Redcon

Deterministic context budgeting for AI coding agents

Stop sending agents 200k tokens of irrelevant code. Redcon scores, compresses, and packs repo context so your agent gets what it actually needs.

Install - Quick Start - How It Works - Docs

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The Problem

AI coding agents burn tokens on irrelevant context. You either:

• Dump the whole repo and pay for 200k input tokens per request, or
• Let the agent grep blindly and waste tool calls figuring out where to look

Redcon solves both. It ranks files by task relevance, compresses them with language-aware strategies (full, snippet, symbol extraction, summary), and packs the result under your token budget. Deterministic, local-first, no embeddings.

Install

Option 1: VS Code Extension (easiest)

1. Install Redcon - Context Budget from the marketplace
2. Open the Redcon sidebar, click Install & Set Up
3. Reload window. Done.

The extension installs the CLI via pip, registers the MCP server for Claude Code, Cursor, and Windsurf, and gives you a sidebar with budget analytics, file rankings, and compression dashboards.

Option 2: CLI + MCP Server

pip install "redcon[mcp]"
redcon init                      # creates redcon.toml + registers MCP

The init command auto-configures MCP so your AI agent can call redcon_rank, redcon_search, redcon_compress, and redcon_budget as native tools.

Option 3: CLI only

pip install redcon
redcon init --no-mcp

Quick Start

# Rank files relevant to a task
redcon plan "add rate limiting to auth API" --repo .

# Pack context under a token budget
redcon pack "refactor payment flow" --repo . --max-tokens 30000

# Compare compression strategies
redcon benchmark "add caching" --repo .

# Audit a PR for context growth
redcon pr-audit --repo . --base origin/main --head HEAD

Output goes to run.json (machine-readable) and run.md (human-readable). Use them in CI, or feed the compressed context directly into your agent.

How It Works

task: "add rate limiting to auth"
       |
       v
  [1] scan    - incremental scan of repo files (cached)
       |
       v
  [2] rank    - score each file: keyword match, imports, file role, git history
       |
       v
  [3] compress - per-file strategy: full / snippet / symbol extraction / summary
       |
       v
  [4] pack    - fit top-N compressed files under token budget, drop the rest
       |
       v
  run.json + run.md + compressed_context ready for your agent

Every step is deterministic. Same input, same output. No embeddings, no random chunking.

MCP Integration (Pull Model)

Instead of pushing a 30k-token blob to your agent, Redcon exposes 6 MCP tools the agent calls on demand:

Tool	What it does
redcon_rank	Top-K files with scores and reasons - call this first
redcon_overview	Lightweight repo map grouped by directory
redcon_compress	Compressed single-file view for cheap inspection
redcon_search	Regex search scoped to ranked files or full repo
redcon_budget	Plan fitting files within a token budget
redcon_run	Run a shell command, return its output compressed

Typical agent flow uses ~5k tokens for exploration instead of 30k for a blob. The agent itself decides what to read in full.

Config gets written automatically to:

• .mcp.json (Claude Code)
• .cursor/mcp.json (Cursor)
• ~/.codeium/windsurf/mcp_config.json (Windsurf)

Command Output Compression

Source files are only half the bloat. The other half is command output: git diff, pytest, cargo test, grep, ls -R. Redcon's redcon_run MCP tool (and redcon run CLI) wraps the call, parses the output, and returns a budget-aware compressed view that preserves every fact the agent actually needs.

Headline reductions on representative inputs:

Compressor	Fixture	Raw tokens	Compact	Ultra
git diff	12 files, 240 hunks	8,078	97.0%	99.5%
pytest	30 failures + 200 passes	2,555	73.8%	99.2%
grep/rg	600 matches across 50 files	7,015	76.9%	99.9%
find	500 paths	3,398	81.3%	99.8%
ls -R	30 dirs x 15 files	1,543	33.5%	99.0%
kubectl events	200-row CrashLoopBackOff	~5,000	91.5%	99.5%
py-spy collapsed	200 stacks	2,385	90.0%	99.0%
json-line log	200 NDJSON records	6,038	91.1%	98.0%
coverage report	50-file grid	738	73.2%	95.0%
psql EXPLAIN ANALYZE	11-node Postgres plan	435	71.3%	93.3%

Quality is enforced separately. Every compressor declares must_preserve_patterns (file paths in a diff, failing test names in pytest, branch name in git status, slowest node operator in EXPLAIN); the M8 quality harness rejects any compressor whose compact output drops a fact present in the raw input. Run it as a CI step:

redcon cmd-quality   # exits non-zero if any compressor regressed
redcon cmd-bench     # markdown table; --json for CI baselines
redcon run "git diff" --quality-floor compact --max-output-tokens 4000

Sixteen compressors ship today: git_diff, git_status, git_log, pytest, cargo_test, npm_test (vitest+jest), go_test, grep, ls, tree, find, lint (ruff+mypy), docker, pkg_install (pip+npm+yarn), kubectl_get/kubectl_events, profiler (py-spy+perf), json_log, coverage, sql_explain (Postgres+MySQL TREE), bundle_stats (webpack + esbuild metafiles). Full per-schema benchmarks: docs/benchmarks/cmd/.

