940 commits

Author	SHA1	Message	Date
Omar Abdelwahab	5c6f713354	Merge branch 'main' into add-mcp-authentication-param	2025-11-10 15:13:45 -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
Omar Abdelwahab	114ab693a5	Merge branch 'main' into add-mcp-authentication-param	2025-11-10 13:19:12 -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
Ashwin Bharambe	fadf17daf3	feat(api)!: deprecate register/unregister resource APIs (#4099) ... Mark all register_* / unregister_* APIs as deprecated across models, shields, tool groups, datasets, benchmarks, and scoring functions. This is the first step toward moving resource mutations to an `/admin` namespace as outlined in https://github.com/llamastack/llama-stack/issues/3809#issuecomment-3492931585. The deprecation flag will be reflected in the OpenAPI schema to warn API users that these endpoints are being phased out. Next step will be implementing the `/admin` route namespace for these resource management operations. - `register_model` / `unregister_model` - `register_shield` / `unregister_shield` - `register_tool_group` / `unregister_toolgroup` - `register_dataset` / `unregister_dataset` - `register_benchmark` / `unregister_benchmark` - `register_scoring_function` / `unregister_scoring_function`	2025-11-10 10:36:33 -08:00
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
Vaishnavi Hire	4341c4c2ac	docs: Add Llama Stack Operator docs (#3983) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> Add documentation for llama-stack-k8s-operator under kubernetes deployment guide. Signed-off-by: Vaishnavi Hire <vhire@redhat.com>	2025-11-10 15:29:15 +01:00
Omar Abdelwahab	c353873774	precommit run	2025-11-07 14:54:33 -08:00
Omar Abdelwahab	1a7ba683e3	Merge branch 'main' into add-mcp-authentication-param	2025-11-07 14:26:06 -08:00
Omar Abdelwahab	ccb870c8fb	precommit	2025-11-07 12:14:42 -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
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
Ashwin Bharambe	a2c4c12384	chore(ui): remove the Streamlit UI (#4097)	2025-11-06 15:51:57 -08:00
Omar Abdelwahab	267c895827	precommit	2025-11-06 13:24:29 -08:00
Ashwin Bharambe	bef1b044bd	refactor(passthrough): use AsyncOpenAI instead of AsyncLlamaStackClient (#4085) ... We'd like to remove the dependence of `llama-stack` on `llama-stack-client`. This is a necessary step. A few small cleanups - Enables `embeddings` now also - Remove ModelRegistryHelper dependency (unused) - Consolidate to auth_credential field via RemoteInferenceProviderConfig - Implement list_models() to fetch from downstream /v1/models ## Test Plan Tested using this script https://gist.github.com/ashwinb/6356463d10f989c0682ab3bff8589581 Output: ``` Listing models from downstream server... Available models: ['passthrough/ollama/nomic-embed-text:latest', 'passthrough/ollama/all-minilm:l6-v2', 'passthrough/ollama/llama3.2-vision:11b', 'passthrough/ollama/llama3.2-vision:latest', 'passthrough/ollama/llama-guard3:1b', 'passthrough/o llama/llama3.2:1b', 'passthrough/ollama/all-minilm:latest', 'passthrough/ollama/llama3.2:3b', 'passthrough/ollama/llama3.2:3b-instruct-fp16', 'passthrough/bedrock/meta.llama3-1-8b-instruct-v1:0', 'passthrough/bedrock/meta.llama3-1-70b-instruct -v1:0', 'passthrough/bedrock/meta.llama3-1-405b-instruct-v1:0', 'passthrough/sentence-transformers/nomic-ai/nomic-embed-text-v1.5'] Using LLM model: passthrough/ollama/llama3.2-vision:11b Making inference request... Response: 4. --- Testing streaming --- Streamed response: ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='1', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='', index=0, logprobs=None)], created=1762381674, m odel='passthrough/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None) ... 5ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='stop', index=0, logprobs=None)], created=1762381674, model='passthrou gh/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None) ```	2025-11-05 18:15:11 -08:00
