LM Studio vs PrivateGPT

LM Studio 36

Read with the local-software lines, since LM Studio and llmster run on the owner's machine with no hosted service behind the API. Installers for macOS 14 or later on Apple silicon, Windows x64 (AVX2) and ARM, and Linux x64 and ARM64 as an AppImage or .deb on Ubuntu 20.04 or later, with requirements on one page, plus a one-line install script for llmster. 0.4.22 (28 August 2026) enabled in-app updates for the AppImage and .deb installs (20). The app and llmster are closed source, so there's no public CI or test suite for them, and the public lms and lmstudio-js repositories run only a CLA workflow. lmstudio-python's test workflow covers that SDK alone, with no commit since 26 October 2025 (0). Open issues in the public bug tracker per the 3 October check (1.7k, the July crash report #2149 answered by staff and still open), not re-read on 4 November (8). Versions run 0.4.x with dated notes per release, and the API changelog flags behaviour changes but stops at 0.4.1, per the 3 October check (8). 0.4.28, pre-1.0 (0). 20+0+8+8+0 = 36.

PrivateGPT 70

Read with the local-software lines, since PrivateGPT runs on the owner's hardware with no hosted service behind it. Homebrew tap, uv tool install from Zylon's own wheel index (not PyPI) and Docker Hub images zylonai/private-gpt 1.0.1 of 18 June 2026. Python 3.11 only, stated in pyproject.toml, and it needs a separate OpenAI-compatible model server (17 of 20). tests.yml runs format, lint, typecheck and the tests affected by each push on main, with the full suite of 217 test files on release pull requests. We couldn't see run results because the GitHub API refused us, and issue #2441 of 1 November reports the Claude spec update workflow failing (15 of 25). GitHub shows 3 open issues of about 1,250, because a stale bot closes quiet issues, two security reports among them, with no maintainer reply (12 of 25). release-please keeps a changelog with breaking changes marked, but no release has been cut since 1.0.1 while main carries 51 commits since 8 August, among them a breaking observability change of 23 October (11 of 15). Version 1.0.1 (15). 17 + 15 + 12 + 11 + 15 = 70.

LM Studio 72

0.4.28 on 31 October 2026 on the dated changelog (30). Eight releases in the 90 days from 6 August, 0.4.21 (12 August) to 0.4.28, plus lmstudio-js tags for SDK 2.0.0 (21 September) and 2.1.0 (14 October) (20). Closed-source app, so the 0 to 15 scale for closed services. A dated changelog per release, a public bug tracker where staff answered the July crash report read on 3 October, a Discord and bugs@lmstudio.ai (10). Official SDKs for TypeScript (@lmstudio/sdk 2.1.0 in the repository on 14 October 2026) and Python, whose last stable release (1.5.0, 22 August 2025) predates API tokens (9). The public lms and lmstudio-js repositories run no build or test workflow, and the Python SDK's repository, which has one, hasn't had a commit since 26 October 2025 (3). 30+20+10+9+3 = 72.

PrivateGPT 41

The last release is 1.0.1, dated 16 June 2026 in the changelog and tagged 18 June, 141 days before the check (10 of 30). No release or dated changelog entry in the 90 days since 8 August (0 of 20). Maintainers commit most days and outside pull requests merged in October, but 23 pull requests are open and the stale bot closes issues, including two security reports, with no maintainer reply (13 of 25). Homebrew tap, wheel index and Docker images ship with releases, and Anthropic SDKs work against it. No product MCP server of its own, though it connects to remote MCP servers (10 of 15). Dependabot, uv.lock, actions pinned by commit since 19 October and dependency CVE upgrades in June, July and August (8 of 10). 10 + 0 + 13 + 10 + 8 = 41.

LM Studio 56

Read with the API lines. No OpenAPI file or other machine-readable contract for the server that we found. Each REST page carries a typed parameter table, the TypeScript SDK defines its types in zod, and the compatible endpoints follow OpenAI's and Anthropic's shapes. Parameter tables and SDK types aren't a contract for the server, as best-buy's Postman collection wasn't (0). An llms.txt of about 2,850 lines at lmstudio.ai that, in what our reader saw, describes the 0.3 app and doesn't mention the v1 REST API, API tokens, llmster or MCP. The docs source is public Markdown on GitHub (6). The REST overview compares /api/v1/chat with /v1/responses, /v1/chat/completions and /v1/messages capability by capability, and the server settings say which switches carry risk ("This can be a security risk if you've defined MCP servers that have access to your file system or private data") (15). Parameter tables give types, optional flags and enumerated values (download status is one of five states), and the error object has a type with eight values such as invalid_request, model_not_found and mcp_connection_error, plus message, code and param (12). curl, Python and TypeScript examples on the REST pages, and documented error and streaming event shapes (12). Versioned paths (/api/v0, then /api/v1 since 0.4.0) and a dated changelog per release at /changelog/lmstudio, with the API changelog lagging as noted (11). 0+6+15+12+12+11 = 56.

