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Reduce LLM token usage by up to 50% through intelligent context pruning. Auto-supersede duplicates, manually discard/distill content, and preserve critical state.

Package Exports

  • @tuanhung303/opencode-acp
  • @tuanhung303/opencode-acp/dist/index.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@tuanhung303/opencode-acp) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

Agentic Context Pruning (ACP)

npm version CI License: MIT

Agent Context Pruning - Making context disappear

Reduce token usage by up to 50% through intelligent context management.

ACP optimizes LLM context windows by automatically pruning obsolete contentβ€”tool outputs, messages, and reasoning blocksβ€”while preserving critical operational state.


πŸ“Š Context Flow Architecture

flowchart TB
    subgraph Input["πŸ“₯ Input Layer"]
        U[User Message]
        T[Tool Outputs]
        M[Assistant Messages]
        R[Thinking Blocks]
    end

    subgraph Processing["βš™οΈ ACP Processing"]
        direction TB
        Auto["Auto-Supersede"]
        Manual["Manual Pruning"]

        subgraph AutoStrategies["Auto-Supersede Strategies"]
            H[Hash-Based<br/>Duplicates]
            F[File-Based<br/>Operations]
            Todo[Todo-Based<br/>Updates]
            URL[Source-URL<br/>Fetches]
            SQ[State Query<br/>Dedup]
        end

        subgraph ManualTools["Manual Tools"]
            D[Discard]
            Dist[Distill]
        end
    end

    subgraph Output["πŸ“€ Optimized Context"]
        Clean[Clean Context<br/>~50% smaller]
        L[LLM Provider]
    end

    U --> Processing
    T --> Auto
    M --> Manual
    R --> Manual

    Auto --> H
    Auto --> F
    Auto --> Todo
    Auto --> URL
    Auto --> SQ

    Manual --> D
    Manual --> Dist

    H --> Clean
    F --> Clean
    Todo --> Clean
    URL --> Clean
    SQ --> Clean
    D --> Clean
    Dist --> Clean

    Clean --> L

    style Input fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    style Processing fill:#f3e5f5,stroke:#4a148c,stroke-width:2px
    style AutoStrategies fill:#fff3e0,stroke:#e65100,stroke-width:1px
    style ManualTools fill:#e8f5e9,stroke:#1b5e20,stroke-width:1px
    style Output fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px

πŸš€ Quick Start

Installation

npm install @tuanhung303/opencode-acp

Add to your OpenCode config:

// opencode.jsonc
{
    "plugin": ["@tuanhung303/opencode-acp@latest"],
}

Basic Usage

ACP handles most pruning automatically. The following tools give agents granular control over context:

// Discard completed work
context({ action: "discard", targets: [["a1b2c3"]] })

// Distill large outputs
context({
    action: "distill",
    targets: [["d4e5f6", "Found 15 TypeScript files"]],
})

// Batch operations
context({
    action: "discard",
    targets: [["hash1"], ["hash2"], ["hash3"]],
})

πŸ“š Documentation

Document Purpose
Validation Guide 43 comprehensive test cases
Test Harness Ready-to-run test scripts
Todo Write Testing Guide Testing todowrite & stuck task detection
Context Architecture Memory management strategies
Decision Tree Visual pruning flowcharts
Limitations Report What cannot be pruned
Changelog Version history and migration guides

πŸ€– Agent Auto Mode

ACP provides the context tool for intelligent context management:

Tool Interface

context({
    action: "discard" | "distill",
    targets: [string, string?][]  // [[target, summary?], ...]
})

Target Types

Type Format Example
Tool outputs 6 hex chars 44136f, 01cb91
Thinking blocks 6 hex chars abc123
Messages 6 hex chars def456

Batch Operations

// Prune multiple items at once
context({
    action: "discard",
    targets: [
        ["44136f"], // Tool output
        ["abc123"], // Thinking block
        ["def456"], // Message
    ],
})

// Distill with shared summary
context({
    action: "distill",
    targets: [
        ["44136f", "Research phase complete"],
        ["01cb91", "Research phase complete"],
    ],
})

πŸ”„ Auto-Supersede Mechanisms

ACP automatically removes redundant content through multiple strategies:

1. Hash-Based Supersede

Duplicate tool calls with identical arguments are automatically deduplicated.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ BEFORE:                             β”‚        β”‚ AFTER:                              β”‚
β”‚                                     β”‚        β”‚                                     β”‚
β”‚   1. read(package.json) #a1b2c3     β”‚   ───► β”‚   ...other work...                  β”‚
β”‚   2. ...other work...               β”‚        β”‚   3. read(package.json) #d4e5f6◄──┐ β”‚
β”‚   3. read(package.json) #d4e5f6     β”‚        β”‚                                     β”‚
β”‚                                     β”‚        β”‚  First call superseded (hash match) β”‚
β”‚  Tokens: ~15,000                    β”‚        β”‚  Tokens: ~10,000  (-33%)            β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

