Memory & context

Two related ideas about what the agent "remembers":

• Context is the current conversation — everything in this run.
• Memory is what carries over between runs and sessions.

Keeping the conversation from getting too big

Every AI model can only read so much text at once. That limit is called its context window, and it's measured in tokens (a token is roughly ¾ of a word). A long agent run — lots of tool calls, lots of results — slowly fills that window, and eventually it overflows and the model errors out.

FitContextWindow prevents that. It keeps the conversation under a token budget by dropping the oldest exchanges, while always keeping the original task and never splitting a tool call from its result:

from agentix import Agent, FitContextWindow

agent = Agent(
    model=m,
    system_prompt="...",
    tools=[...],
    context_strategy=FitContextWindow(max_tokens=180_000, reserve_tokens=4_000),
)

reserve_tokens leaves room for the model's reply. By default the token count is a fast estimate; for an exact count you can plug in a real tokenizer (like tiktoken) — see the example.

→ Runnable example: examples/25_token_context.py

Remembering across sessions

By default, an agent forgets everything once a run ends. Memory lets it recall useful things later — "the user prefers metric units", "last week we decided X".

agentix gives you the interface; you bring the storage (a search index, a vector database, or just a file). A simple keyword-based memory is included so you can start immediately:

from agentix import Agent, InMemoryMemory

memory = InMemoryMemory()
await memory.write("The user's name is Sanjaya.")

agent = Agent(model=m, system_prompt="...", memory=memory)
# Before each run, relevant memories are looked up and added to the agent's context.

Set remember_exchange=True to automatically save each finished conversation back into memory.

Only store trusted content in memory

Memories are added to the agent as trusted instructions. Don't store raw, unchecked tool output there — that would reopen the prompt-injection door the Security model closes.

→ Runnable example: examples/26_memory.py