{
  "entries": [
    {
      "kind": "message",
      "role": "user",
      "text": "Read /app/quickstart.txt and implement the Memgraph getting-started flow in /app using one rootless Podman Memgraph database container without Lab. Create an Alice user linked to Memgraph, run a MATCH query, and confirm the first retrieval returns both names. This machine provides rootless Podman instead of Docker; use Podman-compatible commands.",
      "time_seconds": 2.699
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Reviewing quickstart instructions**",
      "time_seconds": 2.712
    },
    {
      "kind": "tool_call",
      "role": "read",
      "text": "read {\"path\": \"/app/quickstart.txt\"}",
      "time_seconds": 2.712
    },
    {
      "kind": "tool_result",
      "role": "read",
      "text": "<!-- Official source: https://memgraph.com/docs/getting-started -->\n\nGetting started with Memgraph\nGitHubDiscord\nHome\nGetting started\nInstall Memgraph\nDocker\nDebian\nUbuntu\nCentOS\nFedora\nRed Hat\nMemgraph Cloud\nDocker Compose\nKubernetes\nWSL\nDirect download links\nCLI\nFirst steps with Docker\nBuild Memgraph from source\nPackaging Memgraph\nClient libraries\nC#\nGo\nGraphQL\nJava\nJavaScript\nNode.js\nPHP\nPython\nRust\nAI ecosystem\nGraphRAG\nPrerequisites\nKnowledge graph creation\nAtomic Pipelines\nText2Cypher\nLocal Graph Search\nQuery-focused summarization\nAgentic GraphRAG\nExamples & demos\nMCP\nSkills\nAgents\nSQL2Graph\nUnstructured2Graph\nMachine learning\nIntegrations\nFundamentals\nConstraints\nData types\nData durability\nIndexes\nStorage access\nStorage memory usage\nTelemetry\nTransactions\nTriggers\nData modeling\nGraph data model\nLPG vs. RDF\nModeling guides\nModel a knowledge graph\nModel a graph from CSV file\nBest practices\nData migration\nBest practices\nCSV\nPARQUET\nJSON\nCYPHERL\nMigrate from Neo4j\nUsing CSV files\nUsing a single Cypher query\nMigrate from RDBMS using CSV files\nMigrate from RDBMS using MAGE modules\nMigrate Apache Iceberg tables from data lake using Dremio\nMigrate to Memgraph using Apache Spark\nMigrate from Memgraph Platform to Memgraph MAGE\nExport data\nQuerying\nBest practices\nDifferences in Cypher implementations\nCreate graph objects\nRead and modify data\nClauses\nALTER\nCALL\nCASE\nCREATE\nDELETE\nDROP\nEXPLAIN\nFOREACH\nLOAD CSV\nLOAD PARQUET\nMATCH\nMERGE\nOPTIONAL MATCH\nPROFILE\nREMOVE\nRETURN\nSET\nUNION\nUNWIND\nUSING PARALLEL EXECUTION\nWHERE\nWITH\nFunctions\nExpressions\nSchema\nText search\nVector search\nTime to live\nQuery plan\nParallel execution\nExploring datasets\nAnalyzing TED talks\nBackpacking through Europe\nExploring the European road network\nFootball transfers\nGoT deaths\nGraphing the premier league\nMarvel universe\nMovie recommendation\nAdvanced algorithms\nAvailable algorithms\nalgo\nbetweenness_centrality_online\nbetweenness_centrality\nbiconnected_components\nbipartite_matching\nbridges\ncollections\ncsv_utils\ncommunity_detection_online\ncommunity_detection\nconvert\nconvert_c\ndo\ncreate\ncross_database\ncugraph\ncycles\ndate\ndegree_centrality\ndistance_calculator\nelasticsearch_synchronization\nembeddings\nexport_util\ngnn\ngnn_link_prediction\ngnn_node_classification\ngraph_analyzer\ngraph_coloring\ngraph_util\nigraphalg\nimport_util\njson_util\nknn\nkatz_centrality_online\nkatz_centrality\nkmeans_clustering\nlabel\nleiden_community_detection\nllm\nllm_util\nmap\nmath\nmax_flow\nmerge\nmeta_util\nmeta\nmigrate\nmgps\nneighbors\nnode_similarity\nnode\nnode2vec_online\nnode2vec\nnodes\nnxalg\npagerank_online\npagerank\npath\nperiodic\nrefactor\nset_cover\nset_property\ntemporal\ntext\ntemporal_graph_networks\ntsp\nunion_find\nutil_module\nuuid_generator\nvrp\nweakly_connected_components\nxml_module\nDeep path traversal algorithms\nInstall MAGE\nRun algorithms\nUtilize NetworkX\nCustom query modules\nC\nC API\nC example\nC++\nC++ API\nC++ example\nPython\nPython API\nMock Python API\nPython example\nImplement custom query module in Python\nUnderstanding music with modules\nRust\nRust API\nRust example\nManage query modules\nContributing\nMemgraph Lab\nGetting started\nInstallation and deployment\nConnection types\nData migration\nQuerying\nFeatures\nCollections\nCSV file import\nCustom configuration\nData modeling\nGraphChat\nGraph schema\nGraph Style Script (GSS)\nMain building blocks\nBuilt-in elements\nDirective properties\nTutorial\nLayout\nLogs\nMulti-tenancy\nMonitoring\nRun history\nSharing features\nSingle sign-on\nQuery modules\nStreams\nConfiguration\nDatabase management\nAuthentication and authorization\nUsers\nRole-based access control\nMigrating to v3.7 LBAC\nMultiple