Roy Belio	c672a5d792	feat: ability to use postgres as store for starter distro (#4076) ... ## What does this PR do? The starter distribution now comes with all the required packages to support persistent stores—like the agent store, metadata, and inference—using PostgreSQL. Users can enable PostgreSQL support by setting the `ENABLE_POSTGRES_STORE=1` environment variable. This PR consolidates the functionality from the removed `postgres-demo` distribution into the starter distribution, reducing maintenance overhead. **Closes: #2619** **Supersedes: #2851** (rebased and updated) ## Changes Made 1. **Added PostgreSQL support to starter distribution** - New `run-with-postgres-store.yaml` configuration - Automatic config switching via `ENABLE_POSTGRES_STORE` environment variable - Removed separate `postgres-demo` distribution 2. **Updated to new build system** - Integrated postgres switching logic into Containerfile entrypoint - Uses new `storage_backends` and `storage_stores` API - Properly configured both PostgreSQL KV store and SQL store 3. **Updated dependencies** - Added `psycopg2-binary` and `asyncpg` to starter distribution - All postgres-related dependencies automatically included ## How to Use ### With Docker (PostgreSQL): ```bash docker run \ -e ENABLE_POSTGRES_STORE=1 \ -e POSTGRES_HOST=your_postgres_host \ -e POSTGRES_PORT=5432 \ -e POSTGRES_DB=llamastack \ -e POSTGRES_USER=llamastack \ -e POSTGRES_PASSWORD=llamastack \ -e OPENAI_API_KEY=your_key \ llamastack/distribution-starter ``` ### PostgreSQL environment variables: - `POSTGRES_HOST`: Postgres host (default: `localhost`) - `POSTGRES_PORT`: Postgres port (default: `5432`) - `POSTGRES_DB`: Postgres database name (default: `llamastack`) - `POSTGRES_USER`: Postgres username (default: `llamastack`) - `POSTGRES_PASSWORD`: Postgres password (default: `llamastack`) ## Test Plan All pre-commit hooks pass (mypy, ruff, distro-codegen) `llama stack list-deps starter` confirms psycopg2-binary is included Storage configuration correctly uses PostgreSQL backends Container builds successfully with postgres support ## Credits Original work by @leseb in #2851. Rebased and updated by @r-bit-rry to work with latest main. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Sébastien Han @leseb --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: Sébastien Han <seb@redhat.com>	2025-11-05 15:37:06 -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
Omar Abdelwahab	b8c24198eb	precommit	2025-11-05 11:16:11 -08:00
Omar Abdelwahab	09ef0b38c1	Updated the authentication field to take just the token	2025-11-05 10:49:35 -08:00
Omar Abdelwahab	8632c705aa	Merge branch 'main' into add-mcp-authentication-param	2025-11-04 16:20:38 -08:00
ehhuang	95b0493fae	chore: move src/llama_stack/ui to src/llama_stack_ui (#4068) ... # What does this PR do? This better separates UI from backend code, which was a point of confusion often for our beloved AI friends. ## Test Plan CI	2025-11-04 15:21:49 -08:00
Ashwin Bharambe	5850e3473f	fix: remove straggler openapi HTML file	2025-11-04 14:54:33 -08:00
Ashwin Bharambe	0c49a53c97	chore(api)!: remove tool_runtime.rag_tool from the API surface (#4067) ... RAG aka file search is implemented via the Responses API by specifying the file-search tool. The backend implementation remains unchanged. This PR merely removes the directly exposed API surface which allowed users to directly perform searches from the client. This facility is now available via the `client.vector_store.search()` OpenAI compatible API.	2025-11-04 14:50:54 -08:00
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
Omar Abdelwahab	9dbeeaca97	Removed the MCPAuthorization class relying on bearer token	2025-11-03 19:57:58 -08:00
Ashwin Bharambe	053fc0ac39	chore!: remove all deprecated routes (including /openai/v1/ ones) (#4054) ... This PR removes all routes which we had marked deprecated for the 0.3.0 release. This includes: - all the `/v1/openai/v1/` routes (the corresponding /v1 routes still exist of course) - the /agents API (which is superseded completely by Responses + Conversations) - several alpha routes which had a "v1" route to aide transitioning to "v1alpha" This is the corresponding client-python change: https://github.com/llamastack/llama-stack-client-python/pull/294	2025-11-03 19:00:59 -08:00
