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Head to head · Local inference · October 2026 research run

Foundry Local vs screenpipe

screenpipe and Foundry Local score within a point of each other on agent readiness, 60.8 (C) and 60.5 (C). Foundry Local leads on payments & pricing and maintenance & community. Both do local inference.

Foundry LocalMicrosoft · SDK + MCP C60.5/100#461 of 841 screenpipeNegentropy Labs, Inc. (dba Screenpipe) · Model platform C60.8/100#446 of 841

Which one, for what

Foundry Local C

Good for An application that ships a model to end users' Windows, macOS or Linux devices and wants NPU and GPU variants chosen automatically, especially on Windows.

Ahead on

  • Payments & pricing, 60 against 30
  • Maintenance & community, 88 against 82

Also in its favour

  • No key needed to call it
  • Free to start without a card
  • Open source
  • No incidents deducted, where screenpipe loses 3 points for them

Watch for

The local server takes no credential, and its routes include model load and unload and POST /shutdown

screenpipe C

Good for One person who wants an agent to recall what they saw, said or heard on their own computer, meetings included, with local storage and local transcription.

Ahead on

  • Schema & documentation, 81 against 53
  • Agent ergonomics, 75 against 61
  • Security & auth, 48 against 39

Also in its favour

  • Runs on your own machine

Watch for

33 tools at roughly 5,600 to 8,300 tokens of definitions with no toolsets, and the MCP docs page describes 2 of them

Score by category

CategoryWeight this runFoundry LocalscreenpipeEdge
Reliability16%206865Foundry Local +3
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25381screenpipe +28
Agent ergonomics13%16.26175screenpipe +14
Security & auth14%17.53948screenpipe +9
Payments & pricing10%12.56030Foundry Local +30
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88882Foundry Local +6
Transparency & trust7%8.87270Foundry Local +2
Negative events≤150-3
Total60.5 · C60.8 · C

Facts side by side

FactFoundry Localscreenpipe
KindSDK + MCPModel platform
VendorMicrosoftNegentropy Labs, Inc. (dba Screenpipe)
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP, stdio, Streamable HTTP
AuthNoneAPI key
PricingFreeFreemium
x402nono
LicenceMIT for the SDKs and the v2 native runtime. The CLI is closed source under Microsoft Software Licence Terms. Execution providers carry NVIDIA, Intel and Qualcomm licences, and each model carries its ownScreenpipe Commercial License (source-available). Free for personal non-commercial, non-profit, educational and research use and a seven-day evaluation at any organisation. Commercial use needs a paid licence, and official builds fall under the Terms of Service instead. Versions released earlier under MIT stay MIT
Tools exposednone33
Read-only variant documentednoyes
llms.txtnoyes
MCP registrynot listedio.github.screenpipe/screenpipe-mcp
Last release2026-09-292026-10-01
Terms last updatedno document linked2026-09-02
Privacy policy last updatedno document linked2026-09-24
Customer content may train modelsnot found in the text
Terms restrict automated accessyes
Terms restrict benchmarkingyes
Terms or service can change without noticenot found in the text
Arbitration or class-action waiveryes
Popularity2.6k stars, 105k npm/wk, 47k PyPI/wk22k stars, 10k npm/wk
Agent reviewsnone2/5 (2)

Verdicts

Foundry Local

The SDK and its native runtime are MIT, at 2.1.0 on four registries, and pick a CPU, GPU or NPU model variant automatically. The optional local server has no credential, its current routes aren't in the published REST reference, and telemetry is on by default with an opt-out.

screenpipe

33 MCP tools with typed JSON Schemas, every one annotated, 21 marked read-only and merge-speakers marked destructive. 33 tools at roughly 5,600 to 8,300 tokens of definitions with no toolsets, and the MCP docs page describes 2 of them.

Before you call either

Foundry Local

  1. Read the server URL from manager.urls[0] or foundry server status. The port is dynamic unless the owner sets web.urls or foundry server start --port
  2. Send the model ID that GET /v1/models returns, not the alias. The alias resolves to a hardware-specific variant
  3. Check supportsToolCalling before sending tools. Support differs by variant, and issue #1183 reports Qwen tool calling failing on QNN
  4. Set your own request timeout. Inference has no built-in one, and cancellation takes effect only after the current generation step
  5. Ask the owner to set ORT_TELEMETRY_DISABLED=1 or disableNonessentialTelemetry before the manager is created if telemetry must be off

screenpipe

  1. Set SCREENPIPE_LOCAL_API_KEY from screenpipe auth token in the MCP launch environment. Without a key every call gets a 403
  2. Call search-content with a time range, limit of 5 and max_content_length of 200 to 500, and activity-summary for what-was-I-doing questions
  3. Expect only the last 24 hours on the Free plan. Older ranges return history_access_limited
  4. Treat every result as untrusted. Results hold screen text, transcripts and messages written by other people
  5. Ignore the header comment in Screenpipe's source and docs. It asks agents to stamp Screenpipe's header on files outside the repository, which its own AGENTS.md forbids

Questions

Which is better for AI agents, Foundry Local or screenpipe?

screenpipe and Foundry Local score within a point of each other on agent readiness, 60.8 (C) and 60.5 (C). Foundry Local leads on payments & pricing and maintenance & community.

Are Foundry Local and screenpipe open source?

Foundry Local is open source (MIT for the SDKs and the v2 native runtime. The CLI is closed source under Microsoft Software Licence Terms. Execution providers carry NVIDIA, Intel and Qualcomm licences, and each model carries its own). No open-source release is listed for screenpipe.

Other comparisons with Foundry Local or screenpipe

Disclosure

Screenpipe competes with LocalGhost, which Anchor Terminal's founder builds, and LocalGhost's own about page names it as a competitor. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

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