aurora-actr 0.16.0

AURORA: Adaptive Unified Reasoning and Orchestration Architecture with MCP Integration

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Lightweight, private memory and code intelligence for AI coding assistants.
Multi-agent orchestration that runs locally.

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Summary

Aurora - Lightweight Private Memory & Multi-Agent Orchestration

• Private & local - No API keys, no data leaves your machine. Works with Claude Code, Cursor, 20+ tools
• Smart Memory - Indexes code and docs locally. Ranks by recency, relevance, and access patterns
• Code Intelligence - LSP-powered: find unused code, check impact before refactoring, semantic search
• Multi-Agent Orchestration - Decompose goals, spawn agents, coordinate with recovery and state
• Execution - Run task lists with guardrails against dangerous commands and scope creep
• Friction Analysis - Extract learned rules from stuck patterns in past sessions

# New installation
pip install aurora-actr

# Upgrading?
pip install --upgrade aurora-actr
aur --version  # Should show 0.13.2

# Uninstall
pip uninstall aurora-actr

# From source (development)
git clone https://github.com/hamr0/aurora.git
cd aurora && ./install.sh

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Core Features

Smart Memory

aur mem search - Memory with activation decay. Indexes your code using:

• BM25 - Keyword search
• Git signals - Recent changes rank higher
• Tree-sitter/cAST - Code stored as class/method (Python, JS/TS, Go, Java)
• LSP enrichment - Risk level, usage count, complexity (see Code Intelligence below)
• Markdown indexing - Search docs, save tokens

# Terminal
aur mem index .
aur mem search "soar reasoning" --show-scores
Searching memory from /project/.aurora/memory.db...
Found 5 results for 'soar reasoning'

┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━┳━━━━━┳━━━━━━━━━┓
┃ Type   ┃ File                   ┃ Name                 ┃ Lines      ┃ Risk   ┃ Git ┃   Score ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━╇━━━━━╇━━━━━━━━━┩
│ code   │ core.py                │ generate_goals_json  │ 1091-1175  │ MED    │ 8d  │   0.619 │
│ code   │ soar.py                │ <chunk>              │ 1473-1855  │ -      │ 1d  │   0.589 │
│ code   │ orchestrator.py        │ SOAROrchestrator._c… │ 2141-2257  │ HIGH   │ 1d  │   0.532 │
│ code   │ test_goals_startup_pe… │ TestGoalsCommandSta… │ 190-273    │ LOW    │ 1d  │   0.517 │
│ code   │ goals.py               │ <chunk>              │ 437-544    │ -      │ 7d  │   0.486 │
└────────┴────────────────────────┴──────────────────────┴────────────┴────────┴─────┴─────────┘
Avg scores: Activation 0.916 | Semantic 0.867 | Hybrid 0.801
Risk: LOW (0-2 refs) | MED (3-10) | HIGH (11+) · MCP: lsp check/impact/related

Refine your search:
  --show-scores    Detailed score breakdown (BM25, semantic, activation)
  --show-content   Preview code snippets
  --limit N        More results (e.g., --limit 20)
  --type TYPE      Filter: function, class, method, kb, code
  --min-score 0.5  Higher relevance threshold

Detailed Score Breakdown:

┌─ core.py | code | generate_goals_json (Lines 1091-1175) ─────────────────────────────────────┐
│ Final Score: 0.619                                                                           │
│  ├─ BM25:       0.895 * (exact keyword match on 'goals')                                     │
│  ├─ Semantic:   0.865 (high conceptual relevance)                                            │
│  ├─ Activation: 0.014 (accessed 7x, 7 commits, last used 1 week ago)                         │
│  ├─ Git:        7 commits, modified 8d ago, 1769419365                                       │
│  ├─ Files:      core.py, test_goals_json.py                                                  │
│  └─ Used by:    2 files, 2 refs, complexity 44%, risk MED                                    │
└──────────────────────────────────────────────────────────────────────────────────────────────┘

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Code Intelligence (MCP)

Aurora provides fast code intelligence via MCP tools - many operations use ripgrep instead of LSP for 100x speed.

