Local model hosting · vision sidecar · sandboxed agents
lmcoder Debloated model serving & hardened coding sandboxes.
The primary control hub for local LLMs and multimodal vision sidecars, debloating complex runtimes and routing seamlessly between laptops and dedicated RTX inference servers.
go install ubunatic.com/lmcoder/cmd/lmcoder@latestProject snapshot · measured 2026-10-26
Key facts
12-week commit activity
2026-10-26View 12-week data table
| Week starting | Commits |
|---|---|
| 2026-08-03 | 0 |
| 2026-08-10 | 0 |
| 2026-08-17 | 2 |
| 2026-08-24 | 156 |
| 2026-08-31 | 23 |
| 2026-09-07 | 1 |
| 2026-09-14 | 69 |
| 2026-09-21 | 140 |
| 2026-09-28 | 86 |
| 2026-10-05 | 96 |
| 2026-10-12 | 77 |
| 2026-10-19 | 63 |
Key Features
- Transparent proxy routing between local laptop endpoints and upstream dedicated RTX inference servers.
- Self-deploying and cross-compiling binary installation powered by goself.
- Runs coding assistants in rootless Podman containers with single-writable-mount workspaces.
- Multimodal Florence-2 vision sidecars with idle timeouts, candidate streaming, and box filtering.
- Memory budget validation and automated Multi-Token Prediction (MTP) draft acceleration.
Verified Engineering Milestones
- Isolated coding checks and stack install (#047, #156, e96a0a0)
- Managed proxy lifecycle and sampling profile injection (#164, 8037408, d5571f3)
- Florence-2 vision sidecar, box filtering and streaming preview (#223, #224, ad8d8b7)
Stack
Go 1.25.0, Cobra, YAML specs, llama.cpp, OpenAI-compatible HTTP/SSE, rootless Podman, systemd, Vulkan/ROCm, Florence-2
Why lmcoder
Model hosting tuned to developer hardware
Running coding agents against a local LLM sounds simple until the agent needs shell access, the model blows past GPU memory budgets on shared hardware, or a dependency bump silently breaks streaming. Most setups settle for pointing an agent at an unmonitored endpoint with zero sandboxing.
lmcoder turns personal setup customizations into robust software features: debloating the runtime stack, managing memory limits, accelerating inference with MTP draft heads, and isolating agent execution in rootless sandboxes.
Architecture & Capabilities
Built for dedicated GPUs, laptops & remote servers
Seamless Proxy Routing
Routes agent requests transparently between local laptop inference and an upstream RTX server (such as x600), injecting sampling profiles (fast, default, deep) dynamically.
Self-Installing via goself
Deploys, cross-compiles, and updates itself seamlessly across machines using goself, eliminating remote toolchain dependencies.
Multimodal & Vision Sidecar
Runs Florence-2 and vision embedding sidecars with idle timeouts, candidate preview streaming, and degenerate bounding-box filtering.
Hardened Podman Sandboxes
Executes coding agents inside rootless Podman containers with single-writable workspace mounts and automated zombie reaping.
MTP & Memory Budgets
Manages llama-server instances with automated Multi-Token Prediction draft acceleration and strict VRAM/RAM threshold guards.
CLI Workflow
Unified model management
# Serve a model with memory overload guards and MTP acceleration
$ lmcoder serve Qwen3.8-27B-UD-Q4_K_M.gguf
# Route client traffic upstream to an RTX host with dynamic profile injection
$ lmcoder proxy --backend-host 192.168.1.100 --profile fast
# Run sandboxed agent canary against an isolated workspace
$ go run ./scripts/agent-canaries --agent pi --task coding-hello
Project details
Architecture
Layer composition
- Terminal
- CLI, proxy controls, service manager
- Desktop
- Linux, systemd
- Inference / AI
- llama.cpp, OpenAI-compatible API, Florence-2, MTP, Vulkan/ROCm
- Containers / VM
- rootless Podman
- Web
- —
Engineering story
lmcoder: the story behind the project
Second-Vendor Reviews : > - Audited by claude:sonnet:med (session story-reviewer-sonnet ): Approved with corrections . Clarified test counts (19 tests across test_lifecycle.py and test_scan.py ; 14 tests in test_scan.py ), replaced mixed me…
Read the engineering story