harness-utils 0.1.5

Context window management utilities for LLM-based applications

harnessutils

Python library for managing LLM context windows in long-running conversations. Enables indefinite conversation length while staying within token limits.

Installation

uv add harness-utils

Features

• Three-tier context management - Truncation, pruning, and LLM-powered summarization
• Turn processing - Stream event handling with hooks and doom loop detection
• Pluggable storage - Filesystem and in-memory backends
• Zero dependencies - No external runtime requirements
• Type-safe - Full Python 3.12+ type hints

Quick Start

from harnessutils import ConversationManager, Message, TextPart, generate_id

manager = ConversationManager()
conv = manager.create_conversation()

# Add message
msg = Message(id=generate_id("msg"), role="user")
msg.add_part(TextPart(text="Help me debug"))
manager.add_message(conv.id, msg)

# Prune old outputs
manager.prune_before_turn(conv.id)

# Get messages for LLM
model_messages = manager.to_model_format(conv.id)

Context Management

Three tiers handle context overflow:

1. Truncation - Limits tool output size (instant, free)

output = manager.truncate_tool_output(large_output, "tool_name")

2. Pruning - Removes old tool outputs (fast, ~50ms)

result = manager.prune_before_turn(conv.id)
# Keeps recent 40K tokens, removes older outputs

3. Summarization - LLM compression when needed (slow, ~3-5s)

if manager.needs_compaction(conv.id, usage):
    manager.compact(conv.id, llm_client, parent_msg_id)

Turn Processing

Process streaming LLM responses with hooks:

from harnessutils import TurnProcessor, TurnHooks

hooks = TurnHooks(
    on_tool_call=execute_tool,
    on_doom_loop=handle_loop,
)

processor = TurnProcessor(message, hooks)
for event in llm_stream:
    processor.process_stream_event(event)

Includes:

• Tool state machine
• Doom loop detection (3 identical calls)
• Snapshot tracking

Configuration

from harnessutils import HarnessConfig

config = HarnessConfig()
config.truncation.max_lines = 2000
config.pruning.prune_protect = 40_000  # Keep recent 40K tokens
config.model_limits.default_context_limit = 200_000

Storage

from harnessutils import FilesystemStorage, MemoryStorage

# Filesystem (production)
storage = FilesystemStorage(config.storage)

# In-memory (testing)
storage = MemoryStorage()

# Custom (implement StorageBackend protocol)
# See examples/custom_storage_example.py
storage = YourCustomStorage()

Examples

• basic_usage.py - Simple conversation
• ollama_example.py - Ollama integration
• ollama_with_summarization.py - Full three-tier demo
• turn_processing_example.py - Stream processing
• custom_storage_example.py - Custom storage adapter (SQLite)

Development

uv sync                          # Install deps
uv run pytest                    # Run unit tests
uv run mypy src/                 # Type check
uv run python -m evals.runner    # Run evals (quality, budget, performance)

Evals test real-world behavior beyond unit tests:

• Information preservation after compaction
• Token budget compliance
• Performance benchmarks (latency, throughput)

See evals/README.md for details.

License

MIT License - see LICENSE for details.