roles per user and multi-tenant roles\nAuth system integrations\nImpersonate user\nUser profiles\nQuery privileges reference\nBackup and restore\nConfiguration\nDebugging\nEnabling Memgraph Enterprise\nLogs\nMonitoring\nMulti-tenancy\nTenant profiles\nQuery metadata\nServer-side descriptions\nServer-side parameters\nServer stats\nSSL encryption\nSystem configuration\nUpgrades\nSpecific versions\nDeployment\nBest practices\nWorkloads\nMemgraph in cybersecurity\nMemgraph in GraphRAG use cases\nMemgraph in high-throughput workloads\nMemgraph in mission critical workloads\nMemgraph in fraud detection\nMemgraph in supply chain\nEnvironments\nDocker\nLinux\nAWS\nGCP\nAzure\nBenchmarking\nClustering\nReplication\nUnder the hood\nSet up replication cluster with Docker\nSet up replication cluster with K8s\nBest practices\nReference commands\nSystem replication\nHigh availability\nUnder the hood\nQuerying the cluster in HA\nSet up HA cluster with Docker\nSet up HA cluster with Docker Compose\nSet up HA cluster with K8s\nBest practices\nReference commands\nReference architectures\nMigrating to v3.9 HA\nFAQ\nData streams\nTransformation modules\nTransformation modules C API\nTransformations Python API\nManage streams with queries\nGraph stream processing with Kafka\nKafka Connect\nMemgraph Zero\nMemGQL\nQuick Start\nUse Cases\nFederated GQL\nPublic-Private Data\nDistributed Compute\nEnterprise Context\nAgentic Access\nFeatures\nConnect\nto Clickhouse\nto DuckDB\nto Microsoft Fabric\nto SAP HANA\nto Iceberg\nto Memgraph\nto MongoDB\nto Neo4j\nto Oracle\nto PostgreSQL\nto MySQL\nto SQL Server\nto Pinot\nto Snowflake\nSchema File\nMultiple Graphs\nDocker Compose\nReference\nLicensing\nChangelog\nHelp center\nFAQ\nErrors\nAuth\nConnection\nDurability\nHigh availability\nMemory\nModules\nPorts\nPython modules\nReplication\nSnapshots\nSocket\nSSL\nTransactions\nUnknown\nRelease notes\nWhat's coming soon to Memgraph?\nLight\nOn This Page\nInstall Memgraph Platform\nConnect to the database\nImport and query data\nRun advanced algorithms\nQuestion? Give us feedback \u2192Edit this pageScroll to top\nGetting started\nGetting started with Memgraph\nGet Memgraph up and running on your data by following these installation steps\nfor Docker environment. If you\u2019re new to Memgraph or you\u2019re in a developing\nstage, we recommend installing Memgraph\nPlatform. Besides the database,\nyou\u2019ll also have all the tools you might need to analyze your data, such as\ncommand-line interface mgconsole, web interface\nMemgraph Lab and a complete set of algorithms within a\nMAGE library running.\nMemgraph Platform is no longer a single-container application. Before (Memgraph\n< 2.14), Memgraph Platform was a separate Docker image released on Docker Hub.\nThis image is no longer supported, because having database service and client\nservice in the same container is not a good practice. Still, with Memgraph Lab\nbeing released on Docker Hub, it is possible to run Memgraph MAGE and Lab as two\nseparate services, and in that way, running Memgraph Platform as a\nmulti-container application with Docker\nCompose.\nInstall Memgraph Platform\nIf you want to quickly try out Memgraph Platform (Memgraph database + MAGE\nlibrary + Memgraph Lab) for the first time, do the following:\nInstall Docker\nEnsure Docker is running in the\nbackground.\nRun Memgraph Platform\nDepending on your operating system, execute the appropriate command\nin the console:\nLinux/macOSWindows\ncurl https://install.memgraph.com | sh\niwr https://windows.memgraph.com | iex\nThe above command runs a script that downloads a Docker Compose\nfile\nto your system, builds and starts\nmemgraph-mage\nand\nmemgraph-lab\nDocker\nservices in two separate containers. The Docker Compose file is downloaded into\na folder called\nmemgraph-platform\nusually located inside the user folder, e.g.\n/Users/katelatte/memgraph-platform\n. You\u2019ll see the message during the download\nprocess, similar to:\n-e Downloading docker compose file to: /Users/katelatte/memgraph-platform/docker-compose.yml\nTo manage the containers with Docker Compose, position yourself into the\nmemgraph-platform\nfolder. Most commonly used commands are\ndocker compose start\n,\nstop\n,\ndown\nand\nrestart\n. The running containers can be managed with\nDocker as well. To learn how to do that, follow the First steps with Docker\nguide.\nThe above installation process is a quick start, but make sure you explore\nother installation options and choose the\npreferred way of running Memgraph in your environment.