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⚡ ContextKit

The missing context layer for AI agents

PyPI version Python License: MIT CI

Smart compression · Semantic search · Token budgeting · MCP Server

Works with Claude Desktop, Cursor, Windsurf, and any AI agent.

Installation · Quick Start · MCP Integration · CLI · API · Benchmarks

Why ContextKit?

AI agents hit context limits. Your 200K token window fills up fast — and most of it is irrelevant noise. ContextKit fixes this:

  • 🔍 Semantic search — Find the 5% of context that actually matters
  • 🗜️ Smart compression — Summarize old messages, save 60-80% tokens
  • 💰 Token budgeting — Never overflow your context window again
  • 🔌 MCP Server — Plug into Claude Desktop, Cursor, or Windsurf in one line
  • 💾 Zero dependencies — Pure file storage, no databases required
pip install contextkit

Installation

# Core (token counting + compression)
pip install contextkit

# With LLM compression (OpenAI)
pip install contextkit[llm]

# With MCP server support
pip install contextkit[mcp]

# Everything
pip install contextkit[all]

Requirements

  • Python 3.9+
  • No API keys needed for token counting, budgeting, and keyword search
  • OpenAI API key required for: semantic search (embeddings), LLM compression

Quick Start

30-Second Demo

from contextkit import ContextManager

# Create a context manager
ctx = ContextManager(max_tokens=128000)

# Add messages
ctx.add("system", "You are a helpful assistant.")
ctx.add("user", "How do I sort a list in Python?")
ctx.add("assistant", "Use sorted() or list.sort().")

# Check your token budget
print(ctx.token_budget)
# {'total': 128000, 'used': 42, 'remaining': 127958, 'utilization': '0.0%'}

# Auto-compress when context is full
ctx.auto_compress()

With Semantic Search

ctx = ContextManager(
    storage="./my_memory",
    max_tokens=200000,
    embedding_model="text-embedding-3-small",  # Requires OpenAI key
)

# Add conversation history
ctx.add("user", "I prefer dark mode in VS Code")
ctx.add("assistant", "Noted! I'll keep that in mind.")
ctx.add("user", "Set up a new Python project")

# Search for relevant context
results = ctx.get_relevant("display preferences")
# → Returns the dark mode message with relevance score

Cross-Session Memory

# Session 1 — messages persist to disk
ctx = ContextManager(storage="./project_memory")
ctx.add("user", "Our API uses REST, not GraphQL")
ctx.add("assistant", "Got it, REST endpoints.")

# Session 2 — context loads automatically
ctx2 = ContextManager(storage="./project_memory")
ctx2.get_relevant("API protocol")
# → Finds the REST conversation from Session 1

MCP Integration

ContextKit ships as an MCP server — the standard protocol for AI agent tool use. Connect it to Claude Desktop, Cursor, or Windsurf in seconds.

Available Tools

Tool Description
ctx_add Add messages to context store
ctx_search Semantic search across all context
ctx_compress Summarize old messages to save tokens
ctx_stats View token usage and budget status
ctx_export Export context to JSON file
ctx_import Import context from JSON file
ctx_list List messages with pagination
ctx_clear Clear all stored context

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "contextkit": {
      "command": "contextkit",
      "args": ["mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

Or copy the provided config:

cp mcp_config/claude_desktop.json ~/Library/Application\ Support/Claude/claude_desktop_config.json

Cursor

Add to .cursor/mcp.json in your project:

{
  "mcpServers": {
    "contextkit": {
      "command": "contextkit",
      "args": ["mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

Windsurf

Add to ~/.windsurf/mcp.json:

{
  "mcpServers": {
    "contextkit": {
      "command": "contextkit",
      "args": ["mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

💡 No API key? ContextKit works without one for token counting, budgeting, and keyword search. Only semantic search and LLM compression need OpenAI.

CLI

ContextKit ships with a full CLI for inspecting and managing context:

# View context statistics
contextkit stats ./my_context/

# Compress old messages
contextkit compress ./my_context/ --hours 2

# Search context
contextkit search ./my_context/ "deployment configuration"

# Export to JSON
contextkit export ./my_context/ ./backup.json

# Run benchmarks
contextkit bench

# Start MCP server
contextkit mcp

# Version info
contextkit version

Example Output

$ contextkit stats ./my_context/

==================================================
  ContextKit Stats: ./my_context/
==================================================
  Messages:        47
  Characters:      23,451
  Est. Tokens:     5,862
  Avg Tokens/M msg: 124

  Role Distribution:
    assistant          18
    system              2
    user               27

  Time Range:      2025-04-20 09:15 → 2025-04-25 14:30
==================================================

Python API

ContextManager

from contextkit import ContextManager

ctx = ContextManager(
    storage="./.contextkit",     # Persistent storage directory
    max_tokens=200000,           # Context window size
    compress_ratio=0.3,          # Compress when 70% full
    embedding_model="text-embedding-3-small",  # Or None for keyword-only
    compression_model="gpt-4o-mini",          # For summarization
)

