Agentic Context Engine
🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai
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What is Agentic Context Engine?
Kayba is an Agentic Context Engine (ACE) that makes AI agents self-improve from experience by analyzing their execution traces and turning failures into actionable insights. The platform detects silent failures, missed steps, and hallucinations in agent runs, then recursively improves agent performance without requiring retraining or fine-tuning. It works with Claude Code, Codex, browser-use, LangChain, and custom agent frameworks.
Key features include automatic failure detection (policy gaps, missed steps, hallucinations), a hosted dashboard for tracking reliability over time, Skillbook/SkillManager for storing learned strategies, Recursive Reflector for pattern extraction from traces, LiteLLM integration supporting 100+ LLM providers, Opik observability for monitoring costs and token usage, and checkpoint/resume functionality for long training sessions. The framework achieves 20-35% performance improvements on complex tasks while reducing token usage by up to 49%.
Kayba is designed for individual developers building AI agents, teams shipping production agents, and organizations with custom enterprise needs. It's particularly effective for browser automation, question-answering, tool selection, code generation, and multi-step reasoning tasks where agents need to learn patterns from successes and failures. The open-source framework is MIT licensed, while the hosted solution provides dashboard access, team collaboration, and dedicated support.
Agentic Context Engine pricing
Pricing model: Freemium
Open Source (Free): Kayba framework (pip install), Recursive Reflector, Skillbook generation, LiteLLM integration, Community support (Discord), MIT Licensed. Pro ($29/month): Everything in Open Source, Hosted dashboard, Bring your own API key, 10,000 traces/month, Email support, Team collaboration. Enterprise (Contact Us): Everything in Pro, SSO & audit logs, Custom integrations, Dedicated support, SLA guarantees, On-premise deployment.
Agentic Context Engine pros
- Automatically learns from agent execution traces without retraining
- Detects silent failures before they reach users
- 27.4% improvement on pass^1 Tau2 benchmark
- 56.2% improvement on pass^2 Tau2 benchmark
- 83.1% improvement on pass^3 Tau2 benchmark
- 100% improvement on pass^4 Tau2 benchmark
- 49% token usage reduction in browser automation
- Works with Claude Code, Codex, browser-use, LangChain
- Supports 100+ LLM providers via LiteLLM
- Open-source framework with MIT license
- Free tier available for individual developers
- Hosted dashboard for tracking reliability over time
- Skillbook stores learned strategies persistently
- Recursive Reflector extracts actionable insights automatically
- Opik integration for production observability
- Checkpoint and resume for long training sessions
- Integration pattern works with existing agents
- No vector database or fine-tuning required
- Deduplication reduces strategy redundancy
- Custom task environments for domain-specific evaluation
Agentic Context Engine cons
- Requires API key for LLM providers
- Free tier has limited features compared to Pro
- Pro plan at $29/month may be expensive for some
- 10,000 traces/month limit on Pro plan
- Enterprise plan requires contacting sales for pricing
- Learning loop adds computational overhead
- Requires understanding of ACE concepts for advanced usage
- Dependent on external LLM provider availability
- Setup complexity for custom integrations
- Observability features require separate Opik API key
Frequently asked questions about Agentic Context Engine
What is Kayba and how does it work?
Kayba is an Agentic Context Engine that makes AI agents self-improve from experience. It analyzes past agent traces, detects failures like wrong parameters, skipped policies, and bad routing, then turns them into insights that recursively improve your agent. The three-step process is: Analyze (call Kayba from your coding agent to upload execution traces), Insights (Kayba surfaces patterns across traces with categories, severity, and frequency), and Improve (extract insights from failures, turn them into concrete edits, apply them, and feed new traces back into Kayba).
What benchmarks does Kayba improve on?
On Tau2-bench, a real-world agent benchmark by Sierra Research that challenges agents to coordinate with users across complex enterprise domains (using Claude Haiku 4.5), Kayba achieves: pass^1: 41.2% baseline vs 52.5% Kayba (+27.4%), pass^2: 28.3% vs 44.2% (+56.2%), pass^3: 22.5% vs 41.2% (+83.1%), pass^4: 20.0% vs 40.0% (+100.0%). Kayba also achieves 49% token reduction in browser automation and translated 14,000 lines from Python to TypeScript with zero build errors at ~$1.50 learning cost.
Which agent frameworks does Kayba support?
Kayba supports Claude Code, Codex, browser-use, LangChain, the Anthropic SDK, and custom frameworks. Built-in runners include BrowserUse, LangChain, ClaudeCode, LiteLLM, Core, and TraceAnalyser. The integration pattern allows wrapping any existing agent (browser-use, LangChain, Claude Code, Anthropic SDK, or custom) with minimal code changes while preserving execution logic.
What is the Skillbook and how does it work?
The Skillbook is a persistent collection of strategies that evolves with every task. It stores learned strategies with metadata including helpful/harmful/neutral counts. You can add bullets with content and metadata, update bullet content and scores, tag bullets based on performance, remove unhelpful strategies, and get TOON-compressed format for LLM prompts (16-62% token savings). The Skillbook persists to files and can be loaded to resume with learned knowledge.
What are the three ACE roles?
ACE maintains three specialized roles: Agent (executes tasks, enhanced with Skillbook strategies), Reflector (analyzes execution traces to extract what worked and what failed, identifies errors and root causes), and SkillManager (curates the Skillbook by adding, refining, and removing strategies). All roles are backed by PydanticAI agents with structured output validation and route to 100+ LLM providers through LiteLLM.
What is the difference between OfflineAdapter and OnlineAdapter?
OfflineAdapter is for batch training on historical datasets or dedicated training phases. It processes datasets with epochs, supports checkpointing at intervals, and saves checkpoints for resume. OnlineAdapter is for continuous learning during live operation. It processes streaming samples as they arrive (from production) or single samples on-demand, enabling the agent to learn continuously from real-world usage without retraining.
How does Kayba reduce token usage?
Kayba reduces token usage by up to 49% through Skillbook compression using TOON format (16-62% token savings), learning efficient strategies that reduce execution steps (30-50% reduction in browser automation), and avoiding redundant LLM calls by reusing learned patterns. The framework achieves better performance with fewer tokens by encoding successful reasoning patterns and avoiding known pitfalls.
What observability features does Kayba provide?
Kayba provides Opik integration for production-grade observability, automatically tracking LLM calls (Generator, Reflector, Curator), token usage and costs, playbook evolution, and performance metrics. You can view traces at compass.com/opik including cost per role, token efficiency over time, and success rates. Manual logging supports adaptation metrics like epoch, performance score, bullet count, and successful predictions. Logfire auto-instrumentation is available via ace-framework[logfire].
Can I use Kayba with my own API key?
Yes, the Pro plan includes 'Bring your own API key'. You set your API key as an environment variable (OPENAI_API_KEY, ANTHROPIC_API_KEY, or any of 100+ supported providers) and Kayba uses it for all LLM calls. The hosted dashboard doesn't charge for API usage beyond the $29/month plan fee. LiteLLM integration supports OpenAI, Anthropic, Google, Bedrock, Groq, and 100+ other providers.