{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_td4j3yduy4cc","handle":"iflow-mcp-firstbatchxyz-mem-agent-mcp","url":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp","links":{"self":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/record.json","html":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp","markdown":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp","status":"https://wellknown.network/api/v1/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/status","claim":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-firstbatchxyz-mem-agent-mcp/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-firstbatchxyz-mem-agent-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_firstbatchxyz-mem-agent-mcp","summary":"Top-level workspace aggregating agent and mcp_server","description":"# mem-agent-mcp\n\nThis is an MCP server for our model [driaforall/mem-agent](https://huggingface.co/driaforall/mem-agent), which can be connected to apps like Claude Desktop or Lm Studio to interact with an obsidian-like memory system.\n\n## Supported Platforms\n\n- macOS (Metal backend)\n- Linux (with GPU, vLLM backend)\n\n### Platform note: aarch64 (ARM64) Linux\n- On ARM64 Linux, vLLM is not installed by default to avoid build failures (no stable wheels and source builds can fail).\n- Installation will succeed without vLLM; you can:\n  - Use the default OpenRouter/OpenAI path (no local vLLM needed), or\n  - Run vLLM on a compatible x86_64 host and point the client at it (see agent/model.py create_vllm_client).\n\n## Running Instructions\n\n### Using a LiteLLM proxy (OpenAI-compatible)\n- If you have a LiteLLM proxy running locally (e.g., on port 4000), configure the client via .env:\n```\nVLLM_HOST=localhost\nVLLM_PORT=4000\n```\n- Verify connectivity:\n```\ncurl http://localhost:4000/v1/models\n```\n- Then use either of these:\n  - CLI: `make chat-cli`\n  - MCP over STDIO: `make serve-mcp`\n  - MCP over HTTP: `make serve-mcp-http`\n\nNote: On ARM64 Linux, this is the recommended setup instead of vLLM.\n\n1. `make check-uv` (if you have uv installed, skip this step).\n2. `make install`: Installs LmStudio on MacOS.\n3. `make setup`: This will open a file selector and ask you to select the directory where you want to store the memory. \n4. `make run-agent`: If you're on macOS, this will prompt you to select the precision of the model you want to use. 4-bit is very usable as tested, and higher precision models are more reliable but slower.\n5. `make generate-mcp-json`: Generates the `mcp.json` file. That will be used in the next step.\n6. Instructions per app/provider:\n    - Claude Desktop:\n        - Copy the generated `mcp.json` to the where your `claude_desktop.json` is located, then, quit and restart Claude Desktop. Check [this guide](https://modelcontextprotocol.io/quickstart/user) for detailed ins…","publisher":null,"homepage":null,"repository":null,"version":"0.1.0","license":null,"protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_firstbatchxyz-mem-agent-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","summary":"pypi","version":"pypi","description":"pypi"}},"derived":{"capabilities":[{"slug":"ai.model-access","name":"Model Access","confidence":1,"provenance":"derived"}],"categories":["ai"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_firstbatchxyz-mem-agent-mcp","registry":"pypi","observedAt":"2026-11-03T06:28:21.717Z","publishedAt":"2026-02-07T14:11:17.966033Z","latestVersion":"0.1.0"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_firstbatchxyz-mem-agent-mcp","url":"https://pypi.org/project/iflow-mcp_firstbatchxyz-mem-agent-mcp/","firstSeenAt":"2026-09-09T18:22:28.152Z","fetchedAt":"2026-11-03T06:27:08.078Z","normalizedAt":"2026-11-03T06:27:08.078Z"}]},"firstSeenAt":"2026-09-09T18:22:28.152Z","updatedAt":"2026-11-03T06:28:21.717Z"}