---

name: extensions-integrations description: "Install, choose, configure, and troubleshoot AutoGen autogen_ext optional integrations for model clients, tools/workbenches, code executors, memory/cache backends, helper agents, and gRPC worker runtime surfaces." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

AutoGen Extensions Integrations

Use this sub-skill when maintaining existing AutoGen Python applications that depend on autogen_ext optional integrations. AutoGen is in maintenance mode; for brand-new greenfield agent systems, consider Microsoft Agent Framework first. For existing AutoGen systems, this sub-skill helps future agents make safe, precise decisions about extension packages, provider configuration, and service-backed integrations.

Start Here

1. Identify the exact integration surface before installing extras: model client, tool/workbench, code executor, memory/cache backend, helper agent, or gRPC runtime.

2. Install the narrow extra for that surface instead of a broad collection of optional dependencies. See references/optional-extras.md.

3. From this sub-skill directory, inspect the current environment without starting services or making provider calls:

   python scripts/inspect_extensions.py --json
   

4. Configure credentials and services explicitly; do not infer that an importable package means provider access, Docker, Redis, ChromaDB, Jupyter, Playwright, MCP, or Azure resources are usable.

5. Route team/agent orchestration questions to agentchat-workflows, low-level runtime patterns to core-runtime, and Studio/Magentic-One CLI/AG Bench/pyautogen compatibility to tools-studio-bench.

Integration Map

Need	Primary reference	Public imports to check
OpenAI, Azure OpenAI, Anthropic, Azure AI Foundry/GitHub Models, Ollama, llama.cpp, Semantic Kernel, replay clients	references/model-clients.md	autogen_ext.models.openai, autogen_ext.models.azure, autogen_ext.models.anthropic, autogen_ext.models.ollama, autogen_ext.models.llama_cpp, autogen_ext.models.semantic_kernel, autogen_ext.models.replay
MCP tools/workbenches, HTTP tools, LangChain adapters, Azure AI Search, GraphRAG, Semantic Kernel tool adapter	references/tools-workbenches.md	autogen_ext.tools.mcp, autogen_ext.tools.http, autogen_ext.tools.langchain, autogen_ext.tools.azure, autogen_ext.tools.graphrag, autogen_ext.tools.semantic_kernel
Local, Docker, Jupyter, Docker-Jupyter, Azure Container Apps Dynamic Sessions code execution	references/code-executors-and-runtimes.md	autogen_ext.code_executors.local, .docker, .jupyter, .docker_jupyter, .azure
Disk/Redis cache and ChromaDB, Redis, mem0, canvas, task-centric memory	references/optional-extras.md, references/troubleshooting.md	autogen_ext.cache_store.diskcache, .redis, autogen_ext.memory.chromadb, .redis, .mem0, .canvas
Web/file/video surfer and Magentic-One helper agent surfaces inside autogen_ext	references/optional-extras.md, references/troubleshooting.md	autogen_ext.agents.web_surfer, .file_surfer, .video_surfer, .magentic_one
gRPC worker runtime extension package surfaces	references/code-executors-and-runtimes.md	autogen_ext.runtimes.grpc

Safety Defaults

• Treat LocalCommandLineCodeExecutor as trusted-code only; prefer Docker or a controlled remote executor when code is generated by an LLM.
• Do not start Docker containers, Jupyter kernels, MCP servers, browsers, Redis/ChromaDB/mem0 services, or Azure resources just to diagnose imports.
• For MCP stdio servers, review command, args, env, working directory, and filesystem roots before execution; MCP servers are code with the privileges of the process that starts them.
• Never print real API keys, tokens, endpoint secrets, connection strings, Redis passwords, or serialized model configs containing credentials.
• If an extension error is actually about AgentChat team composition, streaming, termination, or state, switch to agentchat-workflows; if it is about routed agents, topics, subscriptions, or distributed runtime design, switch to core-runtime.

References

• references/optional-extras.md — narrow extra selection, dependency boundaries, and safe environment inspection.
• references/model-clients.md — provider client setup, required kwargs/env vars, model metadata, and no-credential diagnostics.
• references/tools-workbenches.md — MCP, HTTP, LangChain, Azure AI Search, GraphRAG, and Semantic Kernel tool integration.
• references/code-executors-and-runtimes.md — local/Docker/Jupyter/Azure executors, gRPC runtime extras, and service-dependent checks.
• references/troubleshooting.md — symptom-to-cause matrix for optional dependencies, credentials, service lifecycles, and serialization.