\nConnect to the database\nYou can connect to the Memgraph instance using the command-line interface\nmgconsole by running the following command in a new terminal:\ndocker run -it memgraph/mgconsole:latest [--host HOST_IP] [--port PORT]\nThe arguments\n--host\nand\n--port\nare only required if you\u2019re running Memgraph on a\nremote server or different port. Otherwise, they can be omitted.\nAlternatively, you can connect to Memgraph using the Memgraph Lab web\napplication available on\nlocalhost:3000\nor\nas a Desktop app.\nImport and query data\nAs a new user, you\u2019ll want to populate your fresh Memgraph installation with\ndata. The method you choose will largely depend on the format of your data.\nMemgraph supports imports from various formats, such as CSV, JSON and using\nqueries listed in a CYPHERL file. For detailed instructions on migrating your\ndata, refer to the data migration documentation.\nIf you\u2019re just getting started and want a quick setup, Memgraph offers a range\nof pre-made datasets ideal for testing and learning. We have prepared more than\n20 datasets you can use for testing and learning. If you wish to use a demo\ndataset, go to Datasets section, chose a dataset and Load dataset.\nNow that the data is imported it is time to run your first Cypher query. Go to\nthe Query Execution section in the sidebar, and then copy-and-paste the\nfollowing code into the Cypher editor.\nMATCH (n)-[r]-(m)\nRETURN n, r, m;\nClick Run query to run the above query that will return all the nodes and\nrelationships in the database and see the result in the Graph results tab.\nThis an example using one of the preloaded datasets:\nIf you\u2019re transitioning from Neo4j to Memgraph, adapting your existing Cypher\nqueries is essential. While both platforms use the Cypher query language, there\nmight be platform-specific features that require adjustments. To assist you, we\nprovide a comprehensive documentation to help you adapt and\noptimize your queries for Memgraph.\nTo start using Memgraph in your application, use one of the following client\nlibraries and follow their getting started guide.\nRun advanced algorithms\nMemgraph offers a range of procedures tailored to address specific graph\nproblems. Built-in deep path traversal algorithms such as BFS, DFS, Weighted\nshortest path, All shortest paths, and K shortest paths can be executed using their specific\nclauses.\nMemgraph comes with expanded set of algorithms called Memgraph Advanced Graph\nExtensions (MAGE) library. MAGE is an open-source\nrepository housing graph algorithms, both by the Memgraph team and the\ncommunity. These are presented as query modules.\nOne of the advanced algorithms at your disposal is the PageRank algorithm. You\ncan run it with the following code:\nCALL pagerank.get()\nYIELD node, rank;\nDiscord\nJoin the community\nGitHub\nAsk a question\nOffice hours\nTalk with Memgraph engineers\nHomeInstall Memgraph\nDocumentation\nGet started\nMigrate data\nQuery data\nCreate an app\nVisualize data\nUse advanced algorithms\nCommunity\nDiscord\nStack Overflow\nTwitter\nMore\nMemgraph Cloud\nMemgraph Playground\nGitHub\nYouTube\nCopyright \u00a9 2026 Memgraph\n\n\n<!-- Official source: https://memgraph.com/blog/how-to-install-memgraph-and-memgraph-lab-with-docker-on-macos -->\n\nHow to Install Memgraph With Docker on macOS\nProduct\nMEMGRAPH\nDatabase\nHigh performance, in-memory, for real-time\nECOSYSTEM\nMemgraph Lab\nVisual interface for your graph data management\nTools & Integrations\nAlready ready for data ingestion and processing\nAI WORKLOADS\nVector Search\nSemantic search for AI-powered applications\nEVALUATION\nBenchmark\nPerformance insights for graph data at scale\nPIPELINES\nZEROETL\nQuery your data as a graph\nUse Cases\nAI WORKLOADS\nGraphRAG\nStructured context that complements vector search\nAI Memory\nSemantic, episodic, and procedural memory in one graph\nAgentic AI\nThe execution graph underneath your AI agents\nGRAPH ANALYTICS\nFraud & Risk Detection\nReal-time fraud detection powered by connected data\n360 Data & Network Exploration\nDeep understanding of structure and behavior in complex networks\nLogistics & Network Optimization\nSupply chains, power grids, and telecom networks\nSee all\u2192\nKnowledge Graph\nConnected knowledge for unified data understanding\nCustomer & User Stories\u2192\nEnterprise\nEnterprise Overview\nSecurity, HA, SLAs, and deployment options\nAI Platform\nThe full Enterprise engine, priced for AI workloads\nPartners\nGet help from teams experienced with Memgraph\nSupport\nDirect access to the engineers who built it\nContact us\u2192\nPricing\nDevelopers\nDocs\nFrom first query to production deployment\nNeo4jDevs\nMigration paths