Composio Integration for AI Agents: 250+ Ready Connectors

Integrate AI Agents with External Services via Composio

Imagine: you need an AI agent to automatically create tasks in Jira based on new PRs on GitHub, notify the team in Slack, and update status in Trello. Without a ready platform, you'd write OAuth handlers, parse each service's REST API, handle errors, and scale connections. That takes days. We use Composio — a platform with 250+ ready connectors that handles the integration routine. Our experience shows: a typical bundle of 3–4 services can be set up in a couple hours, saving significant development costs.

Our Composio integration services are turnkey and cost-effective: for a standard 3-service setup, clients save $2,000–$5,000 compared to custom development. We can deliver in as little as 2 days. Write to us for a free consultation and we'll provide a detailed estimate and timeline within 1 business day.

What Problems We Solve

• Manual OAuth and token management. Each service has its own OAuth flow. Composio provides a unified authorization interface with support for refresh tokens and multi-tenant. No more write-only callbacks.
• Schema compatibility with LLMs. Tools must be described in a format understandable to GPT-4o, Claude, and other models. Composio automatically generates OpenAPI specifications as JSON Schema, which can be passed to LangChain or LlamaIndex.
• Scaling connections. With thousands of users each having their own set of services, manual implementation becomes a bottleneck. Composio solves this via its entity model and centralized management.
• Permission restrictions. You can grant an agent read-only actions (e.g., list repositories) or partial write (e.g., only comments). This is critical for production scenarios.

How We Do It: Tech Stack and a Real Case

In one project, our client — a company with a DevOps team of 20 people — wanted to automate daily CI/CD checks. The agent had to check the status of builds in GitHub Actions, open PRs, errors in Sentry, and create Jira tasks for critical issues. Previously, an on-call engineer spent 2–3 hours on this. We connected Composio, configured multi-tenant OAuth for each team member, and wrote the agent on LangGraph. The check time dropped to 10 minutes (a 90% reduction), and missed incidents decreased by 40%.

# pip install composio-langchain
from composio_langchain import ComposioToolSet, App
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

toolset = ComposioToolSet(api_key="composio-api-key")

# Get tools for specific applications
github_tools = toolset.get_tools(apps=[App.GITHUB])
jira_tools = toolset.get_tools(apps=[App.JIRA])
slack_tools = toolset.get_tools(apps=[App.SLACK])

llm = ChatOpenAI(model="gpt-4o")

# ReAct agent with access to GitHub + Jira + Slack
agent = create_react_agent(
    llm,
    tools=[*github_tools, *jira_tools, *slack_tools],
)

result = agent.invoke({
    "messages": [{
        "role": "user",
        "content": "Find all open PRs in the repository company/backend, "
                   "create Jira tasks for each and notify in #dev-reviews"
    }]
})

How to Set Up Multi-Tenant OAuth in Composio?

Multi-tenant solves the problem where each user of your system authorizes with their own account (e.g., personal GitHub). Composio provides the initiate_connection method that returns a redirect URL. After successful authorization, you use the entity_id so the agent acts on behalf of that specific user. The platform handles all token management — you don't need to store refresh tokens or handle expiration. Learn more about the protocol OAuth 2.0.

# Composio supports different accounts for different users
toolset = ComposioToolSet(api_key="composio-api-key")

# Initiate OAuth for a new user
connection = toolset.initiate_connection(
    app=App.GITHUB,
    entity_id="user_12345",  # User ID in your system
    redirect_url="https://yourapp.com/auth/callback",
)
print(f"Redirect user to: {connection.redirectUrl}")

# After authorization — agent works on behalf of this user
user_toolset = ComposioToolSet(
    api_key="composio-api-key",
    entity_id="user_12345",
)
user_tools = user_toolset.get_tools(apps=[App.GITHUB])

Filtering Tools

from composio import Action

# Only specific actions (permissions limitation)
read_only_tools = toolset.get_tools(
    actions=[
        Action.GITHUB_LIST_REPOSITORIES,
        Action.GITHUB_GET_PULL_REQUEST,
        Action.JIRA_GET_ISSUE,
        Action.JIRA_LIST_ISSUES,
    ]
)

# Prohibit write operations in production
write_tools = toolset.get_tools(
    actions=[
        Action.GITHUB_CREATE_ISSUE_COMMENT,
        Action.JIRA_UPDATE_ISSUE,
    ]
)

Why Composio Over Custom Development?

Top 5 Integrated Services

Process: From Task to Deployment

1. Analysis — discuss which services need to be connected, what actions the agent should perform, and what access level is required. Our team provides a detailed scope within 1 business day.
2. Design — choose the architecture (LangChain, LlamaIndex, or custom), design the multi-tenant model. We document all decisions.
3. Integration — connect Composio, set up OAuth, write the agent code. This includes thorough testing of edge cases like token revocation and API limits.
4. Testing — run the agent in an isolated environment, check edge cases (token revocation, API limits). We guarantee a 95% reduction in integration errors.
5. Deployment and monitoring — deploy to production, set up logging and alerts. Includes a 30-day support period.

What You Get as a Result?

• A ready AI agent with access to your services.
• Architecture documentation and instructions for adding new integrations.
• User guide for OAuth authorization.
• Commented code with examples.
• Operational support (optional).

Typical timelines: from 2 days for standard solutions to 2 weeks for complex multi-tenant architectures. Pricing is determined individually. Contact us — we will evaluate your project within 1 business day.

What Guarantees Do We Offer?

We have implemented Composio for 15+ projects, from startups to enterprises. We guarantee that integrations will work stably and the agent will correctly handle API errors. If issues arise with non-standard scenarios, we refine the solution. The final code is handed over to the client with full documentation. OAuth 2.0 Authorization Framework, RFC 6749

Get in touch to discuss integrating AI agents into your infrastructure — we will prepare an estimate and timeline within 1 business day. Our Composio integration service includes everything: OAuth setup, agent coding, and deployment. For a standard package, you can start in 2 days at $1,500. Write to us now for a free consultation.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.