2. File-Based Supersede (One-File-One-View)

File operations automatically supersede previous operations on the same file.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ BEFORE:                             β”‚        β”‚ AFTER:                              β”‚
β”‚                                     β”‚        β”‚                                     β”‚
β”‚   1. read(config.ts)                β”‚   ───► β”‚                                     β”‚
β”‚   2. write(config.ts)               β”‚        β”‚   3. edit(config.ts)◄────────────┐  β”‚
β”‚   3. edit(config.ts)                β”‚        β”‚                                     β”‚
β”‚                                     β”‚        β”‚  Previous operations pruned         β”‚
β”‚  Tokens: ~18,000                    β”‚        β”‚  Tokens: ~6,000  (-67%)             β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

3. Todo-Based Supersede (One-Todo-One-View)

Todo operations automatically supersede previous todo states.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”        β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ BEFORE:                             β”‚        β”‚ AFTER:                              β”‚
β”‚                                     β”‚        β”‚                                     β”‚
β”‚   1. todowrite: pending             β”‚   ───► β”‚                                     β”‚
β”‚   2. todowrite: in_progress         β”‚        β”‚   3. todowrite: completed◄────────┐ β”‚
β”‚   3. todowrite: completed           β”‚        β”‚                                     β”‚
β”‚                                     β”‚        β”‚  Previous states auto-pruned        β”‚
β”‚  Tokens: ~4,500                     β”‚        β”‚  Tokens: ~1,500  (-67%)             β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜        β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

4. Source-URL Supersede

Identical URL fetches are deduplicatedβ€”only the latest response is retained.

5. State Query Supersede

State queries (ls, find, git status) are deduplicatedβ€”only the latest results matter.


πŸ›‘οΈ Protected Tools

These tools are exempt from pruning to ensure operational continuity:

task, todowrite, todoread, context, batch, write, edit, plan_enter, plan_exit

Additional tools can be protected via configuration:

{
    "commands": {
        "protectedTools": ["my_custom_tool"],
    },
}

βš™οΈ Configuration

ACP uses its own config file with multiple levels:

Priority: Defaults β†’ Global β†’ Config Dir β†’ Project
  • Global: ~/.config/opencode/acp.jsonc
  • Config Dir: $OPENCODE_CONFIG_DIR/acp.jsonc
  • Project: .opencode/acp.jsonc

Default Configuration

{
    "$schema": "https://raw.githubusercontent.com/opencode-acp/opencode-acp/master/acp.schema.json",
    "enabled": true,
    "autoPruneAfterTool": false,
    "pruneNotification": "minimal",

    "commands": {
        "enabled": true,
        "protectedTools": [],
    },

    "tools": {
        "discard": { "enabled": true },
        "distill": { "enabled": true },
        "todoReminder": { "enabled": true },
        "automataMode": { "enabled": true },
    },

    "strategies": {
        "deduplication": { "enabled": false },
        "purgeErrors": { "enabled": false },
        "truncation": { "enabled": false },
        "thinkingCompression": { "enabled": false },
        "supersedeWrites": { "enabled": false },
    },
}

Aggressive Pruning (Opt-In)

Enable for up to 50% token savings:

{
    "strategies": {
        "aggressivePruning": {
            "pruneToolInputs": true, // Strip verbose inputs
            "pruneStepMarkers": true, // Remove step markers
            "pruneSourceUrls": true, // Dedup URL fetches
            "pruneFiles": true, // Mask file attachments
            "pruneSnapshots": true, // Keep latest snapshot
            "pruneRetryParts": true, // Prune failed retries
            "pruneUserCodeBlocks": true, // Truncate old code blocks
            "truncateOldErrors": true, // Truncate old errors
            "aggressiveFilePrune": true, // One-file-one-view
            "stateQuerySupersede": true, // Dedup state queries
        },
    },
}

πŸ“Š Token Savings

Metric Without ACP With ACP Savings
Typical Session ~80k tokens ~40k tokens 50%
Long Session ~150k tokens ~75k tokens 50%
File-Heavy Work ~100k tokens ~35k tokens 65%

Cache Impact: ~65% cache hit rate with ACP vs ~85% without. The token savings typically outweigh the cache miss cost, especially in long sessions.


πŸ§ͺ Testing

Run the comprehensive test suite:

# Load test todos
todowrite({ /* copy from docs/VALIDATION_GUIDE.md */ })

# Run preparation
prep-0 through prep-7

# Execute tests
t1 through t43

# Generate report
report-1 through report-4

See Validation Guide for detailed test procedures.


