Hedgehog Memory

HedgehogMemory

HedgehogMemory gives AI agents infinite persistent memory using a radial compression architecture. Memory is organized as Lines of Nodes — each Node stores the same content at 5 abstraction levels (L0–L4). The L0 one-liner of every node is always loaded at session start (~200 tokens total), so the agent always knows what it knows.

Key guarantee: Memory is NEVER deleted. Old context is only compressed into smaller abstractions. The verbatim original is always recoverable at L4.

Installation

pip install hedgehog-memory
pip install "hedgehog-memory[openai]"   # with OpenAI summarizer (recommended)

Abstraction Levels

Quick Start

from radial_memory import ContextWindowManager
import os

mgr = ContextWindowManager(
    base_path=os.environ.get("HEDGEHOG_MEMORY_PATH", "./memory_store")
)

# SESSION START: get ~200-token origin overview (all L0 summaries)
overview = mgr.reset()
print(overview)  # inject this into your system prompt

# LOAD: find relevant past context by query
result = mgr.load("Python async patterns")
if result.found:
    print(result.content)   # L1 summary by default
    result = result.drill_deeper()     # go to L2
    full = result.load_full_state()    # get verbatim original (L4)

# COMMIT: save current session to memory
mgr.commit(
    topic="Async Python debugging session",
    full_context="Complete session transcript goes here...",
    tags=["python", "async", "debugging"]
)

With OpenAI Summarizer (recommended for quality)

from radial_memory import ContextWindowManager
from radial_memory.summarizer import OpenAISummarizer
import os

summarizer = OpenAISummarizer(
    api_key=os.environ["OPENAI_API_KEY"],
    model="gpt-4o-mini"
)
mgr = ContextWindowManager(
    base_path=os.environ.get("HEDGEHOG_MEMORY_PATH", "./memory_store"),
    summarizer=summarizer
)

Agent Workflow Pattern

Apply this pattern every session:

# 1. SESSION START
overview = mgr.reset()
# overview = all L0 one-liners for every stored node (~200 tokens)
# Inject overview into your system prompt / context window

# 2. QUERY - find relevant past context
result = mgr.load(query=user_request)
if result.found:
    context = result.content  # L1 summary, ~200 chars
    # Need more detail?
    result = result.drill_deeper()   # L2, ~600 chars
    result = result.drill_deeper()   # L3, ~1800 chars
    full = result.load_full_state()  # L4, verbatim original

# 3. WORK - perform task with full context available

# 4. COMMIT - persist session to memory
mgr.commit(
    topic="Brief description of this session",
    full_context=full_session_log,
    tags=["topic1", "topic2"]
)

Status Report

report = mgr.status_report()
# Returns: total lines, total nodes, last commit timestamp
print(report)

Design Principles

• Never deletes — only compresses. The verbatim original is always recoverable.
• Origin always in context — L0 summaries of all nodes load at session start (~200 tokens).
• Radial navigation — query finds the most relevant node by keyword overlap, then drill deeper on demand.
• Pluggable summarizer — swap OpenAI / LiteLLM / custom backends without changing your workflow code.
• Zero mandatory dependencies — pure Python stdlib. Works out-of-the-box with KeywordSummarizer.
• Single-file storage — all memory in one origin.json. Atomic writes, no corruption.

Source & Docs

• GitHub: https://github.com/vvxer/HedgehogMemory
• PyPI: pip install hedgehog-memory
• Issues: https://github.com/vvxer/HedgehogMemory/issues