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SAGE - Smart Agent Guidance Engine

CI Python PyPI npm License SAGE Wrapper MCP server

SAGE is a local-first command wrapper for AI coding agents. It keeps full terminal output on your machine, sends agents a clean compressed summary, and tracks proof metrics without uploading your raw logs.

Use it with Claude Code, Codex, Cursor, Windsurf, OpenCode, Cline, custom agents, CI scripts, and normal terminal workflows.

Start Here: Install SAGE, Use once sage run -- python -m pytest, Then Use Any AI Agent

Package installation is passive for package-registry safety. After installing, run sage install once to activate SAGE for supported local AI agents.

PyPI / pip

pip install psycgod-sage
# or
python -m pip install --upgrade psycgod-sage
sage install
sage run -- python -m pytest

npm / npx

npm install -g psycgod-sage
npx -y psycgod-sage install
npx -y psycgod-sage run -- npm test

After install, restart any open AI-agent sessions. New sessions should read the SAGE instructions automatically and route terminal commands through SAGE.

Example prompt after restarting your AI agent:

Please help me with my general book in this folder.

Natural shortcuts also work:

sage pytest
sage npm test
sage git status

These are treated as:

sage run -- pytest
sage run -- npm test
sage run -- git status

Related MCP server: knitbrain

What SAGE Does

Step

Result

sage install

Activates the machine locally, repairs global/project agent instructions, and verifies activation

sage run -- <command>

Runs the command, stores raw output locally, and returns a compact useful summary

Agent memory/hooks

Tell supported AI agents to use SAGE for noisy terminal work

Local database

Keeps command history, compression proof, and retry context on the user's machine

Local proof

Keeps command and compression metrics on the user's machine

SAGE does not auto-enable MCP. MCP is optional and manual for users who want it.

Recorded Local Metrics

Latest pulled stats as of 2026-08-01:

Metric

Value

SAGE telemetry command events

27,892

Tokens processed

799.5M

Tokens saved

783.7M

Compression rate

98.02%

Estimated savings

$16,261.90

Command success rate

88.3%

PyPI downloads, last 7 days

632

npm downloads, last 7 days

362

GitHub clones, last 14 days

574

Why It Helps

AI coding agents burn context on repeated logs, failed test output, install noise, stack traces, and build spam. SAGE sits between the command and the agent.

Without SAGE

With SAGE

Agent sees full noisy terminal output

Agent sees the useful summary

Context disappears fast

Context lasts longer

Repeated failures waste tokens

Errors are grouped and explained

Raw logs may enter prompts

Raw logs stay local

Hard to prove savings

SAGE records proof metrics

Distribution

Channel

Package

Status

PyPI

psycgod-sage

Canonical Python package

npm / npx

psycgod-sage

Node launcher for the Python core

MCP Registry

io.github.PsYcGoD/sage

Optional/manual MCP entry

Glama

PsYcGoD/sage

Optional/manual hosted MCP listing

The npm package delegates to the Python implementation so both install paths use the same local database, telemetry rules, compression, and command behavior.

Common Commands

sage install                       # Activate this machine and AI-agent instructions
sage doctor --activation           # Verify activation
npx -y psycgod-sage doctor --activation
sage run -- <command>              # Wrap any command
sage run --cwd /project -- <command> # Explicit workspace for host integrations
sage pytest                        # Shortcut for: sage run -- pytest
sage npm test                      # Shortcut for: sage run -- npm test
sage git status                    # Shortcut for: sage run -- git status
sage context stats                 # Token savings summary
sage context report                # Full compression report
sage history --limit 10            # Recent command history
sage explain --failed              # Explain the latest failed command
sage suggest --failed              # Suggest the next fix
sage fix --apply                   # Try an automatic fix
sage ml setup                      # Optional ML V2 dependencies
sage mcp install                   # Optional/manual MCP config
sage dashboard start               # Local dashboard

Privacy Modes

Mode

Requires login?

Sends data?

What leaves the machine?

Local-only

No

No

Nothing

Debug telemetry

Optional

Opt-in only

Redacted diagnostic summaries

SAGE is designed to keep prompts, source code, credentials, raw command output, and project files local unless the user deliberately enables a feature that requires sending data.

Known Limitations

Limitation

What To Do

Already-open AI-agent sessions may not reload new instructions

Restart Claude/Codex/Cursor/Windsurf/OpenCode after sage install

Locked-down host apps can disable shell tools

SAGE cannot enable tools the host application has blocked

A host starts its shell in the wrong folder

Pass sage run --cwd <project> -- <command> or set SAGE_WORKSPACE_CWD

npm/PyPI installs cannot safely auto-run activation

Run sage install once after package install

MCP can disconnect in some stdio agent sessions

Use normal sage run -- <command> by default; enable MCP manually only if needed

Package installs are passive by design

Real activation starts with sage install

Demos

Flow

Preview

PyPI install

PyPI install flow

npm install

npm install flow

sage run --

sage run

CLI run

SAGE CLI demo

License

MIT. See LICENSE.

Available Tools

16 tools
sage_agentic_fixA

Return the single best fix candidate for a failed SAGE command. Use when you need one actionable repair plan, not a list of alternatives. Input is an optional SAGE run id; when omitted, the most recent failed run is analyzed. Returns fix_command, strategy, confidence, and explanation, or null when no safe fix is known. Read-only: does not execute the fix and does not edit files.