and compatibility guides\nPython\nQuery, build, and manage graphs in Python\nNetworkX\nRun NetworkX algorithms on Memgraph data in-memory\nResources\nBlog\nHow-tos, benchmarks, and use case breakdowns\nMemgraph Academy\nStructured courses for getting started with Memgraph\nOn Demand\nWatch case studies, read whitepapers and deep dive\nEvents\nUpcoming webinars, meetups, and speaking events\nPlayground\nInteractive sandbox with preloaded graph datasets\nGet started for freeGet a demo\nGet a demo\nGet started\nBack to blog\nShowcase\nHow to Install Memgraph With Docker on macOS\nBy Memgraph\n9 min readApril 20, 2020\nIntroduction\nMemgraph is an end-to-end platform\ndesigned to solve complex graph problems in real-time and accelerate graph\napplication development. Memgraph supports strongly-consistent ACID\ntransactions and uses the Cypher query language for structuring,\nmanipulating, and exploring data.\nMemgraph Platform includes:\nMemgraph DB - in-memory graph database built for real-time business-critical\napplications\nMemgraph Lab - lightweight and intuitive Cypher and\nBolt compatible visual user interface running in\nbrowser designed to help you import data, develop, debug, and profile database\nqueries and visualize query results.\nmgconsole - command-line interface for running queries\nMAGE - open-source repository that contains graph algorithms and modules that\nextend the Cypher query language and help tackle the most interesting\nand challenging graph analytics problems.\nIn this tutorial, you will install Memgraph Platform on your Mac with Docker and\nMemgraph Lab as a desktop application. You will then test each installation by\nrunning basic Cypher queries to make sure that everything is working correctly.\nPrerequisites\nFor a seamless installation of Memgraph and Memgraph Lab on your Mac, ensure that you have:\nA computer running macOS version 10.13 (High Sierra) or higher.\nAdministrative rights to your macOS and an internet connection.\nDocker Desktop for Mac version\n1.12 or higher installed on your computer.\nBasic knowledge of working with the command line.\nStep 1 \u2014 Downloading and Installing Memgraph\nBefore we go on, it's essential that you have Docker installed on your\nsystem so you are able to\ninstall and run Memgraph.\nOpen iTerm or Terminal, then use the following command to download the Memgraph\nPlatform Docker image and load it.\ndocker pull memgraph/memgraph-platform\nNow, start the Memgraph server by issuing this command:\ndocker run -it -p 7687:7687 -p 3000:3000 -p 7444:7444 -v mg_lib:/var/lib/memgraph memgraph/memgraph-platform\nThe\n-it\nflag allows us to communicate with the database using command-line\ntool\nmgconsole\n. Memgraph DB is exposed on port 7678, Memgraph Lab on port 3000,\nand to be able to read logs from Memgraph Lab the port 7444 also needs to be\nexposed.\nWith the\n-v mg_lib:/var/lib/memgraph\nflag we enable data persistance by mounting a\nvolume where snapshots of the database will be saved. This means that data won't\nbe lost if the database instance is stopped.\nIf successful, you should be greeted with the following message:\nMemgraph Lab is running at localhost:3000\nmgconsole 1.1\nConnected to 'memgraph://127.0.0.1:7678'\nType :help for shell usage\nQuit the shell by typing Ctrl-D(eof) or :quit\nmemgraph>\nAs you can see,\nmgconsole\nopened in the terminal and you can access Memgraph Lab\nby opening\nlocalhost:3000\nin your browser.\nThe next step is to play around by executing some queries using the CLI\n(command-line interface) tool.\nStep 2 \u2014 Querying Memgraph Using a Command-Line Tool\nMemgraph comes with a CLI tool named\nmgconsole\n. It allows you to execute\nCypher queries against the Memgraph DB using the command line.\nMemgraph supports the Cypher query through the\nopenCypher project. Cypher is a SQL-like\ndeclarative query language purposely designed to efficiently query and update\nproperty graphs.\nWe'll now test Memgraph by executing two basic queries that demonstrate the\ncreation and retrieval of data from the database. Each query needs to end with\nthe\n;\n(semicolon) character.\nExecute this query in mgconsole:\nCREATE (u:User {name: \"Alice\"})-[:Likes]->(s:Software {name: \"Memgraph\"});\nThe above will create 2 nodes in the database, one labeled \"User\" with the name \"Alice\" and the other labeled \"Software\" with the name \"Memgraph\". It will also create a relationship that \"Alice\" likes \"Memgraph\".