Cross-call dimension

Beyond per-call compression, four layers compose across an agent session:

• Path aliases (V41): repeated paths like redcon/cmd/pipeline.py collapse to f001 on later mentions. Lazy first-use, never net-negative.
• Content reference ledger (V43): paragraph-shaped blocks above 6 cl100k tokens get session-stable {ref:001} aliases on second-and-later occurrences. Empirically 23% of session output had block-level overlap.
• Symbol aliases (V49): CamelCase types / multi-word snake_case identifiers (>=8 chars) collapse to c001 aliases the same way paths do. Empirically 72% of distinct symbols recur >=2 times per session.
• Snapshot delta vs prior call (V47): when the same argv runs twice, ship only the delta. Schema-aware renderers for pytest (set-diff over failure names), git_diff (file-set with per-file +/- counts), and coverage (per-file pp moves) win meaningfully over generic line-diff. Always picks min(cost_delta, cost_abs) so non-regressive by construction.
• Invariant cert (V93): every COMPACT/VERBOSE output stamps mp_sha=<16hex> over the sorted multiset of (pattern, capture) extracted from raw. Auditors recompute the cert against the compressed text to detect spurious additions or capture thinning - upgrades the existing must-preserve boolean to set-equality.

Empirical measurement on 5 simulated agent sessions (benchmarks/measure_sessions.py): the cross-call layers add +8.3% session-level saving on top of the per-call compressors, with +15% on heavy-overlap sessions (debugging, search-and-edit) and near-zero on distinct-content sessions. V85 adversarial GA fuzzer ratchets all 16 schemas as a hard CI gate (REDCON_V85_ENFORCE=1).

VS Code Extension

Once installed you get:

• Sidebar chat: type a task, send, watch the pack run live
• Dashboard: donut/pie/bar charts for budget, strategies, token impact per file
• Status bar: current budget usage with risk indicator
• CodeLens: compression strategy and token count shown above each file
• File decorations: relevance score badges on files in the explorer
• History: browse past runs, diff them, export to clipboard

Branding: red->navy gradient with triple chevron mark, glass-style UI.

Workspaces (Multi-Repo)

One task can span multiple repos:

name = "backend-services"

[scan]
include_globs = ["**/*.py", "**/*.ts"]

[budget]
max_tokens = 28000
top_files = 24

[[repos]]
label = "auth-service"
path = "../auth-service"

[[repos]]
label = "billing-service"
path = "../billing-service"
ignore_globs = ["tests/fixtures/**"]

Artifacts include workspace, scanned_repos, selected_repos, and repo-qualified paths like auth-service:src/auth.py.

See docs/workspace.md.

Python API

from redcon import RedconEngine

engine = RedconEngine()

# Rank files
plan = engine.plan(task="add user auth", repo=".", top_files=15)

# Pack context
result = engine.pack(
    task="add user auth",
    repo=".",
    max_tokens=30000,
    top_files=25,
)
print(f"Used {result['budget']['estimated_input_tokens']} of {result['max_tokens']} tokens")
print(f"Risk: {result['budget']['quality_risk_estimate']}")

for file in result["compressed_context"]:
    print(f"{file['path']}: {file['strategy']} ({file['compressed_tokens']} tokens)")

Full reference: docs/python-api.md.

Features

• Deterministic scoring: keyword match, import graph, file role (test/docs/prod), git history
• Language-aware compression: Python, TypeScript, JavaScript, Go, Rust, Java, and more
• Command output compression: 16 compressors covering git, test runners, grep/rg, listings, lint, docker, pkg-install, kubectl, profilers, JSON logs, coverage, SQL EXPLAIN, and bundle stats - 70-99% reduction at compact level
• Incremental scanning: cached file metadata with git-aware change detection
• Multi-repo workspaces: single task, multiple repos, shared config
• Budget policies: enforce max tokens, quality risk levels, file counts in CI
• Quality harness: must-preserve regex assertions per compressor, deterministic, robust to truncated/binary input
• Streaming runner: chunked Popen reader with bounded memory and early SIGTERM/SIGKILL when output cap is hit
• Run history: SQLite-backed artifact store for both file packs and command runs, diff/heatmap/drift analysis
• Cost analysis: estimate token costs across GPT-4o, Claude, and other models
• PR auditing: detect context growth in pull requests
• Plugin system: custom scorers, compressors, token estimators, summarizers
• Cache backends: in-memory, local file, Redis
• Doctor command: diagnose environment, Python version, disk space, git availability

Documentation

• Getting Started - first pack in 60 seconds
• CLI Reference - all commands and flags
• Configuration - redcon.toml fields
• Workspaces - multi-repo setup
• Python API - programmatic usage
• Agent Integration - middleware layer
• Plugins - custom extensions
• Architecture - how it all fits together
• Migration Notes - upgrading between versions

License

Dual-licensed. Open-source core + proprietary cloud/enterprise layer.

Component	License
Core engine, CLI, plugins, cache	MIT
Gateway, control plane, agent middleware, LLM integrations, runtime	Proprietary

Commercial licensing: natjiks@gmail.com