πŸ—οΈ Architecture Overview

flowchart TD
    subgraph OpenCode["OpenCode Core"]
        direction TB
        A[User Message] --> B[Session]
        B --> C[Transform Hook]
        C --> D[toModelMessages]
        D --> E[LLM Provider]
    end

    subgraph ACP["ACP Plugin"]
        direction TB
        C --> F[syncToolCache]
        F --> G[injectHashes]
        G --> H[Apply Strategies]
        H --> I[prune]
        I --> C
    end

    style OpenCode fill:#F4F7F9,stroke:#5A6B8A,stroke-width:1.5px
    style ACP fill:#E8F5F2,stroke:#9AC4C0,stroke-width:1.5px

ACP hooks into OpenCode's message flow to reduce context size before sending to the LLM:

  1. Sync Tool Cache - Updates internal tool state tracking
  2. Inject Hashes - Makes content addressable for pruning
  3. Apply Strategies - Runs auto-supersede mechanisms
  4. Prune - Applies manual and automatic pruning rules

πŸ“ Commands

Command Description
/acp List available commands
/acp context Show token usage breakdown
/acp stats Show aggregate pruning statistics
/acp sweep [n] Prune last N tool outputs

πŸ”§ Advanced Features

Todo Reminder

Monitors todowrite usage and prompts when tasks are neglected:

{
    "tools": {
        "todoReminder": {
            "enabled": true,
            "initialTurns": 8, // First reminder after 8 turns without todo update
            "repeatTurns": 4, // Subsequent reminders every 4 turns
            "stuckTaskTurns": 12, // Threshold for stuck task detection
        },
    },
}

Reminder Behavior:

  • First reminder: Fires after initialTurns (8) turns without todowrite
  • Repeat reminders: Fire every repeatTurns (4) turns thereafter
  • Auto-reset: Each todowrite call resets the counter to 0
  • Deduplication: Only ONE reminder exists in context at a time; new reminders replace old ones
  • Stuck task detection: Tasks in in_progress for stuckTaskTurns (12) are flagged with guidance
  • Prunable outputs: Reminder displays a list of prunable tool outputs to help with cleanup

Reminder Sequence:

Turn 0:  todowrite() called (resets counter)
Turn 8:  πŸ”– First reminder (if no todowrite since turn 0)
Turn 12: πŸ”– Repeat reminder
Turn 16: πŸ”– Repeat reminder
...

Automata Mode

Autonomous reflection triggered by "automata" keyword:

{
    "tools": {
        "automataMode": {
            "enabled": true,
            "initialTurns": 8, // Turns before first reflection
        },
    },
}

Stuck Task Detection

Identifies tasks stuck in in_progress for too long:

{
    "tools": {
        "todoReminder": {
            "stuckTaskTurns": 12, // Threshold for stuck detection
        },
    },
}

🚧 Limitations

  • Subagents: ACP is disabled for subagent sessions
  • Cache Invalidation: Pruning mid-conversation invalidates prompt caches
  • Protected Tools: Some tools cannot be pruned by design

πŸ› οΈ Troubleshooting

Error: reasoning_content is missing (400 Bad Request)

Cause: Using Anthropic/DeepSeek/Kimi thinking mode with an outdated ACP version or missing reasoning sync.

Fix:

  1. Update to ACP v3.0.0+: npm install @tuanhung303/opencode-acp@latest
  2. Ensure your config has thinking-compatible settings
  3. See Thinking Mode Compatibility for details

Plugin Not Loading

Symptoms: Commands like /acp return "Unknown command"

Fix:

  1. Verify plugin is in opencode.jsonc: "plugin": ["@tuanhung303/opencode-acp@latest"]
  2. Run npm run build && npm link in the plugin directory
  3. Restart OpenCode

High Token Usage Despite ACP

Check:

  • Is aggressive pruning enabled in config? See Configuration
  • Are you using protected tools excessively? (task, write, edit can't be pruned)
  • Is your session >100 turns? Consider starting a fresh session

πŸ”¬ Provider Compatibility

Thinking Mode APIs (Anthropic, DeepSeek, Kimi)

ACP is fully compatible with extended thinking mode APIs that require the reasoning_content field. The context tool automatically syncs reasoning content to prevent 400 Bad Request errors.

Supported providers: Anthropic, DeepSeek, Kimi
Not required: OpenAI, Google

See the detailed technical documentation for implementation details and the root cause of the original compatibility issue.


