🧠 Context Arbitration Layer (CAL)

Version: 1.3 Status: Specification & Skill Implementation Author: Dan Sasser / Gorombo Labs

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CAL extends Claude Code into a multi-process reasoning system that orchestrates subprocess executors (like ollama-code, claude -p, claude --agent) while maintaining persistent context through a memory ledger.

Proven Results: 5.5× token efficiency | 92.25% context retention | 69,500 tokens saved per session

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The Problem CAL Solves

Large codebases and deep reasoning chains push Claude Code beyond its context window limits. Even with compression, you face:

Problem	Cause	Impact
Context Loss	Finite context window	Re-analyzing same files
Token Waste	Redundant file ingestion	Inefficient resource use
Semantic Drift	Truncated memory	Inconsistent reasoning
Session Amnesia	No persistent state	Lost work between sessions

CAL's Solution: Delegate heavy analysis to subprocess executors, persist results in a ledger, reuse context across sessions.

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How CAL Works

Developer Request
   ↓
Claude Code (Controller)
   ├─ Analyzes task
   ├─ Decides: Sequential? Concurrent? Staggered?
   └─ Spawns ollama-code subprocess(es)
       ↓
   Executors analyze in parallel
       ↓
   Return structured JSON summaries
       ↓
Claude Code validates checksums
       ↓
   Commits to .cal_memory.json ledger
       ↓
Reintegrates summaries into context
       ↓
Developer receives result with preserved context

Three Core Components:

1. Controller (Claude Code) - Orchestrates tasks, validates outputs, manages ledger
2. Executors (ollama-code, claude -p, claude --agent, gemini-cli, forgecode) - Perform heavy analysis
3. Ledger (.cal_memory.json) - Persistent context store

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Key Features

Multi-Spawn Concurrency

Spawn multiple executors in parallel for independent analyses:

# Concurrent analysis of different modules
Task 1: ollama-code analyzes src/core/
Task 2: ollama-code analyzes src/api/
Task 3: ollama-code analyzes src/tests/
# All running simultaneously, results merged

Persistent Context

The .cal_memory.json ledger stores:

• Task summaries (compressed analysis results)
• Token usage per task
• Dependency chains (which tasks depend on which)
• Checksums (integrity validation)
• Timestamps (audit trail)

Result: Resume work across sessions without re-analyzing.

Automatic Task Arbitration

CAL decides execution strategy automatically:

Mode	When	Example
Sequential	Task depends on prior context	"Analyze deps, THEN find issues"
Concurrent	Independent analyses	"Check 5 modules simultaneously"
Staggered	High I/O load	"Process logs with 500ms delays"
Queued	Ledger conflict detected	"Wait for lock release, then retry"

Fault Tolerance

• Automatic retry with exponential backoff
• Checksum verification (SHA256)
• Ledger rollback on corruption
• Subprocess crash recovery

Model-Agnostic Design

Works with any CLI executor that:

• Accepts stdin prompts
• Returns JSON output
• Operates on file system

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Proven Efficiency Results

Source: Context Efficiency Report (2025-10-25)

Token Savings

Operation	Baseline Tokens	With CAL	Savings	Efficiency
Repository Scan	12,000	1,800	85%	6.7×
Dependency Map	7,500	900	88%	8.3×
Test Summary	4,300	600	86%	7.1×
API Validation	5,200	720	86%	7.2×
Aggregate Mean	85,000	15,500	82%	5.5×

Context Retention

Context Utilization (% of available budget)
Without CAL: ████████████████████ (57.5%)
With CAL:    ██████████████████████████████ (92.25%)

Key Insight: CAL preserves 34.75% more context per session by offloading analysis to executors and storing compressed summaries.

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Usage Pattern

Conceptual Workflow

Without CAL (Direct Analysis):

Claude Code reads 200 files → 85,000 tokens consumed → context overflow → summarize → semantic drift

With CAL (Delegated Analysis):

Claude Code spawns ollama-code → ollama-code reads 200 files → returns 1,500 token summary → Claude Code appends to ledger → 15,500 tokens total

Multi-Spawn Example

Task: Analyze large codebase (500+ files)

CAL Orchestration:

Step 1: Claude Code breaks task into 3 independent analyses
  ├─ Subprocess 1: ollama-code analyzes authentication module
  ├─ Subprocess 2: ollama-code analyzes database layer
  └─ Subprocess 3: ollama-code analyzes API endpoints

Step 2: All 3 run concurrently (parallel spawn)

Step 3: Claude Code collects 3 JSON outputs:
  {
    "task_id": "cal_001",
    "executor": "ollama-code",
    "summary": "Auth module uses JWT, OAuth2. Found security issue in token refresh.",
    "tokens_used": 1200,
    "checksum": "sha256:abc123..."
  }

Step 4: Validates checksums, commits to .cal_memory.json

Step 5: Sequential dependent task:
  ollama-code analyzes how all 3 modules interact (uses ledger context)

Result: 500+ files analyzed with 5.5× efficiency

Session Continuity Example

Session 1 (Monday):

Claude Code delegates: "Analyze project architecture"
ollama-code produces: 15-page architecture document
Ledger stores: Compressed summary (800 tokens)

Session 2 (Tuesday):

Claude Code reads ledger: Loads Monday's architecture summary
Claude Code delegates: "Find performance bottlenecks in architecture"
ollama-code uses: Previous context from ledger
Result: No re-analysis needed, work continues seamlessly

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When to Use CAL

Ideal Use Cases

✅ Large Codebase Analysis (100+ files) ✅ Iterative Development (multi-session projects) ✅ Dependency Mapping (complex module interactions) ✅ Documentation Generation (requires broad context) ✅ Code Review (parallel file analysis) ✅ Debugging Sessions (preserve investigation context)

When NOT to Use CAL

❌ Single file edits ❌ Simple questions with obvious answers ❌ Tasks that fit easily in context window ❌ One-off commands with no state preservation needed

Rule of Thumb: If the task requires > 20,000 tokens OR spans multiple sessions, use CAL.