Ashwin Bharambe	cb40da210f	fix: update tests for OpenAI-style models endpoint (#4053) ... The llama-stack-client now uses /`v1/openai/v1/models` which returns OpenAI-compatible model objects with 'id' and 'custom_metadata' fields instead of the Resource-style 'identifier' field. Updated api_recorder to handle the new endpoint and modified tests to access model metadata appropriately. Deleted stale model recordings for re-recording. **NOTE: CI will be red on this one since it is dependent on https://github.com/llamastack/llama-stack-client-python/pull/291/files landing. I verified locally that it is green.**	2025-11-03 17:30:08 -08:00
Omar Abdelwahab	1143db0f64	added a fix	2025-11-03 16:55:13 -08:00
Sébastien Han	4a5ef65286	chore!: remove SDG API (#4035) ... # What does this PR do? This API hasn't received any traction and close to zero interest from the community. Let's revisit in the future if things change. Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-03 16:12:06 -08:00
Ashwin Bharambe	44096512b5	feat: add custom_metadata to OpenAIModel to unify /v1/models with /v1/openai/v1/models (#4051) ... We need to remove `/v1/openai/v1` paths shortly. There is one trouble -- our current `/v1/openai/v1/models` endpoint provides different data than `/v1/models`. Unfortunately our tests target the latter (llama-stack customized) behavior. We need to get to true OpenAI compatibility. This is step 1: adding `custom_metadata` field to `OpenAIModel` that includes all the extra stuff we add in the native `/v1/models` response. This can be extracted on the consumer end by look at `__pydantic_extra__` or other similar fields. This PR: - Adds `custom_metadata` field to `OpenAIModel` class in `src/llama_stack/apis/models/models.py` - Modified `openai_list_models()` in `src/llama_stack/core/routing_tables/models.py` to populate custom_metadata Next Steps 1. Update stainless client to use `/v1/openai/v1/models` instead of `/v1/models` 2. Migrate tests to read from `custom_metadata` 3. Remove `/v1/openai/v1/` prefix entirely and consolidate to single `/v1/models` endpoint	2025-11-03 15:56:07 -08:00
Omar Abdelwahab	d0a8878337	MCP authentication parameter implementation	2025-11-03 15:48:56 -08:00
Sébastien Han	d4aa348b60	chore: remove HTML generation for openapi spec (#4039) ... # What does this PR do? This seems to be an ancient artifact when we were using readthedocs? Now docusaurus read the specs directly. --------- Signed-off-by: Sébastien Han <seb@redhat.com>	2025-11-03 18:03:40 +01:00
raghotham	62603d25c2	chore(api)!: /v1/inspect only lists v1 apis by default (#3948) ... # What does this PR do? Allow filtering for v1alpha, v1beta, deprecated and v1. Backward incompatible change since by default it only returns v1 apis now. ## Test Plan added unit test	2025-10-31 11:55:46 -07:00
Jiayi Ni	fa7699d2c3	feat: Add rerank API for NVIDIA Inference Provider (#3329) ... # What does this PR do? Add rerank API for NVIDIA Inference Provider. <!-- If resolving an issue, uncomment and update the line below --> Closes #3278 ## Test Plan Unit test: ``` pytest tests/unit/providers/nvidia/test_rerank_inference.py ``` Integration test: ``` pytest -s -v tests/integration/inference/test_rerank.py --stack-config="inference=nvidia" --rerank-model=nvidia/nvidia/nv-rerankqa-mistral-4b-v3 --env NVIDIA_API_KEY="" --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com" ```	2025-10-30 21:42:09 -07:00
Sébastien Han	b4ea05ada9	chore: add batches to openapi schema (#3980) ... # What does this PR do? While working on https://github.com/llamastack/llama-stack/pull/3944 I realized that the batches API wasn't generated. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-10-30 07:08:35 -07:00