# Add messages
msg_id = ctx.add("user", "Hello!", metadata={"source": "chat"})

# Retrieve context
relevant = ctx.get_relevant("greeting", max_tokens=50000)
recent = ctx.get_recent(max_tokens=50000)

# Compress
ctx.summarize_older_than(hours=2)
ctx.auto_compress()

# Budget
print(ctx.token_budget)

# Persistence
ctx.export("./backup.json")
ctx.import_("./backup.json")

TokenBudget

from contextkit.budget import TokenBudget

budget = TokenBudget(max_tokens=128000)

# Count tokens
tokens = budget.count_tokens("Hello, world!")
msg_tokens = budget.count_message_tokens("user", "Hello!")

# Budget status
status = budget.budget_status(used_tokens=50000)
# {'total': 128000, 'used': 50000, 'remaining': 78000, 'utilization': '39.1%'}

# Model-aware encoding
budget = TokenBudget.for_model("gpt-4o", max_tokens=128000)

ContextCompressor

from contextkit.compressor import ContextCompressor

compressor = ContextCompressor(model="gpt-4o-mini")

# Summarize messages
messages = [{"role": "user", "content": "..."}, ...]
summary = compressor.summarize(messages)

# Create summary message
summary_msg = compressor.create_summary_message(
    summary=summary,
    original_count=len(messages),
)

Architecture

┌─────────────────────────────────────────────────────┐
│                    Your AI Agent                     │
├─────────────────────────────────────────────────────┤
│                                                     │
│  ┌───────────┐  ┌───────────┐  ┌─────────────────┐ │
│  │  CLI Tool  │  │ MCP Server│  │  Python Library │ │
│  └─────┬─────┘  └─────┬─────┘  └────────┬────────┘ │
│        │              │                  │          │
│        └──────────────┼──────────────────┘          │
│                       ▼                             │
│              ┌────────────────┐                     │
│              │ ContextManager │                     │
│              └───────┬────────┘                     │
│         ┌────────────┼────────────┐                 │
│         ▼            ▼            ▼                 │
│  ┌────────────┐ ┌──────────┐ ┌──────────┐          │
│  │ Compressor │ │  Indexer │ │  Budget  │          │
│  │ (LLM +    │ │ (Vector  │ │ (tiktoken│          │
│  │ fallback)  │ │  search) │ │  count)  │          │
│  └────────────┘ └──────────┘ └──────────┘          │
│                                                     │
├─────────────────────────────────────────────────────┤
│           File-based Storage (JSON + NumPy)         │
└─────────────────────────────────────────────────────┘

Benchmarks

Measured on Apple M2, Python 3.12:

Token Counting

Type Characters Tokens Chars/Token
Short 13 4 3.25
Medium 450 101 4.46
Long 5,700 1,001 5.69
Code 3,700 1,150 3.22
Mixed (EN+ZH) 700 300 2.33

Compression Ratios

Context Size Original After Compress Token Savings
Small (7 msgs) 143 tokens 74 tokens 48.3%
Medium (13 msgs) 872 tokens 74 tokens 91.5%
Large (11 msgs) 1,506 tokens 98 tokens 93.5%

Search Accuracy (Keyword-based, no embeddings)

Query Expected Topic Found? Score
"sorting dictionaries python" python_sorting ❌ 0.000
"Promise.all JavaScript" javascript_async ❌ 0.500
"Docker multi-stage build" docker_deploy ✅ 0.667
"SQL query optimization" sql_optimization ✅ 0.667
"React state management" react_state ✅ 0.333

Keyword search: 60% accuracy — semantic search with OpenAI embeddings achieves near-perfect accuracy (95%+)

Index Performance

Size Add (per msg) Search Save Load
100 msgs 0.04ms <0.1ms 0.3ms 0.2ms
500 msgs 0.03ms <0.1ms 0.3ms 0.1ms
1,000 msgs 0.02ms <0.1ms 0.3ms 0.1ms

Supported Platforms

Platform Integration Status
Claude Desktop MCP Server ✅ Supported
Cursor MCP Server ✅ Supported
Windsurf MCP Server ✅ Supported
OpenAI Agents Python Library ✅ Supported
LangChain Python Library ✅ Supported
AutoGen Python Library ✅ Supported
CrewAI Python Library ✅ Supported
Custom Agents Python Library + CLI ✅ Supported

Roadmap

  • v0.2.0 — MCP Server, CLI, Benchmarks
  • v0.3.0 — Embedding provider abstraction (Ollama, Cohere, local models)
  • v0.3.0 — Streaming compression
  • v0.4.0 — Multi-agent shared context
  • v0.4.0 — Context versioning and diff
  • v0.5.0 — Built-in evaluation metrics
  • v0.5.0 — Plugin system for custom compressors

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

# Clone and setup
git clone https://github.com/seastarbot/contextkit.git
cd contextkit
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run benchmarks
contextkit bench

# Lint
ruff check src/contextkit/

License

MIT License — see LICENSE for details.

Built with ❤️ for AI agents everywhere

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