Tool	Action	Speed	Purpose
lsp	check	~1s	Quick usage count before editing
lsp	impact	~2s	Full impact analysis with top callers
lsp	deadcode	2-20s	Find all unused symbols in directory
lsp	imports	<1s	Find all files that import a module
lsp	related	~50ms	Find outgoing calls (dependencies)
mem_search	-	<1s	Semantic search with LSP enrichment

Risk levels: LOW (0-2 refs) → MED (3-10) → HIGH (11+)

When to use:

• Before editing: lsp check to see what depends on it
• Before refactoring: lsp impact to assess risk
• Understanding dependencies: lsp related to see what a function calls
• Finding importers: lsp imports to see who imports a module
• Finding code: mem_search instead of grep for semantic results
• After changes: lsp deadcode to clean up orphaned code

Language support:

• Python: Full (LSP + tree-sitter complexity + import filtering + indexing)
• JavaScript/TypeScript: LSP refs + tree-sitter indexing + import filtering
• Go: LSP refs + tree-sitter indexing + import filtering
• Java: LSP refs + tree-sitter indexing + import filtering

See Code Intelligence Guide for all 16 features and implementation details.

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Memory-Aware Planning (Terminal)

aur goals - Decomposes any goal into subgoals:

1. Looks up existing memory for matches
2. Breaks down into subgoals
3. Assigns your existing subagents to each subgoal
4. Detects capability gaps - tells you what agents to create

Works across any domain (code, writing, research).

$ aur goals "how can i improve the speed of aur mem search that takes 30 seconds loading when
it starts" -t claude
╭──────────────────────────────────────── Aurora Goals ───────────────────────────────────────╮
│ how can i improve the speed of aur mem search that takes 30 seconds loading when it starts  │
╰─────────────────────────────────────── Tool: claude ────────────────────────────────────────╯
╭──────────────────────────────── Plan Decomposition Summary ─────────────────────────────────╮
│ Subgoals: 5                                                                                 │
│                                                                                             │
│   [++] Locate and identify the 'aur mem search' code in the codebase: @code-developer       │
│   [+] Analyze the startup/initialization logic to identify performance bottlenecks:         │
│ @code-developer (ideal: @performance-engineer)                                              │
│   [++] Review system architecture for potential design improvements (lazy loading, caching, │
│ indexing): @system-architect                                                                │
│   [++] Implement optimization strategies (lazy loading, caching, indexing, parallel         │
│ processing): @code-developer                                                                │
│   [++] Measure and validate performance improvements with benchmarks: @quality-assurance    │
╰─────────────────────────────────────────────────────────────────────────────────────────────╯

╭────────────────────────────────────────── Summary ──────────────────────────────────────────╮
│ Agent Matching: 4 excellent, 1 acceptable                                                   │
│ Gaps Detected: 1 subgoals need attention                                                    │
│ Context: 1 files (avg relevance: 0.60)                                                      │
│ Complexity: COMPLEX                                                                         │
│ Source: soar                                                                                │
│                                                                                             │
│ Warnings:                                                                                   │
│   ! Agent gaps detected: 1 subgoals need attention                                          │
│                                                                                             │
│ Legend: [++] excellent | [+] acceptable | [-] insufficient                                  │
╰─────────────────────────────────────────────────────────────────────────────────────────────╯

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Memory-Aware Research (Terminal)

aur soar - Research questions using your codebase:

1. Looks up existing memory for matches
2. Decomposes question into sub-questions
3. Utilizes existing subagents
4. Spawns agents on the fly
5. Simple multi-orchestration with agent recovery (stateful)

aur soar "write a 3 paragraph sci-fi story about a bug the gained llm conscsiousness" -t claude
╭──────────────────────────────────────── Aurora SOAR ────────────────────────────────────────╮
│ write a 3 paragraph sci-fi story about a bug the gained llm conscsiousness                  │
╰─────────────────────────────────────── Tool: claude ────────────────────────────────────────╯
Initializing...


