# How to Reset the Conversation Context of a Needle Agent: A Complete Guide

> Reset Needle agent conversation context with agent.reset(). Clear the sliding window and start fresh conversations in cactus-compute/needle. Full guide included.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: how-to-guide
- Published: 2026-09-06

---

**Use `agent.reset()` to clear the 256-token sliding window and increment the internal reset counter, starting a fresh conversation with no prior context.**

Resetting the conversation context of a **Needle agent** is essential when you want to discard previous exchanges and begin a new dialogue. The Needle library maintains conversation state across two layers: a Python-side wrapper managing worker processes and a native C-level engine tracking token memory. This article explains exactly how the reset mechanism works and how to verify it succeeded.

## How Conversation State Works in Needle

Needle stores conversation context in two locations:

- **`Needle` class instance** – the Python wrapper that coordinates with a background worker process
- **Native engine** – a C library maintaining a **256-token sliding window** and a global `resets` counter exposed via `needle_reset`

When you invoke `agent.reset()`, the library orchestrates a multi-step sequence to clear both layers.

## The Reset Sequence: Step by Step

According to the [cactus-compute/needle](https://github.com/cactus-compute/needle) source code, calling `reset()` triggers the following flow:

| Step | Action | Location |
|------|--------|----------|
| a | Re-bind to current generation (reload latest model files) | `self._bind()` in [[`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py)](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py#L304-L306) |
| b | Forward reset request to worker process (if active) | `self._worker.reset()` in [[`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py)](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py#L306-L307) |
| c | Worker translates request to native call | `_request({"operation": "reset"})` → `lib.needle_reset()` in [[`needle/_worker.py`](https://github.com/cactus-compute/needle/blob/main/needle/_worker.py)](https://github.com/cactus-compute/needle/blob/main/needle/_worker.py#L299-L301) |
| d | Native library clears token window and increments counter | `needle_reset` in C library, exposed via [[`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py)](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py#L145-L146) |
| e | Subsequent `complete` or `run` calls start fresh; JSON response includes `"resets": <N>` | Verified in [[`tests/test_worker.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_worker.py)](https://github.com/cactus-compute/needle/blob/main/tests/test_worker.py#L86-L94) |

## Resetting from Python Code

The primary method to reset conversation context is calling `reset()` on your agent instance.

```python
import needle

# Create an agent with a tool

@needle.tool
def echo(msg: str):
    """Return the same message."""
    return {"msg": msg}

agent = needle.Needle(tools=[echo])

# Run initial conversation

print(agent.run("Say hello")["results"])

# → [{'msg': 'hello'}]

# Reset the conversation context

agent.reset()

# Continue with fresh context — previous exchange is forgotten

print(agent.run("What did I just say?")["results"])

# → []  # Model has no memory of prior message

```

## Verifying the Reset with the Counter

The native engine tracks how many times the context has been cleared. Inspect the `"resets"` field in any response to confirm your reset succeeded.

```python
resp = agent.run("first message")
print(resp["resets"])   # → 0

agent.reset()

resp = agent.run("second message")
print(resp["resets"])   # → 1 — confirms reset occurred

```

## Resetting via the Playground HTTP Server

Needle's Playground UI exposes the same reset functionality through an HTTP endpoint.

```bash

# Trigger reset on running Playground server

curl http://127.0.0.1:7860/reset

```

This endpoint executes identical logic to `agent.reset()`, clearing the native token window and incrementing the counter. The implementation resides in [[`needle/playground/server.py`](https://github.com/cactus-compute/needle/blob/main/needle/playground/server.py)](https://github.com/cactus-compute/needle/blob/main/needle/playground/server.py).

## Key Source Files for Reset Behavior

| File | Purpose |
|------|---------|
| [[`needle/__init__.py`](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py)](https://github.com/cactus-compute/needle/blob/main/needle/__init__.py) | Public `Needle` class with `reset()` orchestration |
| [[`needle/_worker.py`](https://github.com/cactus-compute/needle/blob/main/needle/_worker.py)](https://github.com/cactus-compute/needle/blob/main/needle/_worker.py) | Worker process forwarding reset to native layer |
| [[`needle/playground/server.py`](https://github.com/cactus-compute/needle/blob/main/needle/playground/server.py)](https://github.com/cactus-compute/needle/blob/main/needle/playground/server.py) | HTTP `/reset` endpoint for UI access |
| [[`tests/test_worker.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_worker.py)](https://github.com/cactus-compute/needle/blob/main/tests/test_worker.py) | Unit tests validating reset behavior and counter |

## Summary

- Call **`agent.reset()`** to clear conversation context in Needle
- The reset clears a **256-token sliding window** in the native C engine
- Verify success by checking the **`"resets"`** counter in response objects
- Use **`curl http://host:port/reset`** for Playground server resets
- The reset mechanism spans Python wrapper, worker process, and native library layers

## Frequently Asked Questions

### What happens to the model's memory when I call `agent.reset()`?

The native C library immediately discards all tokens in the 256-token sliding window. The model loses all prior context and treats the next input as the start of a new conversation. The `resets` counter increments to track this operation.

### Can I reset context without creating a new Needle instance?

Yes. `agent.reset()` exists specifically to avoid the overhead of re-instantiating the `Needle` class. It preserves your tool bindings and configuration while only clearing the conversation history.

### How do I confirm a reset actually occurred?

Inspect the `"resets"` field in any response from `agent.run()` or `agent.complete()`. This integer increments by one with each successful reset. The test suite in [[`tests/test_worker.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_worker.py)](https://github.com/cactus-compute/needle/blob/main/tests/test_worker.py#L86-L94) demonstrates this validation pattern.

### Does the Playground `/reset` endpoint behave differently from `agent.reset()`?

No. Both paths invoke the same underlying `needle_reset` function in the native library. The Playground endpoint simply provides HTTP access to the identical reset sequence used by the Python API.