\nNext, lets find the created nodes and relationships by executing this second query:\nMATCH (u:User)-[l]->(s) RETURN u, l, s;\nThe query returns the following result:\n| u | l | s |\n| ---------------------- | -------| ------------------------------|\n| (:User {name: \"Alice\"})| [Likes]| (:Software {name: \"Memgraph\"})|\nLastly, type\n:quit\nin the prompt, or press\nCTRL + D\nor\nCTRL + C\nto exit the\nmgconsole\ntool.\nExcellent! You have now installed Memgraph and used its command-line tool\nmgconsole\nto run a simple query.\nNow, aside from a CLI tool, Memgraph also has an integrated development\nenvironment called Memgraph Lab to help you manage and execute various tasks on\nyour databases.\nWhen you install and run Memgraph Platform, Memgraph Lab is available at\nlocalhost:3000\n. If you need Memgraph Lab to connect to a remote server you\ncan also install it as a desktop application.\nIn the next step we will show you how to install Memgraph Lab as a desktop\napplication. If you don't need it, skip to Step 4 where you will learn how to\nconnect to Memgraph DB and execute queries using Memgraph Lab.\nStep 3 \u2014 Installing Memgraph Lab\nStart by selecting and downloading the Memgraph Lab build for macOS from\nMemgraph's Download Hub.\nOnce downloaded, installing the app on your system is similar to how you would\ninstall most Mac apps \u2014 by opening the\n.dmg\nfile and dragging the app\n(Memgraph Lab) into the\n/Applications\nfolder.\nMake sure that Memgraph is running as explained on Step 1. You won't be able to connect if the server is not running in the first\nplace!\nOpen the Memgraph Lab app.\nNow, depending on your macOS' security preferences, it might block the app from\nrunning the first time. This is because it can't recognize the developer of the\napp, which in this case, is Memgraph.\nThis is normal and nothing you should worry about. Just press the OK button to\ndismiss the dialog box.\nWhat you need to do next is to allow macOS to run Memgraph Lab. To do this,\nopen your system's System\nPreferences\nand then click Security & Privacy preferences.\nNow, inside the Security & Privacy preference pane, ensure that the General\ntab is active and look to the Allow apps downloaded from: section. Any option\nor setting that will allow Memgraph Lab to run will be available there.\nFor more information about allowing unidentified apps to run on macOS, see the\narticles Open an app from an unidentified\ndeveloper and the Safely\nopen apps on your Mac.\nAfter updating your security preferences, try rerunning Memgraph Lab. Just click\nOpen if macOS asks you if you really want to open it. This is again part of\nmacOS' security precautions.\nMemgraph Lab will now open, and you should be presented with the Home screen. It\nlooks the same as Home screen of Memgraph Lab that is running in the browser at\nlocalhost:3000\n.\nNow that we have Memgraph Lab installed, lets connect to Memgraph DB and run a\nfew basic queries to make sure everything works properly.\nStep 4 \u2014 Testing the Memgraph Lab Installation\nMemgraph Lab Home screen should look tha same if the database is up and running\nregardless if you are using Memgraph Lab as a desktop app, or it is running in\nbrowser at\nlocalhost:3000\n.\nIf Memgraph Lab cannot find a running database instance run\nMemgraph like we did in Step 1.\nClick CONNECT NOW to start connecting to the Memgraph server using default\nvalues for username and password (those values are empty by default), and\nendpoint (default values for Host is\nlocalhost\nand default Port is\n7687\n).\nIf you need to change any of those values click Connect Manually.\nIn the Overview tab you should be able to see there are 2 nodes and 1\nrelationship present because we created them via\nmgconsole\n. You can also find\nout how much memory those entities are using.\nIf you go to the Graph Schema tab and click Generate graph schema you can\nsee the existing node labels, edge types, their properties and how they interact\nwith each other.\nLet's test Memgraph Lab by running a similar query we ran in Step 2.\nFirst, click the Query tab on the left sidebar.\nNext, enter this query at the query editor which is located at the top of the screen:\nCREATE (u:User {name: \"Sean\"})-[:Likes]->(s:Software {name: \"MemgraphLab\"});\nThe query above creates 2 new nodes and a relationship between them.\nLastly, click Run or press\nCmd + Enter\nto execute the query.\nIf no error message appeared, that means your query executed successfully and in\nthe Summary tab you check the times and cost estimates of the query.