Integrating Microsoft Foundry with OpenClaw: Step by Step Model Configuration
Step 1: Deploying Models on Microsoft Foundry Let us kick things off in the Azure portal. To get our OpenClaw agent thinking like a genius, we need to deploy our models in Microsoft Foundry. For this guide, we are going to focus on deploying gpt-5.2-codex on Microsoft Foundry with OpenClaw. Navigate to your AI Hub, head over to the model catalog, choose the model you wish to use with OpenClaw and hit deploy. Once your deployment is successful, head to the endpoints section. Important: Grab your Endpoint URL and your API Keys right now and save them in a secure note. We will need these exact values to connect OpenClaw in a few minutes. Step 2: Installing and Initializing OpenClaw Next up, we need to get OpenClaw running on your machine. Open up your terminal and run the official installation script: curl -fsSL https://openclaw.ai/install.sh | bash The wizard will walk you through a few prompts. Here is exactly how to answer them to link up with our Azure setup: First Page (Model Selection): Choose "Skip for now". Second Page (Provider): Select azure-openai-responses. Model Selection: Select gpt-5.2-codex , For now only the models listed (hosted on Microsoft Foundry) in the picture below are available to be used with OpenClaw. Follow the rest of the standard prompts to finish the initial setup. Step 3: Editing the OpenClaw Configuration File Now for the fun part. We need to manually configure OpenClaw to talk to Microsoft Foundry. Open your configuration file located at ~/.openclaw/openclaw.json in your favorite text editor. Replace the contents of the models and agents sections with the following code block: { "models": { "providers": { "azure-openai-responses": { "baseUrl": "https://<YOUR_RESOURCE_NAME>.openai.azure.com/openai/v1", "apiKey": "<YOUR_AZURE_OPENAI_API_KEY>", "api": "openai-responses", "authHeader": false, "headers": { "api-key": "<YOUR_AZURE_OPENAI_API_KEY>" }, "models": [ { "id": "gpt-5.2-codex", "name": "GPT-5.2-Codex (Azure)", "reasoning": true, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 400000, "maxTokens": 16384, "compat": { "supportsStore": false } }, { "id": "gpt-5.2", "name": "GPT-5.2 (Azure)", "reasoning": false, "input": ["text", "image"], "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }, "contextWindow": 272000, "maxTokens": 16384, "compat": { "supportsStore": false } } ] } } }, "agents": { "defaults": { "model": { "primary": "azure-openai-responses/gpt-5.2-codex" }, "models": { "azure-openai-responses/gpt-5.2-codex": {} }, "workspace": "/home/<USERNAME>/.openclaw/workspace", "compaction": { "mode": "safeguard" }, "maxConcurrent": 4, "subagents": { "maxConcurrent": 8 } } } } You will notice a few placeholders in that JSON. Here is exactly what you need to swap out: Placeholder Variable What It Is Where to Find It <YOUR_RESOURCE_NAME> The unique name of your Azure OpenAI resource. Found in your Azure Portal under the Azure OpenAI resource overview. <YOUR_AZURE_OPENAI_API_KEY> The secret key required to authenticate your requests. Found in Microsoft Foundry under your project endpoints or Azure Portal keys section. <USERNAME> Your local computer's user profile name. Open your terminal and type whoami to find this. Step 4: Restart the Gateway After saving the configuration file, you must restart the OpenClaw gateway for the new Foundry settings to take effect. Run this simple command: openclaw gateway restart Configuration Notes & Deep Dive If you are curious about why we configured the JSON that way, here is a quick breakdown of the technical details. Authentication Differences Azure OpenAI uses the api-key HTTP header for authentication. This is entirely different from the standard OpenAI Authorization: Bearer header. Our configuration file addresses this in two ways: Setting "authHeader": false completely disables the default Bearer header. Adding "headers": { "api-key": "<key>" } forces OpenClaw to send the API key via Azure's native header format. Important Note: Your API key must appear in both the apiKey field AND the headers.api-key field within the JSON for this to work correctly. The Base URL Azure OpenAI's v1-compatible endpoint follows this specific format: https://<your_resource_name>.openai.azure.com/openai/v1 The beautiful thing about this v1 endpoint is that it is largely compatible with the standard OpenAI API and does not require you to manually pass an api-version query parameter. Model Compatibility Settings "compat": { "supportsStore": false } disables the store parameter since Azure OpenAI does not currently support it. "reasoning": true enables the thinking mode for GPT-5.2-Codex. This supports low, medium, high, and xhigh levels. "reasoning": false is set for GPT-5.2 because it is a standard, non-reasoning model. Model Specifications & Cost Tracking If you want OpenClaw to accurately track your token usage costs, you can update the cost fields from 0 to the current Azure pricing. Here are the specs and costs for the models we just deployed: Model Specifications Model Context Window Max Output Tokens Image Input Reasoning gpt-5.2-codex 400,000 tokens 16,384 tokens Yes Yes gpt-5.2 272,000 tokens 16,384 tokens Yes No Current Cost (Adjust in JSON) Model Input (per 1M tokens) Output (per 1M tokens) Cached Input (per 1M tokens) gpt-5.2-codex $1.75 $14.00 $0.175 gpt-5.2 $2.00 $8.00 $0.50 Conclusion: And there you have it! You have successfully bridged the gap between the enterprise-grade infrastructure of Microsoft Foundry and the local autonomy of OpenClaw. By following these steps, you are not just running a chatbot; you are running a sophisticated agent capable of reasoning, coding, and executing tasks with the full power of GPT-5.2-codex behind it. The combination of Azure's reliability and OpenClaw's flexibility opens up a world of possibilities. Whether you are building an automated devops assistant, a research agent, or just exploring the bleeding edge of AI, you now have a robust foundation to build upon. Now it is time to let your agent loose on some real tasks. Go forth, experiment with different system prompts, and see what you can build. If you run into any interesting edge cases or come up with a unique configuration, let me know in the comments below. Happy coding!
