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Ingero Alternatives
Similar projects and alternatives to ingero
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ollama
Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
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SaaSHub
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mcp-client-for-ollama
Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved preferences.
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edot-autopilot
Business-aware observability for any codebase — 53 smoke tests across 4 tiers (Native OTel SDK → Manual wrap → Monkey-patch → HTTP sidecar), covering 50+ technologies including NVIDIA GPU/CUDA. All signals: traces + logs + metrics → Elastic.
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nvsonar
Discontinued Active GPU diagnostic tool that identifies performance bottlenecks, detects anomalous patterns, and gives actionable recommendations [GET https://api.github.com/repos/btursunbayev/nvsonar: 404 - Not Found // See: https://docs.github.com/rest]
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gpuxray
A lightweight GPU observability tool focused on per-process GPU metrics, with optional deep tracing powered by eBPF.
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parca-agent
eBPF based always-on CPU/GPU profiler auto-discovering targets in Kubernetes and systemd, zero code changes or restarts needed!
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better ingero alternative or higher similarity.
ingero discussion
ingero reviews and mentions
Posts with mentions or reviews of ingero.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-05-28.
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Tracing torch.cuda.empty_cache() on an RTX 4090 - Where Do the 53 MB Go?
# 1. Build git clone https://github.com/ingero-io/ingero.git cd ingero && make build # 2. Create the MCP config (points to this post's investigation DB) cat > /tmp/ingero-mcp.json << 'EOF' { "mcpServers": { "ingero": { "command": "./bin/ingero", "args": ["mcp", "--db", "investigations/pytorch-173382-empty-cache.db"] } } } EOF # 3. Install ollmcp (MCP client for Ollama) pip install ollmcp # 4. Investigate with a local model ollmcp -m qwen3:32b -j /tmp/ingero-mcp.json
The trace database is in the Ingero repo. Connect any MCP-compatible AI:
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What GitHub Uses eBPF For (and the Layer They Have Not Ported Yet)
git clone https://github.com/ingero-io/ingero.git cd ingero # Open the captured DB in the MCP server (works with Claude Code, # Cursor, ollmcp, or any MCP client) ./bin/ingero mcp --db investigations/vllm-37343-logprobs-amplification.db # Or query directly via SQL ./bin/ingero query --db investigations/vllm-37343-logprobs-amplification.db \ --since 2h --op cudaLaunchKernel --json | jq .
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One Kernel, Zero Sidecars: Tracing AI Workloads Without an Agent on Every Host
This is one of the reasons we picked eBPF over an SDK approach. The other reasons are listed in the project README, but cost-at-fleet-scale is the one most people don’t notice until the fleet is already large.
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MCP Tools Are New API Surfaces. eBPF Sees What They Actually Touch.
# 1. install curl -fsSL https://github.com/ingero-io/ingero/releases/latest/download/install.sh | sh # 2. capture the tool server's footprint for one minute ingero trace --pid $(pgrep -f your-mcp-server) --duration 60s \ --out /tmp/mcp.db # 3. inspect what the tool actually did ingero query /tmp/mcp.db "SELECT * FROM cuda_events LIMIT 20" ingero query /tmp/mcp.db "SELECT * FROM net_events LIMIT 20" ingero query /tmp/mcp.db "SELECT * FROM io_events LIMIT 20"
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A Cluster Stall Looks Healthy on Every Host. The Cause Is in the Pattern Across Hosts.
curl -fsSL -o echo-fanin-demo.db \ https://github.com/ingero-io/ingero-fleet/raw/main/investigations/echo-fanin-demo.db duckdb echo-fanin-demo.db
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GPU Utilization Is a Counter, Not a Cause
# 1. Capture a fresh trace (Linux, recent kernel, NVIDIA driver, root or CAP_BPF + CAP_PERFMON) sudo ingero check sudo ingero trace --duration 120s --db /tmp/vllm.db # 2. Or skip the capture and query the prebuilt DB git clone https://github.com/ingero-io/ingero.git cd ingero
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What Happens When an AI Agent Gets Kernel-Level GPU Traces
We tested this on a real investigation: a PyTorch DataLoader bottleneck where DataLoader was 114x slower than direct tensor indexing on an RTX 4090. The trace database is in the Ingero repo for independent reproduction.
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MCP as Observability Interface: Connecting AI Agents to Kernel Tracepoints
The project is open source. The investigation database from this post is available for download. Claude (or any MCP client) can connect to it and run an investigation:
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Catching a vLLM Latency Spike with eBPF and an Open-Weight LLM
git clone https://github.com/ingero-io/ingero.git cd ingero && make build
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A note from our sponsor - SaaSHub
www.saashub.com | 9 Jun 2026
Stats
Basic ingero repo stats
10
84
9.7
5 days ago
ingero-io/ingero is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of ingero is C.
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