\nWe will now retrieve all the nodes and relationships we've created by executing the following Cypher query:\nMATCH (u:User)-[l]->(s) RETURN u, l, s;\nYou should get a result that looks similar to this:\nYou now have Memgraph Lab working correctly on your system. Memgraph Lab's\nvisual presentation of query results is one of its best features. It helps you\nunderstand the relationships of your nodes compared to the tabular format\npresented by the\nmgconsole\ntool we introduced in Step 2. This makes Memgraph\nLab the preferred tool for both beginners learning Memgraph and the Cypher query\nlanguage, and advanced Memgraph users working on complex queries.\nConclusion\nIn this tutorial, you installed Memgraph Platform and Memgraph Lab on macOS. You\ntested Memgraph by executing Cypher queries using a command-line tool\nmgconsole\nand a visual interface Memgraph Lab.\nIf you are interested in learning more about each product, please consult the\nrespective Memgraph and Memgraph\nLab.\nIf you are new to Memgraph or graph databases in general, finishing this\ntutorial marks an excellent starting point for further learning and something\nyou should build upon. Here are our other tutorials you can follow:\nExploring the Marvel comic universe social network with\nMemgraph.\nExploring Game of Thrones deaths with\nMemgraph\nIf you have any questions or would like to get any help with Memgraph, please\ncheck out our community.\nJoin us on Discord!\nFind other developers performing graph analytics in real time with Memgraph.\nJoin now\nProduct\nDatabaseTools & integrationsLabVector searchBenchmark\nEnterprise\nOverviewAI PlatformPricingPartnersSupportLegal\nResources\nUse CasesBlogOn demandEventsPlaygroundGraphRAG demo\nDevelopers\nDocsPythonNetworkXNeo4j Devs\nCompany\nAbout usCareersContact us\n\u00a9 2026 Memgraph Ltd. All rights reserved.\nTerms & Privacy\n",
      "time_seconds": 4.569
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Planning rootless Podman Memgraph setup**",
      "time_seconds": 4.57
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ ls -la /app && find /app -maxdepth 2 -type f -not -name quickstart.txt -print",
      "time_seconds": 4.57
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "total 28\ndrwxrwxr-x+  1 root root    66 Aug 30 17:35 .\ndrwxr-xr-x  15 root root   340 Aug 30 17:35 ..\n-rw-rw-r--+  1 root root 23243 Aug 30 16:36 quickstart.txt\n-rw-rw-r--+  1 root root   884 Aug 30 16:36 source-receipt.json\n/app/source-receipt.json\n",
      "time_seconds": 6.995
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Planning rootless Podman Memgraph setup**",
      "time_seconds": 6.996
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ podman --version && podman ps -a --format '{{.Names}}\\t{{.Image}}\\t{{.Status}}'",
      "time_seconds": 6.996
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "podman version 6.1.0\n",
      "time_seconds": 11.12
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Pulling memgraph container image**",
      "time_seconds": 11.12
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ podman pull docker.io/memgraph/memgraph:latest",
      "time_seconds": 11.12
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "time=\"2026-08-30T17:35:38-07:00\" level=warning msg=\"\\\"/run/user/1000\\\" directory set by $XDG_RUNTIME_DIR does not exist. Either create the directory or unset $XDG_RUNTIME_DIR.: faccessat /run/user/1000: no such file or directory: Trying to pull image in the event that it is a public image.\"\nTrying to pull docker.io/memgraph/memgraph:latest...\nGetting image source signatures\nCopying blob sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1\nCopying blob sha256:5230bff73c81167d6174a39c10eff1f02643c3a865db3de78eec966f875c4878\nCopying blob sha256:41140838cd967104f816a16f4132acefcc18315b8585b5918eaed7dbf9e164c1\nCopying blob sha256:074d2f178d5199c5b6742e006789b6090b847839251b6815afb9adbddfed51e2\nCopying config sha256:e57cdd6a27c909d16b38d69bf1dc744b9707f966bc82c139ebe2c76e7187de14\nWriting manifest to image destination\ne57cdd6a27c909d16b38d69bf1dc744b9707f966bc82c139ebe2c76e7187de14\n",
      "time_seconds": 15.384
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Verifying port availability for memgraph container**",
      "time_seconds": 15.384
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ podman run -d --name memgraph -p 7687:7687 docker.io/memgraph/memgraph:latest",
      "time_seconds": 15.384
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "time=\"2026-08-30T17:35:41-07:00\" level=warning msg=\"\\\"/run/user/1000\\\" directory set by $XDG_RUNTIME_DIR does not exist. Either create the directory or unset $XDG_RUNTIME_DIR.: faccessat /run/user/1000: no such file or directory: Trying to pull image in the event that it is a public image.\"\nd1cfa594a72b128e287f84a401a9afbc7fa9eb554bce8508bdd04f32c3adc183\n",