[ORCHESTRATOR] Phase 1: Assess
  Analyzing query complexity...
  Complexity: MEDIUM

[ORCHESTRATOR] Phase 2: Retrieve
  Looking up memory index...
  Matched: 10 chunks from memory

[LLM → claude] Phase 3: Decompose
  Breaking query into subgoals...
  ✓ 1 subgoals identified

[LLM → claude] Phase 4: Verify
  Validating decomposition and assigning agents...
  ✓ PASS (1 subgoals routed)

                                      Plan Decomposition
┏━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ #    ┃ Subgoal                                       ┃ Agent                ┃ Match        ┃
┡━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩
│ 1    │ Write a 3-paragraph sci-fi short story about  │ @creative-writer*    │ ✗ Spawned    │
└──────┴───────────────────────────────────────────────┴──────────────────────┴──────────────┘
╭────────────────────────────────────────── Summary ──────────────────────────────────────────╮
│ 1 subgoal • 0 assigned • 1 spawned                                                          │
│                                                                                             │
│ Spawned (no matching agent): @creative-writer                                               │
╰─────────────────────────────────────────────────────────────────────────────────────────────

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Task Execution (Terminal)

aur spawn - Takes predefined task list and executes with:

• Stop gates for feature creep
• Dangerous command detection (rm -rf, etc.)
• Budget limits

aur spawn tasks.md --verbose

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Friction Analysis (Terminal)

aur friction - Analyze stuck patterns across your coding sessions:

aur friction ~/.claude/projects
Per-Project:
aurora        56% BAD (40/72)  median: 16.0  🔴
mcp-gov       40% BAD (2/5)    median: 0.5   🟡
liteagents     0% BAD (0/1)    median: 0.0   ✅

Session Extremes:
WORST: aurora/0203-1630-11eb903a  peak=225  turns=127
BEST:  liteagents/0202-2121-8d8608e1  peak=0  turns=4

Last 2 Weeks:
2026-02-02  15 sessions  10 BAD  ██████░░░░  67%
2026-02-03  29 sessions  12 BAD  ████░░░░░░  41%
2026-02-04   6 sessions   2 BAD  ███░░░░░░░  33%

Verdict: ✓ USEFUL
Intervention predictability: 93%

Identifies sessions where you got stuck and extracts learned rules ("antigens") to add to CLAUDE.md or your AI tool's instructions - preventing the same mistakes.

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Planning Workflow

Terminal                    In your AI tool (Claude Code, Cursor, etc.)
────────                    ─────────────────────────────────────────────
aur init
aur goals "Add auth"  →     /aur:plan add-auth  →  /aur:implement add-auth
     ↓                           ↓                        ↓
 goals.json               PRD + tasks.md              Code changes
 (subgoals, agents)       (ready to execute)          (validated)

Step	Command	Output
Setup (once)	aur init + complete project.md	.aurora/ directory, indexed codebase
Decompose	aur goals "goal"	Subgoals mapped to agents + source files
Plan	/aur:plan [id]	PRD, design doc, tasks.md
Implement	/aur:implement [id]	Code changes with validation
Regen tasks	/aur:tasks [id]	Regenerate tasks after PRD edits (optional)

Quick prototype? Skip aur goals and run /aur:plan directly.

See 3 Simple Steps Guide for detailed walkthrough.

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Quick Start

# Install (or upgrade with --upgrade flag)
pip install aurora-actr

# Initialize project (once per project)
cd your-project/
aur init                        # Creates .aurora/project.md

# IMPORTANT: Complete .aurora/project.md manually
# Ask your agent: "Please complete the project.md with our architecture and conventions"
# This context improves planning accuracy

# Index codebase for memory
aur mem index .

# Plan with memory context
aur goals "Add user authentication"

# In your CLI tool (Claude Code, Cursor, etc.):
/aur:plan add-user-authentication
/aur:implement add-user-authentication

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Commands Reference

Terminal

Command	Description
aur init	Initialize Aurora in project
aur doctor	Check installation and dependencies
aur mem index .	Index code and docs
aur mem search "query"	Search memory from terminal
aur goals "goal"	Decompose goal, match agents, find gaps
aur soar "question"	Multi-agent research with memory
aur spawn tasks.md	Execute task list with guardrails
aur friction <dir>	Analyze session friction patterns

Slash Commands (in AI tools)

Command	Description
/aur:plan [id]	Generate PRD, design, tasks from goal
/aur:tasks [id]	Regenerate tasks after PRD edits
/aur:implement [id]	Execute plan tasks sequentially
/aur:archive [id]	Archive completed plan

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Supported Tools

Works with 20+ CLI tools: Claude Code, Cursor, Aider, Cline, Windsurf, Gemini CLI, and more.

Configuration is per-project (not global) to keep your CLI clean:

cd /path/to/project
aur init --tools=claude,cursor

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Documentation

• Commands Reference
• Tools Guide
• Flows Guide
• Troubleshooting

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License

MIT License - See LICENSE