# Agent Memory
Use Hypabase as persistent, structured memory for AI agents across sessions, with session-tagged provenance.
## Multi-session persistence
Hypabase persists to SQLite. An agent can write memory in one session and read it in the next:
```python
from hypabase import Hypabase
# --- Session 1: Agent records task context ---
with Hypabase("agent_memory.db") as hb:
with hb.context(source="session_1", confidence=0.9):
hb.node("user_alice", type="user")
hb.node("task_write_report", type="task")
hb.node("doc_quarterly", type="document")
hb.edge(
["user_alice", "task_write_report", "doc_quarterly"],
type="assigned",
)
```
```python
# --- Session 2: Agent reopens, queries, adds new context ---
with Hypabase("agent_memory.db") as hb:
# Session 1 data is still there
alice_edges = hb.edges(containing=["user_alice"])
# Returns the "assigned" edge from session 1
with hb.context(source="session_2", confidence=0.85):
hb.node("tool_spreadsheet", type="tool")
hb.edge(
["user_alice", "task_write_report", "tool_spreadsheet"],
type="uses_tool",
)
```
```python
# --- Session 3: Agent queries across all sessions ---
with Hypabase("agent_memory.db") as hb:
# All data from all sessions
assert len(hb.nodes()) == 4
assert len(hb.edges()) == 2
# Cross-session path discovery
paths = hb.paths("doc_quarterly", "tool_spreadsheet")
# doc_quarterly → user_alice → tool_spreadsheet (across sessions)
# Track which session contributed what
sources = hb.sources()
# [
# {"source": "session_1", "edge_count": 1, "avg_confidence": 0.9},
# {"source": "session_2", "edge_count": 1, "avg_confidence": 0.85},
# ]
```
## Key patterns
### Session tracking via provenance
Use `source` to track which session or agent interaction created each memory:
```python
with hb.context(source=f"session_{session_id}", confidence=0.9):
# All memories in this block are tagged with the session
hb.edge([user, task, resource], type="context")
```
### Confidence decay
Lower confidence for older or uncertain memories:
```python
# Fresh interaction — high confidence
with hb.context(source="session_current", confidence=0.95):
hb.edge(["user", "preference_dark_mode"], type="prefers")
# Inferred from past behavior — lower confidence
with hb.context(source="inference_engine", confidence=0.6):
hb.edge(["user", "preference_vim"], type="likely_prefers")
```
### Context retrieval
When the agent needs to recall context about an entity:
```python
def get_agent_context(hb, entity_id, min_confidence=0.7):
"""Retrieve all high-confidence memories about an entity."""
edges = hb.edges(
containing=[entity_id],
min_confidence=min_confidence,
)
neighbors = hb.neighbors(entity_id)
return {
"relationships": edges,
"connected_entities": neighbors,
}
```
### Decision traces
Record why the agent made a decision:
```python
with hb.context(source="planning_step_3", confidence=0.88):
hb.edge(
["decision_use_react", "requirement_speed", "constraint_team_skill"],
type="decision_trace",
properties={"reasoning": "React chosen due to team familiarity"},
)
```
Later, the agent (or a human) can audit the decision:
```python
decisions = hb.edges(type="decision_trace")
for d in decisions:
print(f"Decision involved: {d.node_ids}")
print(f"Source: {d.source}, Confidence: {d.confidence}")
print(f"Reasoning: {d.properties.get('reasoning')}")
```