      "time_seconds": 18.843
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ for i in $(seq 1 30); do if podman exec memgraph mgconsole --host 127.0.0.1 --port 7687 --execute 'RETURN 1;' 2>/dev/null | grep -q '1'; then echo ready; break; fi; sleep 1; done; podman logs --tail 30 memgraph",
      "time_seconds": 18.843
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "[2026-08-31 00:35:42.984] [warning] --metrics-format=JSON is deprecated and will be removed in a future release. Please use OpenMetrics instead.\nYou are running Memgraph v3.12.0\nTo get started with Memgraph, visit https://memgr.ph/start\n",
      "time_seconds": 58.407
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Inspecting mgconsole command status**",
      "time_seconds": 58.407
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ podman ps --format '{{.Names}}\\t{{.Status}}\\t{{.Ports}}'; podman exec memgraph mgconsole --help | head -80",
      "time_seconds": 58.407
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "memgraph\tUp 42 seconds\t0.0.0.0:7687->7687/tcp\nmgconsole: Memgraph bolt client.\nThe client can be run in interactive or non-interactive mode.\n\n\n  Flags from /home/mg/memgraph/build/mgconsole/mgconsole/build/src/gflags/src/gflags-proj/src/gflags.cc:\n    -flagfile (load flags from file) type: string default: \"\"\n    -fromenv (set flags from the environment [use 'export FLAGS_flag1=value'])\n      type: string default: \"\"\n    -tryfromenv (set flags from the environment if present) type: string\n      default: \"\"\n    -undefok (comma-separated list of flag names that it is okay to specify on\n      the command line even if the program does not define a flag with that\n      name.  IMPORTANT: flags in this list that have arguments MUST use the\n      flag=value format) type: string default: \"\"\n\n  Flags from /home/mg/memgraph/build/mgconsole/mgconsole/build/src/gflags/src/gflags-proj/src/gflags_completions.cc:\n    -tab_completion_columns (Number of columns to use in output for tab\n      completion) type: int32 default: 80\n    -tab_completion_word (If non-empty, HandleCommandLineCompletions() will\n      hijack the process and attempt to do bash-style command line flag\n      completion on this value.) type: string default: \"\"\n\n  Flags from /home/mg/memgraph/build/mgconsole/mgconsole/build/src/gflags/src/gflags-proj/src/gflags_reporting.cc:\n    -help (show help on all flags [tip: all flags can have two dashes])\n      type: bool default: false currently: true\n    -helpful (show help on all flags -- same as -help) type: bool\n      default: false\n    -helpmatch (show help on modules whose name contains the specified substr)\n      type: string default: \"\"\n    -helpon (show help on the modules named by this flag value) type: string\n      default: \"\"\n    -helppackage (show help on all modules in the main package) type: bool\n      default: false\n    -helpshort (show help on only the main module for this program) type: bool\n      default: false\n    -helpxml (produce an xml version of help) type: bool default: false\n    -version (show version and build info and exit) type: bool default: false\n\n\n\n  Flags from /home/mg/memgraph/build/mgconsole/mgconsole/src/main.cpp:\n    -batch_size (A single batch size only when --import-mode=batched-parallel.)\n      type: int32 default: 1000\n    -collect_parser_stats (Collect parsing statistics only when\n      --import-mode=parser) type: bool default: true\n    -csv_delimiter (Character used to separate fields.) type: string\n      default: \",\"\n    -csv_doublequote (Controls how instances of quotechar(\") appearing inside a\n      field should themselves be quoted. When true, the character is doubled.\n      When false, the escapechar is used as a prefix to the quotechar. If\n      `csv-doublequote` is false, 'csv-escapechar' must be set.) type: bool\n      default: true\n    -csv_escapechar (Character used to escape the quotechar(\") if the\n      `csv-doublequote` flag is false.) type: string default: \"\"\n    -fit_to_screen (Fit output width to screen width.) type: bool\n      default: false\n    -history (Use the specified directory to save history.) type: string\n      default: \"~/.memgraph\"\n    -host (Server address. It can be a DNS resolvable hostname.) type: string\n      default: \"127.0.0.1\"\n    -import_mode (Import mode defines the way how the queries will be executed.\n      `serial` mode will try to execute queries in the specified input order.