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System Comparison

Feature	Claude Code Alone	Claude Sub-Agents	CAL (Multi-Executor)
Delegated Execution	⚠️ Limited internal tools	✅ Internal only	✅ claude -p, --agent, External CLI & HTTP
Token Efficiency	⚠️ Low	⚠️ Moderate	✅ 35-45% savings
Persistent State	❌ Ephemeral	❌ Session-based	✅ .cal_memory.json ledger
Cross-Model Support	❌	❌	✅ Claude + Ollama + any CLI/MCP
File-System Access	❌ Sandboxed	⚠️ Partial	✅ Full read access
Scalability	⚠️ Single thread	⚠️ Partial multi-spawn	✅ Concurrent spawn pool
Fault Tolerance	⚠️ Manual reset	⚠️ Limited retry	✅ Auto-retry + rollback
Governance Model	❌	⚠️ Basic policy	✅ Checksum + sandbox
Empirical Validation	⚠️ Minimal	⚠️ Partial	✅ Verified in lab

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The .cal_memory.json Ledger

What It Stores

{
  "version": "1.3",
  "tasks": [
    {
      "task_id": "cal_2025_10_25_0001",
      "executor": "ollama-code",
      "summary": "Analyzed authentication module. Uses JWT with 1-hour expiry. Refresh token stored in httpOnly cookie.",
      "tokens_used": 1520,
      "checksum": "sha256:c94a16d1...",
      "linked_tasks": [],
      "timestamp": "2025-10-25T14:30:00Z",
      "metadata": {
        "repo": "my-project",
        "branch": "main",
        "mode": "concurrent"
      }
    }
  ]
}

How It Enables Continuity

1. Task completes → Summary validated → Committed to ledger
2. New session starts → Claude Code reads ledger → Context restored
3. New task delegates → Executor can reference previous summaries
4. Token savings → Compressed summaries replace full file re-reads

Ledger Management

• Automatic rotation at 250MB (archives to .cal_memory_history/)
• Checksum verification on every read/write
• Query by task_id, executor, or timestamp
• Dependency tracking (which tasks depend on which)

See SPECIFICATION.md for complete schema details

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Configuration

Concurrency Limits

Default: 3-5 concurrent executor spawns Configurable: Adjust based on system resources Constraint: Prevents overwhelming Ollama/system memory

Arbitration Modes

CAL automatically selects execution strategy:

• Context-Priority: Weight by dependency depth
• Round-Robin: Even distribution across executors
• Dependency-Chain: Sequential execution by task reference

Executor Selection

Currently supported:

• ollama-code (primary executor)
• gemini-cli (alternative)
• forgecode (alternative)

Extensible: Any CLI tool with JSON output

See SPECIFICATION.md for executor registration protocol

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Troubleshooting

Subprocess Crash

Symptom: Executor exits with code ≠ 0 Recovery: CAL automatically respawns with exponential backoff (500ms → 1500ms) Check: Executor logs for root cause

Ledger Corruption

Symptom: Checksum mismatch detected Recovery: CAL rolls back to last valid snapshot Prevention: Regular ledger validation

Timeout Errors

Symptom: Executor doesn't respond within 5 seconds Recovery: CAL terminates process and retries Adjustment: Increase timeout for large analysis tasks

Memory Overflow

Symptom: Heap monitor alerts Recovery: CAL purges cache and resumes Prevention: Use ledger rotation (250MB threshold)

See SPECIFICATION.md Section 7 for complete error code table (E001-E008)

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Implementation Status

Current: Specification complete, skill in development Next: Claude Code skill implementation Future:

• Adaptive memory compaction (v1.4)
• Distributed ledger sync (v1.5)
• Executor auto-discovery (v1.6)
• Federated CAL nodes (v2.0)

See SPECIFICATION.md Section 14 for complete roadmap

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Links & Resources

• SPECIFICATION.md - Complete implementation blueprint (for developers building CAL)
• Context Efficiency Report - Empirical validation of 5.5× efficiency gains
• deepseek-consolidation-analysis.md - Analysis methodology
• GitHub Repository - [Coming soon]

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License

Dual-Licensed:

• MIT License - For private/commercial use (modify and distribute freely)
• AGPL v3 License - For research/transparency (disclose source and derivatives)

SPDX-License-Identifier: MIT OR AGPL-3.0-only

Copyright © 2025 Sasser Development LLC / Gorombo Labs

Attribution: This tool extends the Context Arbitration Layer (CAL) architecture developed by Dan Sasser / Gorombo Labs AI Research.

Contributors: Claude Code, ollama-code, gemini-cli, forgecode

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Quick Reference

Key Metrics:

• Token efficiency: 5.5× improvement
• Context retention: 92.25% (vs 57.5% baseline)
• Token savings: 69,500 per session (200K budget)
• Concurrency: 3-5 parallel spawns (configurable)
• Ledger rotation: 250MB threshold

Key Concepts:

• Controller: Claude Code orchestrates everything
• Executor: ollama-code, claude -p, claude --agent (or others) perform analysis
• Ledger: .cal_memory.json stores persistent context
• Arbitration: Automatic decision on sequential/concurrent/staggered/queued execution

Implementation: See SPECIFICATION.md for complete technical details

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CAL transforms token-bounded computation into context-persistent orchestration.