# Context Management
Long-running agents accumulate messages that exceed the model's context window. yoagent provides token tracking, overflow detection, tiered compaction, and execution limits.
## Token Estimation
Fast estimation without external tokenizer dependencies:
```rust
use yoagent::context::{estimate_tokens, message_tokens, total_tokens};
estimate_tokens("Hello world"); // ~3 tokens (chars / 4)
message_tokens(&agent_message); // estimate for a single message
total_tokens(&messages); // estimate for all messages
```
## Context Tracking
`ContextTracker` combines real token counts from provider responses with estimation for new messages — more accurate than pure estimation:
```rust
use yoagent::context::ContextTracker;
let mut tracker = ContextTracker::new();
// After each assistant response, record the real usage:
tracker.record_usage(&assistant_usage, message_index);
// Get current context size (real usage + estimated trailing):
let tokens = tracker.estimate_context_tokens(agent.messages());
// After compaction, reset the tracker:
tracker.reset();
```
When no usage data is available, it falls back to chars/4 estimation.
## Context Overflow Detection
When the context exceeds a model's window, providers return overflow errors. yoagent detects these automatically across all major providers.
### HTTP-level detection
Providers that check before streaming (Google, Bedrock, Vertex) return `ProviderError::ContextOverflow`:
```rust
use yoagent::provider::ProviderError;
match agent.prompt("...").await {
// The loop already handles this — but you can also match it:
Err(ProviderError::ContextOverflow { message }) => {
// Compact and retry
}
_ => {}
}
```
`ProviderError::classify()` auto-detects overflow from error messages covering Anthropic, OpenAI, Google, AWS Bedrock, xAI, Groq, OpenRouter, llama.cpp, LM Studio, MiniMax, Kimi, GitHub Copilot, and generic patterns.
### Message-level detection
SSE-based providers (Anthropic, OpenAI) return overflow as a `StopReason::Error` message. Check with:
```rust
if message.is_context_overflow() {
// Compact and retry
}
```
### Handling overflow in your application
yoagent provides the detection and building blocks. Your application wires the compaction strategy:
```rust
// Proactive: check before each prompt
let tokens = tracker.estimate_context_tokens(agent.messages());
if tokens > context_window - reserve {
let compacted = compact_messages(agent.messages().to_vec(), &config);
agent.replace_messages(compacted);
}
// Reactive: catch overflow errors
// ... on ContextOverflow or message.is_context_overflow():
// compact, then retry with agent.continue_loop()
```
For LLM-based summarization (asking the model to summarize old messages), implement that in your application layer — yoagent provides `replace_messages()` and `compact_messages()` as building blocks.
## ContextConfig
```rust
pub struct ContextConfig {
pub max_context_tokens: usize, // Default: 100,000
pub system_prompt_tokens: usize, // Default: 4,000
pub keep_recent: usize, // Default: 10
pub keep_first: usize, // Default: 2
pub tool_output_max_lines: usize, // Default: 50
}
```
### Auto-Derivation from ModelConfig
When you set a `ModelConfig` but don't explicitly set a `ContextConfig`, the compaction budget is automatically derived from the model's `context_window` — reserving 80% for context and 20% for output:
```rust
// MiniMax with 1M context → compacts at 800K (no manual config needed)
let agent = Agent::new(OpenAiCompatProvider)
.with_model_config(ModelConfig::minimax("MiniMax-Text-01", "MiniMax Text 01"))
.with_api_key(api_key);
// Anthropic with 200K context → compacts at 160K
let agent = Agent::new(AnthropicProvider)
.with_model_config(ModelConfig::anthropic("claude-sonnet-4-20250514", "Claude Sonnet 4"))
.with_api_key(api_key);
```
The priority chain:
1. Explicit `with_context_config(...)` → always wins
2. Has `model_config` → auto-derives from `context_window` (80%)
3. Neither → `ContextConfig::default()` (100K)
You can also derive manually:
```rust
let config = ContextConfig::from_context_window(1_000_000);
// config.max_context_tokens == 800_000
```
## Tiered Compaction
`compact_messages()` tries each level in order, stopping as soon as messages fit the budget:
### Level 1: Truncate Tool Outputs
Replaces long tool outputs with head + tail (keeping first N/2 and last N/2 lines). This is the cheapest — preserves conversation structure, typically saves 50-70% in coding sessions.
### Level 2: Summarize Old Turns
Keeps the last `keep_recent` messages in full detail. Older assistant messages are replaced with one-line summaries like `"[Summary] [Assistant used 3 tool(s)]"`, and their tool results are dropped.
### Level 3: Drop Middle Messages
Keeps `keep_first` messages from the start and `keep_recent` from the end, dropping everything in between. A marker message notes how many were removed.
## ExecutionLimits
Prevents runaway agents:
```rust
pub struct ExecutionLimits {
pub max_turns: usize, // Default: 50
pub max_total_tokens: usize, // Default: 1,000,000
pub max_duration: Duration, // Default: 600s (10 min)
}
```
When a limit is reached, the agent stops with a message like `"[Agent stopped: Max turns reached (50/50)]"`.
## Disabling Context Management
```rust
let agent = Agent::new(provider)
.without_context_management();
```
This sets both `context_config` and `execution_limits` to `None`.