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foundry-knowledge
Knowledge retrieval (RAG) via Foundry file_search and azure_ai_search tools. Agentic retrieval with citations — uses Responses API.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Knowledge retrieval (RAG) via Foundry file_search and azure_ai_search tools. Agentic retrieval with citations — uses Responses API.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
Pair with a kars cluster and offload heavy tasks to governed cloud sandboxes with GPU / foundation-model inference / Azure AI services, or communicate with other agents over end-to-end encrypted AgentMesh. Triggers on natural-language intents like "offload to the cloud", "run this on Azure", "ask my cluster to…", "send a message to agent X", "who is on the mesh", "check my inbox", "is my offload done".
Behavioral governance for OpenClaw agents via AGT — tool-level policy, inter-agent trust, audit logging.
Spawn secure isolated sub-agent sandboxes, delegate tasks via AGT mesh, receive results, and destroy sub-agents. Uses the kars_spawn, kars_mesh_send, kars_mesh_inbox, and kars_spawn_destroy tools.
Query and inspect Foundry prompt agents and invoke Foundry tools via the Responses API. OpenClaw is the orchestrator — Foundry provides managed AI services.
Python code execution via Azure AI Foundry Responses API with code_interpreter tool. Data analysis, charts, and math in a managed sandbox.
Manage persistent conversations via Foundry Conversations API. Create conversations, add messages, and maintain history across sessions.
| name | foundry-knowledge |
| description | Knowledge retrieval (RAG) via Foundry file_search and azure_ai_search tools. Agentic retrieval with citations — uses Responses API. |
| metadata | {"openclaw":{"requires":{"env":["FOUNDRY_PROJECT_ENDPOINT"]},"primaryEnv":"FOUNDRY_PROJECT_ENDPOINT"}} |
You have access to knowledge retrieval via Foundry's built-in search tools:
Both return results with citations. No hosted agent needed — uses direct Responses API.
All requests: http://localhost:8443 with ?api-version=2025-11-15-preview. Auth is automatic.
Requires vector store IDs from previously uploaded files:
curl -s -X POST 'http://localhost:8443/openai/responses?api-version=2025-11-15-preview' \
-H 'Content-Type: application/json' \
-d '{"model":"gpt-4.1","input":"What does the Q3 report say about revenue?","tools":[{"type":"file_search","vector_store_ids":["vs_abc123"]}],"store":false}'
Requires an AI Search index configured as a connection in the Foundry project:
curl -s -X POST 'http://localhost:8443/openai/responses?api-version=2025-11-15-preview' \
-H 'Content-Type: application/json' \
-d '{"model":"gpt-4.1","input":"Find documents about security best practices","tools":[{"type":"azure_ai_search","azure_ai_search":{"indexes":[{"index_name":"my-index","project_connection_id":"/connections/my-search"}]}}],"store":false}'
curl -s 'http://localhost:8443/indexes?api-version=2025-11-15-preview'
File search requires uploading files to Foundry vector stores first. Azure AI Search requires an index and connection configured in the project. If neither is set up, the tools will return empty results — the agent should fall back to its training data.