\n      `batched-parallel` will batched and parallelize query execution. NOTE:\n      batched-parallel is an experimental feature, the behavior might be\n      unexpected because it depends on how the underlying database system is\n      configured (e.g., in the transactional setup there might be many\n      serialization errors, while in the analytical setup, ordering of\n      nodes/edges is very important. `parser` mode will just print info about\n      the provided queries. NOTE: `parser` mode won't execute any query against\n      the underlying database system.) type: string default: \"serial\"\n    -no_history (Do not save history.) type: bool default: false\n    -output_format (Query output format can be csv, tabular or cypherl. If\n      output format is not tabular `fit-to-screen` flag is ignored.)\n      type: string default: \"tabular\"\n    -password (Database password.) type: string default: \"\"\n    -port (Server port.) type: int32 default: 7687\n    -print_parser_stats (Print parser statistics for each query only when\n      --import-mode=parser) type: bool default: true\n    -term_colors (Use terminal colors syntax highlighting.) type: bool\n      default: false\n",
      "time_seconds": 60.808
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Identifying missing execute flag**",
      "time_seconds": 60.809
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ podman exec memgraph mgconsole --help 2>&1 | grep -E 'execute|query|file|username|non' | tail -30",
      "time_seconds": 60.809
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "The client can be run in interactive or non-interactive mode.\n    -flagfile (load flags from file) type: string default: \"\"\n    -tab_completion_word (If non-empty, HandleCommandLineCompletions() will\n    -import_mode (Import mode defines the way how the queries will be executed.\n      `serial` mode will try to execute queries in the specified input order.\n      `batched-parallel` will batched and parallelize query execution. NOTE:\n      the provided queries. NOTE: `parser` mode won't execute any query against\n    -print_parser_stats (Print parser statistics for each query only when\n    -username (Database username.) type: string default: \"\"\n    -verbose_execution_info (Output the additional information about query such\n      as query cost, parsing, planning and execution times.) type: bool\n    -workers_number (The number of threads to execute batches in parallel, only\n",
      "time_seconds": 62.849
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Constructing and piping Memgraph query**",
      "time_seconds": 62.85
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ printf '%s\\n%s\\n' 'CREATE (u:User {name: \"Alice\"})-[:Likes]->(s:Software {name: \"Memgraph\"});' 'MATCH (u:User)-[l]->(s) RETURN u.name AS user, s.name AS software;' | podman exec -i memgraph mgconsole --host 127.0.0.1 --port 7687 --no-history",
      "time_seconds": 62.85
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "+------------+------------+\n| user       | software   |\n+------------+------------+\n| \"Alice\"    | \"Memgraph\" |\n+------------+------------+\n",
      "time_seconds": 67.161
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Planning rootless Podman Memgraph setup**",
      "time_seconds": 67.163
    },
    {
      "kind": "tool_call",
      "role": "bash",
      "text": "$ podman ps -a --format '{{.Names}}\\t{{.Image}}\\t{{.Status}}\\t{{.Ports}}' && printf 'Verification:\\n' && printf '%s\\n' 'MATCH (u:User)-[:Likes]->(s:Software) RETURN u.name AS user, s.name AS software;' | podman exec -i memgraph mgconsole --host 127.0.0.1 --port 7687 --no-history",
      "time_seconds": 67.163
    },
    {
      "kind": "tool_result",
      "role": "bash",
      "text": "memgraph\tdocker.io/memgraph/memgraph:latest\tUp 54 seconds\t0.0.0.0:7687->7687/tcp\nVerification:\n+------------+------------+\n| user       | software   |\n+------------+------------+\n| \"Alice\"    | \"Memgraph\" |\n+------------+------------+\n",
      "time_seconds": 72.486
    },
    {
      "kind": "thinking",
      "role": "assistant",
      "text": "**Confirming rootless single-container setup**",
      "time_seconds": 72.487
    },
    {
      "kind": "text",
      "role": "assistant",
      "text": "Implemented with one rootless Podman container:\n\n- Container: `memgraph`\n- Image: `memgraph/memgraph:latest`\n- Port: `7687`\n- No Memgraph Lab container\n\nCreated and verified:\n\n```text\nuser     software\n\"Alice\"  \"Memgraph\"\n```",
      "time_seconds": 72.487
    }
  ],
  "schema_version": 1,
  "source": "pi-session-jsonl",
  "source_sha256": "49634a412c6076570f8abcc39e37d04a526a9e8cfd3d85419e11a3891f3c14a6",
  "started_at_epoch_ms": 1788136524088
}