Charlie Doern	e8ecc99524	fix!: remove chunk_id property from Chunk class (#3954) ... # What does this PR do? chunk_id in the Chunk class executes actual logic to compute a chunk ID. This sort of logic should not live in the API spec. Instead, the providers should be in charge of calling generate_chunk_id, and pass it to `Chunk`. this removes the incorrect dependency between Provider impl and API impl Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-10-29 18:59:59 -07:00
Omar Abdelwahab	e6b27db30a	docs: A getting started notebook featuring simple agent examples. (#3955) ... # What does this PR do? Getting started notebook featuring simple agent examples. --------- Co-authored-by: Omar Abdelwahab <omara@fb.com>	2025-10-29 14:13:34 -04:00
Nathan Weinberg	b90c6a2c8b	fix(docs): remove leftover telemetry sidebar section (#3961) ... Leftover telemetry section was preventing `npm run build` from completing successfully Signed-off-by: Nathan Weinberg <nweinber@redhat.com>	2025-10-29 11:20:13 -04:00
ehhuang	1f9d48cd54	feat: openai files provider (#3946) ... # What does this PR do? - Adds OpenAI files provider - Note that file content retrieval is pretty limited by `purpose` https://community.openai.com/t/file-uploads-error-why-can-t-i-download-files-with-purpose-user-data/1357013?utm_source=chatgpt.com ## Test Plan Modify run yaml to use openai files provider: ``` files: - provider_id: openai provider_type: remote::openai config: api_key: ${env.OPENAI_API_KEY:=} metadata_store: backend: sql_default table_name: openai_files_metadata # Then run files tests ❯ uv run --no-sync ./scripts/integration-tests.sh --stack-config server:ci-tests --inference-mode replay --setup ollama --suite base --pattern test_files ```	2025-10-28 16:25:03 -07:00
raghotham	feabcdd67b	docs: add documentation on how to use custom run yaml in docker (#3949) ... as title test plan: ```yaml # custom-ollama-run.yaml version: 2 image_name: starter external_providers_dir: /.llama/providers.d apis: - inference - vector_io - files - safety - tool_runtime - agents providers: inference: # Single Ollama provider for all models - provider_id: ollama provider_type: remote::ollama config: url: ${env.OLLAMA_URL:=http://localhost:11434} vector_io: - provider_id: faiss provider_type: inline::faiss config: persistence: namespace: vector_io::faiss backend: kv_default files: - provider_id: meta-reference-files provider_type: inline::localfs config: storage_dir: /.llama/files metadata_store: table_name: files_metadata backend: sql_default safety: - provider_id: llama-guard provider_type: inline::llama-guard config: excluded_categories: [] tool_runtime: - provider_id: rag-runtime provider_type: inline::rag-runtime agents: - provider_id: meta-reference provider_type: inline::meta-reference config: persistence: agent_state: namespace: agents backend: kv_default responses: table_name: responses backend: sql_default max_write_queue_size: 10000 num_writers: 4 storage: backends: kv_default: type: kv_sqlite db_path: /.llama/kvstore.db sql_default: type: sql_sqlite db_path: /.llama/sql_store.db stores: metadata: namespace: registry backend: kv_default inference: table_name: inference_store backend: sql_default max_write_queue_size: 10000 num_writers: 4 conversations: table_name: openai_conversations backend: sql_default registered_resources: models: # All models use the same 'ollama' provider - model_id: llama3.2-vision:latest provider_id: ollama provider_model_id: llama3.2-vision:latest model_type: llm - model_id: llama3.2:3b provider_id: ollama provider_model_id: llama3.2:3b model_type: llm # Embedding models - model_id: nomic-embed-text-v2-moe provider_id: ollama provider_model_id: toshk0/nomic-embed-text-v2-moe:Q6_K model_type: embedding metadata: embedding_dimension: 768 shields: [] vector_dbs: [] datasets: [] scoring_fns: [] benchmarks: [] tool_groups: [] server: port: 8321 telemetry: enabled: true vector_stores: default_provider_id: faiss default_embedding_model: provider_id: ollama model_id: toshk0/nomic-embed-text-v2-moe:Q6_K ``` ```bash docker run -it --pull always -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT -v ~/.llama:/root/.llama -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml -e RUN_CONFIG_PATH=/app/custom-run.yaml -e OLLAMA_URL=http://host.docker.internal:11434/ llamastack/distribution-starter:0.3.0 --port $LLAMA_STACK_PORT ```	2025-10-28 16:05:44 -07:00