πŸ“¦ npm Package

Package: @tuanhung303/opencode-acp
License: MIT
Repository: https://github.com/tuanhung303/opencode-agent-context-pruning

Installation Methods

# Via npm
npm install @tuanhung303/opencode-acp

# Via OpenCode config
# Add to opencode.jsonc: "plugin": ["@tuanhung303/opencode-acp@latest"]

# Via URL (for agents)
curl -s https://raw.githubusercontent.com/tuanhung303/opencode-acp/master/README.md

CI/CD

  • CI: Every PR triggers linting, type checking, and unit tests
  • CD: Merges to main auto-publish to npm

🀝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Run tests: npm test
  4. Submit a pull request

πŸ“„ License

MIT Β© tuanhung303


⚠️ Known Pitfalls for Agents β€” Critical rules when modifying ACP code

Read this section before modifying ACP code. These are hard-won lessons from debugging production issues.

1. Always Fetch Messages in All Code Paths

❌ WRONG:

async function executeContextToolDiscard(ctx, toolCtx, hashes) {
    const { state, logger } = ctx

    // Validate hashes...

    if (validHashes.length === 0) {
        // Early return without fetching messages
        const currentParams = getCurrentParams(state, [], logger)  // ← BUG: Empty array
        return "No valid hashes"
    }

    // Only fetch messages in success path
    const messages = await client.session.messages(...)
}

βœ… CORRECT:

async function executeContextToolDiscard(ctx, toolCtx, hashes) {
    const { client, state, logger } = ctx

    // ALWAYS fetch messages first - required for thinking mode API compatibility
    const messagesResponse = await client.session.messages({
        path: { id: toolCtx.sessionID },
    })
    const messages = messagesResponse.data || messagesResponse

    // ALWAYS initialize session - syncs reasoning_content
    await ensureSessionInitialized(client, state, toolCtx.sessionID, logger, messages)

    // Now validate hashes...

    if (validHashes.length === 0) {
        const currentParams = getCurrentParams(state, messages, logger) // ← Use actual messages
        return "No valid hashes"
    }
}

Why? Anthropic's thinking mode API requires reasoning_content on all assistant messages with tool calls. Skipping ensureSessionInitialized causes 400 errors.


2. Never Skip ensureSessionInitialized

This function syncs reasoning_content from message parts to msg.info. Without it:

error, status code: 400, message: thinking is enabled but reasoning_content is missing
in assistant tool call message at index 2

Rule: Call ensureSessionInitialized at the START of every context tool function, before any early returns.


3. Thinking Mode: Distill, Don't Discard Reasoning

❌ WRONG:

// Completely removing reasoning_content breaks API
state.prune.reasoningPartIds.push(partId)
// No replacement content β†’ field removed β†’ API error

βœ… CORRECT:

// Convert discard to distill with minimal placeholder
if (reasoningHashes.length > 0) {
    const minimalSummaries = reasoningHashes.map(() => "β€”")
    await executeContextReasoningDistill(
        ctx,
        toolCtx,
        reasoningHashes.map((h, i) => [h, minimalSummaries[i]]),
    )
}

Why? The reasoning_content field must exist (even if minimal) for thinking mode providers.


4. Test with Non-Existing Hashes

Always test context tool changes with:

  1. Valid existing hashes (success path)
  2. Non-existing hashes like "zzzzzz" (no-op path)
  3. Mix of valid and invalid hashes

The no-op path is where most bugs hide because it's less tested.


5. Provider-Specific Behavior

Provider Thinking Mode reasoning_content Required Notes
Anthropic Extended thinking βœ… Yes Strict validation
DeepSeek DeepThink βœ… Yes Similar to Anthropic
Kimi K1 thinking βœ… Yes Similar to Anthropic
OpenAI N/A ❌ No No thinking mode
Google N/A ❌ No No thinking mode

Always test with Anthropic when modifying context/pruning logicβ€”it has the strictest API validation.


6. The Transform Hook Timing

User Message β†’ Transform Hook β†’ ensureReasoningContentSync β†’ API Call
                    ↑
            Context tool runs HERE

If context tool returns early (no-op), the transform hook may not have synced reasoning_content yet. That's why each context tool function must call ensureSessionInitialized itself.


7. Debug Checklist

When you see reasoning_content is missing errors:

  1. Check the error index - "at index 2" means the 3rd message in the array
  2. Verify message fetch - Is the function fetching messages before early returns?
  3. Verify session init - Is ensureSessionInitialized called in ALL code paths?
  4. Check the no-op path - Does the early return path have proper initialization?
  5. Test with invalid hash - context({ action: "discard", targets: [["zzzzzz"]] })

πŸ—¨οΈ For LLM Agents β€” Quick pruning reference and safety guidelines

If you're an AI assistant reading this:

Quick Pruning Reference:

  • Light pressure (<50%): No action
  • Moderate (50-75%): Prune old tool outputs using context({ action: "discard", targets: [[hash]] })
  • High (75-90%): Anchor critical info, then batch prune non-essential items
  • Critical (>90%): Aggressively discard all disposable context

Always Protect: Active todos, current file edits, user requirements

Ask the user: "Would you like me to run a context optimization check?"