The moment a local LLM agent runs out of context, the conversation stops making sense. Turn number eight tries to reference a file the model has already dropped from its window, tool call output eats 6000 tokens on a routine step, and the next reply is a wet apology instead of a plan. Getting a home-run 7B or 13B model to hold a task through fifty steps is less about model size and more about what enters and leaves the window.
We tested seven apps and libraries that manage context around local LLMs. Some are memory stores that persist across sessions, some are prompt engines that summarize before overflow, and one is the Ollama server itself with the right settings. Below is what actually kept our test agent on task through a 90-minute coding session with a 13B model.
What to look for in a context manager
- Automatic summarization. The tool should notice when the window is nearing capacity and roll up older turns without losing intent.
- Retrieval over ballooning history. A file, a doc, or a past chat referenced by embedding is cheaper than the same content stuffed back into the prompt.
- Persistent memory across sessions. The agent should remember what you told it two days ago about your project without you repeating yourself.
- Tool call token discipline. Big tool outputs (grep results, HTTP responses) should be trimmed or referenced by handle rather than piped raw into the next turn.
- Ollama-native or OpenAI-compatible. The tool should speak the same protocol as your local runtime.
- Local-only mode. Nothing about context management should require phoning home to a cloud service.
Quick comparison
| App | Best for | License | Free plan | Starting price | Rating |
|---|---|---|---|---|---|
| LangChain | Full framework for context orchestration | MIT | Free | Free | 4.5 |
| LlamaIndex | Retrieval-first agents | MIT | Free | Free | 4.6 |
| Mem0 | Long-term memory store | Apache 2.0 | Self-host free | About $19/mo hosted | 4.5 |
| Chroma | Embedded vector database | Apache 2.0 | Free | Free | 4.5 |
| Continue | VS Code agent with context rules | Apache 2.0 | Free | Free | 4.6 |
| Open WebUI | Chat UI with document and memory pipes | BSD-3 | Free | Free | 4.7 |
| Ollama | Runtime with per-model context settings | MIT | Free | Free | 4.7 |
The apps
1. LangChain — Best full framework for context orchestration
LangChain is the framework that most local-LLM projects reach for the moment context management becomes a real problem. Conversation buffers with token limits, automatic summarization chains, message history stores, and a memory API that plugs into any LLM protocol are all first-class primitives.
For an agent that runs many turns with tools, the ConversationTokenBufferMemory combined with a MapReduceSummarize chain keeps the window from blowing up. It integrates cleanly with Ollama and llama.cpp servers.
Where it falls short: The API surface is enormous, and the fastest path is not always obvious. Weekly releases sometimes break minor imports. Debugging a long chain requires the LangSmith UI or careful logging.
Pricing:
- Free: Full library, MIT license
- Paid: LangSmith cloud tracing tier from about $39/user/mo
Platforms: Python, JavaScript on Windows, macOS, Linux
Download: LangChain
Bottom line: Pick this when the agent needs many primitives (memory, retrieval, tool routing) in one place.
2. LlamaIndex — Best retrieval-first agents
LlamaIndex treats context as a query problem. Rather than pasting a whole document into a prompt, it indexes the document with embeddings and pulls back only the paragraphs the model needs on this turn. The result is a much smaller working window and a much longer effective memory.
The library ships with connectors for PDFs, Markdown, code repositories, Notion, and databases. Its ChatEngine primitive maintains conversation state while retrieving relevant chunks per turn.
Where it falls short: The retrieval-heavy design assumes you have documents to index. For a pure chat agent with no external corpus, it is more setup than plain memory buffers.
Pricing:
- Free: MIT license
- Paid: LlamaCloud managed hosting from about $50/mo
Platforms: Python, TypeScript on Windows, macOS, Linux
Download: LlamaIndex
Bottom line: The right pick when the agent’s job involves reasoning over documents rather than a rolling chat.
3. Mem0 — Best long-term memory store
Mem0 (formerly Embedchain) is a memory service designed to give an agent recall across sessions. It ingests conversations, extracts durable facts, indexes them, and injects the relevant ones into the next prompt automatically. The result is an agent that “remembers” your project structure, coding style, or ongoing task without the user replaying it.
Self-hosted mode runs against a local Postgres or SQLite and reads and writes through a Python client. A hosted tier exists for teams that do not want to run infrastructure.
Where it falls short: Memory extraction quality depends on the model. A 7B model produces noisier memories than a 13B or a hosted frontier model. Some tuning of the extractor prompt is often needed.
Pricing:
- Free: Self-hosted Apache 2.0
- Paid: Hosted from about $19/mo for individual, higher for teams
Platforms: Python client on Windows, macOS, Linux; hosted API
Download: Mem0
Bottom line: The obvious choice when you want the agent to remember your project between reboots.
4. Chroma — Best embedded vector database
Chroma is a small, embedded vector database that runs in-process with a Python or JavaScript app. Embed a chunk with your model of choice, insert it into a Chroma collection, and query by cosine similarity to pull it back later. No server, no cluster, no ops.
For local LLM context management, Chroma is the storage layer under most of the retrieval patterns in LangChain and LlamaIndex. It also runs as a lightweight server for cases where multiple processes share the same store.