Ian Miller	5598f61e12	feat(responses)!: introduce OpenAI compatible prompts to Responses API (#3942) ... # 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 making changes to Responses API scheme to introduce OpenAI compatible prompts there. Change to the API only, therefore currently no implementation at all. However, the follow up PR with actual implementation will be submitted after current PR lands. The need of this functionality was initiated in #3514. > Note, #3514 is divided on three separate PRs. Current PR is the second of three. <!-- 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.* --> CI	2025-10-28 09:31:27 -07:00
ehhuang	b7dd3f5c56	chore!: BREAKING CHANGE: vector_db_id -> vector_store_id (#3923) ... # What does this PR do? ## Test Plan CI vector_io tests will fail until next client sync passed with https://github.com/llamastack/llama-stack-client-python/pull/286 checked out locally	2025-10-27 14:26:06 -07:00
IAN MILLER	98a5047f9d	feat(prompts): attach prompts to storage stores in run configs (#3893) ... # 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 attaching prompts to storage stores in run configs. It allows to specify prompts as stores in different distributions. The need of this functionality was initiated in #3514 > Note, #3514 is divided on three separate PRs. Current PR is the first of three. <!-- 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.* --> Manual testing and updated CI unit tests Prerequisites: 1. `uv run --with llama-stack llama stack list-deps starter | xargs -L1 uv pip install` 2. `llama stack run starter ` ``` INFO 2025-10-23 15:36:17,387 llama_stack.cli.stack.run:100 cli: Using run configuration: /Users/ianmiller/llama-stack/llama_stack/distributions/starter/run.yaml INFO 2025-10-23 15:36:17,423 llama_stack.cli.stack.run:157 cli: HTTPS enabled with certificates: Key: None Cert: None INFO 2025-10-23 15:36:17,424 llama_stack.cli.stack.run:159 cli: Listening on ['::', '0.0.0.0']:8321 INFO 2025-10-23 15:36:17,749 llama_stack.core.server.server:521 core::server: Run configuration: INFO 2025-10-23 15:36:17,756 llama_stack.core.server.server:524 core::server: apis: - agents - batches - datasetio - eval - files - inference - post_training - safety - scoring - tool_runtime - vector_io image_name: starter providers: agents: - config: persistence: agent_state: backend: kv_default namespace: agents responses: backend: sql_default max_write_queue_size: 10000 num_writers: 4 table_name: responses provider_id: meta-reference provider_type: inline::meta-reference batches: - config: kvstore: backend: kv_default namespace: batches provider_id: reference provider_type: inline::reference datasetio: - config: kvstore: backend: kv_default namespace: datasetio::huggingface provider_id: huggingface provider_type: remote::huggingface - config: kvstore: backend: kv_default namespace: datasetio::localfs provider_id: localfs provider_type: inline::localfs eval: - config: kvstore: backend: kv_default namespace: eval provider_id: meta-reference provider_type: inline::meta-reference files: - config: metadata_store: backend: sql_default table_name: files_metadata storage_dir: /Users/ianmiller/.llama/distributions/starter/files provider_id: meta-reference-files provider_type: inline::localfs inference: - config: api_key: '********' url: https://api.fireworks.ai/inference/v1 provider_id: fireworks provider_type: remote::fireworks - config: api_key: '********' url: https://api.together.xyz/v1 provider_id: together provider_type: remote::together - config: {} provider_id: bedrock provider_type: remote::bedrock - config: api_key: '********' base_url: https://api.openai.com/v1 provider_id: openai provider_type: remote::openai - config: api_key: '********' provider_id: anthropic provider_type: remote::anthropic - config: api_key: '********' provider_id: gemini provider_type: remote::gemini - config: api_key: '********' url: https://api.groq.com provider_id: groq provider_type: remote::groq - config: api_key: '********' url: https://api.sambanova.ai/v1 provider_id: sambanova provider_type: remote::sambanova - config: {} provider_id: sentence-transformers provider_type: inline::sentence-transformers post_training: - config: checkpoint_format: meta provider_id: torchtune-cpu provider_type: inline::torchtune-cpu safety: - config: excluded_categories: [] provider_id: llama-guard provider_type: inline::llama-guard - config: {} provider_id: code-scanner provider_type: inline::code-scanner scoring: - config: {} provider_id: basic provider_type: inline::basic - config: {} provider_id: llm-as-judge provider_type: inline::llm-as-judge - config: openai_api_key: '********' provider_id: braintrust provider_type: inline::braintrust tool_runtime: - config: api_key: '********' max_results: 3 provider_id: brave-search provider_type: remote::brave-search - config: api_key: '********' max_results: 3 provider_id: tavily-search provider_type: remote::tavily-search - config: {} provider_id: rag-runtime provider_type: inline::rag-runtime - config: {} provider_id: model-context-protocol provider_type: remote::model-context-protocol vector_io: - config: persistence: backend: kv_default namespace: vector_io::faiss provider_id: faiss provider_type: inline::faiss - config: db_path: /Users/ianmiller/.llama/distributions/starter/sqlite_vec.db persistence: backend: kv_default namespace: vector_io::sqlite_vec provider_id: sqlite-vec provider_type: inline::sqlite-vec registered_resources: benchmarks: [] datasets: [] models: [] scoring_fns: [] shields: [] tool_groups: - provider_id: tavily-search toolgroup_id: builtin::websearch - provider_id: rag-runtime toolgroup_id: builtin::rag vector_stores: [] server: port: 8321 storage: backends: kv_default: db_path: /Users/ianmiller/.llama/distributions/starter/kvstore.db type: kv_sqlite sql_default: db_path: /Users/ianmiller/.llama/distributions/starter/sql_store.db type: sql_sqlite stores: conversations: backend: sql_default table_name: openai_conversations inference: backend: sql_default max_write_queue_size: 10000 num_writers: 4 table_name: inference_store metadata: backend: kv_default namespace: registry prompts: backend: kv_default namespace: prompts telemetry: enabled: true vector_stores: default_embedding_model: model_id: nomic-ai/nomic-embed-text-v1.5 provider_id: sentence-transformers default_provider_id: faiss version: 2 INFO 2025-10-23 15:36:20,032 llama_stack.providers.utils.inference.inference_store:74 inference: Write queue disabled for SQLite to avoid concurrency issues WARNING 2025-10-23 15:36:20,422 llama_stack.providers.inline.telemetry.meta_reference.telemetry:84 telemetry: OTEL_EXPORTER_OTLP_ENDPOINT is not set, skipping telemetry INFO 2025-10-23 15:36:22,379 llama_stack.providers.utils.inference.openai_mixin:436 providers::utils: OpenAIInferenceAdapter.list_provider_model_ids() returned 105 models INFO 2025-10-23 15:36:22,703 uvicorn.error:84 uncategorized: Started server process [17328] INFO 2025-10-23 15:36:22,704 uvicorn.error:48 uncategorized: Waiting for application startup. INFO 2025-10-23 15:36:22,706 llama_stack.core.server.server:179 core::server: Starting up Llama Stack server (version: 0.3.0) INFO 2025-10-23 15:36:22,707 llama_stack.core.stack:470 core: starting registry refresh task INFO 2025-10-23 15:36:22,708 uvicorn.error:62 uncategorized: Application startup complete. INFO 2025-10-23 15:36:22,708 uvicorn.error:216 uncategorized: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) ``` As you can see, prompts are attached to stores in config Testing: 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_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e163f","variables":["name","company","role","tone"],"is_default":false}% ` 2. Get prompt: `curl -X GET http://localhost:8321/v1/prompts/pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e163f` `{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.","version":1,"prompt_id":"pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e163f","variables":["name","company","role","tone"],"is_default":false}% ` 