streamctx 0.4.1

Context health monitoring for AI agents — detect poisoning, drift, loops

StreamCtx

Your AI agent is silently corrupting its own context. StreamCtx detects it — and fixes it.

Install

pip install streamctx

2-Line Setup

import streamctx
streamctx.start()  # patches OpenAI + Anthropic automatically

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The Problem Nobody Talks About

You ship an AI agent. It works perfectly in demos.

Then in production:

• Agent gets stuck repeating the same failed action 58 times
• Context from step 3 contradicts context from step 7
• Agent hallucinates a tool call, writes it to memory, references it forever
• Your $0.50 task costs $50 because nobody set a limit

Every LLM observability tool tracks tokens. Nobody tracks context health.

Until now.

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What StreamCtx Does

1. Context Poison Detection

result = streamctx.scan(messages)
print(result["health_score"])   # 25/100
print(result["warnings"])
# ⚠️  Repeated errors: 'failed' 4x — agent stuck in loop
# 🔴  Context severely poisoned — resume from checkpoint

2. Context Diff — See Exactly What Changed

diff = streamctx.context_diff(step3_msgs, step7_msgs, step_a=3, step_b=7)
print(diff["summary"])
# ⚠️  System prompt REMOVED — agent lost instructions
# ⚠️  Contradiction: 'use gpt' added but 'use claude' removed
# Drift Score: 50/100

3. Auto-Checkpoint + Resume

session_id = streamctx.get_session_id()
messages = streamctx.resume(session_id)
# Pick up exactly where agent left off

4. 50% Token Compression

result = streamctx.compress(messages, max_tokens=2000)
# 140 tokens → 70 tokens (50% reduction)

5. Self-Healing

stats = streamctx.healing_stats()
# failures: 1, recoveries: 1

6. Full Session Report

streamctx.report()
streamctx.stop()

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Storage Backends

StreamCtx works out of the box with zero config (SQLite, local file). For production, point it at Supabase for managed, multi-user persistence:

# .env
STREAMCTX_BACKEND=supabase
SUPABASE_URL=your-project-url
SUPABASE_KEY=your-api-key

No code changes needed — same API, different backend.

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Feature Comparison

Feature	StreamCtx	Langfuse	LangSmith	Mem0
Token tracking	YES	YES	YES	NO
Cost estimation	YES	YES	YES	NO
Context Poison Det.	YES	NO	NO	NO
Context Diff	YES	NO	NO	NO
Auto-checkpoint	YES	NO	NO	NO
50% Compression	YES	NO	NO	NO
Self-healing	YES	NO	NO	NO
Zero config	YES	NO	NO	NO
Open source	YES	YES	NO	NO

| Core features free forever | YES | Partial | NO | NO (Graph Memory paywalled) |

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Quick Start

import streamctx
from openai import OpenAI

streamctx.start()
client = OpenAI()

messages = [{"role": "user", "content": "Hello!"}]
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages,
)

result = streamctx.scan(messages)
print(result["health_score"])
print(result["recommendation"])

streamctx.report()
streamctx.stop()

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API Reference

streamctx.start()                        # start tracking
streamctx.stop()                         # stop tracking
streamctx.report()                       # print full report
streamctx.wrap(client)                   # manually wrap client

streamctx.scan(messages)                 # context health score
streamctx.context_diff(a, b)             # compare two steps

streamctx.checkpoint()                   # save checkpoint
streamctx.resume(session_id)             # resume from checkpoint
streamctx.get_session_id()               # current session ID

streamctx.compress(messages)             # 50% token compression
streamctx.healing_stats()                # self-healing stats

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Why StreamCtx?

Most tools answer: "How many tokens did I use?"

StreamCtx answers: "Why is my agent broken — and how do I fix it?"

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Pricing

The core SDK — all 6 features above, plus the SQLite backend — is free forever, MIT-licensed, with no feature gates. No credit card, no signup, no locked features.

A managed offering (hosted Supabase backend + observability dashboard + team access) is planned for teams who want infrastructure handled for them. The features themselves will never move behind a paywall.

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Roadmap

Done:

• Token tracking + cost estimation
• Context poison detection
• Context diff + drift scoring
• Auto-checkpoint + resume
• 50% token compression
• Self-healing engine
• Supabase storage backend

Coming:

• Context budget manager (v0.4.0)
• Visual dashboard
• Multi-agent support

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License

MIT — Sneh R Joshi

Built by a solo founder who got tired of AI agents silently going insane.