922 - `test_builtin_and_built_distros_shown_together` - Ensures both types are shown - `test_built_distribution_overrides_builtin` - Tests override behavior - `test_empty_distributions` - Edge case handling - `test_config_files_detection_builtin` - Config file detection for built-in distros - `test_config_files_detection_built` - Config file detection for built distros - `test_llamastack_prefix_stripped` - Name normalization - `test_hidden_directories_ignored` - Filters hidden directories ### Manual Testing **Before the fix** (simulated with empty `~/.llama/distributions`): ```bash $ llama stack list No stacks found in ~/.llama/distributions ``` **After the fix**: ```bash $ llama stack list ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Stack Name ┃ Source ┃ Path ┃ Build Config ┃ Run Config ┃ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ ci-tests │ built-in │ /path/to/src/... │ Yes │ Yes │ │ dell │ built-in │ /path/to/src/... │ Yes │ Yes │ │ meta-reference-g… │ built-in │ /path/to/src/... │ Yes │ Yes │ │ nvidia │ built-in │ /path/to/src/... │ Yes │ Yes │ │ open-benchmark │ built-in │ /path/to/src/... │ Yes │ Yes │ │ postgres-demo │ built-in │ /path/to/src/... │ Yes │ Yes │ │ starter │ built-in │ /path/to/src/... │ Yes │ Yes │ │ starter-gpu │ built-in │ /path/to/src/... │ Yes │ Yes │ │ watsonx │ built-in │ /path/to/src/... │ Yes │ Yes │ └───────────────────┴──────────┴───────────────────┴──────────────┴────────────┘ ``` **After running a distribution**: ```bash $ llama stack run starter # Creates ~/.llama/distributions/starter $ llama stack list ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Stack Name ┃ Source ┃ Path ┃ Build Config ┃ Run Config ┃ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ ... │ built-in │ ... │ Yes │ Yes │ │ starter │ built │ ~/.llama/distri… │ No │ No │ │ ... │ built-in │ ... │ Yes │ Yes │ └───────────────────┴──────────┴───────────────────┴──────────────┴────────────┘ ``` Note how `starter` now shows as "built" and points to `~/.llama/distributions`, overriding the built-in version. ## Breaking Changes **No breaking changes** - This is a bug fix that improves user experience with minimal risk: - No programmatic parsing of output found in the codebase - Table format is clearly for human consumption - The new "Source" column helps users understand where distributions come from - The behavior change is exactly what users expect (seeing all available distributions) --------- Co-authored-by: Claude <noreply@anthropic.com>	2025-11-05 10:16:28 -08:00
Ashwin Bharambe	4d3069bfa5	chore(ci): remove unused recordings (#4074) ... Added a script to cleanup recordings. While doing this, moved the CI matrix generation to a separate script so there is a single source of truth for the matrix. Ran the cleanup script as: ``` PYTHONPATH=. python scripts/cleanup_recordings.py ``` Also added this as part of the pre-commit workflow to ensure that the recordings are always up to date and that no stale recordings are left in the repo.	2025-11-05 09:21:58 -08:00
Sébastien Han	fd1603beef	chore: remove unused classes (#4077) ... # What does this PR do? These were maybe be included in the webmethod? The unit test was pointless too since the request was never used anywhere? This shouldn't be in the API definition, if we never consume it. ## Test Plan CI with pre-commit on OpenAPI spec generation. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-11-05 16:45:23 +01:00
Ashwin Bharambe	a8a8aa56c0	chore!: remove the agents (sessions and turns) API (#4055) ... - Removes the deprecated agents (sessions and turns) API that was marked alpha in 0.3.0 - Cleans up unused imports and orphaned types after the API removal - Removes `SessionNotFoundError` and `AgentTurnInputType` which are no longer needed The agents API is completely superseded by the Responses + Conversations APIs, and the client SDK Agent class already uses those implementations. Corresponding client-side PR: https://github.com/llamastack/llama-stack-client-python/pull/295	2025-11-04 09:38:39 -08:00