Explore
MCP servers and skills: search, filter by where they run, and compare.
sql-benchmarks-dagsterrctruta
LocalSQL Benchmarking Laboratory using Dagster for Orchestration
PythonApache-2.0★ 2Few starsNo key
MCP servers and skills: search, filter by where they run, and compare.
SQL Benchmarking Laboratory using Dagster for Orchestration
PythonApache-2.0★ 2Few starsNo key
AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scena
OfficialPythonApache-2.0★ 2.4kNo key
Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read
PythonMIT★ 6.2kNo key
Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo w
PythonMIT★ 32kNo key
A cyber brain for your AI. It never forgets a detail, remembers exactly what you said, and learns how you work over time. Free, local, works with Cursor, Claude Code, Codex, OpenClaw, Hermes and more.
PythonMIT★ 911No key
A reading path for CPU performance engineering, from one instruction to production inference. Primary sources only, with a runnable benchmark for every section.
PythonMIT★ 1.2kNo key
Auditable memory layer for AI agents: zero-LLM-call local ingest (~10ms/msg, air-gapped), matches Mem0 on accuracy at ~1000x lower ingest cost, bi-temporal belief-state, MCP server. Honest LoCoMo/Long
PythonApache-2.0★ 10No key
LynxMCP: local MCP server for the code questions grep can't answer. Call graph and blast radius, hybrid semantic + lexical code search on ONNX Runtime, library docs and PDFs as sources. No cloud, no P
PythonApache-2.0★ 10No key
The memory your AI should have had from the start. Automatic capture, automatic recall, 100% local. One SQLite file, zero cloud. Works with Claude Code, Claude CLI, Cursor, Codex CLI, Gemini CLI.
PythonAGPL-3.0★ 381No key
AI agent identity, signed credentials, delegated authority and portable governance evidence. Python core and offline verifier SDKs.
PythonApache-2.0★ 874No key
Graph memory for AI agents — decisions, context, and session history that survive across every conversation. Works with any LLM.
PythonMIT★ 33No key
Lightweight and portable LLM sandbox runtime (code interpreter) Python library.
PythonMIT★ 1.1kNo key
Cut AI context cost without trusting the compressor. Every reduction is reversible, byte-exact recoverable, and carries an auditable receipt. Local-first, works through proxy, MCP, SDK, or agent wrapp
PythonApache-2.0★ 477No key
Text-to-SQL access control: schema selection that gives the LLM only the tables the caller is allowed to read. Row-level-security-aware table pruning for LangChain, MCP and any SQL agent. Postgres, Or
PythonApache-2.0★ 104No key
Your agents are guessing at APIs. Give them the actual Agent-Native spec. 1500+ API's Ready To-Use skills, Compile any API spec into a lean, agent-native format. 10× smaller. OpenAPI, GraphQL, AsyncAP
PythonApache-2.0★ 346No key
A type-checker and contract layer for AI agent tool calls, deny-by-default, in-process, Pydantic-only. Strict argument validation, ghost-argument stripping, and self-healing retries for MCP servers an
PythonApache-2.0★ 16No key
Persistent memory for AI coding agents — MCP server, REST API & Python SDK. Works with Claude Code, Cursor, Windsurf and any LLM agent.
PythonApache-2.0★ 4Few starsNo key
Persistent local memory for AI coding agents — Claude Code, Codex CLI, Cursor, any MCP client. Temporal knowledge graph, procedural memory, AST codebase ingest, cross-project analogy. LongMemEval R@5
PythonMIT★ 73No key
Forecast what will happen, including outcomes of your decisions.
PythonMIT★ 57Needs a key
Generate and validate ISO 20022 payment files (pain.001 v03-v12, pain.008) from CSV, SQLite, JSON, or Parquet. XSD and SEPA scheme validation, pain.002 and camt.053 parsers and builders, plus a CLI, R
PythonApache-2.0★ 49No key
MCP server for Windows desktop automation. Incremental perception instead of a screenshot per step: measured 16.9x fewer tokens per action than the alternatives, with the benchmarks to check it.
PythonMIT★ 4Few starsNo key
System 1 decision models (Jev, Laya, Cua-S1) as brain for agents: Browser use, computer use, games and robotics
PythonApache-2.0★ 149No key
A full-featured MCP server for the Kaggle API — competitions, datasets, kernels, models, benchmarks, and discussions.
PythonMIT★ 4Few starsNo key
Long-term, cross-project memory for AI coding agents. Your own Obsidian vault as the source of truth. Daemonless and without opaque databases, your memory belongs to you.
PythonApache-2.0★ 66No key
Who stands behind it
