Performance¶

Echo Agent's bottlenecks are usually model-call latency and local data I/O. This page covers the tuning levers that exist in the configuration.

Where the time goes¶

Typical request breakdown:

┌─────────────────────────────────────────────────────────────┐
│ Model call (60-80%)  │ Tool execution (10-25%) │ I/O (5-15%) │
└─────────────────────────────────────────────────────────────┘

Bottleneck	Symptom	Where to tune
Model latency	Long wait before the first token	Model selection, routing, compression
SQLite I/O	Slow memory retrieval	Storage path, periodic VACUUM
Process memory	OOM or swapping	Spill settings, session trimming
Knowledge retrieval	Slow similarity search	Chunking, index rebuild
Tool execution	External calls time out	Per-tool timeouts, parallel calls

Model calls¶

Routing by task type¶

Route scenarios to appropriate models to balance cost against speed. Routing rules live in models.routes and match on task_types:

models:
  default_model: claude-sonnet-4-5
  routes:
    - task_types: [simple]
      provider: openai
      model: gpt-4o-mini
      max_tokens: 1000
    - task_types: [coding]
      provider: anthropic
      model: claude-sonnet-4-5
      max_tokens: 8000
      fallback_models: [gpt-4o]

There is no models.routing section grouping models by scenario name — routing is always expressed as a routes list with task_types.

Context compression¶

Echo Agent does not cache model responses (there is no models.cache in the configuration). The lever for reducing repeated input cost is context compression, under compression:

compression:
  enabled: true
  trigger_ratio: 0.7          # compress when context usage hits this ratio
  summary_target_ratio: 0.2   # target share after compression
  tool_pruning_enabled: true  # prune historical tool output

Parallel tool calls¶

Independent tools run in parallel. The setting is agent.tool_concurrency, enabled by default:

agent:
  tool_concurrency:
    enabled: true
    max_concurrent: 4

Model fallback

Configure fallback models so a slow or failing primary model switches to a faster alternative. See Model routing and fallback.

SQLite¶

Echo Agent stores metadata and memory in SQLite.

WAL mode¶

SQLite connection parameters (journal_mode, synchronous, cache_size, mmap_size and other PRAGMAs) cannot be tuned through configuration — there is no database section, and the code sets none of these PRAGMAs.

What is configurable is the set of storage paths, under storage:

storage:
  database_path: data/echo_agent.db
  sessions_dir: data/sessions
  memory_dir: data/memory
  logs_dir: data/logs
  spill_dir: data/spill

Database-level optimization therefore comes down to two things: keep database_path on a local SSD rather than a network filesystem, and run VACUUM periodically to reclaim fragmentation.

Index maintenance¶

# Check database size and fragmentation
sqlite3 ~/.echo-agent/data/echo_agent.db "
  SELECT page_count * page_size as size_bytes FROM pragma_page_count(), pragma_page_size();
"

# Rebuild indexes (offline operation)
sqlite3 ~/.echo-agent/data/echo_agent.db "REINDEX;"

# Reclaim space
sqlite3 ~/.echo-agent/data/echo_agent.db "VACUUM;"

VACUUM regularly

SQLite does not shrink the file automatically after bulk deletes. Schedule VACUUM during low-load periods.

Memory¶

Context and history size¶

Controlled by session (runtime has a single field, single_instance, unrelated to memory):

session:
  context_window_tokens: 0        # 0 means use the model's own window
  max_history_messages: 500       # history entries retained
  compression_window_cap: 200000  # upper bound on the compression window
  expiry_hours: 72                # session expiry

Spill¶

Oversized tool output spills to disk. The section is top-level spill, not under runtime:

spill:
  enabled: true
  max_inline_chars: 6000     # spill past this many characters
  max_total_mb: 512          # cap on total spill directory size
  retention_days: 7          # retention window
  sweep_interval_hours: 6    # cleanup scan interval

The spill directory path comes from storage.spill_dir. Artifacts are read back on demand with the read_spill tool — see the tool reference.

Monitoring¶

# Process status and resource usage
echo-agent status

# Linux: detailed memory maps
cat /proc/$(pgrep -f echo-agent)/status | grep -i vm

Knowledge retrieval¶

Chunking and retrieval size¶

The knowledge index is a local JSON file, not an HNSW-style vector index, so there are no ef_construction / ef_search / m knobs and no knowledge.index subsection. What you can tune is chunking and retrieval size:

knowledge:
  enabled: true
  chunk_size: 1200        # document chunk size (default 1200)
  chunk_overlap: 120      # chunk overlap (default 120)
  max_results: 5          # results per retrieval
  auto_index: true        # index automatically after changes
  index_path: data/knowledge_index.json
  docs_dir: data/knowledge

Chunks that are too small increase result count and recall noise; too large and you lose precision. Changes take effect only after an index rebuild.

Index rebuild¶

The index is a derived artifact: when its structure is damaged, rebuild rather than repair. Use the knowledge_index tool (action: rebuild) or the Dashboard's Knowledge page.

Embedding and reranking parameters live in the memory section (embedding_backend, rerank_enabled, rerank_top_k and so on) — see the configuration reference.

Network¶

There is no unified network section, and no connection-pool or global-timeout fields. Network settings sit with their consumers:

Setting	Purpose
`execution.network_policy`	Master outbound switch, defaults to `deny`
`tools.web.proxy`	Proxy for the web tool
`tools.web.timeout_seconds`	Web tool timeout, default `30`
`tools.browser.nav_timeout_sec`	Browser navigation timeout
`channels.telegram.proxy`	Proxy for the Telegram channel
`gateway.media_download_concurrency`	Gateway media download concurrency, default `4`

execution:
  network_policy: allow

tools:
  web:
    enabled: true
    proxy: "http://proxy:8080"
    timeout_seconds: 30

All outbound requests pass the shared SSRF check in echo_agent/security/net_guard.py; private addresses are rejected by default. Open the relevant allow_private_addresses only when intranet access is genuinely required.

Resource guidance¶

Minimum¶

Resource	Minimum	Notes
CPU	2 cores	Needed for parallel tool execution
Memory	2 GB	Base runtime plus SQLite cache
Disk	1 GB	Database, logs, spill
Network	Stable connection	Model API calls

Recommended¶

Resource	Recommended	Notes
CPU	4 cores	Smooth tool parallelism
Memory	4-8 GB	Comfortable cache headroom
Disk	10 GB SSD	Fast I/O
Network	Low latency	Less time waiting on model calls

Diagnostics¶

# Runtime status and resource usage
echo-agent status

# Dependency status
echo-agent deps status

# Validate configuration (flags implausible values)
echo-agent config validate

# Cost analysis (find expensive operations)
echo-agent cost --group-by model