uacos
Allows running local real-model evaluation using Ollama.
UACOS
UACOS is a local-first reliability, context, and safety runtime for AI coding agents.
UACOS is not a Goose clone or general chat/coding agent. External agents propose changes. UACOS prepares bounded context, validates semantic scope/risk, enforces policy and approval, applies changes through guarded transactions, runs verification, retries bounded failures, and records durable evidence.
Start here
Related MCP server: AI Knowledge Center MCP
Requirements
Python 3.9+
Optional: Ollama for local real-model evaluation
Optional: Codex CLI, Claude Code, or Goose for real-provider E2E; availability is never assumed
Three-step workflow
1. Initialize and inspect
python -m pip install -e .
uacos-flow setup --repo . --task "fix login bug safely"
uacos-flow doctor --repo .2. Build bounded context
uacos-flow assist --repo . --task "fix login bug safely" --max-tokens 60003. Guard and apply verified changes
uacos-flow guard \
--repo . \
--patch change.diff \
--task "fix login bug safely" \
--allowed-file app/auth.py \
--test "pytest -q"
uacos-flow apply-safe \
--repo . \
--patch change.diff \
--allowed-file app/auth.py \
--test "pytest -q" \
--yesExisting uacos ... commands remain available for lower-level and backward-compatible workflows.
Core capabilities
Multi-language semantic graph: Python, JavaScript/TypeScript, Rust, Go, Java, C, and C++.
Canonical symbols, cross-file imports, calls, inheritance/interfaces, route/service/data relationships, and test/source relationships.
Evidence-weighted context selection with symbol/subgraph slicing and dynamic budgets.
Durable agent runtime with resume, retry policy, no-progress detection, cancellation/deadlines, and replay.
Semantic diff risk, stale-patch preconditions, YAML policy, patch-bound human approval, guarded apply/test/rollback, and rollback verification.
Real HTTP JSON-RPC MCP client with timeout/payload bounds, pagination guards, request-id validation, and localhost network E2E.
Evidence-first real-agent E2E runner for Codex CLI, Claude Code, and Goose when explicitly installed/configured.
Comparative ground-truth benchmark evaluator for
full_repovsgrepvsuacos.Machine-checkable final closure gate that keeps implementation readiness separate from real-provider and benchmark evidence.
Useful commands
uacos-flow list
uacos-flow status --repo .
uacos-flow prepare --repo . --summary
uacos-flow orchestrate --spec "upgrade safely until tests pass" --agent goose --test "pytest -q" --max-iterations 3
uacos-flow benchmark --repo . --manifest evals/benchmark_suite.json
python scripts/provider_readiness.py
python scripts/final_closure_check.py --repo .Evidence and claims
The repository separates implementation tests from real-world evidence.
A fixture, mock, binary probe, or localhost contract test must not be presented as a successful real-provider run. Real Agent E2E is complete only when an installed/configured provider actually executes and its archived evidence reports status=passed.
Comparative benchmark claims require real observations using the same provider/model for all comparison modes, at least three repeats per task/mode, and explicit ground truth.
Safe baseline claim:
UACOS reduces unnecessary repository context sent to AI coding agents by selecting task-relevant files and validates candidate changes through local reliability and safety gates.
Do not claim 80-90% or 99% token savings unless an archived benchmark directly supports the exact statement.
Evidence outputs
Depending on the workflow, UACOS writes evidence including:
reports/uacos_performance_report.jsonreports/uacos_benchmark_suite_report.jsonreports/release_gate_report.jsonreports/provider_readiness.jsonreports/real-agent-e2e/summary.jsonreports/comparative_ground_truth_benchmark.jsonreports/final_closure_report.json.uacos/real_agent_e2e/.uacos/run_state/.uacos/replay/.uacos/patch_lifecycle/latest_patch_lifecycle_report.json
Product proof package
Use these before publishing claims or customer-facing material:
Community listing
UACOS is listed in awesome-cli-coding-agents under Agent infrastructure. This is a community-maintained project listing, not a certification or vendor endorsement.
Documentation
Use docs/README.md as the main documentation index.
This server cannot be deployed
Maintenance
Related MCP Connectors
Persistent memory, knowledge graph, task management and more for AI agents via MCP.
shared AI-context layer for teams — persistent memory your agents search and update over MCP
- memnodeOAuthdev.memnode
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Codebase graphs, caller impact analysis, and recorded project context for AI coding agents.
Related MCP Servers
- AlicenseNot gradedqualityAmaintenanceMCP server for local-first code intelligence, providing structural code graph, semantic search, and impact analysis to AI agents.2MIT
- AlicenseNot gradedqualityCmaintenanceLocal-first MCP server that provides project context, verification gates, and structured tools for coding agents to discover knowledge, run diagnostics, and execute allowlisted commands within a repository.16 npmMIT
- AlicenseNot gradedqualityAmaintenanceA persistent code-intelligence MCP server that builds a queryable knowledge graph of your codebase, enabling AI assistants to perform cross-file structural reasoning, dependency analysis, and blast radius detection.9MIT

Vibgrate AI Contextofficial
AlicenseNot gradedqualityAmaintenanceLocal-first MCP server that gives AI assistants codebase intelligence—code graph, drift analysis, vulnerability attribution, and version-correct library docs—all from the user's machine.1,307 npm5Apache 2.0