3. Query sqlite KV storage to check created prompt: ``` sqlite> .mode column sqlite> .headers on sqlite> SELECT * FROM kvstore WHERE key LIKE 'prompts:v1:%'; key value expiration ------------------------------------------------------------ ------------------------------------------------------------ ---------- prompts:v1:pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e {"prompt_id": "pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab 163f:1 5f6e163f", "prompt": "Hello {{name}}! You are working at {{c ompany}}. Your role is {{role}} at {{company}}. Remember, {{ name}}, to be {{tone}}.", "version": 1, "variables": ["name" , "company", "role", "tone"], "is_default": false} prompts:v1:pmpt_a90e09e67acfe23776f2778c603eb6c17e139dab5f6e 1 163f:default sqlite> ```	2025-10-27 11:12:12 -07:00
Luis Tomas Bolivar	63422e5b36	fix!: Enhance response API support to not fail with tool calling (#3385) ... # What does this PR do? Introduces two main fixes to enhance the stability of Responses API when dealing with tool calling responses and structured outputs. ### Changes Made 1. It added OpenAIResponseOutputMessageMCPCall and ListTools to OpenAIResponseInput but https://github.com/llamastack/llama-stack/pull/3810 got merge that did the same in a different way. Still this PR does it in a way that keep the sync between OpenAIResponsesOutput and the allowed objects in OpenAIResponseInput. 2. Add protection in case self.ctx.response_format does not have type attribute BREAKING CHANGE: OpenAIResponseInput now uses OpenAIResponseOutput union type. This is semantically equivalent - all previously accepted types are still supported via the OpenAIResponseOutput union. This improves type consistency and maintainability.	2025-10-27 09:33:02 -07:00
Luis Tomas Bolivar	f18b5eb537	fix: Avoid BadRequestError due to invalid max_tokens (#3667) ... This patch ensures if max tokens is not defined, then is set to None instead of 0 when calling openai_chat_completion. This way some providers (like gemini) that cannot handle the `max_tokens = 0` will not fail Issue: #3666	2025-10-27 09:27:21 -07:00
ehhuang	509676641a	chore: update run configs (#3902) ... # What does this PR do? telemetry was deprecated ## Test Plan	2025-10-24 15:03:06 -07:00
ehhuang	2a1a813308	chore: update docs for telemetry api removal (#3900) ... # What does this PR do? Telemetry is no longer an API/provider. ## Test Plan	2025-10-24 13:57:28 -07:00
Francisco Arceo	4566eebe05	feat: Add static file import system for docs (#3882) ... # What does this PR do? Add static file import system for docs - Use `remark-code-import` plugin to embed code at build time - Support importing Python code with syntax highlighting using `raw-loader` + `ReactMarkdown` One caveat is that currently when embedding markdown with code used the syntax highlighting isn't behaving but I'll investigate that in a follow up. ## Test Plan Python Example: <img width="1372" height="995" alt="Screenshot 2025-10-23 at 9 22 18 PM" src="https://github.com/user-attachments/assets/656d2c78-4d9b-45a4-bd5e-3f8490352b85" /> Markdown example: <img width="1496" height="1070" alt="Screenshot 2025-10-23 at 9 22 38 PM" src="https://github.com/user-attachments/assets/6c0a07ec-ff7c-45aa-b05f-8c46acd4445c" /> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-10-24 14:01:33 -04:00
ehhuang	9916cb3b17	chore: support default model in moderations API (#3890) ... # What does this PR do? https://platform.openai.com/docs/api-reference/moderations supports optional model parameter. This PR adds support for using moderations API with model=None if a default shield id is provided via safety config. ## Test Plan added tests manual test: ``` > SAFETY_MODEL='together/meta-llama/Llama-Guard-4-12B' uv run llama stack run starter > curl http://localhost:8321/v1/moderations \ -H "Content-Type: application/json" \ -d '{ "input": [ "hello" ] }' ```	2025-10-23 16:03:53 -07:00