> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# LearnConfig
> Configure continuous learning to capture patterns and preferences from agent interactions
Configure agents to learn from interactions, capturing patterns, preferences, and insights to improve future responses.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
subgraph "Continuous Learning"
A[🗣️ Interaction] --> B[🧠 Learn]
B --> C[💾 Store]
C --> D[🔄 Improve]
end
classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
classDef process fill:#F59E0B,stroke:#7C90A0,color:#fff
classDef store fill:#189AB4,stroke:#7C90A0,color:#fff
classDef output fill:#10B981,stroke:#7C90A0,color:#fff
class A input
class B process
class C store
class D output
```
## Quick Start
Enable learning with default settings:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Learning Agent",
instructions="You are a helpful assistant",
learn=True
)
```
Enable specific learning capabilities:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, LearnConfig
agent = Agent(
name="Learning Agent",
instructions="You are a helpful assistant",
learn=LearnConfig(
persona=True, # User preferences
insights=True, # Observations
patterns=True, # Reusable knowledge
scope="private"
)
)
```
Cap store size and expire stale entries automatically:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, LearnConfig
agent = Agent(
name="Assistant",
instructions="Adapts to user preferences",
learn=LearnConfig(
persona=True,
max_entries=1000, # Cap per store
retention_days=90, # Drop stale entries after 90 days
),
)
```
***
## Configuration Options
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import LearnConfig
config = LearnConfig(
# Learning capabilities
persona=True, # User preferences and profile
insights=True, # Observations and learnings
thread=True, # Session/conversation context
patterns=False, # Reusable knowledge patterns
decisions=False, # Decision logging
feedback=False, # Outcome signals
improvements=False, # Self-improvement proposals
# Scope configuration
scope="private",
# Storage configuration
store_path=None, # Custom storage path
backend="file", # Storage backend (file, sqlite, redis, mongodb)
# Retention (0 = unbounded, existing data unaffected)
max_entries=0, # Max entries per store (0 = no cap)
retention_days=0, # Days until stale (0 = never expires)
# Learning mode
mode="disabled", # How to extract learnings automatically
)
```
| Parameter | Type | Default | Description |
| ---------------- | --------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `persona` | `bool` | `True` | Learn user preferences and profile |
| `insights` | `bool` | `True` | Capture observations and learnings |
| `thread` | `bool` | `True` | Maintain session/conversation context |
| `patterns` | `bool` | `False` | Store reusable knowledge patterns |
| `decisions` | `bool` | `False` | Log decision-making process |
| `feedback` | `bool` | `False` | Capture outcome signals |
| `improvements` | `bool` | `False` | Store self-improvement proposals |
| `scope` | `str \| LearnScope` | `"private"` | Visibility scope (`private`, `shared`) |
| `store_path` | `str \| None` | `None` | Custom path for learning storage |
| `backend` | `str \| LearnBackend` | `"file"` | Storage backend (`file`, `sqlite`, `redis`, `mongodb`) |
| `mode` | `str \| LearnMode` | `"disabled"` | Learning extraction mode (`disabled`, `agentic`) |
| `max_entries` | `int` | `0` | Maximum entries per store. `0` = unbounded (default, existing behaviour unchanged). When the cap is exceeded, the store prunes least-used / oldest entries on the next write or `prune()` call. Evicted entries are archived to a `.archive.json` sidecar. |
| `retention_days` | `int` | `0` | Soft expiry in days. `0` = no time-based pruning (default). Entries older than this with no recent use become eligible for pruning. |
Both `max_entries` and `retention_days` default to `0` (unbounded). Existing agents are unaffected unless you explicitly set these values. There is no silent eviction — entries are only pruned when a cap is set.
***
## Bounded Retention
Retention limits keep long-running agents healthy without losing data — evicted entries are saved to a `.archive.json` sidecar file next to the store, so nothing is permanently lost.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
subgraph "Bounded Retention"
A[💡 New entry] --> B[💾 Store]
B --> C{🚦 Over cap?}
C -->|No| D[Keep]
C -->|Yes| E[📦 Archive sidecar]
E --> F[Active store stays small]
end
classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
classDef proc fill:#F59E0B,stroke:#7C90A0,color:#fff
classDef ok fill:#10B981,stroke:#7C90A0,color:#fff
classDef archive fill:#8B0000,stroke:#7C90A0,color:#fff
class A,B input
class C proc
class D,F ok
class E archive
```
### How pruning works
| Trigger | When |
| ----------------- | ------------------------------------------------------------ |
| On write | Automatically when `max_entries > 0` and the cap is exceeded |
| On `prune()` call | Manually, for one-off cleanup |
Entries are scored by **least used first, then oldest** — the most valuable, recently accessed learnings are always kept.
The archive sidecar is written to the same directory as the store file, named `.archive.json` (e.g., `persona.archive.json`).
### Manual pruning
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, LearnConfig
agent = Agent(
learn=LearnConfig(max_entries=1000, retention_days=90)
)
# Run across all configured stores; returns evicted count per store
evicted = agent.memory.learn().prune()
print(f"Pruned: {evicted}")
# e.g. {"persona": 12, "insights": 3}
```
`prune()` is also called automatically on every write when limits are set. Manual calls are only needed for one-off cleanup of a long-running agent.
***
## Common Patterns
### Pattern 1: Full Learning Profile
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, LearnConfig
agent = Agent(
name="Full Learning Agent",
instructions="You are a personalized assistant",
learn=LearnConfig(
persona=True,
insights=True,
thread=True,
patterns=True,
decisions=True,
feedback=True,
improvements=True
)
)
```
### Pattern 2: Shared Team Learning
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, LearnConfig
agent = Agent(
name="Team Agent",
instructions="Share learnings with team",
learn=LearnConfig(
patterns=True,
insights=True,
scope="shared"
)
)
```
### Pattern 3: Compliance-Ready Agent
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, LearnConfig
agent = Agent(
name="Compliance Agent",
instructions="Process requests with data retention limits",
learn=LearnConfig(
persona=True,
insights=True,
max_entries=500,
retention_days=30, # Purge entries older than 30 days
)
)
```
***
## Best Practices
Both `max_entries` and `retention_days` default to `0` (unbounded). Unless your agent runs for months or handles compliance requirements, the defaults are fine. Stores only grow with actual interactions.
For agents that run continuously and interact with many users, set `max_entries=1000` to keep store files small and search fast. The archive sidecar preserves evicted entries if you need to audit them.
For compliance requirements (e.g., GDPR, data minimization), set `retention_days` to automatically make old entries eligible for pruning. Pair with `max_entries` for proactive pruning on every write.
Keep learnings private unless you explicitly need shared team knowledge. This protects user data.
Evicted entries are never hard-deleted. Find the `.archive.json` sidecar (same directory as the store file) to recover them. The archive is append-only JSON.
***
## Related
User-flow guide: retention caps, archival, and the prune API
Learn about the Agent Learn concept