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Why This Approach? Migrating large objects (LOBs) between PostgreSQL servers can be challenging due to size, performance, and complexity. Traditional methods often involve exporting to files and re-importing, which adds overhead and slows down the process. Also, there could be some Cloud restrictions that limit the usage of other tools like: Pgcopydb: Welcome to pgcopydb’s documentation! — pgcopydb 0.17~dev documentation Or other techniques like using RLS: SoporteDBA: PostgreSQL pg_dump filtering data by using Row Level Security (RLS) This solution introduces a parallelized migration script that: Reads directly from pg_largeobject. Splits work across multiple processes using the MOD() function on loid. Streams data via PIPES—no intermediate files. Scales easily by adjusting parallel degree. *Possible feature*: Commit size logic to supports resume logic by excluding already migrated LOBs. Key Benefits Direct streaming: No temporary files, reducing disk I/O. Parallel execution: Faster migration by leveraging multiple processes. Simple setup: Just two helper scripts for source and destination connections. Reference Scripts reference: Moving data with PostgreSQL COPY and \COPY commands | Microsoft Community Hub Source Connection -- pgsource.sh -- To connect to source database -- PLEASE REVIEW CAREFULLY THE CONNECTION STRINGS TO CONNECT TO SOURCE SERVER PGPASSWORD=<password> psql -t -h <sourceservername>.postgres.database.azure.com -U <username> <database> -- Permissions to execute chmod +x pgsource.sh Destination Connection -- pgdestination.sh -- To connect to target database -- PLEASE REVIEW CAREFULLY THE CONNECTION STRINGS TO CONNECT TO DESTINATION SERVER PGPASSWORD=<password> psql -t -h <destinationservername>.postgres.database.azure.com -U <username> <database> -- Permissions to execute chmod +x pgdestination.sh Parallel Migration Script -- transferlobparallel.sh -- To perform the parallel migrations of lobs echo > nohup.out echo 'ParallelDegree: '$1 'DateTime: '`date +"%Y%m%d%H%M%S"` # Check if no large objects to migrate count=$(echo "select count(1) from pg_largeobject;"|./pgsource.sh) count=$(echo "$count" | xargs) if [ "$count" -eq 0 ]; then echo "There are no large objects to migrate. Stopping the script." exit 0 fi par=$1 for i in $(seq 1 $1); do nohup /bin/bash <<EOF & echo "\copy (select data from (select 0 as rowsort, 'begin;' as data union select 1, concat('SELECT pg_catalog.lo_create(', lo.loid, ');SELECT pg_catalog.lo_open(', lo.loid, ', 131072);SELECT pg_catalog.lowrite(0,''', string_agg(lo.data, '' ORDER BY pageno), ''');SELECT pg_catalog.lo_close(0);') from pg_largeobject lo where mod(lo.loid::BIGINT,$par)=$i-1 group by lo.loid union select 2, 'commit;') order by rowsort) to STDOUT;"|./pgsource.sh|sed 's/\\\\\\\/\\\\/g'|./pgdestination.sh; echo "Execution thread $i finished. DateTime: ";date +"%Y%m%d%H%M%S"; EOF done tail -f nohup.out|grep Execution -- Permissions to execute chmod +x transferlobparallel.sh -- NOTE Please pay attention during the script execution, it will never finish as last line is “tail -f nohup…”, you need to monitor if all the threads already finished or checking in a different session the “psql” processes are still working or not. How to Run ./transferlobparallel.sh <ParallelDegree> Example: ./transferlobparallel.sh 3 Runs 3 parallel processes to migrate LOBs directly to the destination server. Performance Results Please find basic metrics taking into consideration that client linux VM with accelerated networking was co-located in same region/AZ than source and target Azure Database for PostgreSQL Flexible servers, servers and linux client based in Standard 4CPU SKUs, no other special configurations. 16 threads: ~11 seconds for 500MB. CPU usage: ~15% at 16 threads. Estimated migration for 80GB: ~30 minutes Key Takeaways This approach dramatically reduces migration time and complexity. By combining PostgreSQL’s pg_largeobject with parallelism and streaming, without intermediate storage, and using only psql client command as required client software. Disclaimer The script is provided as it is. Please review carefully when running/testing, the script is just a starting point to show how to migrate large objects in a parallelized way without intermediate storage, but it can be also implemented/improved using other methods as mentioned..NET MAUI on Linux with Visual Studio Code
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