Where it falls short: Not a full memory framework. You wire the retrieval logic yourself. Query latency on very large collections (millions of chunks) trails Qdrant and Milvus.
Pricing:
- Free: Apache 2.0
- Paid: Chroma Cloud beta available for teams
Platforms: Python, JavaScript, in-process on Windows, macOS, Linux
Download: Chroma
Bottom line: The clean pick when you just need a place to store embeddings without spinning up a server.
5. Continue — Best VS Code agent with context rules
Continue is the open-source VS Code (and JetBrains) extension that turns a local Ollama or llama.cpp instance into a coding agent inside the editor. Its config.yaml sets context rules per project: which files to include automatically, which to summarize, and which to ignore.
The @ command palette pulls in specific context by name: @file for the current file, @codebase for a semantic search across the repo, @docs for external documentation. Each @ reference is a controlled context injection rather than a paste.
Where it falls short: Configuration is YAML-heavy and takes a session to tune. Some retrieval configurations bias too heavily toward small files.
Pricing:
- Free: Apache 2.0
- Paid: Hub features (shared assistants, private hubs) from about $10/user/mo
Platforms: VS Code, JetBrains, Windows, macOS, Linux
Download: Continue
Bottom line: The right pick when the agent lives in your editor and needs the right slice of the repo, not the whole tree.
6. Open WebUI — Best chat UI with document and memory pipes
Open WebUI is the ChatGPT-style front end for local models with two features that solve context problems directly: document RAG pipes and per-user memory. Upload a PDF or paste a URL, and Open WebUI chunks, embeds, and retrieves during the chat. The memory panel lets the user save durable notes the model consults on every turn.
The Pipelines feature lets you swap in custom filters (summarization, redaction, tool routing) that run before or after the model call. Advanced users write their own pipeline in Python and drop it in the plugin folder.
Where it falls short: Not headless. Every agent path ends in a chat window. Automation-first workflows use Continue or LangChain instead.
Pricing:
- Free: BSD-3
- Paid: None; enterprise support available
Platforms: Docker, Kubernetes, Python on Windows, macOS, Linux
Download: Open WebUI
Bottom line: Pick this when a person is doing the chatting and the “run out of context” problem is really an “attachments and memory” problem.
7. Ollama — Best runtime with per-model context settings
Ollama is the local model runtime almost every other tool on this page talks to. The context management story here is not a framework but two flags: num_ctx in the model file to raise the window size, and the keep_alive parameter to hold the KV cache in RAM between calls.
Raising num_ctx from the default 4096 to 32768 on a 13B model quietly solves most “agent forgets” reports before anyone touches LangChain. The trade-off is memory footprint and per-token throughput.
Where it falls short: No memory, no retrieval, no summarization. It only holds what you send it in the prompt.
Pricing:
- Free: MIT
- Paid: None
Platforms: Windows, macOS, Linux, ARM64
Download: Ollama
Bottom line: Set num_ctx first. Then reach for the frameworks above. In that order.
How to pick the right one
- If you are hitting a hard wall at 4096 tokens: Ollama. Change
num_ctxbefore rewriting your app. - If you want a framework that covers memory, retrieval, and tool routing: LangChain.
- If the agent is answering questions over documents: LlamaIndex.
- If the agent needs to remember what happened last week: Mem0.
- If you already run LangChain and just need somewhere to put embeddings: Chroma.
- If the agent lives in VS Code: Continue.
- If a human is doing the chatting and needs uploads plus memory: Open WebUI.
FAQ
Why does my local LLM agent forget things mid-task?
Every model has a hard token limit for the prompt window. When conversation, tool output, and instructions exceed that limit, the runtime silently drops earlier tokens. The agent still generates, but with a truncated view of the task. Raise num_ctx on the model, and add a summarizer that folds older turns into a compressed history.
What is the difference between context management and memory?
Context management is what fits in the current prompt window (usually short-lived). Memory is what persists across prompts, sessions, and reboots (long-lived). Mem0 and Open WebUI cover memory; LangChain, LlamaIndex, and Continue focus on context.
How much context can a local LLM handle?
Depends on the model and your RAM budget. Llama 3.1 8B supports 128K tokens if the runtime honors it. In practice, 32K to 64K keeps latency reasonable on consumer hardware. Very long contexts also increase perplexity, so trimming is often better than stuffing.
Do these tools work with LM Studio and llama.cpp?
LangChain, LlamaIndex, Continue, and Open WebUI all speak the OpenAI-compatible API that LM Studio and llama.cpp’s server expose. Mem0 and Chroma are storage layers and work with any provider you point them at.
Is Mem0 open source?
Yes. Mem0’s core library is Apache 2.0 and self-hostable. The paid hosted tier is a convenience layer, not a gating layer for the OSS.
Can I use these on Apple Silicon?
All seven picks run on Apple Silicon natively. Ollama, Continue, and Open WebUI have first-class ARM64 builds. LangChain, LlamaIndex, Mem0, and Chroma are Python or Node libraries and work anywhere Python or Node runs.