PrivateGPT 83

An OpenAPI file of 38 paths in the repository (fern/openapi/openapi.json, version 1.0.1), also served by each instance at /openapi.json, with the messages API modelled on Anthropic's (25). docs.privategpt.dev serves llms.txt, a Markdown copy of every page and a docs MCP server (10). The docs explain each endpoint group, startup and model discovery, and a compatibility table says which parts of the Claude API are supported, partial or missing, such as prompt caching and OAuth (15 of 20). Pydantic models with literal type discriminators and required fields, while settings and tool context are wide objects (12 of 15). curl examples for each guide and a /v1/messages/validate route, with error shapes not documented as a list (9 of 15). /v1 paths and a dated changelog, though releases trail main by more than four months (12 of 15). 25 + 10 + 15 + 12 + 9 + 12 = 83.

LM Studio 59

Read with the tool checklist. Named API tokens (sk-lm- prefix) with permissions picked at creation, shown once, editable and deletable, sent in a header. Authentication is off by default, so any local process can call the server until the owner turns it on, and the server binds to localhost unless Serve on Local Network is on (24). Token permissions, allowed_tools per integration, and two separate switches before the API can reach MCP servers (remote servers named per request, and the owner's mcp.json servers, which also need authentication on). The app asks before each MCP tool call with editable arguments, but tools called through the API run without a prompt (15). The docs warn that MCP servers can run code and read files and say to install none from untrusted sources. Nothing on injected instructions in tool results or documents (6). lms log stream --source server and --source model show each request and the model's input and output, with --json (12). No security.txt (lmstudio.ai answers that path with a Hub web page), no SECURITY.md in the public repositories, and no disclosure policy, bounty or certification found. The changelog's "Security hardening" lines in 0.4.16 and 0.4.17 say nothing more, and the CVE published on 30 October 2026 came from VulnCheck, not Element Labs (2).

PrivateGPT 32

Read with the tool checklist. Authentication is off by default, and when on it is one static secret compared with the whole Authorization header, with a sample secret:key value in settings.yaml. There are no per-user keys or scopes, private-gpt serve binds 0.0.0.0 by default and CORS allows every origin, so a default start answers anyone on the network. The docs disagree on the header (Bearer, Basic or an Anthropic key) (6 of 30). Tools are chosen per request and code execution has internet off by default, but bash runs as a host subprocess with resource limits, not in a container, and nothing asks for approval (6 of 20). Web fetch, web search, documents and MCP servers bring untrusted text to the model. A public prompt-injection report (#2270) was closed by the stale bot, and no guidance was found beyond a 19 October change that bounds tool output (3 of 15). OpenTelemetry traces cover every request, model call, tool and sandbox step, off by default, with local JSON lines exporters (10 of 15). SECURITY.md since 23 July 2026 asks for private reports through GitHub or Discord. No bounty, no certification, and the June and July security reports in issues were closed by a bot with no reply (7 of 20). 6 + 6 + 3 + 10 + 7 = 32.

LM Studio 69

Read with the API lines. /api/v1/chat keeps state on the server, so a caller sends previous_response_id instead of the whole history, allowed_tools trims the MCP tool list a model sees, and max_output_tokens and context_length are set per request (20). Model listing has no paging. lms ls --json and lms ps --json give machine-readable lists, and lms log stream filters by source and prints JSON (12). Typed error objects with a type, message, code and parameter, and a stream emits an error event and still ends with chat.end (16). Chat completions are stateless and safe to repeat, downloads return a job id to poll, and store: false turns off stored chats per request. No retry or idempotency guidance (8). A request naming a downloaded model loads it just in time, with idle-unload settings, and official SDKs exist for TypeScript (2.0.0) and Python, though Python's last stable release is from August 2025 (13).

PrivateGPT 64

Read for an HTTP API an agent calls on a local instance. Token counting, top_k style retrieval settings, citation output and response byte caps let a caller size context (18 of 25). Artifact listing by namespace and retrieval filters, with no documented cursor pagination (12 of 20). FastAPI validation errors name the field and the validate route checks a request before running it, but there is no error catalogue (12 of 20). Async messages and ingestion return task ids with status, cancel and delete routes. No idempotency keys were found (10 of 20). Anthropic SDKs and Claude Code work by pointing ANTHROPIC_BASE_URL at the server, but a model server and an embedding server have to be configured first (12 of 15). 18 + 12 + 12 + 10 + 12 = 64.