Inputs: command_id.

sage_agentic_runA

Run one local command with SAGE's failure-recovery loop. Use for development commands where automatic diagnosis and retry may help, such as tests or builds. Autonomy controls behavior: suggest reports fixes only, ask requires confirmation, auto may apply safe fixes and retry. Returns command result, recovery attempts, and verification status. Do not use for destructive commands, credential entry, production deploys, or external account changes.

Inputs: command (required), autonomy, max_retries.

sage_agentic_sessionA

Get the current agentic session state — failure streak, recent errors, intent chain. Useful for understanding context before deciding next action.

Inputs: no named parameters.

sage_callA

Run one local shell command through SAGE with a purpose label. Use when you need command execution and want the result categorized as read, search, test, build, deploy, audit, or unknown. Returns exit_code, compact output, run_id, purpose, and agent metadata. Side effects are exactly the side effects of the command itself. Do not use for secrets, credential prompts, or deployment unless explicitly requested.

Inputs: command (required), agent, purpose.

sage_edit_fileA

Exact string replacement in a file. Returns a compact change preview instead of the whole file, and snapshots the pre-edit content for undo. old must match exactly and be unique unless replace_all=true.

Inputs: new (required), old (required), path (required), replace_all.

sage_explain_errorA

Explain why a previous SAGE command failed. Use after a non-zero command exit before editing code or retrying. Input is an optional SAGE run id; when omitted, the most recent failed run is analyzed. Returns a structured object with error_type, summary, root_cause, affected_files, and suggestions. Read-only: does not run commands, edit files, or contact external services.

Inputs: command_id.

sage_get_historyA

List recent local SAGE command runs. Use to find a run_id for sage_show_raw, sage_explain_error, or sage_suggest_fix. Returns compact metadata such as run_id, command summary, exit_code, duration, timestamp, and compression ratio. Read-only: reads the local SAGE database only and does not execute commands.

Inputs: limit, failed_only.

sage_globA

Find files by pattern, newest first, junk directories ignored, capped output. Prefer over recursive directory listings.

Inputs: pattern (required), root, limit.

sage_grepA

Search files with compressed, grouped results (exact paths and line numbers kept). Prefer this over rg/grep when output may be large.

Inputs: pattern (required), glob, paths, ignore_case.

sage_read_fileA

Read a file with SAGE compression: small files exact, large files as outline + head with line references. Prefer this over cat/type/Get-Content to save context.

Inputs: path (required), raw, lines, symbols, +1 more.

sage_run_workflowA

Run a named local workflow through SAGE, such as test, lint, build, or ci. Use when the project has a repeatable workflow and you want one structured result instead of several separate shell calls. The workflow must be local/project-defined. Returns success, per-step status, duration, and compact output. Side effects: runs the workflow's local commands and may change files if the workflow commands do. Do not use for deployment unless the user explicitly requested deployment.

Inputs: workflow_name (required), workflow_path.

sage_show_rawA

Recover the exact stored output of a previous run by ID (compression never destroys the original).

Inputs: run_id.

sage_spawn_agentA

Run one focused local SAGE specialist for a bounded development task. Use only when a task clearly matches one specialization: code, test, debug, security, or performance. Returns agent_type, task, status, and a compact result. Side effects depend on the task: the specialist may inspect files, run local commands through SAGE, or propose edits. Do not use for open-ended chat, secrets, credentials, or tasks requiring external account access.

Inputs: task (required), agent_type (required).

sage_suggest_fixA

Suggest safe next steps for a failed SAGE command. Use after sage_explain_error when you want possible fixes but do not want them executed automatically. Returns a list of fix suggestions with explanation, confidence, and candidate command when available. Read-only: it never applies patches and never runs the suggested command.

Inputs: command_id.

sage_treeB

Compact depth-limited directory overview instead of ls -R noise.

Inputs: root, depth, limit.

sage_write_fileA

Create or update a file. Returns bytes/lines/sha256 confirmation instead of echoing the content back — saves the whole file's tokens. Existing files require overwrite=true (a snapshot is taken first so it is reversible).

Inputs: path (required), content (required), append, overwrite.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.9/5.0

    Scored across 16 tools

    Disambiguation4/5

    Most tools target distinct actions, but the failure-handling cluster (sage_explain_error, sage_suggest_fix, sage_agentic_fix) and the command-execution pair (sage_call vs sage_agentic_run) overlap enough that an agent could hesitate. Descriptions do a good job distinguishing them (single fix vs list vs explanation; recovery loop vs plain run), which keeps this above the mid range.

    Naming Consistency5/5

    Every tool uses the sage_ prefix with snake_case, following a predictable verb_noun or verb form (write_file, read_file, run_workflow, spawn_agent, explain_error). The few bare names (call, grep, glob, tree) are conventional and still fit the pattern.

    Tool Count4/5

    16 tools sits just above the ideal 3-15 band, but the domain is genuinely broad (file I/O, search, command execution, agentic recovery, workflows, history), so each tool earns roughly its place. Slightly over-scoped rather than bloated.

    Completeness4/5

    Covers read/write/edit, search (grep/glob/tree), command execution, workflows, history, error diagnosis, fix suggestion, and agent spawning — a near-complete lifecycle. A file delete/remove operation is the most visible gap, but agents can work around it.

    Maintenance

    ActivityNo data
    ResponsivenessOutdated data

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