Release v6.5.0 into Main#921
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* add context window to models * test coverage * pre-commit * pr comments * tests * pre-commit * metrics dashboard token usage update * unit tests * cr comments * pre-commit --------- Co-authored-by: jmharold <jmharold@amazon.com> Co-authored-by: Evan Stohlmann <estohlmann@gmail.com>
…ly runs with cleanup
* fix streamed guardrail responses --------- Co-authored-by: jmharold <jmharold@amazon.com>
Replace aws_s3_tools with aws_operator_tools for broader AWS API coverage. Update mcp-workbench docs and README; refresh package-lock.json. Made-with: Cursor
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| const ecrAccounts = [...new Set([...accountNumbersEcr, accountNumber].filter(Boolean))]; |
estohlmann
approved these changes
Apr 6, 2026
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v6.5.0
Key Features
Self-Service RAG Administration
A new RAG Admin role gives designated users full control over RAG repository operations, document ingestion, collection management, and pipeline configuration without granting full system administrator privileges. This reduces the operational bottleneck where every RAG change required a system administrator. Self-service RAG is especially useful in multi-tenant environments.
Operations Metrics Dashboard
New dashboard reports track metrics across models and clusters, including inference latency, token usage, and batch ingestion job status. For example, customers can use the new input/output token reports to derive costs across users, groups, and models. This is useful for multi-tenant environments with a variety of end-user orgs. Also, model containers publish Prometheus metrics for vLLM, TEI, and TGI, and batch ingestion jobs report totals and failures for RAG document ingestion.
Integrating Externally Deployed Models
Administrators can register deployed models that are not LISA-managed by providing a URL that can be accessed from the LiteLLM ECS cluster. These models appear and behave like other models in the platform.
AWS Session Credentials
LISA now lets you attach AWS credentials to a chat session. While that session is active, MCP tools can use those credentials to call AWS APIs, so tool-based workflows can reach AWS resources in the same context as the conversation instead of requiring separate per-tool setup.
An example of a tool using this can be seen: lib/serve/mcp-workbench/src/examples/sample_tools/aws_operator_tools.py
Other Key Changes
Acknowledgements
Full Changelog: https://github.com/awslabs/LISA/compare/v6.4.0..v6.5.0