How to Manage OpenClaw Sessions and Context Pruning
Long conversations fill the context window, causing errors, higher costs, and slower responses. Effective session management with intelligent context pruning keeps OpenClaw responsive while preserving important conversation history. This guide covers session lifecycle, pruning strategies, archiving, and monitoring.
Why This Is Hard to Do Yourself
These are the common pitfalls that trip people up.
Context window limits
Claude models have finite context windows (200k tokens). Long sessions hit the limit and fail.
What to prune
Pruning too aggressively loses important context. Pruning too little fills the window. Finding the balance is hard.
Cost vs. quality tradeoff
Larger context windows cost more. Aggressive pruning saves money but degrades conversation quality.
Session storage
Thousands of sessions create storage overhead. Old sessions need cleanup without losing valuable data.
Step-by-Step Guide
Understand OpenClaw session lifecycle
Sessions have distinct phases that affect management strategy.
# Session lifecycle phases:
#
# 1. Active: User is actively conversing (last message < 5 min ago)
# - Full context retained
# - No pruning
#
# 2. Idle: User paused (5 min < last message < 1 hour)
# - Light pruning eligible
# - Keep recent N messages
#
# 3. Stale: Likely abandoned (1 hour < last message < 24 hours)
# - Aggressive pruning
# - Keep only critical context
#
# 4. Expired: Definitely abandoned (last message > 24 hours)
# - Archive to cold storage
# - Remove from active memory
# Check session status:
openclaw admin sessions list --status active
openclaw admin sessions list --status stale --older-than 1hConfigure session TTL and cleanup
Set time-to-live for different session states.
# In config/sessions/lifecycle.yaml:
session_lifecycle:
ttl:
active: 1h # Keep in memory for 1 hour of inactivity
idle: 24h # Move to disk after 24 hours
archived: 90d # Delete archives after 90 days
cleanup:
enabled: true
schedule: "0 * * * *" # Run hourly
batch_size: 100
persistence:
active_sessions: memory
idle_sessions: redis
archived_sessions: s3
# In config/sessions/redis.yaml:
redis:
host: localhost
port: 6379
db: 0
key_prefix: "openclaw:session:"
ttl_seconds: 86400 # 24 hoursWarning: Setting TTL too short will delete active sessions. Start with conservative values (24h for idle, 90d for archives) and adjust based on usage patterns.
Set up context window limits
Configure maximum context size per model.
# In config/context/limits.yaml:
context_limits:
claude-opus-4:
max_tokens: 200000
warning_threshold: 180000 # Warn at 90%
hard_limit: 195000 # Start pruning at 97.5%
claude-sonnet-3.5:
max_tokens: 200000
warning_threshold: 180000
hard_limit: 195000
# Default for unknown models:
default:
max_tokens: 100000
warning_threshold: 90000
hard_limit: 95000
# Per-user overrides:
user_limits:
alice@example.com:
max_tokens: 200000 # Power user
bob@example.com:
max_tokens: 50000 # Limited userConfigure pruning strategies (FIFO, relevance-based)
Choose how to prune old messages.
# In config/context/pruning.yaml:
pruning:
enabled: true
strategy: hybrid # "fifo", "relevance", "hybrid"
# FIFO: Remove oldest messages first (simple, predictable)
fifo:
keep_recent: 50 # Always keep last 50 messages
# Relevance: Use embeddings to keep most relevant messages
relevance:
enabled: true
model: text-embedding-3-small
similarity_threshold: 0.7
keep_top_n: 30
# Hybrid: FIFO but always keep high-relevance messages
hybrid:
fifo_weight: 0.3
relevance_weight: 0.7
min_keep: 20 # Always keep at least 20 messages
max_keep: 100 # Never exceed 100 messages
# Message importance rules:
importance:
- type: system_message
priority: high # Never prune system messages
- type: code_output
priority: medium
- type: chat
priority: lowWarning: Relevance-based pruning requires computing embeddings, which adds latency and cost. Use FIFO for most use cases and relevance only when conversation quality is critical.
Implement session archiving
Archive old sessions to cold storage.
# In config/sessions/archiving.yaml:
archiving:
enabled: true
triggers:
- type: age
older_than: 7d
action: archive
- type: inactivity
inactive_for: 24h
action: archive
- type: size
larger_than: 1mb
action: compress_and_archive
storage:
provider: s3
s3:
bucket: openclaw-session-archives
region: us-east-1
prefix: sessions/
compression:
enabled: true
format: gzip
retrieval:
allow_restore: true
restore_ttl: 24h # Restored sessions expire after 24h
# Archive old sessions manually:
openclaw admin sessions archive --older-than 7d
# Restore archived session:
openclaw admin sessions restore --session-id sess_abc123Monitor context usage
Track token usage and pruning effectiveness.
# In config/monitoring/context.yaml:
context_monitoring:
enabled: true
metrics:
- type: context_size
per: session
interval: 1m
- type: pruning_events
per: session
interval: 1h
- type: token_usage
per: user
interval: 1h
alerts:
- metric: context_size
threshold: 180000
action: notify_user
- metric: pruning_frequency
threshold: 5
window: 1h
action: investigate
# View context usage:
openclaw admin context stats --session sess_abc123
# Show pruning history:
openclaw admin context pruning-log --session sess_abc123
# Export context metrics:
openclaw admin context export --format json > context-metrics.jsonSession Management Is Easy to Get Wrong
TTL configuration, pruning strategies, archiving, monitoring — balancing context quality with performance and cost requires careful tuning. Our experts configure production-grade session management for OpenClaw deployments.
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